[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"\"}":3,"_public_publisher_byId_da19403e-15b4-4b2c-9d09-474f7d03f67d":656,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"totalCitation\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:da19403e-15b4-4b2c-9d09-474f7d03f67d,\"}":777},{"meta":4,"data":6},{"total":5},"117",[7,60,176,207,299,413,445,480,543,572],{"id":8,"createTime":9,"updateTime":10,"relativeEntities":11,"slug":12,"properties":13,"entityType":24,"verifyStatus":25,"verifyTime":26,"verifyNote":27,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":29,"subjectFields":30,"manageAffiliations":31,"indexDatabases":32,"url":33,"thumbnailPath":34,"statistic":35,"gsStatistic":26,"type":26,"analyzePriority":26},"5a09599f-54d1-44c3-8f16-3847ac552024","2023-08-17T04:28:50.832+00:00","2025-10-13T23:58:52.517+00:00",[],"T%E1%BA%A1p-ch%C3%AD-Truy%E1%BB%81n-nhi%E1%BB%85m-Vi%E1%BB%87t-Nam",{"country":14,"issn":16,"introduce":18,"title":21},{"VOID":15},"VN",{"VOID":17},"08667829",{"EN":19,"VI":20},"{\"ops\":[{\"insert\":\"Vietnam Journal of Infectious Diseases is a social - professional forum of the Vietnam Society for Infectious Diseases, whose responsibility is to introduce the researches, the scientific advances in Vietnam and from the world; to contribute to the improvement of knowledges for health care staffs about clinic, treatment, guideline, prevention, epidemiology for infectious diseases, HIV\u002FAIDS and community health care; to provide, exchange and publish scientific information to the public accurately and promptly.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"Vietnam Journal of Infectious Diseases publishes in the whole country; serves the professional work for researchers, clinical staffs, administrators, undergraduate and postgraduate students who are working and studying in the infections, tropical diseases and HIV\u002FAIDS from central to local levels, also organizations and individuals in the world who are interested in infectious and tropical diseases and prevention for HIV\u002FAIDS.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Editor in Chief:\"},{\"attributes\":{\"header\":4},\"insert\":\"\\n\"},{\"insert\":\"Prof. Dr. Nguyen Van Kinh. - Chairman of Editorial Board\"},{\"attributes\":{\"align\":\"justify\",\"blockquote\":true},\"insert\":\"\\n\"},{\"insert\":\"Prof. Dr. Nguyen Van Mui.\"},{\"attributes\":{\"align\":\"justify\",\"blockquote\":true},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Headquarter:\"},{\"attributes\":{\"header\":4},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Address:\"},{\"insert\":\" Level 6, National Hospital for Tropical Diseases, No. 78 Giai Phong Street, Dong Da District, Hanoi city, Vietnam.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Tel:\"},{\"insert\":\" \"},{\"attributes\":{\"color\":\"#008d45\",\"background\":\"transparent\",\"link\":\"https:\u002F\u002Fbvnd.vojs.vn\u002Findex.php\u002Fvjid\u002Fmanagement\u002Fsettings\u002Fcontext\u002Ftel:02435765464\"},\"insert\":\"02435765464\"},{\"insert\":\"; \"},{\"attributes\":{\"color\":\"#008d45\",\"background\":\"transparent\",\"link\":\"https:\u002F\u002Fbvnd.vojs.vn\u002Findex.php\u002Fvjid\u002Fmanagement\u002Fsettings\u002Fcontext\u002Ftel:0913552672\"},\"insert\":\"0913552672\"},{\"insert\":\"; \"},{\"attributes\":{\"color\":\"#008d45\",\"background\":\"transparent\",\"link\":\"https:\u002F\u002Fbvnd.vojs.vn\u002Findex.php\u002Fvjid\u002Fmanagement\u002Fsettings\u002Fcontext\u002Ftel:0913228072\"},\"insert\":\"0913228072\"},{\"insert\":\".\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Email\"},{\"insert\":\": \"},{\"attributes\":{\"color\":\"#008d45\",\"background\":\"transparent\",\"link\":\"https:\u002F\u002Fbvnd.vojs.vn\u002Findex.php\u002Findex\u002Fadmin\u002Fcontexts\u002Fmailto:tungphamvan52@gmail.com\"},\"insert\":\"tungphamvan52@gmail.com\"},{\"insert\":\"; \"},{\"attributes\":{\"color\":\"#008d45\",\"background\":\"transparent\",\"link\":\"https:\u002F\u002Fbvnd.vojs.vn\u002Findex.php\u002Fvjid\u002Fmanagement\u002Fsettings\u002Fcontext\u002Fmailto:tapchitruyennhiem@gmail.com\"},\"insert\":\"tapchitruyennhiem@gmail.com\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Bank account:\"},{\"insert\":\" 0021000275119, Vietcombank Hanoi Branch. Transaction office No. 7, 402 Tran Khat Chan, Hai Ba Trung District, Hanoi city, Vietnam.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"\\n\"}]}","{\"ops\":[{\"insert\":\"Tạp chí Truyền nhiễm Việt Nam là cơ quan ngôn luận của Hội Truyền nhiễm Việt Nam, chịu sự lãnh đạo, chỉ đạo trực tiếp của Thường vụ Ban Chấp hành Hội Truyền nhiễm Việt Nam; sự kiểm tra, giám sát về nghiệp vụ báo chí của các cơ quan chức năng quản lý báo chí thuộc Bộ Thông tin và Truyền thông và Ban Tuyên giáo Trung ương Đảng.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"Tạp chí có trách nhiệm giới thiệu các công trình nghiên cứu khoa học, những tiến bộ khoa học trên thế giới và trong nước, góp phần nâng cao kiến thức khoa học cho cán bộ y tế về lâm sàng, điều trị, hướng dẫn, dự phòng, dịch tễ học các bệnh truyền nhiễm, HIV\u002FAIDS và chăm sóc sức khỏe cộng đồng; cung cấp, trao đổi, phổ biến các thông tin khoa học tới công chúng chính xác và kịp thời.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"Tạp chí xuất bản 03 tháng\u002F01 kỳ và được phát hành qua Bưu điện và tự phát hành trong phạm vi toàn quốc.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"Lãnh đạo Tạp chí: \"},{\"attributes\":{\"header\":4},\"insert\":\"\\n\"},{\"insert\":\"Tổng Biên tập: ThS.BSCKII Nguyễn Trung Cấp\"},{\"attributes\":{\"align\":\"justify\",\"blockquote\":true},\"insert\":\"\\n\"},{\"insert\":\"Phó Tổng Biên tập:\"},{\"attributes\":{\"align\":\"justify\",\"blockquote\":true},\"insert\":\"\\n\"},{\"insert\":\"            TS.BSCKII Phạm Ngọc Thạch\"},{\"attributes\":{\"align\":\"justify\",\"blockquote\":true},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Trụ sở tòa soạn:\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"Địa chỉ: Tầng 6, Bệnh viện Bệnh Nhiệt đới Trung ương, 78 đường Giải Phóng, phường Phương Mai, quận Đống Đa, thành phố Hà Nội.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Điện thoại:\"},{\"insert\":\" \"},{\"attributes\":{\"color\":\"#008d45\",\"background\":\"transparent\",\"link\":\"https:\u002F\u002Fbvnd.vojs.vn\u002Findex.php\u002Fvjid\u002Fmanagement\u002Fsettings\u002Fcontext\u002Ftel:02435765464\"},\"insert\":\"02435765464\"},{\"insert\":\" - \"},{\"attributes\":{\"color\":\"#008d45\",\"background\":\"transparent\",\"link\":\"https:\u002F\u002Fbvnd.vojs.vn\u002Findex.php\u002Fvjid\u002Fmanagement\u002Fsettings\u002Fcontext\u002Ftel:0913228072\"},\"insert\":\"0913228072\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Email\"},{\"insert\":\": \"},{\"attributes\":{\"color\":\"#008d45\",\"background\":\"transparent\",\"link\":\"https:\u002F\u002Fbvnd.vojs.vn\u002Findex.php\u002Findex\u002Fadmin\u002Fcontexts\u002Fmailto:tungphamvan52@gmail.com\"},\"insert\":\"tungphamvan52@gmail.com\"},{\"insert\":\"; \"},{\"attributes\":{\"color\":\"#008d45\",\"background\":\"transparent\",\"link\":\"https:\u002F\u002Fbvnd.vojs.vn\u002Findex.php\u002Fvjid\u002Fmanagement\u002Fsettings\u002Fcontext\u002Fmailto:tapchitruyennhiem@gmail.com\"},\"insert\":\"tapchitruyennhiem@gmail.com\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Tài khoản:\"},{\"insert\":\" Số 0021000275119 Ngân hàng Ngoại thương Việt Nam, chi nhánh Hà Nội (Vietcombank Hà Nội). Phòng giao dịch số 7, 402 Trần Khát Chân, quận Hai Bà Trưng, Thành phố Hà Nội.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"\\n\"}]}",{"EN":22,"VI":23},"Vietnam Journal of Infectious Diseases","Tạp chí Truyền nhiễm Việt Nam","PUBLISHER","VERIFIED",null,"Admin update database","PENDING",51,[],[],[],"https:\u002F\u002Ftruyennhiemvietnam.vn\u002Findex.php\u002Fvjid","\u002Fapi\u002Fpublic\u002Ffile\u002Fpublisher\u002F5a09599f-54d1-44c3-8f16-3847ac552024\u002Fadb0e52bf25e0de580fd4289519cb8bf.jpg",{"impactFactor":36,"impactFactorByYear":37,"i10Index":36,"i10IndexLast5Year":36,"totalPublication":41,"totalPublicationByYear":42,"totalCitation":48,"totalCitationByYear":49,"totalCitationPerPublication":54,"totalCitationPerPublicationByYear":55,"hindexLast5Year":59,"hindex":59},0,{"2022":38,"2023":39,"2024":40},0.01,0.03,0.13,315,{"2020":43,"2021":44,"2022":45,"2023":46,"2024":47},63,58,57,65,72,43,{"2020":50,"2021":51,"2022":52,"2023":53},8,15,9,11,0.14,{"2020":40,"2021":56,"2022":57,"2023":58},0.26,0.16,0.17,3,{"id":61,"createTime":62,"updateTime":63,"relativeEntities":64,"slug":65,"properties":66,"entityType":24,"verifyStatus":25,"verifyTime":78,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":79,"subjectFields":80,"manageAffiliations":81,"indexDatabases":136,"url":137,"thumbnailPath":26,"statistic":138,"gsStatistic":155,"type":175,"analyzePriority":26},"25b6bd10-676c-40c0-8dc3-356d1679a284","2023-05-19T02:22:33.430+00:00","2026-06-18T23:33:57.141+00:00",[],"T%E1%BA%A1p-ch%C3%AD-Y-D%C6%B0%E1%BB%A3c-h%E1%BB%8Dc-C%E1%BA%A7n-Th%C6%A1",{"country":67,"issn":68,"introduce":70,"title":73,"gsId":76},{"VOID":15},{"VOID":69},"23541210",{"EN":71,"VI":72},"\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">04\u002F10\u002F2015 Ministry of Information and Communications allowed Can Tho journal of medicine and pharmacy to operate (102 \u002FGP-BTTTT)\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">07\u002F16\u002F2015 Can Tho journal of medicine and pharmacy is internationally recognized: ISSN 2354-1210\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">In 2016, The journal has been included in the list of medical science journals by The State Council for professorship which is awarded a work score of 0-0.5 points for a published article.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Can Tho Journal of Medicine and Pharmacy welcome original works that haven’t been submitted or published in other medical journals. Posts must contain content related to one of the journal’s categories.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The content published\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The journal is divided into 3 categories:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Scientific research article: are valuable scientific works, which have been researched and accepted.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Overview of medicine, biology and pharmacy: serving the objective of continuing training in the fields of medicine, biology and pharmacy; to systematize classical and modern knowledge.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Update information on new knowledge about medicine, biology, pharmacy in the country and in the world.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Scope\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Publication and introduction of scientific research in the fields:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">+ Medicine (internal medicine, surgery, pediatrics, obstetrics and gynecology, odonto-stomatology, laboratory, oncology, traditional medicine, nursing).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">+ Biology (genetics, biotechnology).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">+ Pharmacology (pharmaceutics, drug quality analysis-control, synthetic pharmaceutical chemistry, biochemistry, pharmacognosy, botany, clinical pharmacy).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- To enhance the quality of undergraduate, postgraduate education, scientifically researching and meet the necessary treatment in hospital.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Introducing the updated domestic and oversea information about science technology to promote scientific research and exchanging technology in local, other universities.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">- Exchanging pharmaceutical and medical information for social health developing in the Mekong Delta and Vietnam.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">The object\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Postgraduate students, student of Can Tho University of Medicine and Pharmacy, scientists from schools, research institutes, hospitals, health centers, pharmaceutical companies of the Mekong Delta; other provinces and regions in Vietnam and other country.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Address\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Headquarters of Can Tho Journal of Medicine and Pharmacy, located Scientific Research and International Cooperation Office: 179 Nguyen Van Cu Street, An Khanh Ward, Ninh Kieu District, Can Tho City, Vietnam.\u003C\u002Fspan>\u003C\u002Fp>","\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Ngày 16\u002F7\u002F2015, Tạp chí Y Dược học Cần Thơ được cấp chỉ số quốc tế: ISSN 2354-1210.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Từ tháng 4\u002F2016, Tạp chí đã được Hội đồng Giáo sư ngành Y đưa vào danh sách các tạp chí khoa học Y học được tính điểm công trình 0-0,5 điểm cho một bài báo đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Năm 2020 Tạp chí Y Dược học Cần Thơ đã được phê duyệt vào danh mục của các Hội đồng Giáo sư ngành Dược học được tính điểm công trình 0-0,5 điểm cho một bài báo đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ ra 12 số\u002Fnăm, 180-200 trang\u002Fsố.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Từ tháng 12\u002F2022 Tạp chí Y Dược học Cần Thơ là thành viên của hệ thống Crossref và từ tháng 01\u002F2023 tạp chí thực hiện bình duyệt online kín 2 chiều nhằm tăng tính minh bạch, tin cậy của các công trình nghiên cứu khoa học và đảm bảo tốt nhất chất lượng khoa học của bài viết.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tôn chỉ, mục đích và phạm vi của tạp chí\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tôn chỉ và mục đích hoạt động của tạp chí: xuất bản nhằm mục đích phổ biến kết quả từ các đề tài nghiên cứu khoa học; giao lưu trao đổi khoa học, chia sẻ kinh nghiệm, học tập, đồng thời cập nhật thông tin khoa học mới trong các lĩnh vực y, sinh, dược học trong và ngoài nước.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Phạm vi của tạp chí: Tạp chí xuất bản được chia thành 3 chuyên mục: (i) Bài báo nghiên cứu khoa học là kết quả công trình nghiên cứu khoa học có giá trị đã được triển khai nghiên cứu, (ii) Bài tổng quan y, sinh, dược học: phục vụ mục tiêu đào tạo liên tục trong lĩnh vực y, sinh, dược học; nhằm hệ thống hóa những kiến thức kinh điển và hiện đại; (iii) Thông tin cập nhật kiến thức mới về y, sinh, dược học trong nước và trên thế giới.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Chính sách truy cập mở\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ áp dụng chính sách truy cập mở đối với các bài báo đã xuất bản đến với độc giả, nhằm mở rộng cơ hội tiếp cận các kết quả nghiên cứu chất lượng cao và tăng cường trao đổi kiến thức. Tạp chí đăng tải trực tuyến (miễn phí) toàn văn các bài báo được công bố trên website của Tạp chí (https:\u002F\u002Ftapchi.ctump.edu.vn).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đạo đức xuất bản\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ cam kết tuân thủ đạo đức xuất bản phù hợp với các hướng dẫn và tiêu chuẩn của the Committee on Publication Ethics (COPE), tuân thủ các nguyên tắc của COPE’s Core Practices, Best Practices Guidelines for Journal Editors và Guidelines on Good Publication Practices.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Bản thảo bài báo chỉ được chấp nhận khi được tác giả chịu trách nhiệm chính cam kết các nội dung sau: Các nội dung của bản thảo chưa được đăng tải toàn bộ hoặc một phần ở các tạp chí khác; Tất cả các tác giả đều có đóng góp một cách đáng kể vào quá trình nghiên cứu hoặc chuẩn bị bản thảo và cùng chịu trách nhiệm về các nội dung của bản thảo; Tuân thủ các biện pháp đảm bảo đạo đức nghiên cứu (ví dụ thỏa thuận đồng ý tham gia nghiên cứu).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Cam kết bảo mật\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí cam kết thực hiện và tuân thủ các quy định của luật và các văn bản hướng dẫn liên quan đến bảo mật thông tin cá nhân trên không gian mạng. Các thông tin mà người dùng (tác giả, độc giả, biên tập viên, người phản biện) nhập vào các biểu mẫu trên Hệ thống Quản lý xuất bản trực tuyến của tạp chí chỉ được sử dụng vào các mục đích đã được tuyên bố rõ ràng và sẽ không được cung cấp cho bất kỳ bên thứ ba nào khác, hay dùng vào bất kỳ mục đích nào khác.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Phí gửi bài\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Lệ phí gửi đăng bài: 1.000.000đ\u002Fbài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Lệ phí gửi đăng nhanh: 1.500.000đ\u002Fbài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đối với tác giả là cán bộ viên chức thuộc Trường Đại học Y Dược Cần Thơ thì được hỗ trợ 50% lệ phí gửi đăng bài.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đối với sinh viên thực hiện đề tài nghiên cứu khoa học cấp trường được hỗ trợ 100% lệ phí đăng bài ( Tác giả gửi đính kèm “ Quyết định về việc giao tổ chức thực hiện đề tài nghiên cứu khoa học cấp Trường của sinh viên”).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Hình thức nộp lệ phí:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Tiền mặt:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Nộp trực tiếp tại Phòng Tài chính - Kế toán, Trường Đại học Y Dược Cần Thơ, số 179 Nguyễn Văn Cừ, P. An Khánh, Q. Ninh Kiều, thành phố Cần Thơ.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Chuyển khoản:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tên Tài khoản: Trường ĐHYD Cần Thơ, Số TK: 0111000115668, tại ngân hàng Vietcombank chi nhánh Cần Thơ.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Thời gian: Áp dụng từ ngày 01\u002F02\u002F2023.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">* Phí gửi bài không được hoàn trả khi bài viết bị từ chối hoặc tác giả xin rút bài viết.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Quy trình phản biện bài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ thực hiện quy trình phản biện kín hai chiều nghiêm ngặt. Danh tính của những người phản biện không được tiết lộ cho các tác giả và ngược lại. Quy trình thẩm định bài báo đăng gồm các bước sau:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tiếp nhận bản thảo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tác giả liên hệ gửi bản thảo đến Tạp chí qua hệ thống trực tuyến tại website: https:\u002F\u002Ftapchi.ctump.edu.vn. Hướng dẫn về cách đăng ký, gửi bài và chuẩn bị bản thảo được cung cấp trên website của Tạp chí.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sàng lọc sơ bộ\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sau khi Tòa soạn nhận được bài báo của tác giả, Ban Thư ký sẽ tiến hành kiểm tra sơ bộ bài báo (các yêu cầu về nội dung và hình thức). Những bài báo không đúng quy cách hoặc có nội dung không phù hợp hoặc vi phạm bản quyền sẽ bị từ chối (Ban Thư ký thông báo phản hồi đến tác giả trong vòng 1 tuần). Những bài báo đủ điều kiện, được Ban Thư ký tòa soạn chuyển đến Ban Biên tập có cùng chuyên môn với nội dung bài báo để đề xuất người phản biện. Thời gian kể từ khi Ban Biên tập nhận bài báo đến khi đề xuất người phản biện bài báo chậm nhất là 5 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Vòng phản biện\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Ban Thư ký gửi bài và yêu cầu phản biện đến 02 phản biện độc lập.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Các phản biện gởi nhận xét cho Ban Thư ký. Thời gian từ khi gửi bài cho phản biện đến khi nhận ý kiến của phản biện tối đa là 20 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Xử ký kết quả phản biện\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Nếu ý kiến đồng ý cho đăng và không cần chỉnh sửa, Ban Thư ký tiếp tục đăng bài theo qui trình.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Nếu ý kiến đồng ý đăng và cần chỉnh sửa, Ban Thư ký sẽ thông tin đến tác giả chỉnh sửa theo yêu cầu của người phản biện. Thời gian chỉnh sửa và gửi lại kéo dài không quá 2 tuần, từ khi tác giả bài báo nhận được thông tin (Quá trình này có thể lặp lại tối đa 2 lần\u002F1 bài báo). Khi có sự thống nhất, đồng ý của người phản biện; bài báo được tiếp tục đăng theo qui trình.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">3. Những bài báo có chất lượng không đạt yêu cầu, cả 2 phản biện không đồng ý cho đăng sẽ bị Tòa soạn từ chối đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Xuất bản\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Ban Thư ký tổng hợp các bản thảo đã được tác giả hoàn thiện sau thẩm định trình Ban Biên tập xem xét, Tổng Biên tập phê duyệt, quyết định bài đăng theo các tiêu chí: sự phù hợp nội dung với tôn chỉ và mục đích, thể loại bài viết (ưu tiên các bài có bài có nghiên cứu chuyên sâu, hàm lượng khoa học cao), đóng góp mới bài báo, bài báo được ưu tiên đăng trong số gần nhất của Tạp chí theo thứ tự: tính thời sự, chất lượng bài báo và thời gian gửi bài.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Ban Biên tập và Ban Thư ký biên tập bản thảo, chế bản, đọc rà soát lỗi. Thời gian hoàn thành từ 10-15 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">3. Ban Thư ký có trách nhiệm thông báo cho tác giả bài báo (bằng e-mail) về tình hình phê duyệt bài báo, thời gian, số kỳ, tập xuất bản bài báo theo qui định.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">4. Danh sách bài báo theo số Tạp chí được in ấn và phát hành trong năm định kỳ được công bố chính thức trên website: https:\u002F\u002Ftapchi.ctump.edu.vn\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>",{"EN":74,"VI":75},"Cantho Journal of Medicine and Pharmacy","Tạp chí Y Dược học Cần Thơ",{"VOID":77},"wcQ1uqwAAAAJ","2023-05-30T08:17:21.868+00:00",32,[],[82],{"id":83,"createTime":84,"updateTime":85,"relativeEntities":86,"slug":87,"properties":88,"entityType":98,"verifyStatus":25,"verifyTime":99,"verifyNote":26,"syncStatus":28,"languages":100,"translateLanguages":26,"viewCount":103,"url":104,"parentIds":105,"statistic":106},"6413896b-eca9-442b-a73f-182a58a0ce40","2023-06-12T14:59:13.446+00:00","2026-06-19T02:29:32.871+00:00",[],"Tr%C6%B0%E1%BB%9Dng-%C4%90%E1%BA%A1i-h%E1%BB%8Dc-Y-D%C6%B0%E1%BB%A3c-C%E1%BA%A7n-Th%C6%A1",{"country":89,"title":90,"address":93,"abbreviation":96},{"VOID":15},{"EN":91,"VI":92},"Can Tho University of Medicine and Pharmacy","Trường Đại học Y Dược Cần Thơ",{"VI":94,"EN":95},"Số 179, đường Nguyễn Văn Cừ, phường An Khánh, quận Ninh Kiều, thành phố Cần Thơ, Việt Nam","No 179, Nguyen Van Cu street, An Khanh ward, Ninh Kieu district, Can Tho city, Vietnam",{"VOID":97},"ctump","AFFILIATION","2023-08-01T14:07:27.977+00:00",[101,102],"VI","EN",12,"http:\u002F\u002Fwww.ctump.edu.vn\u002F",[],{"impactFactor":36,"impactFactorByYear":107,"i10Index":111,"i10IndexLast5Year":59,"totalPublication":112,"totalPublicationByYear":113,"totalCitation":122,"totalCitationByYear":123,"totalCitationPerPublication":128,"totalCitationPerPublicationByYear":129,"hindexLast5Year":135,"hindex":135},{"2022":108,"2023":109,"2024":110,"2025":38},0.1,0.05,0.07,4,1489,{"2013":114,"2014":115,"2015":115,"2016":111,"2017":115,"2018":59,"2019":114,"2020":116,"2021":117,"2022":118,"2023":119,"2024":120,"2025":121,"2026":111},2,1,10,84,281,680,260,156,313,{"2013":59,"2014":114,"2018":124,"2021":125,"2022":126,"2023":127},17,36,123,132,0.21,{"2013":130,"2014":114,"2018":131,"2021":132,"2022":133,"2023":134},1.5,5.67,0.43,0.44,0.19,6,[],"https:\u002F\u002Ftapchi.ctump.edu.vn\u002Findex.php\u002Fctump",{"impactFactor":36,"impactFactorByYear":139,"i10Index":114,"i10IndexLast5Year":114,"totalPublication":140,"totalPublicationByYear":141,"totalCitation":147,"totalCitationByYear":148,"totalCitationPerPublication":151,"totalCitationPerPublicationByYear":152,"hindexLast5Year":135,"hindex":135},{"2022":38,"2023":36,"2024":39,"2025":38},1956,{"0":114,"2019":114,"2020":50,"2021":142,"2022":143,"2023":144,"2024":145,"2025":146},76,403,789,305,371,524,{"0":114,"2019":135,"2021":124,"2022":149,"2023":150,"2024":111,"2025":114},287,206,0.27,{"0":115,"2019":59,"2021":153,"2022":154,"2023":56,"2024":38,"2025":38},0.22,0.71,{"impactFactor":26,"impactFactorByYear":26,"i10Index":114,"i10IndexLast5Year":114,"totalPublication":156,"totalPublicationByYear":157,"totalCitation":163,"totalCitationByYear":164,"totalCitationPerPublication":169,"totalCitationPerPublicationByYear":170,"hindexLast5Year":135,"hindex":135},461,{"0":158,"2019":114,"2021":159,"2022":160,"2023":161,"2024":158,"2025":162},7,37,296,107,5,419,{"2021":59,"2022":59,"2023":165,"2024":166,"2025":167,"2026":168},71,126,171,41,0.91,{"2021":171,"2022":38,"2023":172,"2024":173,"2025":174},0.08,0.66,18,34.2,"JOURNAL",{"id":177,"createTime":178,"updateTime":179,"relativeEntities":180,"slug":181,"properties":182,"entityType":24,"verifyStatus":25,"verifyTime":193,"verifyNote":26,"syncStatus":28,"languages":194,"translateLanguages":26,"viewCount":36,"subjectFields":195,"manageAffiliations":196,"indexDatabases":197,"url":198,"thumbnailPath":26,"statistic":26,"gsStatistic":199,"type":175,"analyzePriority":26},"f8d0bf97-8d89-482e-b58c-2fc481a0b79b","2025-10-27T06:27:08.591+00:00","2026-06-18T23:33:41.667+00:00",[],"T%E1%BA%A1p-ch%C3%AD-Khoa-h%E1%BB%8Dc-v%C3%A0-C%C3%B4ng-ngh%E1%BB%87-nhi%E1%BB%87t-%C4%91%E1%BB%9Bi",{"country":183,"introduce":184,"gsId":186,"title":188,"issn":191},{"VOID":15},{"EN":185},"\u003Cp style=\"text-align:justify;\">     Journal of Tropical Science and Engineering (JTSE) is a multidisciplinary scientific journal, licensed to operate as a print journal in 2012 and an electronic journal in 2024 (License No.1479\u002FGP-BTTTT dated August 20, 2012 and No.91\u002FGP-BTTTT dated April 9, 2024 issued by the Ministry of Information and Communications of Vietnam). The JTSE is headquartered in Hanoi.\u003C\u002Fp>\u003Cp style=\"text-align:justify;\">        The JTSE is published every 3 months (4 issues\u002Fyear), publishing research results and overview articles in 3 groups of fields: Tropical Ecology and Environment; Chemistry and Material Sciences; Biomedicine and Pharmacy. In 2022, the JTSE registered the international identifier Digital Object Identifier (DOI): 10.58334\u002Fvrtc.jtst and assigned DOI codes to all articles of the journal. The members of the Editorial Board of the JTSE are prestigious scientists and leading scientists from Vietnam and many countries in the world. The JTSE has been recognized by the Vietnam State Council for Professorship to score scientific articles in Chemistry, Medicine and Biology with scores ranging from 0-0.75 points.\u003C\u002Fp>\u003Cp style=\"text-align:justify;\">      Currently, the JTSE is building and perfecting a set of criteria and making efforts to join the List of prestigious  international journals with a roadmap to enter Scopus and SCIE in the coming time.\u003C\u002Fp>",{"VOID":187},"MS2_GJQAAAAJ",{"VI":189,"EN":190},"Tạp chí Khoa học và Công nghệ nhiệt đới","Journal of Tropical Science and Engineering",{"VOID":192},"08667535","2025-10-27T06:27:25.058+00:00",[101,102],[],[],[],"https:\u002F\u002Ftapchikhcnnd.com.vn",{"impactFactor":26,"impactFactorByYear":26,"i10Index":36,"i10IndexLast5Year":36,"totalPublication":200,"totalPublicationByYear":201,"totalCitation":161,"totalCitationByYear":203,"totalCitationPerPublication":153,"totalCitationPerPublicationByYear":205,"hindexLast5Year":114,"hindex":114},481,{"0":115,"2020":115,"2021":115,"2022":114,"2024":115,"2025":202,"2026":115},474,{"2017":114,"2018":115,"2019":111,"2020":162,"2021":162,"2022":116,"2023":50,"2024":162,"2025":204,"2026":52},52,{"2020":162,"2021":162,"2022":162,"2024":162,"2025":206,"2026":52},0.11,{"id":208,"createTime":209,"updateTime":210,"relativeEntities":211,"slug":212,"properties":213,"entityType":24,"verifyStatus":25,"verifyTime":221,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":222,"subjectFields":223,"manageAffiliations":224,"indexDatabases":225,"url":226,"thumbnailPath":26,"statistic":227,"gsStatistic":276,"type":175,"analyzePriority":26},"a3d1e82a-57e2-40f9-940e-f5fa8b9ef64a","2023-06-01T07:11:26.039+00:00","2026-06-18T23:33:28.573+00:00",[],"VNU-Journal-of-Science-Earth-and-Environmental-Sciences",{"issn":214,"title":216,"country":218,"gsId":219},{"VOID":215},"26159279",{"EN":217},"VNU Journal of Science: Earth and Environmental Sciences",{"VOID":15},{"VOID":220},"UmXD8vEAAAAJ","2023-06-01T07:17:59.210+00:00",27,[],[],[],"https:\u002F\u002Fjs.vnu.edu.vn\u002FEES",{"impactFactor":36,"impactFactorByYear":228,"i10Index":234,"i10IndexLast5Year":36,"totalPublication":235,"totalPublicationByYear":236,"totalCitation":246,"totalCitationByYear":247,"totalCitationPerPublication":261,"totalCitationPerPublicationByYear":262,"hindexLast5Year":103,"hindex":103},{"2010":229,"2012":40,"2013":230,"2014":230,"2015":231,"2016":54,"2017":171,"2018":109,"2019":110,"2020":57,"2021":232,"2022":233,"2023":151,"2024":58},0.02,0.04,0.09,0.31,0.4,19,656,{"2008":237,"2009":238,"2010":239,"2011":238,"2012":240,"2013":241,"2014":239,"2015":242,"2016":243,"2017":244,"2018":245,"2019":165,"2020":125,"2021":168,"2022":125,"2023":222,"2024":111},23,24,22,28,25,20,146,29,78,1038,{"2008":248,"2009":249,"2010":250,"2011":251,"2012":252,"2013":253,"2014":254,"2015":255,"2016":256,"2017":257,"2018":161,"2019":258,"2020":259,"2021":29,"2022":260},119,64,66,86,38,73,59,31,129,39,97,53,26,1.58,{"2008":263,"2009":264,"2010":59,"2011":265,"2012":266,"2013":267,"2014":268,"2015":269,"2016":270,"2017":271,"2018":272,"2019":272,"2020":273,"2021":274,"2022":275},5.17,2.67,3.58,1.36,2.92,2.68,1.55,0.88,1.34,1.37,1.47,1.24,0.72,{"impactFactor":26,"impactFactorByYear":26,"i10Index":173,"i10IndexLast5Year":135,"totalPublication":277,"totalPublicationByYear":278,"totalCitation":279,"totalCitationByYear":280,"totalCitationPerPublication":288,"totalCitationPerPublicationByYear":289,"hindexLast5Year":158,"hindex":298},113,{"2007":114,"2008":103,"2009":173,"2010":116,"2011":51,"2012":239,"2013":103,"2014":162,"2015":53,"2016":135},616,{"2009":162,"2010":114,"2011":52,"2012":124,"2013":222,"2014":255,"2015":48,"2016":44,"2017":281,"2018":282,"2019":259,"2020":283,"2021":284,"2022":285,"2023":286,"2024":257,"2025":287,"2026":59},54,60,49,47,34,44,33,5.45,{"2009":290,"2010":291,"2011":292,"2012":293,"2013":294,"2014":295,"2015":296,"2016":297},0.28,0.2,0.6,0.77,2.25,6.2,3.91,9.67,14,{"id":300,"createTime":301,"updateTime":302,"relativeEntities":303,"slug":304,"properties":305,"entityType":24,"verifyStatus":25,"verifyTime":316,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":234,"subjectFields":317,"manageAffiliations":318,"indexDatabases":388,"url":389,"thumbnailPath":390,"statistic":391,"gsStatistic":409,"type":175,"analyzePriority":26},"a7166325-6c9e-4db3-8a59-7c879701a372","2023-07-31T04:28:49.231+00:00","2026-06-18T23:33:22.428+00:00",[],"VNU-Journal-of-Science-Policy-and-Management-Studies",{"country":306,"issn":307,"eissn":309,"title":311,"gsId":314},{"VOID":15},{"VOID":308},"26159295",{"VOID":310},"25881116",{"EN":312,"VI":313},"VNU Journal of Science: Policy and Management Studies","Tạp chí Nghiên cứu Chính sách và Quản lý",{"VOID":315},"xKW2E_cAAAAJ","2023-08-01T03:23:46.688+00:00",[],[319],{"id":320,"createTime":321,"updateTime":322,"relativeEntities":323,"slug":324,"properties":325,"entityType":98,"verifyStatus":25,"verifyTime":335,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":336,"url":337,"parentIds":338,"statistic":339},"12466116-8f60-4b01-8faf-2411483a0977","2023-05-30T09:37:38.472+00:00","2026-06-19T02:14:28.352+00:00",[],"Vietnam-National-University",{"country":326,"title":327,"abbreviation":330,"address":332},{"VOID":15},{"EN":328,"VI":329},"Vietnam National University, Hanoi","Đại học Quốc gia Hà Nội",{"VOID":331},"VNU",{"VI":333,"EN":334},"Số 144 Xuân Thủy, Phường Dịch Vọng Hậu, Quận Cầu Giấy, Hà Nội, Việt Nam","No. 144 Xuan Thuy Street, Dich Vong Hau Ward, Cau Giay District, Hanoi, Vietnam","2023-08-02T13:58:17.029+00:00",16,"http:\u002F\u002Fwww.vnu.edu.vn\u002F",[],{"impactFactor":36,"impactFactorByYear":340,"i10Index":285,"i10IndexLast5Year":162,"totalPublication":344,"totalPublicationByYear":345,"totalCitation":354,"totalCitationByYear":355,"totalCitationPerPublication":368,"totalCitationPerPublicationByYear":369,"hindexLast5Year":336,"hindex":336},{"2014":109,"2015":231,"2016":231,"2017":206,"2018":341,"2019":110,"2020":342,"2021":343,"2022":171,"2023":231,"2024":110,"2025":39},0.06,0.18,0.35,1247,{"2003":115,"2004":115,"2005":114,"2006":59,"2007":111,"2008":162,"2009":59,"2010":50,"2011":124,"2012":336,"2013":260,"2014":244,"2015":250,"2016":281,"2017":346,"2018":347,"2019":348,"2020":349,"2021":350,"2022":351,"2023":352,"2024":353,"2025":255,"2026":50},94,88,83,67,176,185,187,93,1644,{"2004":111,"2005":115,"2006":52,"2007":298,"2008":356,"2009":298,"2010":286,"2011":357,"2012":244,"2013":358,"2014":359,"2015":360,"2016":361,"2017":362,"2018":363,"2019":364,"2020":365,"2021":366,"2022":367,"2023":367,"2024":162},13,81,48,70,151,115,109,259,174,181,121,101,1.32,{"2004":111,"2005":370,"2006":59,"2007":371,"2008":372,"2009":373,"2010":374,"2011":375,"2012":376,"2013":377,"2014":378,"2015":379,"2016":380,"2017":381,"2018":382,"2019":383,"2020":384,"2021":385,"2022":386,"2023":387,"2024":109},0.5,3.5,2.6,4.67,5.5,4.76,1.81,1.85,2.41,2.29,2.13,1.16,2.94,2.1,2.7,0.69,0.55,0.54,[],"https:\u002F\u002Fjs.vnu.edu.vn\u002FPaM","\u002Fapi\u002Fpublic\u002Ffile\u002Fpublisher\u002Fa7166325-6c9e-4db3-8a59-7c879701a372\u002Fecfe83f4bcc60c7023ae04d8900fe0ac.jpg",{"impactFactor":36,"impactFactorByYear":392,"i10Index":111,"i10IndexLast5Year":36,"totalPublication":394,"totalPublicationByYear":395,"totalCitation":399,"totalCitationByYear":400,"totalCitationPerPublication":293,"totalCitationPerPublicationByYear":403,"hindexLast5Year":158,"hindex":158},{"2018":109,"2019":171,"2020":393,"2021":108,"2022":54,"2023":206,"2024":54},0.23,320,{"2015":50,"2016":239,"2017":286,"2018":396,"2019":397,"2020":159,"2021":398,"2022":168,"2023":79,"2024":255},30,35,40,247,{"2015":115,"2016":242,"2017":401,"2018":222,"2019":402,"2020":79,"2021":239,"2022":50,"2023":116},77,50,{"2015":404,"2016":169,"2017":405,"2018":406,"2019":407,"2020":408,"2021":386,"2022":291,"2023":232},0.12,1.75,0.9,1.43,0.86,{"impactFactor":26,"impactFactorByYear":26,"i10Index":36,"i10IndexLast5Year":36,"totalPublication":103,"totalPublicationByYear":410,"totalCitation":234,"totalCitationByYear":411,"totalCitationPerPublication":261,"totalCitationPerPublicationByYear":412,"hindexLast5Year":114,"hindex":59},{"2015":114,"2016":116},{"2016":115,"2017":114,"2018":114,"2019":59,"2020":114,"2021":59,"2022":114,"2023":114,"2024":115,"2026":115},{"2016":108},{"id":414,"createTime":415,"updateTime":416,"relativeEntities":417,"slug":418,"properties":419,"entityType":24,"verifyStatus":25,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":238,"subjectFields":428,"manageAffiliations":429,"indexDatabases":430,"url":431,"thumbnailPath":26,"statistic":432,"gsStatistic":438,"type":175,"analyzePriority":26},"16e65a3f-d274-47dd-8691-56d76eb58c3d","2023-05-30T09:10:06.062+00:00","2026-06-18T23:33:17.679+00:00",[],"Vietnam-Journal-of-Otorhinolaryngology-Head-and-Neck-Surgery",{"country":420,"issn":421,"title":423,"gsId":426},{"VOID":15},{"VOID":422},"18593704",{"EN":424,"VI":425},"Vietnam Journal of Otorhinolaryngology - Head and Neck Surgery","Tạp chí Tai Mũi Họng Việt Nam",{"VOID":427},"L78B_eIAAAAJ",[],[],[],"https:\u002F\u002Ftapchitaimuihong.vn\u002Findex.php\u002Ftmh",{"impactFactor":36,"impactFactorByYear":433,"i10Index":36,"i10IndexLast5Year":36,"totalPublication":434,"totalPublicationByYear":435,"totalCitation":111,"totalCitationByYear":436,"totalCitationPerPublication":230,"totalCitationPerPublicationByYear":437,"hindexLast5Year":115,"hindex":115},{"2024":229},95,{"2022":52,"2023":286,"2024":255,"2025":53},{"2023":111},{"2023":231},{"impactFactor":26,"impactFactorByYear":26,"i10Index":36,"i10IndexLast5Year":36,"totalPublication":240,"totalPublicationByYear":439,"totalCitation":234,"totalCitationByYear":440,"totalCitationPerPublication":441,"totalCitationPerPublicationByYear":442,"hindexLast5Year":114,"hindex":114},{"2023":53,"2024":116,"2025":135,"2026":115},{"2024":59,"2025":298,"2026":114},0.68,{"2024":443,"2025":444,"2026":114},0.3,2.33,{"id":446,"createTime":447,"updateTime":448,"relativeEntities":449,"slug":450,"properties":451,"entityType":24,"verifyStatus":25,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":396,"subjectFields":460,"manageAffiliations":461,"indexDatabases":462,"url":463,"thumbnailPath":464,"statistic":465,"gsStatistic":472,"type":175,"analyzePriority":26},"25988add-c322-4eda-afda-63b1559bb824","2023-04-07T03:49:55.817+00:00","2026-06-18T23:32:23.776+00:00",[],"T%E1%BA%A1p%20ch%C3%AD%20Y%20-%20D%C6%B0%E1%BB%A3c%20h%E1%BB%8Dc%20qu%C3%A2n%20s%E1%BB%B1",{"country":452,"issn":453,"title":455,"gsId":458},{"VOID":15},{"VOID":454},"18590748",{"EN":456,"VI":457},"Journal of Military Pharmaco-medicine","Tạp chí Y - Dược học quân sự",{"VOID":459},"_pmQ8IEAAAAJ",[],[],[],"https:\u002F\u002Fjmpm.vn\u002Findex.php\u002Fjmpm","\u002Fapi\u002Fpublic\u002Ffile\u002Fpublisher\u002F25988add-c322-4eda-afda-63b1559bb824\u002F09c6bc42157cf5ece95edc971ccddb52.jpg",{"impactFactor":36,"impactFactorByYear":466,"i10Index":36,"i10IndexLast5Year":36,"totalPublication":467,"totalPublicationByYear":468,"totalCitation":135,"totalCitationByYear":470,"totalCitationPerPublication":38,"totalCitationPerPublicationByYear":471,"hindexLast5Year":115,"hindex":115},{"2023":229,"2024":38},475,{"2022":168,"2023":365,"2024":469,"2025":124},236,{"2022":114,"2023":111},{"2022":109,"2023":229},{"impactFactor":26,"impactFactorByYear":26,"i10Index":36,"i10IndexLast5Year":36,"totalPublication":277,"totalPublicationByYear":473,"totalCitation":401,"totalCitationByYear":474,"totalCitationPerPublication":441,"totalCitationPerPublicationByYear":475,"hindexLast5Year":59,"hindex":59},{"2015":115,"2018":114,"2019":115,"2020":115,"2021":59,"2022":103,"2023":159,"2024":287,"2025":237},{"2020":115,"2021":115,"2022":115,"2023":52,"2024":237,"2025":79,"2026":116},{"2020":115,"2021":476,"2022":171,"2023":477,"2024":478,"2025":479},0.33,0.24,0.7,1.39,{"id":481,"createTime":482,"updateTime":483,"relativeEntities":484,"slug":485,"properties":486,"entityType":24,"verifyStatus":25,"verifyTime":26,"verifyNote":27,"syncStatus":28,"languages":496,"translateLanguages":26,"viewCount":45,"subjectFields":497,"manageAffiliations":498,"indexDatabases":525,"url":526,"thumbnailPath":527,"statistic":528,"gsStatistic":26,"type":26,"analyzePriority":26},"2b8d7b12-2d20-4777-be98-077f44f03c69","2023-09-07T07:40:58.286+00:00","2025-07-13T21:21:00.542+00:00",[],"VNU-Journal-of-Social-Sciences-and-Humanities",{"country":487,"issn":488,"introduce":490,"title":493},{"VOID":15},{"VOID":489},"23541172",{"EN":491,"VI":492},"{\"ops\":[{\"insert\":\"VNU Journal of Social Sciences and Humanities (ISSN 2354-1172) is a double-blind peer-reviewed journal published by University of Social Sciences and Humanities, Vietnam National University, Hanoi, Vietnam, under the publication permit no. 155\u002FGP-BTTTT, issued on 11\"},{\"attributes\":{\"script\":\"super\"},\"insert\":\"th \"},{\"insert\":\"May, 2015 by Ministry of Information and Communications. The journal publishes four Vietnamese issues and two English issues per year.\\nCurrently, there are 35 reputable professors in the editorial board. The main objectives of the journal include: providing an intellectual platform for Vietnamese and international scholars; promoting interdisciplinary studies in social sciences and humanities; becoming the leading journal in social sciences and humanities in Vietnam; being indexed by worldwide databases and having academic recognition internationally in the near future.\\nThe journal is currently indexed by Google Scholar, WorldCat, Open Archives, Cosmos Impact Factor, Advanced Sciences Index, Scientific Indexing Services, CrossRef, EBSCO Information Services and Vietnam National University’s digital archive.\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Journal of Social Sciences and Humanities-Vietnam\"},{\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"ISSN 2354-1172, email: tapchikhxhnv@gmail.com, tckhxhnv@vnu.edu.vn\"},{\"insert\":\"\\n\"}]}","{\"ops\":[{\"insert\":\"Được thành lập ngày 31\u002F8\u002F2015 (giấy phép hoạt động số 155\u002FGP-BVHTT ngày 11 tháng 5 năm 2015 của Bộ Thông tin và Truyền thông, mã số tiêu chuẩn quốc tế ISSN 2354-1172), Tạp chí Khoa học Xã hội và Nhân văn (Journal of Social Sciences and Humanities) là ấn phẩm khoa học chính thức, duy nhất của Trường Đại học Khoa học Xã hội và Nhân văn, ĐHQG Hà Nội, phát triển và kế thừa Chuyên san Khoa học Xã hội và Nhân văn, Tạp chí Khoa học, ĐHQG Hà Nội.\\nTạp chí xuất bản định kỳ (04 số tiếng Việt\u002Fnăm và 02 số tiếng Anh\u002Fnăm), có nhiệm vụ \"},{\"attributes\":{\"italic\":true},\"insert\":\"công bố, giới thiệu các công trình nghiên cứu khoa học khoa học xã hội và nhân văn của các tác giả là các nhà khoa học trong và ngoài nước, phục vụ giảng dạy, học tập và nghiên cứu khoa học\"},{\"insert\":\". Hội đồng biên tập của Tạp chí hiện bao gồm 33 nhà khoa học có uy tín trong nước và quốc tế. Tạp chí tập trung và ưu tiên đăng tải những bài báo theo định hướng của tinh thần cởi mở, sáng tạo, nhanh chóng vươn lên để tiếp cận và sánh ngang với các tạp chí có uy tín hàng đầu của khu vực và trên thế giới. Nội dung chính của Tạp chí bao gồm các Bài nghiên cứu (khoảng 6000 đến 15000 từ), các bài điểm sách, thông tin khoa học (khoảng 300 đến 1500 từ) được trình bày theo đúng cấu trúc và chuẩn mực của một tạp chí khoa học.\\nCác bài viết của Tạp chí hiện đang được trích dẫn bởi Google Scholar, WorldCat, Open Archives, Cosmos Impact Factor, Advanced Sciences Index, Scientific Indexing Services, CrossRef, EBSCO Information Services.\\nMọi thông tin xin liên hệ: \"},{\"attributes\":{\"italic\":true},\"insert\":\"Phòng Tạp chí, 701 - E, Trường Đại học Khoa học Xã hội và Nhân văn, 336 Nguyễn Trãi, Thanh Xuân, Hà Nội. ĐT: 024.35581984; email: tckhxhnv@vnu.edu.vn \"},{\"insert\":\"hoặc \"},{\"attributes\":{\"italic\":true},\"insert\":\"tapchikhxhnv@gmail.com \"},{\"insert\":\"\\n\"}]}",{"EN":494,"VI":495},"VNU Journal of Social Sciences and Humanities","Tạp chí Khoa học Xã hội và Nhân văn",[101,102],[],[499],{"id":500,"createTime":501,"updateTime":502,"relativeEntities":503,"slug":504,"properties":505,"entityType":98,"verifyStatus":25,"verifyTime":509,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":222,"url":26,"parentIds":510,"statistic":511},"8b6e349b-0daf-4895-9c3f-85f30f1bfd42","2023-07-31T12:55:58.430+00:00","2026-06-19T02:30:07.899+00:00",[],"Tr%C6%B0%E1%BB%9Dng-%C4%90%E1%BA%A1i-h%E1%BB%8Dc-Khoa-h%E1%BB%8Dc-X%C3%A3-h%E1%BB%99i-v%C3%A0-Nh%C3%A2n-v%C4%83n-%C4%90%E1%BA%A1i-h%E1%BB%8Dc-Qu%E1%BB%91c-gia-H%C3%A0-N%E1%BB%99i",{"title":506},{"EN":507,"VI":508},"VNU University of Social Sciences and Humanities","Trường Đại học Khoa học Xã hội và Nhân văn, Đại học Quốc gia Hà Nội","2023-08-02T15:28:49.057+00:00",[],{"impactFactor":36,"impactFactorByYear":512,"i10Index":59,"i10IndexLast5Year":115,"totalPublication":514,"totalPublicationByYear":515,"totalCitation":518,"totalCitationByYear":519,"totalCitationPerPublication":520,"totalCitationPerPublicationByYear":521,"hindexLast5Year":162,"hindex":162},{"2016":229,"2017":230,"2018":229,"2021":230,"2022":58,"2023":513,"2024":38},0.15,365,{"2013":115,"2014":158,"2015":125,"2016":252,"2017":44,"2018":516,"2019":286,"2020":396,"2021":259,"2022":517,"2023":396,"2024":115,"2025":59,"2026":115},21,42,169,{"2015":283,"2016":135,"2017":52,"2018":50,"2019":298,"2020":53,"2021":359,"2022":115,"2023":115},0.46,{"2015":266,"2016":57,"2017":57,"2018":522,"2019":523,"2020":524,"2021":368,"2022":229,"2023":39},0.38,0.32,0.37,[],"http:\u002F\u002Fjournal.ussh.vnu.edu.vn\u002Findex.php\u002Fvjossh","\u002Fapi\u002Fpublic\u002Ffile\u002Fpublisher\u002F2b8d7b12-2d20-4777-be98-077f44f03c69\u002F0bd0751202944a4b4165b482e6e623e4.png",{"impactFactor":36,"impactFactorByYear":529,"i10Index":162,"i10IndexLast5Year":115,"totalPublication":530,"totalPublicationByYear":531,"totalCitation":537,"totalCitationByYear":538,"totalCitationPerPublication":343,"totalCitationPerPublicationByYear":540,"hindexLast5Year":135,"hindex":135},{"2016":341,"2017":39,"2018":229,"2019":38,"2020":110,"2021":231,"2022":231,"2023":341,"2024":39},764,{"2015":255,"2016":245,"2017":532,"2018":259,"2019":533,"2020":534,"2021":535,"2022":536,"2023":534,"2024":254,"2025":50},111,106,68,92,89,265,{"2015":539,"2016":241,"2017":168,"2018":51,"2019":349,"2020":260,"2021":244,"2022":298,"2023":114},46,{"2015":541,"2016":523,"2017":524,"2018":290,"2019":542,"2020":522,"2021":523,"2022":57,"2023":39},1.48,0.63,{"id":544,"createTime":545,"updateTime":546,"relativeEntities":547,"slug":548,"properties":549,"entityType":24,"verifyStatus":25,"verifyTime":26,"verifyNote":558,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":50,"subjectFields":559,"manageAffiliations":560,"indexDatabases":561,"url":562,"thumbnailPath":563,"statistic":564,"gsStatistic":26,"type":26,"analyzePriority":26},"6ec01bd0-15c0-469a-86ac-41339076ae0a","2023-08-10T07:08:33.153+00:00","2026-01-31T21:19:06.362+00:00",[],"T%E1%BA%A1p-ch%C3%AD-Da-li%E1%BB%85u-h%E1%BB%8Dc-Vi%E1%BB%87t-Nam",{"country":550,"issn":551,"introduce":553,"title":555},{"VOID":15},{"VOID":552},"18594824",{"VI":554},"{\"ops\":[{\"insert\":\"Tạp chí “Da liễu học Việt Nam” (Tiếng Anh: Vietnamese Journal of Dermatology and Venereology) thuộc Hội Da liễu Việt Nam, xuất bản 4 số mỗi năm bằng tiếng Việt hoặc tiếng Anh.\\nTạp chí Da liễu học Việt Nam hoạt động với mục đích, tôn chỉ là phổ biến, trao đổi thông tin trong lĩnh vực chuyên ngành da liễu; đăng tải các công trình nghiên cứu khoa học; chuyển giao công nghệ - kinh tế và khoa học kỹ thuật liên quan đến lĩnh vực da liễu.\\nPhạm vi của tạp chí là tất cả các bài báo khoa học, bài tổng quan, giới thiệu ca lâm sàng, … có liên quan tới chuyên ngành da liễu trong và ngoài nước. Tạp chí công bố các công trình nghiên cứu liên quan đến mô hình bệnh tật, các phương pháp chẩn đoán, điều trị, dự phòng và phục hồi chức năng các bệnh thuộc chuyên ngành da liễu. Ngoài ra, tạp chí còn đăng tải các bài tổng quan, cập nhật thông tin, kiến thức, hướng dẫn chẩn đoán, điều trị trong chuyên ngành da liễu trong nước và quốc tế; đăng tải các bài ca lâm sàng đặc biệt trong chuyên ngành da liễu.\\nTạp chí Da liễu học Việt Nam được biết tới là một tạp chí chuyên ngành có uy tín trong lĩnh vực da liễu. Các bài báo về nghiên cứu khoa học đăng trong Tạp chí được bình duyệt một cách nghiêm ngặt bởi ít nhất 2 chuyên gia. Hội đồng biên tập tạp chí bao gồm các nhà khoa học có uy tín (Giáo sư, Phó Giáo sư, Tiến sĩ, Bác sĩ…) trong chuyên ngành da liễu nhằm đảm bảo chất lượng và tính khách quan, khoa học cho các bài viết đăng trên Tạp chí.\\n\"}]}",{"EN":556,"VI":557},"Vietnamese Journal of Dermatology and Venereology","Tạp chí Da liễu học Việt Nam","Admin Import",[],[],[],"https:\u002F\u002Fvjdv.vn\u002Findex.php\u002Fvjdv","\u002Fapi\u002Fpublic\u002Ffile\u002Fpublisher\u002F6ec01bd0-15c0-469a-86ac-41339076ae0a\u002F3cbc81720e58429dc7b1c4d7ab1935ca.jpg",{"impactFactor":36,"impactFactorByYear":565,"i10Index":36,"i10IndexLast5Year":36,"totalPublication":566,"totalPublicationByYear":567,"totalCitation":116,"totalCitationByYear":570,"totalCitationPerPublication":109,"totalCitationPerPublicationByYear":571,"hindexLast5Year":115,"hindex":115},{"2023":38,"2024":230},182,{"2022":568,"2023":45,"2024":569},69,56,{"2022":52,"2023":115},{"2022":40,"2023":229},{"id":573,"createTime":574,"updateTime":575,"relativeEntities":576,"slug":577,"properties":578,"entityType":24,"verifyStatus":25,"verifyTime":26,"verifyNote":27,"syncStatus":28,"languages":587,"translateLanguages":26,"viewCount":283,"subjectFields":588,"manageAffiliations":589,"indexDatabases":647,"url":648,"thumbnailPath":649,"statistic":650,"gsStatistic":26,"type":26,"analyzePriority":26},"19221551-7519-47ff-a892-331d1139c64b","2023-09-12T07:03:05.744+00:00","2026-01-24T20:54:40.144+00:00",[],"T%E1%BA%A1p-ch%C3%AD-Khoa-h%E1%BB%8Dc-S%E1%BB%A9c-kho%E1%BA%BB-%C4%90%E1%BA%A1i-h%E1%BB%8Dc-Qu%E1%BB%91c-gia-Th%C3%A0nh-ph%E1%BB%91-H%E1%BB%93-Ch%C3%AD-Minh",{"country":579,"issn":580,"introduce":582,"title":584},{"VOID":15},{"VOID":581},"27349446",{"EN":583},"{\"ops\":[{\"attributes\":{\"bold\":true},\"insert\":\"1. History\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"Science and Technology Development Journal\"},{\"insert\":\" (STDJ) (ISSN 2734-9446), Vietnam National University - Ho Chi Minh City (VNU-HCM) was established in 1997. And the first issue was published in January 1998 with ISSN 1859-0128. Since then, STDJ has become the most important scientific forum of scientists from VNU-HCM as well as other universities. The magazine has undergone 20 years of development and has become a bridge for scientific exchanges, as well as enriching reference materials for the faculty, doctoral students, students of VNU-HCM in particular and other universities, institutes...\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"Science and Technology Development Journal - Health Sciences (STDJ-HS) is a subjournal of Science and Technology Development Journal since 2020.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\" \"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"attributes\":{\"bold\":true},\"insert\":\"2. Focus and Scope\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"Publishing articles with contents on healthcare, research projects in the field of health, advanced directions in health education and management. Therefore, STDJ-HS will accept articles from doctors, administrators, teachers, researchers, graduate students and fellows. The authors will be responsible for the accuracy of the data, opinions, opinions and material cited in the article.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"Facilitating the exchange of scientific and technological information and act as a bridge between theory and practice in the community of educators, scientists, managers, policy makers and enterprises in and out of the country.\"},{\"attributes\":{\"align\":\"justify\"},\"insert\":\"\\n\"},{\"insert\":\"\\n\"}]}",{"EN":585,"VI":586},"VNUHCM JOURNAL OF HEALTH SCIENCES","Tạp chí Khoa học Sức khoẻ Đại học Quốc gia Thành phố Hồ Chí Minh",[101,102],[],[590],{"id":591,"createTime":592,"updateTime":593,"relativeEntities":594,"slug":595,"properties":596,"entityType":98,"verifyStatus":25,"verifyTime":606,"verifyNote":26,"syncStatus":28,"languages":607,"translateLanguages":26,"viewCount":608,"url":609,"parentIds":610,"statistic":611},"fc4c2560-868c-4677-acb1-8686bef88727","2023-06-26T04:48:23.774+00:00","2026-06-19T01:46:17.248+00:00",[],"%C4%90%E1%BA%A1i-h%E1%BB%8Dc-Qu%E1%BB%91c-gia-Th%C3%A0nh-ph%E1%BB%91-H%E1%BB%93-Ch%C3%AD-Minh",{"country":597,"title":598,"abbreviation":601,"address":603},{"VOID":15},{"EN":599,"VI":600},"Vietnam National University Ho Chi Minh City","Đại học Quốc gia Thành phố Hồ Chí Minh",{"VOID":602},"VNUHCM",{"VI":604,"EN":605},"khu phố 6, Phường Linh Trung, Thành phố Thủ Đức, Thành phố Hồ Chí Minh, Việt Nam","Quarter 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam","2023-08-16T14:13:07.134+00:00",[101,102],45,"https:\u002F\u002Fvnuhcm.edu.vn\u002F",[],{"impactFactor":36,"impactFactorByYear":612,"i10Index":285,"i10IndexLast5Year":51,"totalPublication":613,"totalPublicationByYear":614,"totalCitation":624,"totalCitationByYear":625,"totalCitationPerPublication":634,"totalCitationPerPublicationByYear":635,"hindexLast5Year":124,"hindex":124},{"2014":36,"2015":38,"2016":109,"2017":39,"2018":229,"2019":38,"2020":110,"2021":171,"2022":110,"2023":110,"2024":171,"2025":229},3708,{"2005":115,"2006":115,"2007":115,"2008":135,"2009":111,"2010":53,"2011":52,"2012":568,"2013":615,"2014":351,"2015":616,"2016":469,"2017":617,"2018":127,"2019":618,"2020":619,"2021":620,"2022":621,"2023":622,"2024":623,"2025":159,"2026":50},164,266,177,226,603,931,314,246,74,2399,{"2006":115,"2008":50,"2010":162,"2011":242,"2012":222,"2013":43,"2014":626,"2015":627,"2016":628,"2017":629,"2018":258,"2019":630,"2020":631,"2021":632,"2022":633,"2023":353,"2024":115},250,124,157,162,152,385,559,128,0.65,{"2006":115,"2008":636,"2010":637,"2011":638,"2012":639,"2013":522,"2014":640,"2015":641,"2016":642,"2017":643,"2018":644,"2019":642,"2020":645,"2021":292,"2022":646,"2023":522,"2024":38},1.33,0.45,2.22,0.39,1.35,0.47,0.67,0.92,0.73,0.64,0.41,[],"http:\u002F\u002Fstdjhs.scienceandtechnology.com.vn\u002Findex.php\u002Fstdjhs","\u002Fapi\u002Fpublic\u002Ffile\u002Fpublisher\u002F19221551-7519-47ff-a892-331d1139c64b\u002F2e10768b7d1e380c36a25ed0c714dd7e.png",{"impactFactor":36,"impactFactorByYear":651,"i10Index":36,"i10IndexLast5Year":36,"totalPublication":652,"totalPublicationByYear":653,"totalCitation":516,"totalCitationByYear":654,"totalCitationPerPublication":151,"totalCitationPerPublicationByYear":655,"hindexLast5Year":59,"hindex":59},{"2022":57,"2023":108,"2024":39},79,{"2020":116,"2021":240,"2022":239,"2023":52,"2024":162,"2025":162},{"2020":59,"2021":51,"2022":59},{"2020":443,"2021":387,"2022":54},{"code":657,"data":658,"meta":26},"SUCCESS",{"id":659,"createTime":660,"updateTime":661,"relativeEntities":662,"slug":663,"properties":664,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":671,"manageAffiliations":720,"indexDatabases":730,"url":755,"thumbnailPath":26,"statistic":756,"gsStatistic":26,"type":175,"analyzePriority":26},"da19403e-15b4-4b2c-9d09-474f7d03f67d","2024-04-11T06:38:19.496+00:00","2025-11-21T09:50:24.288+00:00",[],"SN-Computer-Science",{"issn":665,"eissn":667,"title":669},{"VOID":666},"2661-8907",{"VOID":668},"2662-995X",{"EN":670},"SN Computer Science",[672,680,688,696,704,712],{"id":673,"createTime":674,"updateTime":675,"relativeEntities":676,"label":677,"description":679,"parentId":26,"standard":26,"scholarHubFieldId":26},"e8c4e2fa-b139-4154-a158-595bd93d381c","2023-05-29T10:24:07.692+00:00","2023-11-21T07:15:31.396+00:00",[],{"EN":678},"Computational Theory and Mathematics",{},{"id":681,"createTime":682,"updateTime":683,"relativeEntities":684,"label":685,"description":687,"parentId":26,"standard":26,"scholarHubFieldId":26},"6a3cb349-a9fc-40fb-9aa8-c9946de1e629","2023-05-29T10:24:01.672+00:00","2023-11-21T06:16:02.793+00:00",[],{"EN":686},"Artificial Intelligence",{},{"id":689,"createTime":690,"updateTime":691,"relativeEntities":692,"label":693,"description":695,"parentId":26,"standard":26,"scholarHubFieldId":26},"bb1bad44-29ec-44f6-a988-069cb69215fd","2023-05-29T10:24:07.710+00:00","2023-11-21T08:09:16.461+00:00",[],{"EN":694},"Computer Science Applications",{},{"id":697,"createTime":698,"updateTime":699,"relativeEntities":700,"label":701,"description":703,"parentId":26,"standard":26,"scholarHubFieldId":26},"b177825c-9d9c-4720-9088-005a06cd20a2","2023-05-29T10:24:12.249+00:00","2023-11-21T07:47:26.903+00:00",[],{"EN":702},"Computer Science (miscellaneous)",{},{"id":705,"createTime":706,"updateTime":707,"relativeEntities":708,"label":709,"description":711,"parentId":26,"standard":26,"scholarHubFieldId":26},"f0f0ec9c-bb68-4288-a6f1-ee3d47dc35eb","2023-05-29T10:24:08.348+00:00","2023-11-21T07:29:12.845+00:00",[],{"EN":710},"Computer Networks and Communications",{},{"id":713,"createTime":714,"updateTime":715,"relativeEntities":716,"label":717,"description":719,"parentId":26,"standard":26,"scholarHubFieldId":26},"4e896d33-26c2-4e3f-a97e-e17cfeda3425","2023-05-29T10:24:07.471+00:00","2023-11-21T02:13:17.501+00:00",[],{"EN":718},"Computer Graphics and Computer-Aided Design",{},[721],{"id":722,"createTime":723,"updateTime":724,"relativeEntities":725,"slug":726,"properties":727,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"url":26,"parentIds":729,"statistic":26},"26a19206-5cad-4456-bb2f-49abd254fbc6","2024-04-19T01:59:34.612+00:00","2025-11-21T10:06:47.646+00:00",[],"SPRINGER",{"title":728},{"EN":726},[],[731],{"id":732,"indexDatabase":733,"url":745,"indexYears":746,"academicFieldIds":747,"indexDatabaseRanking":754},"16de44d4-0db7-4a68-b6a4-866808e18004",{"id":734,"createTime":735,"updateTime":736,"relativeEntities":737,"label":738,"description":740,"key":742,"publicationTags":743,"standard":26},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9","2023-05-22T09:57:18.509+00:00","2025-11-21T10:07:52.274+00:00",[],{"EN":739,"VI":739},"Scopus - Elsevier",{"EN":739,"VI":741},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[744],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F21101083109","2020-2025",[748,749,750,751,752,753],"bccef185-f7d5-4f2e-881a-2dc383ea6c57","06e80d88-c77a-4e7d-afe5-4dd75e7cdac8","212b8bab-be53-49b1-9ceb-c528868800fc","ea95f5ee-62c8-4ccb-8e3e-d3a8bc9e97cb","b4ee5f56-1514-428f-b517-37576cea076b","ee665440-0967-40f8-92e7-239e7375196a","NONE","https:\u002F\u002Flink.springer.com\u002Fjournal\u002F42979",{"impactFactor":36,"impactFactorByYear":757,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":761,"totalCitation":767,"totalCitationByYear":768,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":773,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},1.23,0.98,4038,{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},543,813,740,1069,318,4780,{"2020":769,"2021":770,"2022":771},2631,1345,311,1.18,{"2020":774,"2021":775,"2022":776},4.85,1.65,0.42,{"meta":778,"data":780},{"total":779},"1759",[781,1274,1569,1862,2113,2446,2617,2805,3046,3814],{"id":782,"createTime":783,"updateTime":784,"relativeEntities":785,"slug":786,"properties":787,"entityType":802,"verifyStatus":25,"verifyTime":784,"verifyNote":803,"syncStatus":28,"languages":804,"translateLanguages":26,"viewCount":36,"primaryUrl":805,"fullTextUrl":26,"authors":806,"publicationType":919,"publisherRelationship":920,"citationCount":942,"citationInfo":943,"publishDate":947,"publishYear":948,"citationAnalyzeStatus":949,"lastCitationAnalyze":950,"indexDatabases":26,"openAccess":26,"references":951,"isForceReanalyzing":1273},"35c707b6-8c6f-4d44-8177-1c165b219e57","2024-04-13T22:38:59.714+00:00","2024-09-03T13:37:20.343+00:00",[],"Forecasting-Models-for-Coronavirus-Disease-COVID-19-A-Survey-of-the-State-of-the-Art",{"mag":788,"keywords":790,"pmc":791,"openalex":793,"abstract":795,"title":796,"pm":798,"doi":800},{"VOID":789},"3016236951",{},{"VOID":792},"7289234",{"VOID":794},"W3016236951",{},{"EN":797},"Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art",{"VOID":799},"33063048",{"VOID":801},"10.1007\u002Fs42979-020-00209-9","PUBLICATION","Auto Verify",[102],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-020-00209-9",[807,825,843,864,883,903],{"id":808,"sortIndex":115,"researcher":26,"roles":809,"affiliations":810,"properties":820},"ccf87c1b-85a8-4fd0-92a3-8c65f64210c1",[],[811],{"id":26,"sortIndex":36,"affiliation":812,"properties":26},{"id":813,"createTime":814,"updateTime":814,"relativeEntities":815,"slug":816,"properties":817,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"67dcdfbc-7338-48d1-84e5-862f44c8dfb0","2024-04-13T22:38:59.734+00:00",[],"Department-of-Computer-Engineering-Smt-Kashibai-Navale-College-of-Engineering-Pune-Maharashtra-India",{"title":818},{"EN":819},"Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Pune, Maharashtra, India",{"openalex":821,"title":823},{"VOID":822},"A5008445326",{"EN":824},"Asmita Balasaheb Kalamkar",{"id":826,"sortIndex":162,"researcher":26,"roles":827,"affiliations":828,"properties":838},"0eafa613-9154-4785-bf5d-b8f84bf9c342",[],[829],{"id":26,"sortIndex":36,"affiliation":830,"properties":26},{"id":831,"createTime":832,"updateTime":832,"relativeEntities":833,"slug":834,"properties":835,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"2e1ab979-92b1-4e26-8d73-75d0e55d16ad","2024-04-13T22:38:59.781+00:00",[],"Faculty-of-Computers-and-Information-Information-Technology-Department-Cairo-University-Giza-Egypt",{"title":836},{"EN":837},"Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt",{"openalex":839,"title":841},{"VOID":840},"A5085432173",{"EN":842},"Aboul Ella Hassanien",{"id":844,"sortIndex":111,"researcher":26,"roles":845,"affiliations":846,"properties":857},"02ff6ae6-b48e-40d7-8d22-678617adcce6",[],[847],{"id":26,"sortIndex":36,"affiliation":848,"properties":26},{"id":849,"createTime":850,"updateTime":851,"relativeEntities":852,"slug":853,"properties":854,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"d5b81416-54d6-4364-a128-7c1eca8397c7","2023-12-14T13:27:04.763+00:00","2024-09-30T21:21:36.175+00:00",[],"School-of-Information-Technology-and-Engineering-Vellore-Institute-of-Technology-Vellore-India",{"title":855},{"VI":856},"School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India",{"openalex":858,"orcid":860,"title":862},{"VOID":859},"A5090666420",{"VOID":861},"https:\u002F\u002Forcid.org\u002F0000-0003-1804-8590",{"EN":863},"Jyotismita Chaki",{"id":865,"sortIndex":59,"researcher":26,"roles":866,"affiliations":867,"properties":876},"80520395-8cf7-4360-8558-75a851c628f9",[],[868],{"id":26,"sortIndex":36,"affiliation":869,"properties":26},{"id":870,"createTime":871,"updateTime":871,"relativeEntities":872,"slug":26,"properties":873,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"46688714-3631-439e-8bb8-b837e4a9c0e5","2024-02-05T15:33:05.704+00:00",[],{"title":874},{"VI":875},"Department of Information Technology, Techno International New Town, Kolkata, India",{"openalex":877,"orcid":879,"title":881},{"VOID":878},"A5000975435",{"VOID":880},"https:\u002F\u002Forcid.org\u002F0000-0001-8437-498X",{"EN":882},"Nilanjan Dey",{"id":884,"sortIndex":114,"researcher":26,"roles":885,"affiliations":886,"properties":896},"2a258794-ae04-44c4-9ba6-c95bd24d5580",[],[887],{"id":26,"sortIndex":36,"affiliation":888,"properties":26},{"id":889,"createTime":890,"updateTime":890,"relativeEntities":891,"slug":892,"properties":893,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"3697f6ec-041b-49e1-8440-7993e3188446","2024-04-13T22:38:59.745+00:00",[],"Department-of-Communication-Media-and-Information-Technologies-Aalborg-University-Copenhagen-Denmark",{"title":894},{"EN":895},"Department of Communication, Media and Information Technologies, Aalborg University, Copenhagen, Denmark",{"openalex":897,"orcid":899,"title":901},{"VOID":898},"A5082942970",{"VOID":900},"https:\u002F\u002Forcid.org\u002F0000-0001-5474-6826",{"EN":902},"Parikshit N. Mahalle",{"id":904,"sortIndex":36,"researcher":26,"roles":905,"affiliations":906,"properties":912},"fd3666a3-7f62-41ab-9f48-22ef4645d66f",[],[907],{"id":26,"sortIndex":36,"affiliation":908,"properties":26},{"id":813,"createTime":814,"updateTime":814,"relativeEntities":909,"slug":816,"properties":910,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":911},{"EN":819},{"openalex":913,"orcid":915,"title":917},{"VOID":914},"A5080409343",{"VOID":916},"https:\u002F\u002Forcid.org\u002F0000-0001-6623-8722",{"EN":918},"Gitanjali R. Shinde","ARTICLE",{"url":26,"publisher":921,"properties":26},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":922,"slug":663,"properties":923,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":927,"manageAffiliations":928,"indexDatabases":929,"url":755,"thumbnailPath":26,"statistic":937,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":924,"eissn":925,"title":926},{"VOID":666},{"VOID":668},{"EN":670},[],[],[930],{"id":732,"indexDatabase":931,"url":745,"indexYears":746,"academicFieldIds":936,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":932,"label":933,"description":934,"key":742,"publicationTags":935,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":938,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":939,"totalCitation":767,"totalCitationByYear":940,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":941,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},214,{"total":942,"publishYear":26,"statisticByYear":944},{"2020":241,"2021":945,"2022":946,"2023":285,"2024":59},85,61,"2020-07-01",2020,"ERROR_IN_ANALYZE_CITATION","2024-04-14T09:25:31.653+00:00",[952,955,958,962,966,969,973,976,980,984,988,992,996,1000,1003,1006,1009,1013,1017,1021,1025,1029,1033,1037,1040,1044,1048,1052,1056,1059,1062,1066,1070,1074,1078,1082,1086,1089,1093,1097,1101,1105,1109,1112,1115,1119,1123,1127,1131,1135,1139,1143,1146,1150,1154,1157,1161,1165,1168,1171,1175,1179,1183,1187,1190,1194,1198,1202,1206,1209,1213,1216,1220,1224,1227,1231,1235,1239,1242,1246,1250,1254,1258,1261,1265,1269],{"id":26,"text":953,"url":26,"identifiers":954},"Dicker RC, Coronado F, Koo D, Parrish RG. Principles of epidemiology in public health practice; an introduction to applied epidemiology and biostatistics. 2006",{},{"id":26,"text":956,"url":26,"identifiers":957},"Gordis L. Epidemiology. 4th ed. Philadelphia, PA: Elsevier Saunders; 2014.",{},{"id":26,"text":959,"url":26,"identifiers":960},"Platt C. King death: the black death and its aftermath in late-medieval England. Oxon, U.K.: Routledge; 2014.",{"doi":961},"10.4324\u002F9781315072371",{"id":26,"text":963,"url":26,"identifiers":964},"DeWitte SN. Mortality risk and survival in the aftermath of the medieval black death. PLoS ONE. 2014;9(5):e96513.",{"doi":965},"10.1371\u002Fjournal.pone.0096513",{"id":26,"text":967,"url":26,"identifiers":968},"Diamond J. Guns, germs, and steel: the fates of human societies. New York: Norton; 2009.",{},{"id":26,"text":970,"url":26,"identifiers":971},"Frieden NM. The Russian cholera epidemic, 1892–93, and medical professionalization. J Soc History. 1977;10(4):538.",{"doi":972},"10.1353\u002Fjsh\u002F10.4.538",{"id":26,"text":974,"url":26,"identifiers":975},"McKibbin WJ, Sidorenko AA. Global macroeconomic consequences of pandemic influenza. analysis. Sydney, Australia: Lowy Institute for International Policy; 2006.",{},{"id":26,"text":977,"url":26,"identifiers":978},"Dixon S, McDonald S, Roberts J. AIDS and economic growth in Africa: a panel data analysis. J Int Develop. 2001;13(4):411–26.",{"doi":979},"10.1002\u002Fjid.795",{"id":26,"text":981,"url":26,"identifiers":982},"Flahault A, Valleron AJ. HIV and travel, no rationale for restrictions. Lancet. 1990;336(8724):1197–8.",{"doi":983},"10.1016\u002F0140-6736(90)92820-8",{"id":26,"text":985,"url":26,"identifiers":986},"Keogh-Brown MR, Smith RD. The economic impact of SARS: how does the reality match the predictions? Health Policy. 2008;88(1):110–20.",{"doi":987},"10.1016\u002Fj.healthpol.2008.03.003",{"id":26,"text":989,"url":26,"identifiers":990},"Achonu C, Laporte A, Gardam MA. The financial impact of controlling a respiratory virus outbreak in a teaching hospital: lessons learned from SARS. Canad J Public Health. 2005;96(1):52–4.",{"doi":991},"10.1007\u002FBF03404018",{"id":26,"text":993,"url":26,"identifiers":994},"Tizzoni M, Bajardi P, Poletto C, Ramasco JJ, Balcan D, et al. Real-time numerical forecast of global epidemic spreading: case study of 2009 A\u002FH1N1pdm. BMC Med. 2012;10(1):165.",{"doi":995},"10.1186\u002F1741-7015-10-165",{"id":26,"text":997,"url":26,"identifiers":998},"Jain S, Kamimoto L, Bramley AM, Schmitz AM, Benoit SR, et al. Hospitalized patients with 2009 H1N1 influenza in the United States, April–June 2015. New England J Med. 2009;361(20):1935–44.",{"doi":999},"10.1056\u002FNEJMoa0906695",{"id":26,"text":1001,"url":26,"identifiers":1002},"Park J, Kim J. Hong Kong sets ‘serious’ response to South Korea’s MERS outbreak.” Reuters, June 8. 2015",{},{"id":26,"text":1004,"url":26,"identifiers":1005},"Pandemic risk. Background paper for world development report 2014: Risk and opportunity; managing risk for development, World Bank, Washington.",{},{"id":26,"text":1007,"url":26,"identifiers":1008},"UNDP (United Nations Development Programme). A socio-economic impact assessment of the Zika virus in Latin America and the Caribbean: with a focus on Brazil, Colombia, and Suriname. UNDP, New York: Synthesis report; 2017.",{},{"id":26,"text":1010,"url":26,"identifiers":1011},"Fong SJ, Li G, Dey N, Crespo RG Herrera-Viedma E. Finding an accurate early forecasting model from small dataset: a case of 2019-ncov novel coronavirus outbreak. arXiv preprint arXiv:2003.10776. 2020",{"doi":1012},"10.9781\u002Fijimai.2020.02.002",{"id":26,"text":1014,"url":26,"identifiers":1015},"Gaudart J, Ghassani M, Mintsa J, Waku J, Rachdi M, Doumbo OK, Demongeot J (2010) Demographic and spatial factors as causes of an epidemic spread, the copule approach: application to the retro-prediction of the black death epidemy of 1346. In: 2010 IEEE 24th International conference on advanced information networking and applications workshops (pp 751–758). IEEE.",{"doi":1016},"10.1109\u002FWAINA.2010.79",{"id":26,"text":1018,"url":26,"identifiers":1019},"Greenhalgh D, HAY G. Mathematical modelling of the spread of HIV\u002FAIDS amongst injecting drug users. Math Med Biol J IMA. 1997;14(1):11–38.",{"doi":1020},"10.1093\u002Fimammb\u002F14.1.11",{"id":26,"text":1022,"url":26,"identifiers":1023},"Kim D, Hong S, Choi S, Yoon T. Analysis of transmission route of MERS coronavirus using decision tree and Apriori algorithm. In: 2016 18th International conference on advanced communication technology (ICACT). 2016. (pp 559–565). IEEE.",{"doi":1024},"10.1109\u002FICACT.2016.7423472",{"id":26,"text":1026,"url":26,"identifiers":1027},"Amiroch S, Pradana MS, Irawan MI, Mukhlash I. Maximum likelihood method on the construction of phylogenetic tree for identification the spreading of SARS epidemic. In: 2018 International symposium on advanced intelligent informatics (SAIN) 2018. (pp 137–141). IEEE.",{"doi":1028},"10.1109\u002FSAIN.2018.8673334",{"id":26,"text":1030,"url":26,"identifiers":1031},"Hu B, Gong J. Support vector machine based classification analysis of SARS spatial distribution. In: 2010 Sixth international conference on natural computation. 2010 (vol. 2, pp. 924–927). IEEE.",{"doi":1032},"10.1109\u002FICNC.2010.5583921",{"id":26,"text":1034,"url":26,"identifiers":1035},"Sultana N, Sharma N. Statistical models for predicting swine flu incidences in India. In: 2018 First international conference on secure cyber computing and communication (ICSCCC). 2018 (pp. 134–138). IEEE.",{"doi":1036},"10.1109\u002FICSCCC.2018.8703300",{"id":26,"text":1038,"url":26,"identifiers":1039},"World Health Organization online available on https:\u002F\u002Fwww.who.int\u002Femergencies\u002Fdiseases\u002Fnovel-coronavirus-2019\u002F.",{},{"id":26,"text":1041,"url":26,"identifiers":1042},"Bhatt C, Dey N, Ashour AS (eds). (2017). Internet of things and big data technologies for next generation healthcare.",{"doi":1043},"10.1007\u002F978-3-319-49736-5",{"id":26,"text":1045,"url":26,"identifiers":1046},"Hassanien AE, Dey N, Borra S (eds) (2018). Medical big data and internet of medical things: advances, challenges and applications. CRC Press, Boca Raton.",{"doi":1047},"10.1201\u002F9781351030380",{"id":26,"text":1049,"url":26,"identifiers":1050},"Lan K, Wang DT, Fong S, Liu LS, Wong KK, Dey N. A survey of data mining and deep learning in bioinformatics. J Med Syst. 2018;42(8):139.",{"doi":1051},"10.1007\u002Fs10916-018-1003-9",{"id":26,"text":1053,"url":26,"identifiers":1054},"Jain A, Bhatnagar V. Concoction of ambient intelligence and big data for better patient ministration services. Int J Ambient Comput Intell (IJACI). 2017;8(4):19–30.",{"doi":1055},"10.4018\u002FIJACI.2017100102",{"id":26,"text":1057,"url":26,"identifiers":1058},"Bhattacharjee S. Statistical investigation of relationship between spread of coronavirus disease (COVID-19) and environmental factors based on study of four mostly affected places of China and five mostly affected places of Italy. arXiv preprint arXiv:2003.11277. 2020",{},{"id":26,"text":1060,"url":26,"identifiers":1061},"Toda AA. Susceptible-infected-recovered (sir) dynamics of covid-19 and economic impact. arXiv preprint arXiv:2003.11221. 2020",{},{"id":26,"text":1063,"url":26,"identifiers":1064},"Caccavo D. Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model. medRxiv. 2020",{"doi":1065},"10.1101\u002F2020.03.19.20039388",{"id":26,"text":1067,"url":26,"identifiers":1068},"Siwiak MM, Szczesny P, Siwiak MP. From a single host to global spread. The global mobility based modelling of the COVID-19 pandemic implies higher infection and lower detection rates than current estimates. medRxiv. 2020",{"doi":1069},"10.1101\u002F2020.03.21.20040444",{"id":26,"text":1071,"url":26,"identifiers":1072},"Zareie B, Roshani A, Mansournia MA, Rasouli MA, Moradi G. A model for COVID-19 prediction in Iran based on China parameters. medRxiv. 2020",{"doi":1073},"10.1101\u002F2020.03.19.20038950",{"id":26,"text":1075,"url":26,"identifiers":1076},"Teles P. Predicting the evolution Of SARS-Covid-2 in Portugal using an adapted SIR Model previously used in South Korea for the MERS outbreak. arXiv preprint https:\u002F\u002FarXiv:2003.10047. 2020",{"doi":1077},"10.1101\u002F2020.03.18.20038612",{"id":26,"text":1079,"url":26,"identifiers":1080},"Russo L, Anastassopoulou C, Tsakris A, Bifulco GN, Campana EF, Toraldo G, Siettos C. Tracing DAY-ZERO and forecasting the fade out of the COVID-19 outbreak in Lombardy, Italy: a compartmental modelling and numerical optimization approach. medRxiv. 2020",{"doi":1081},"10.1101\u002F2020.03.17.20037689",{"id":26,"text":1083,"url":26,"identifiers":1084},"Liu P, Beeler P, Chakrabarty RK. COVID-19 progression timeline and effectiveness of response-to-spread interventions across the United States. medRxiv. 2020",{"doi":1085},"10.1101\u002F2020.03.17.20037770",{"id":26,"text":1087,"url":26,"identifiers":1088},"Nadim SS, Ghosh I, Chattopadhyay J. Short-term predictions and prevention strategies for COVID-2019: a model based study. arXiv preprint https:\u002F\u002FarXiv:2003.08150. 2020",{},{"id":26,"text":1090,"url":26,"identifiers":1091},"Hossain M, Junus A, Zhu X, Jia P, Wen TH, Pfeiffer D, Yuan HY. The effects of border control and quarantine measures on global spread of COVID-19. In: Alvin and Zhu, Xiaolin and Jia, Pengfei and Wen, Tzai-Hung and Pfeiffer, Dirk and Yuan, Hsiang-Yu, The effects of border control and quarantine measures on global spread of COVID-19 .2020 (March 2, 2020).",{"doi":1092},"10.1101\u002F2020.03.13.20035261",{"id":26,"text":1094,"url":26,"identifiers":1095},"Rocha Filho TM, dos Santos FSG, Gomes VB, Rocha TA, Croda JH, Ramalho WM, Araujo WN Expected impact of COVID-19 outbreak in a major metropolitan area in Brazil. medRxiv. 2020.",{"doi":1096},"10.1101\u002F2020.03.14.20035873",{"id":26,"text":1098,"url":26,"identifiers":1099},"Traini MC, Caponi C, De Socio GV. Modelling the epidemic 2019-nCoV event in Italy: a preliminary note. medRxiv. 2020.",{"doi":1100},"10.1101\u002F2020.03.14.20034884",{"id":26,"text":1102,"url":26,"identifiers":1103},"Giordano G, Blanchini F, Bruno R, Colaneri P, Di Filippo A, Di Matteo A, Colaneri M. Modelling the COVID-19 epidemic andimplementation of population-wide interventions in Italy. Nat Med. 2020;1–6.",{"doi":1104},"10.1038\u002Fs41591-020-0883-7",{"id":26,"text":1106,"url":26,"identifiers":1107},"Wangping J, Ke H, Yang S, Wenzhe C, Shengshu W, Shanshan Y, Miao L.  Extended SIR prediction of the epidemics trend ofCOVID-19 in Italy and compared with Hunan, China. Front Med. 2020;7:169.",{"doi":1108},"10.3389\u002Ffmed.2020.00169",{"id":26,"text":1110,"url":26,"identifiers":1111},"Li L, Yang Z, Dang Z, Meng C, Huang J, Meng H, Shao Y. Propagation analysis and prediction of the COVID-19. Infect Dis Model. 2020;5:282–92.",{},{"id":26,"text":1113,"url":26,"identifiers":1114},"La S, Bogoch II, Ruktanonchai N, Watts AG, Li Y, Yu J, Lv X, Yang W, Hongjie Y, Khan K, Li Z. Assessing spread risk of Wuhan novel coronavirus within and beyond China, January–April 2020: a travel network-based modelling study. 2020.",{},{"id":26,"text":1116,"url":26,"identifiers":1117},"Zhu X, Zhang A, Xu S, Jia P, Tan X, Tian J, Wei T, Quan Z, Yu J. Spatially explicit modeling of 2019-nCoV epidemic trend based on mobile phone data in mainland China. medRxiv. 2020.",{"doi":1118},"10.1101\u002F2020.02.09.20021360",{"id":26,"text":1120,"url":26,"identifiers":1121},"Volpert V, Banerjee M, Petrovskii S. On a quarantine model of coronavirus infection and data analysis. Math Modell Nat Phenomena. 2020;15:24.",{"doi":1122},"10.1051\u002Fmmnp\u002F2020006",{"id":26,"text":1124,"url":26,"identifiers":1125},"Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the novel coronavirus (2019-nCoV) outbreak. medRxiv. 2020.",{"doi":1126},"10.1101\u002F2020.02.11.20022186",{"id":26,"text":1128,"url":26,"identifiers":1129},"Li C, Chen LJ, Chen X, Zhang M, Pang CP, Chen H. Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020. Eurosurveillance. 2020;25(10):2000199.",{"doi":1130},"10.2807\u002F1560-7917.ES.2020.25.10.2000199",{"id":26,"text":1132,"url":26,"identifiers":1133},"Bayham J, Fenichel EP. The impact of school closure for COVID-19 on the US healthcare workforce and the net mortality effects. medRxiv. 2020.",{"doi":1134},"10.1101\u002F2020.03.09.20033415",{"id":26,"text":1136,"url":26,"identifiers":1137},"Giuliani D, Dickson MM, Espa G, Santi F. Modelling and predicting the spread of coronavirus (COVID-19) infection in NUTS-3 Italian regions. arXiv preprint arXiv:2003.06664. 2020.",{"doi":1138},"10.21203\u002Frs.3.rs-20501\u002Fv3",{"id":26,"text":1140,"url":26,"identifiers":1141},"Lu J. A new, simple projection model for COVID-19 pandemic. medRxiv. 2020.",{"doi":1142},"10.1101\u002F2020.03.21.20039867",{"id":26,"text":1144,"url":26,"identifiers":1145},"Webb G. Predicting the number of reported and unreported cases for the COVID-19 epidemic in South Korea, Italy, France and Germany. medRxiv. 2020.",{},{"id":26,"text":1147,"url":26,"identifiers":1148},"Victor AO. Mathematical predictions for Covid-19 as a global pandemic. medRxiv. 2020.",{"doi":1149},"10.2139\u002Fssrn.3555879",{"id":26,"text":1151,"url":26,"identifiers":1152},"Wang H, Zhang Y, Lu S, Wang S. Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19. medRxiv. 2020.",{"doi":1153},"10.1101\u002F2020.03.21.20040139",{"id":26,"text":1155,"url":26,"identifiers":1156},"Botha AE, Dednam W. A simple iterative map forecast of the COVID-19 pandemic. arXiv preprint arXiv:2003.10532. 2020.",{},{"id":26,"text":1158,"url":26,"identifiers":1159},"Coelho FC, Lana RM, Cruz OG, Villela D, Bastos LS, Pastore y Piontti A, Davis JT, Vespignani A, Codeco C, Gomes MF. Assessing the potential impacts of COVID-19 in Brasil: mobility, morbidity and impact to the health system. medRxiv. 2020.",{"doi":1160},"10.2139\u002Fssrn.3559609",{"id":26,"text":1162,"url":26,"identifiers":1163},"Weber A, Ianelli F, Goncalves S. Trend analysis of the COVID-19 pandemic in China and the rest of the world. arXiv preprint https:\u002F\u002FarXiv:2003.09032. 2020.",{"doi":1164},"10.1101\u002F2020.03.19.20037192",{"id":26,"text":1166,"url":26,"identifiers":1167},"Sameni R. Mathematical modeling of epidemic diseases; a case study of the COVID-19 coronavirus. arXiv preprint https:\u002F\u002FarXiv:2003.11371. 2020.",{},{"id":26,"text":1169,"url":26,"identifiers":1170},"Long C, Ying Q, Fu X, Li Z, Gao Y. Forecasting the cumulative number of COVID-19 deaths in China: a Boltzmann function-based modeling study. medRxiv. 2020.",{},{"id":26,"text":1172,"url":26,"identifiers":1173},"Dowd JB, Andriano L, Brazel DM, Rotondi V, Block P, Ding X, Mills MC. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceed nat acad  sci 2020;117(18):9696–9698.",{"doi":1174},"10.1073\u002Fpnas.2004911117",{"id":26,"text":1176,"url":26,"identifiers":1177},"He X, Lau EH, Wu P, Deng X, Wang J, Hao X, Lau YC, Wong JY, Guan Y, Tan X, Mo X. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. 2020;26(5):672–675.",{"doi":1178},"10.1038\u002Fs41591-020-0869-5",{"id":26,"text":1180,"url":26,"identifiers":1181},"Giannakeas V, Bhatia D, Warkentin MT, Bogoch I, Stall NM. Estimating the maximum daily number of incident COVID-19 cases manageable by a healthcare system. medRxiv. 2020.",{"doi":1182},"10.1101\u002F2020.03.25.20043711",{"id":26,"text":1184,"url":26,"identifiers":1185},"Banerjee A, Pasea L, Harris S, Gonzalez-Izquierdo A, Torralbo A, Shallcross L, Noursadeghi M, Pillay D, Pagel C, Wong WK, Langenberg C. Estimating excess 1-year mortality from COVID-19 according to underlying conditions and age in England: a rapid analysis using NHS health records in 3.8 million adults. medRxiv. 2020.",{"doi":1186},"10.1101\u002F2020.03.22.20040287",{"id":26,"text":1188,"url":26,"identifiers":1189},"Siegenfeld AF, Bar-Yam Y. Eliminating COVID-19: a community-based analysis. arXiv preprint https:\u002F\u002FarXiv:2003.10086. 2020.",{},{"id":26,"text":1191,"url":26,"identifiers":1192},"Chen B, Liang H, Yuan X, Hu Y, Xu M, Zhao Y, Zhang B, Tian F, Zhu X. Roles of meteorological conditions in COVID-19 transmission on a worldwide scale. MedRxiv. 2020.",{"doi":1193},"10.1101\u002F2020.03.16.20037168",{"id":26,"text":1195,"url":26,"identifiers":1196},"Ma Y, Zhao Y, Liu J, He X, Wang B, Fu S, Yan J, Niu J, Luo B. Effects of temperature variation and humidity on the mortality of COVID-19 in Wuhan. medRxiv. 2020.",{"doi":1197},"10.1101\u002F2020.03.15.20036426",{"id":26,"text":1199,"url":26,"identifiers":1200},"Shi P, Dong Y, Yan H, Li X, Zhao C, Liu W, He M, Tang S, Xi S. The impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak-evidence from China. MedRxiv. 2020.",{"doi":1201},"10.1101\u002F2020.03.22.20038919",{"id":26,"text":1203,"url":26,"identifiers":1204},"Fong SJ, Li G, Dey N, Crespo RG, Herrera-Viedma E. Composite monte carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Appl Soft Comput. 2020;106282.",{"doi":1205},"10.1016\u002Fj.asoc.2020.106282",{"id":26,"text":1207,"url":26,"identifiers":1208},"Batista M. Estimation of the final size of the second phase of the coronavirus COVID-19 epidemic by the logistic model.",{},{"id":26,"text":1210,"url":26,"identifiers":1211},"Hu Z, Ge Q, Li S, Jin L, Xiong M. Evaluating the effect of public health intervention on the global-wide spread trajectory of Covid-19. medRxiv. 2020.",{"doi":1212},"10.1101\u002F2020.03.11.20033639",{"id":26,"text":1214,"url":26,"identifiers":1215},"Jia L, Li K, Jiang Y, Guo X. Prediction and analysis of coronavirus disease 2019. arXiv preprint https:\u002F\u002FarXiv:2003.05447. 2020.",{},{"id":26,"text":1217,"url":26,"identifiers":1218},"Kumar J, Hembram KPSS. Epidemiological study of novel coronavirus (COVID-19). 2020 arXiv preprint https:\u002F\u002FarXiv:2003.11376.",{"doi":1219},"10.18203\u002F2394-6040.ijcmph20210828",{"id":26,"text":1221,"url":26,"identifiers":1222},"DeCaprio D, Gartner J, Burgess T, Kothari S, Sayed S. Building a COVID-19 vulnerability index. arXiv preprint https:\u002F\u002FarXiv:2003.07347. 2020.",{"doi":1223},"10.1101\u002F2020.03.16.20036723",{"id":26,"text":1225,"url":26,"identifiers":1226},"Santosh K, Das D, Pal U. Truncated inception net: COVID-19 outbreak screening using chest X-rays. PREPRINT (Version 1) available at Research Square 3, 2020.",{},{"id":26,"text":1228,"url":26,"identifiers":1229},"Santosh KC. AI-driven tools for coronavirus outbreak: need of active learning and cross-population train\u002Ftest models on multitudinal\u002Fmultimodal data. J Med Syst. 2020;44(5)1–5 https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10916-020-01562-1.",{"doi":1230},"10.1007\u002Fs10916-020-01562-1",{"id":26,"text":1232,"url":26,"identifiers":1233},"Dey N, Rajinikant V, Fong SJ, Kaiser MS, Mahmud M. Social-group-optimization assisted kapur’s entropy and morphological segmentation for automated detection of COVID-19 infection from computed tomography images. 2020.",{"doi":1234},"10.20944\u002Fpreprints202005.0052.v1",{"id":26,"text":1236,"url":26,"identifiers":1237},"Wagh CS, Mahalle PN, Wagh SJ. Epidemic peak for COVID-19 in India, 2020. Preprints 2020, 2020050176 (https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202005.0176.v1).",{"doi":1238},"10.20944\u002Fpreprints202005.0176.v1",{"id":26,"text":1240,"url":26,"identifiers":1241},"Rajinikanth V, Dey N, Raj ANJ, Hassanien AE, Santosh KC, Raja N. Harmony-search and otsu based system for coronavirus disease (COVID-19) detection using lung CT scan images. 2020 arXiv preprint arXiv:2004.03431.",{},{"id":26,"text":1243,"url":26,"identifiers":1244},"Bhapkar HR, Mahalle P, Dhotre PS. Virus graph and COVID-19 pandemic: a graph theory approach. Preprints 2020, 2020040507 (https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202004.0507.v1).",{"doi":1245},"10.20944\u002Fpreprints202004.0507.v1",{"id":26,"text":1247,"url":26,"identifiers":1248},"Bullock J, Pham KH, Lam CS, Luengo-Oroz M. Mapping the landscape of artificial intelligence applications against COVID-19. arXiv preprint arXiv:2003.11336. 2020.",{"doi":1249},"10.1613\u002Fjair.1.12162",{"id":26,"text":1251,"url":26,"identifiers":1252},"Mahalle PN, Sable NP, Mahalle NP, Shinde GR Data analytics: COVID-19 prediction using multimodal data. Preprints 2020, 2020040257 (https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202004.0257.v1).",{"doi":1253},"10.20944\u002Fpreprints202004.0257.v1",{"id":26,"text":1255,"url":26,"identifiers":1256},"Ardabili SF, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy AR, Reuter U, Rabczuk T, Atkinson PM. Covid-19 outbreak prediction with machine learning. Available at SSRN 3580188. 2020.",{"doi":1257},"10.31234\u002Fosf.io\u002F5dyfc",{"id":26,"text":1259,"url":26,"identifiers":1260},"COVID-19 in India: guidance from the IndiaSIM Model- March 24, 2020. https:\u002F\u002Fcddep.org\u002Fcovid-19\u002F.",{},{"id":26,"text":1262,"url":26,"identifiers":1263},"Dey N, Fong S, Song W, Cho K Forecasting energy consumption from smart home sensor network by deep learning. In: International conference on smart trends for information technology and computer communications 2017 (pp. 255–265). Springer, Singapore.",{"doi":1264},"10.1007\u002F978-981-13-1423-0_28",{"id":26,"text":1266,"url":26,"identifiers":1267},"Hu S, Liu M, Fong S, Song W, Dey N, Wong R. Forecasting China future MNP by deep learning. In: Behavior engineering and applications 2018 (pp. 169–210). Springer, Cham.",{"doi":1268},"10.1007\u002F978-3-319-76430-6_8",{"id":26,"text":1270,"url":26,"identifiers":1271},"Singh N, Mohanty SR. Short term price forecasting using adaptive generalized neuron model. Int J Ambient Comput Intell (IJACI). 2018;9(3):44–56.",{"doi":1272},"10.4018\u002FIJACI.2018070104",false,{"id":1275,"createTime":1276,"updateTime":1277,"relativeEntities":1278,"slug":1279,"properties":1280,"entityType":802,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":1291,"translateLanguages":26,"viewCount":36,"primaryUrl":1292,"fullTextUrl":26,"authors":1293,"publicationType":919,"publisherRelationship":1394,"citationCount":1416,"citationInfo":1417,"publishDate":1419,"publishYear":948,"citationAnalyzeStatus":949,"lastCitationAnalyze":1277,"indexDatabases":26,"openAccess":26,"references":1420,"isForceReanalyzing":1273},"1c82ed0d-5880-4189-b5db-f388a6ebe3e7","2024-04-12T03:30:56.633+00:00","2024-04-13T12:38:21.744+00:00",[],"Breast-Cancer-Prediction-A-Comparative-Study-Using-Machine-Learning-Techniques",{"mag":1281,"keywords":1283,"openalex":1284,"abstract":1286,"title":1287,"doi":1289},{"VOID":1282},"3083171672",{},{"VOID":1285},"W3083171672",{},{"EN":1288},"Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques",{"VOID":1290},"10.1007\u002Fs42979-020-00305-w",[102],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-020-00305-w",[1294,1313,1327,1346,1364,1378],{"id":1295,"sortIndex":36,"researcher":26,"roles":1296,"affiliations":1297,"properties":1306},"91def779-8709-4545-832d-238fe25aa2cf",[],[1298],{"id":26,"sortIndex":36,"affiliation":1299,"properties":26},{"id":1300,"createTime":1301,"updateTime":1301,"relativeEntities":1302,"slug":26,"properties":1303,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"4c30cb06-7d1b-4556-9390-ec86ff1f27e8","2023-12-18T04:26:02.096+00:00",[],{"title":1304},{"VI":1305},"Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh",{"openalex":1307,"orcid":1309,"title":1311},{"VOID":1308},"A5061448749",{"VOID":1310},"https:\u002F\u002Forcid.org\u002F0000-0002-4535-5978",{"EN":1312},"Md. Milon Islam",{"id":1314,"sortIndex":114,"researcher":26,"roles":1315,"affiliations":1316,"properties":1322},"9860ccaa-174d-4a1b-8681-c65a16e80f3f",[],[1317],{"id":26,"sortIndex":36,"affiliation":1318,"properties":26},{"id":1300,"createTime":1301,"updateTime":1301,"relativeEntities":1319,"slug":26,"properties":1320,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1321},{"VI":1305},{"openalex":1323,"title":1325},{"VOID":1324},"A5017113728",{"EN":1326},"Hasib Iqbal",{"id":1328,"sortIndex":111,"researcher":26,"roles":1329,"affiliations":1330,"properties":1339},"aa412210-7226-4d87-a10c-26ead771e585",[],[1331],{"id":26,"sortIndex":36,"affiliation":1332,"properties":26},{"id":1333,"createTime":1334,"updateTime":1334,"relativeEntities":1335,"slug":26,"properties":1336,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"f2e9073b-53ba-4246-80e6-243655778529","2024-01-04T23:34:26.391+00:00",[],{"title":1337},{"VI":1338},"Department of Computer Science, Stony Brook University, Stony Brook, USA",{"openalex":1340,"orcid":1342,"title":1344},{"VOID":1341},"A5037795630",{"VOID":1343},"https:\u002F\u002Forcid.org\u002F0000-0002-4386-0356",{"EN":1345},"Mahmudul Hasan",{"id":1347,"sortIndex":162,"researcher":26,"roles":1348,"affiliations":1349,"properties":1359},"6351aa83-90c4-4c1f-84a8-eb7db5a15feb",[],[1350],{"id":26,"sortIndex":36,"affiliation":1351,"properties":26},{"id":1352,"createTime":1353,"updateTime":1353,"relativeEntities":1354,"slug":1355,"properties":1356,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"5cbda0b9-9c07-46f1-8b5f-183333e30738","2024-04-12T03:30:58.042+00:00",[],"Faculty-of-Computing-Universiti-Malaysia-Pahang-Gambang-Malaysia",{"title":1357},{"EN":1358},"Faculty of Computing, Universiti Malaysia Pahang, Gambang, Malaysia",{"openalex":1360,"title":1362},{"VOID":1361},"A5048170076",{"EN":1363},"Muhammad Nomani Kabir",{"id":1365,"sortIndex":115,"researcher":26,"roles":1366,"affiliations":1367,"properties":1373},"5eca990a-77dd-4154-bad4-7f6ed371f9cd",[],[1368],{"id":26,"sortIndex":36,"affiliation":1369,"properties":26},{"id":1300,"createTime":1301,"updateTime":1301,"relativeEntities":1370,"slug":26,"properties":1371,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1372},{"VI":1305},{"openalex":1374,"title":1376},{"VOID":1375},"A5081094175",{"EN":1377},"Md. Rezwanul Haque",{"id":1379,"sortIndex":59,"researcher":26,"roles":1380,"affiliations":1381,"properties":1387},"85108616-8153-4214-b16f-47bc89e95bf8",[],[1382],{"id":26,"sortIndex":36,"affiliation":1383,"properties":26},{"id":1352,"createTime":1353,"updateTime":1353,"relativeEntities":1384,"slug":1355,"properties":1385,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1386},{"EN":1358},{"openalex":1388,"orcid":1390,"title":1392},{"VOID":1389},"A5032869074",{"VOID":1391},"https:\u002F\u002Forcid.org\u002F0000-0002-2700-236X",{"EN":1393},"Muzamir Hasan",{"url":26,"publisher":1395,"properties":26},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":1396,"slug":663,"properties":1397,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":1401,"manageAffiliations":1402,"indexDatabases":1403,"url":755,"thumbnailPath":26,"statistic":1411,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":1398,"eissn":1399,"title":1400},{"VOID":666},{"VOID":668},{"EN":670},[],[],[1404],{"id":732,"indexDatabase":1405,"url":745,"indexYears":746,"academicFieldIds":1410,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":1406,"label":1407,"description":1408,"key":742,"publicationTags":1409,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":1412,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":1413,"totalCitation":767,"totalCitationByYear":1414,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":1415,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},158,{"total":1416,"publishYear":26,"statisticByYear":1418},{"2020":162,"2021":516,"2022":282,"2023":259,"2024":124},"2020-09-01",[1421,1424,1427,1430,1433,1437,1440,1443,1447,1451,1455,1459,1462,1466,1470,1474,1478,1482,1485,1489,1492,1496,1500,1503,1507,1511,1515,1519,1522,1525,1529,1532,1536,1539,1543,1546,1550,1554,1558,1562,1565],{"id":26,"text":1422,"url":26,"identifiers":1423},"Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiol Soc N Am. 2018;286(3):800–9.",{},{"id":26,"text":1425,"url":26,"identifiers":1426},"Breast Cancer: Statistics, Approved by the Cancer.Net Editorial Board, 04\u002F2017. [Online]. Available: http:\u002F\u002Fwww.cancer.net\u002Fcancer-types\u002Fbreast-cancer\u002Fstatistics. Accessed 26 Aug 2018.",{},{"id":26,"text":1428,"url":26,"identifiers":1429},"Mori M, Akashi-Tanaka S, Suzuki S, Daniels MI, Watanabe C, Hirose M, Nakamura S. Diagnostic accuracy of contrast-enhanced spectral mammography in comparison to conventional full-field digital mammography in a population of women with dense breasts. Springer. 2016;24(1):104–10.",{},{"id":26,"text":1431,"url":26,"identifiers":1432},"Kurihara H, Shimizu C, Miyakita Y, Yoshida M, Hamada A, Kanayama Y, Tamura K. Molecular imaging using PET for breast cancer. Springer. 2015;23(1):24–32.",{},{"id":26,"text":1434,"url":26,"identifiers":1435},"Azar AT, El-Said SA. Probabilistic neural network for breast cancer classification. Neural Comput Appl. 2013;23(6):1737–51.",{"doi":1436},"10.1007\u002Fs00521-012-1134-8",{"id":26,"text":1438,"url":26,"identifiers":1439},"Nagashima T, Suzuki M, Yagata H, Hashimoto H, Shishikura T, Imanaka N, Miyazaki M. Dynamic-enhanced MRI predicts metastatic potential of invasive ductal breast cancer. Springer. 2002;9(3):226–30.",{},{"id":26,"text":1441,"url":26,"identifiers":1442},"Park CS, Kim SH, Jung NY, Choi JJ, Kang BJ, Jung HS. Interobserver variability of ultrasound elastography and the ultrasound BI-RADS lexicon of breast lesions. Springer. 2013;22(2):153–60.",{},{"id":26,"text":1444,"url":26,"identifiers":1445},"Ayon SI, Islam MM, Hossain MR. Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE J Res. 2020;. https:\u002F\u002Fdoi.org\u002F10.1080\u002F03772063.2020.1713916.",{"doi":1446},"10.1080\u002F03772063.2020.1713916",{"id":26,"text":1448,"url":26,"identifiers":1449},"Muhammad LJ, Islam MM, Usman SS, Ayon SI. Predictive data mining models for novel coronavirus (COVID-19) infected patients’ recovery. SN Comput Sci. 2020;1(4):206.",{"doi":1450},"10.1007\u002Fs42979-020-00216-w",{"id":26,"text":1452,"url":26,"identifiers":1453},"Islam MM, Iqbal H, Haque MR, Hasan MK. Prediction of breast cancer using support vector machine and K-Nearest neighbors. In: Proc. IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, 2017, pp. 226–229.",{"doi":1454},"10.1109\u002FR10-HTC.2017.8288944",{"id":26,"text":1456,"url":26,"identifiers":1457},"Haque MR, Islam MM, Iqbal H, Reza MS, Hasan MK. Performance evaluation of random forests and artificial neural networks for the classification of liver disorder. In: Proc. International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, 2018, pp. 1–5.",{"doi":1458},"10.1109\u002FIC4ME2.2018.8465658",{"id":26,"text":1460,"url":26,"identifiers":1461},"Ayon SI, Islam MM. Diabetes prediction: a deep learning approach. Int J Inf Eng Electron Bus (IJIEEB). 2019;11(2):21–7.",{},{"id":26,"text":1463,"url":26,"identifiers":1464},"Islam MZ, Islam MM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images, 2020. pp. 1–20.",{"doi":1465},"10.1016\u002Fj.imu.2020.100412",{"id":26,"text":1467,"url":26,"identifiers":1468},"Hasan MK, Islam MM, Hashem MMA. Mathematical model development to detect breast cancer using multigene genetic programming. In: 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), pp. 574–579, 2016.",{"doi":1469},"10.1109\u002FICIEV.2016.7760068",{"id":26,"text":1471,"url":26,"identifiers":1472},"Sakri SB, Rashid NBA, Zain ZM. Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access. 2018;6:29637–47.",{"doi":1473},"10.1109\u002FACCESS.2018.2843443",{"id":26,"text":1475,"url":26,"identifiers":1476},"Juneja K, Rana C. An improved weighted decision tree approach for breast cancer prediction. In: International Journal of Information Technology, 2018.",{"doi":1477},"10.1007\u002Fs41870-018-0184-2",{"id":26,"text":1479,"url":26,"identifiers":1480},"Yue W, et al. Machine learning with applications in breast cancer diagnosis and prognosis. Designs. 2018;2(2):13.",{"doi":1481},"10.3390\u002Fdesigns2020013",{"id":26,"text":1483,"url":26,"identifiers":1484},"Banu AB, Subramanian PT. Comparison of Bayes classifiers for breast cancer classification. Asian Pac J Cancer Prev (APJCP). 2018;19(10):2917–20.",{},{"id":26,"text":1486,"url":26,"identifiers":1487},"Chaurasia V, Pal S, Tiwari B. Prediction of benign and malignant breast cancer using data mining techniques. J Algorithms Comput Technol. 2018;12(2):119–26.",{"doi":1488},"10.1177\u002F1748301818756225",{"id":26,"text":1490,"url":26,"identifiers":1491},"Azar AT, El-Metwally SM. Decision tree classifiers for automated medical diagnosis. Neural Comput Appl. 2012;23(7–8):2387–403.",{},{"id":26,"text":1493,"url":26,"identifiers":1494},"Senapati MR, Mohanty AK, Dash S, Dash PK. Local linear wavelet neural network for breast cancer recognition. Neural Comput Appl. 2013;22(1):125–31.",{"doi":1495},"10.1007\u002Fs00521-011-0670-y",{"id":26,"text":1497,"url":26,"identifiers":1498},"Senapati MR, Panda G, Dash PK. Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection. Neural Comput Appl. 2014;24(3–4):745–53.",{"doi":1499},"10.1007\u002Fs00521-012-1286-6",{"id":26,"text":1501,"url":26,"identifiers":1502},"Hasan MK, Islam MM, Hashem MMA (2016) Mathematical model development to detect breast cancer using multigene genetic programming. In: Proc. 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, 2016, pp. 574–579.",{"doi":1469},{"id":26,"text":1504,"url":26,"identifiers":1505},"Azar AT, El-Said SA. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl. 2013;24(5):1163–77.",{"doi":1506},"10.1007\u002Fs00521-012-1324-4",{"id":26,"text":1508,"url":26,"identifiers":1509},"Ferreira P, Dutra I, Salvini R, Burnside E. Interpretable models to predict Breast Cancer. In: Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, 2016, pp. 1507–1511.",{"doi":1510},"10.1109\u002FBIBM.2016.7822745",{"id":26,"text":1512,"url":26,"identifiers":1513},"Jhajharia S, Verma S, Kumar R. A cross-platform evaluation of various decision tree algorithms for prognostic analysis of breast cancer data. In: Proc. International Conference on Inventive Computation Technologies (ICICT), Coimbatore, 2016, pp. 1–7.",{"doi":1514},"10.1109\u002FINVENTIVE.2016.7830107",{"id":26,"text":1516,"url":26,"identifiers":1517},"Islam MM, Rahaman A, Islam MR. Development of smart healthcare monitoring system in IoT environment. SN Comput Sci. 2020;1(3):185.",{"doi":1518},"10.1007\u002Fs42979-020-00195-y",{"id":26,"text":1520,"url":26,"identifiers":1521},"Rahaman A, Islam M, Islam M, Sadi M, Nooruddin S. Developing IoT based smart health monitoring systems: a review. Rev d’Intell Artif. 2019;33(6):435–40.",{},{"id":26,"text":1523,"url":26,"identifiers":1524},"Breast Cancer Wisconsin (Original) Data Set, [Online]. https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fmachine-learning-databases\u002Fbreast-cancer-wisconsin\u002Fbreast-cancer-wisconsin.data. Accessed 25 Aug 2018.",{},{"id":26,"text":1526,"url":26,"identifiers":1527},"James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. 1st ed. New York: Springer; 2013.",{"doi":1528},"10.1007\u002F978-1-4614-7138-7",{"id":26,"text":1530,"url":26,"identifiers":1531},"Guido S, Mller AC. Introduction to machine learning with python. Sebastopol: O’Reilly Media Inc.; 2016.",{},{"id":26,"text":1533,"url":26,"identifiers":1534},"Dwivedi AK. Performance evaluation of different machine learning techniques for prediction of heart disease. Neural Comput Appl. 2016;29(10):685–93.",{"doi":1535},"10.1007\u002Fs00521-016-2604-1",{"id":26,"text":1537,"url":26,"identifiers":1538},"Ratner B. Statistical and machine-learning data mining: techniques for better predictive modeling and analysis of big data. Oxford: Chapman and Hall\u002FCRC; 2017.",{},{"id":26,"text":1540,"url":26,"identifiers":1541},"Dong L, Wesseloo J, Potvin Y, Li X. Discrimination of mine seismic events and blasts using the fisher classifier, naive bayesian classifier and logistic regression. Rock Mech Rock Eng. 2015;49(1):183–211.",{"doi":1542},"10.1007\u002Fs00603-015-0733-y",{"id":26,"text":1544,"url":26,"identifiers":1545},"Hosmer DW Jr, Lemeshow S. Applied logistic regression. New York: Wiley; 2004.",{},{"id":26,"text":1547,"url":26,"identifiers":1548},"Schumacher M, Roner R, Vach W. Neural networks and logistic regression: part I. Comput Stat Data Anal. 1996;21(6):661–82.",{"doi":1549},"10.1016\u002F0167-9473(95)00032-1",{"id":26,"text":1551,"url":26,"identifiers":1552},"Vach W, Roner R, Schumacher M. Neural networks and logistic regression: part II. Comput Stat Data Anal. 1996;21(6):683–701.",{"doi":1553},"10.1016\u002F0167-9473(95)00033-X",{"id":26,"text":1555,"url":26,"identifiers":1556},"Hajmeer M, Basheer I. Comparison of logistic regression and neural network-based classifiers for bacterial growth. Food Microbiol. 2003;20(1):43–55.",{"doi":1557},"10.1016\u002FS0740-0020(02)00104-1",{"id":26,"text":1559,"url":26,"identifiers":1560},"Xu Y, Zhu Q, Wang J. Breast cancer diagnosis based on a kernel orthogonal transform. Neural Comput Appl. 2012;21(8):1865–70.",{"doi":1561},"10.1007\u002Fs00521-011-0547-0",{"id":26,"text":1563,"url":26,"identifiers":1564},"Latchoumi TP, Parthiban L. Abnormality detection using weighed particle swarm optimization and smooth support vector machine. Biomed Res. 2017;28:4749–51.",{},{"id":26,"text":1566,"url":26,"identifiers":1567},"Kumar UK, Nikhil MBS, Sumangali K. Prediction of breast cancer using voting classifier technique. In: Proc. IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Chennai, 2017, pp. 108–114.",{"doi":1568},"10.1109\u002FICSTM.2017.8089135",{"id":1570,"createTime":1571,"updateTime":1572,"relativeEntities":1573,"slug":1574,"properties":1575,"entityType":802,"verifyStatus":25,"verifyTime":1592,"verifyNote":803,"syncStatus":28,"languages":1593,"translateLanguages":26,"viewCount":36,"primaryUrl":1594,"fullTextUrl":26,"authors":1595,"publicationType":919,"publisherRelationship":1649,"citationCount":248,"citationInfo":1671,"publishDate":1674,"publishYear":1672,"citationAnalyzeStatus":28,"lastCitationAnalyze":1675,"indexDatabases":26,"openAccess":26,"references":1676,"isForceReanalyzing":1273},"25bbb85d-75f1-43d2-808c-b9bc64fe3f57","2024-04-15T17:20:27.729+00:00","2026-04-29T03:07:11.020+00:00",[],"Usability-Evaluation-of-Artificial-Intelligence-Based-Voice-Assistants-The-Case-of-Amazon-Alexa",{"mag":1576,"keywords":1578,"pmc":1579,"openalex":1581,"abstract":1583,"title":1584,"pm":1586,"doi":1588,"gsPaper":1590},{"VOID":1577},"3118998664",{},{"VOID":1580},"7798382",{"VOID":1582},"W3118998664",{},{"EN":1585},"Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa",{"VOID":1587},"33458698",{"VOID":1589},"10.1007\u002Fs42979-020-00424-4",{"VOID":1591},"[\"1030686334177548709\"]","2024-05-10T14:21:06.325+00:00",[102],"http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-020-00424-4",[1596,1617,1635],{"id":1597,"sortIndex":114,"researcher":26,"roles":1598,"affiliations":1599,"properties":1608},"a26994cf-9b4b-46e5-aa68-0d10c680c12d",[],[1600],{"id":26,"sortIndex":36,"affiliation":1601,"properties":26},{"id":1602,"createTime":1603,"updateTime":1603,"relativeEntities":1604,"slug":26,"properties":1605,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"c7b0c22b-cd87-4457-a5a6-e6c9e16b30d0","2023-12-26T03:52:50.471+00:00",[],{"title":1606},{"VI":1607},"School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand",{"openalex":1609,"orcid":1611,"title":1613,"gsAuthor":1615},{"VOID":1610},"A5006533392",{"VOID":1612},"https:\u002F\u002Forcid.org\u002F0000-0001-8062-1947",{"EN":1614},"Chonlameth Arpnikanondt",{"VOID":1616},"[\"cdAY8HkAAAAJ\"]",{"id":1618,"sortIndex":115,"researcher":26,"roles":1619,"affiliations":1620,"properties":1626},"3d94f577-a0b1-4a94-9112-ae94220672dc",[],[1621],{"id":26,"sortIndex":36,"affiliation":1622,"properties":26},{"id":1602,"createTime":1603,"updateTime":1603,"relativeEntities":1623,"slug":26,"properties":1624,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1625},{"VI":1607},{"openalex":1627,"orcid":1629,"title":1631,"gsAuthor":1633},{"VOID":1628},"A5046949056",{"VOID":1630},"https:\u002F\u002Forcid.org\u002F0000-0002-7305-1015",{"EN":1632},"Debajyoti Pal",{"VOID":1634},"[\"N3Trro8AAAAJ\"]",{"id":1636,"sortIndex":36,"researcher":26,"roles":1637,"affiliations":1638,"properties":1644},"a392bb25-3b7f-426a-9230-2b6cab6cae8d",[],[1639],{"id":26,"sortIndex":36,"affiliation":1640,"properties":26},{"id":1602,"createTime":1603,"updateTime":1603,"relativeEntities":1641,"slug":26,"properties":1642,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1643},{"VI":1607},{"openalex":1645,"title":1647},{"VOID":1646},"A5028864931",{"EN":1648},"Dilawar Shah Zwakman",{"url":26,"publisher":1650,"properties":26},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":1651,"slug":663,"properties":1652,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":1656,"manageAffiliations":1657,"indexDatabases":1658,"url":755,"thumbnailPath":26,"statistic":1666,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":1653,"eissn":1654,"title":1655},{"VOID":666},{"VOID":668},{"EN":670},[],[],[1659],{"id":732,"indexDatabase":1660,"url":745,"indexYears":746,"academicFieldIds":1665,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":1661,"label":1662,"description":1663,"key":742,"publicationTags":1664,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":1667,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":1668,"totalCitation":767,"totalCitationByYear":1669,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":1670,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},{"total":248,"publishYear":1672,"statisticByYear":1673},2021,{"2021":162,"2022":239,"2023":241,"2024":396,"2025":222,"2026":162},"2021-02-01","2026-04-29T03:07:11.019+00:00",[1677,1680,1684,1687,1691,1695,1699,1703,1707,1711,1715,1718,1722,1726,1730,1734,1738,1742,1746,1750,1754,1758,1762,1766,1770,1774,1778,1782,1786,1790,1794,1798,1802,1806,1810,1814,1818,1822,1826,1830,1833,1837,1841,1845,1848,1852,1856,1859],{"id":26,"text":1678,"url":26,"identifiers":1679},"Statista, “Smart Speaker Market Value Worldwide 2014–2025,” [Online]. https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F1022823\u002Fworldwide-smart-speaker-market-revenue\u002F, Accessed 8 Sep 2020.",{},{"id":26,"text":1681,"url":26,"identifiers":1682},"Ki CW, Cho E, Lee JE. Can an intelligent assistant (IPA) be your friend? Para-friendship development mechanism between IPAs and their users. Comput Hum Behav. 2020;111:1–10. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.chb.2020.106412.",{"doi":1683},"10.1016\u002Fj.chb.2020.106412",{"id":26,"text":1685,"url":26,"identifiers":1686},"Statista, “Factors surrounding preference of voice assistants over websites and applications, worldwide,” [Online]. https:\u002F\u002Fwww.statista.com\u002Fstatistics\u002F801980\u002Fworldwide-preference-voice-assistant-websites-app\u002F, Accessed 8 Sep 2020.",{},{"id":26,"text":1688,"url":26,"identifiers":1689},"McLean G, Frimpong KO. Hey Alexa … examine the variables influencing the use of artificial intelligent in-home voice assistants. Comput Hum Behav. 2019;99:28–37. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.chb.2019.05.009.",{"doi":1690},"10.1016\u002Fj.chb.2019.05.009",{"id":26,"text":1692,"url":26,"identifiers":1693},"Pal D, Arpnikanondt C, Funilkul S, Chutimaskul W. The adoption analysis of voice based smart IoT products. IEEE Internet Things J. 2020;7(11):10852–67. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2020.2991791.",{"doi":1694},"10.1109\u002FJIOT.2020.2991791",{"id":26,"text":1696,"url":26,"identifiers":1697},"Feng L, Wei W. An empirical study on user experience evaluation and identification of critical UX issues. Sustainability. 2019;11(8):1–19. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu11082432.",{"doi":1698},"10.3390\u002Fsu11082432",{"id":26,"text":1700,"url":26,"identifiers":1701},"Oliver RL. A cognitive model of the antecedents and consequences of satisfaction decisions. J Mark Res. 1980;17(4):460–9. https:\u002F\u002Fdoi.org\u002F10.2307\u002F3150499.",{"doi":1702},"10.2307\u002F3150499",{"id":26,"text":1704,"url":26,"identifiers":1705},"Bhattacharjee A. Understanding information systems continuance: an expectation-confirmation model. MIS Q. 2001;25(3):351–70. https:\u002F\u002Fdoi.org\u002F10.2307\u002F3250921.",{"doi":1706},"10.2307\u002F3250921",{"id":26,"text":1708,"url":26,"identifiers":1709},"Parasuraman A, Zeithaml VA, Berry LL. A conceptual model of service quality and its implications for future research. J Mark. 1985;49(4):41–50. https:\u002F\u002Fdoi.org\u002F10.2307\u002F1251430.",{"doi":1710},"10.2307\u002F1251430",{"id":26,"text":1712,"url":26,"identifiers":1713},"Kocaballi AB, Laranjo L, Coiera E. Measuring user experience in conversational interfaces: a comparison of six questionnaires. In: Proc. of 32nd international BCS human computer interaction conference (HCI) July 4–6; 2018. pp. 1–12.",{"doi":1714},"10.14236\u002Fewic\u002FHCI2018.21",{"id":26,"text":1716,"url":26,"identifiers":1717},"Lewis JR. Standardized questionnaires for voice interaction design. ACIXD J. 2016;1(1):1–16.",{},{"id":26,"text":1719,"url":26,"identifiers":1720},"Lewis JR. Measuring perceived usability: SUS, UMUX, and CSUQ ratings for four everyday products. Int J Hum-Comput Interact. 2018;35(15):1404–19. https:\u002F\u002Fdoi.org\u002F10.1080\u002F10447318.2018.1533152.",{"doi":1721},"10.1080\u002F10447318.2018.1533152",{"id":26,"text":1723,"url":26,"identifiers":1724},"Lewis JR. The system usability scale: past, present, and future. Int J Hum Comput Interact. 2018;34(7):577–90. https:\u002F\u002Fdoi.org\u002F10.1080\u002F10447318.2018.1455307.",{"doi":1725},"10.1080\u002F10447318.2018.1455307",{"id":26,"text":1727,"url":26,"identifiers":1728},"Kocaballi AB, Laranjo L, Coiera E. Understanding and measuring user experience in conversational interfaces. Interact Comput. 2019;31(2):192–207. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fiwc\u002Fiwz015.",{"doi":1729},"10.1093\u002Fiwc\u002Fiwz015",{"id":26,"text":1731,"url":26,"identifiers":1732},"Murad C, Munteanu C, Cowan BR, Clark L. Revolution or evolution? Speech interaction and HCI design guidelines. IEEE Pervasive Comput. 2019;18(2):33–45. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMPRV.2019.2906991.",{"doi":1733},"10.1109\u002FMPRV.2019.2906991",{"id":26,"text":1735,"url":26,"identifiers":1736},"Cowan BR, et al. What can i help you with?: Infrequent users’ experiences of intelligent personal assistants. In: Proc. 19th international conference on human-computer interaction with mobile devices and services; 2017. pp. 1–12. https:\u002F\u002Fdoi.org\u002F10.1145\u002F3098279.3098539.",{"doi":1737},"10.1145\u002F3098279.3098539",{"id":26,"text":1739,"url":26,"identifiers":1740},"Silva AB, et al. Intelligent personal assistants: a systematic literature review. Expert Syst Appl. 2020;147:1–14. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eswa.2020.113193.",{"doi":1741},"10.1016\u002Fj.eswa.2020.113193",{"id":26,"text":1743,"url":26,"identifiers":1744},"Kawase T, Okamoto M, Fukutomi T, Takahashi Y. Speech enhancement parameter adjustment to maximize accuracy of automatic speech recognition. IEEE Trans Consum Electron. 2020;66(2):125–33. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTCE.2020.2986003.",{"doi":1745},"10.1109\u002FTCE.2020.2986003",{"id":26,"text":1747,"url":26,"identifiers":1748},"Kumar AJ, Schmidt C, Kohler J. A knowledge graph based speech interface for question answering systems. Speech Commun. 2017;92:1–12. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.specom.2017.05.001.",{"doi":1749},"10.1016\u002Fj.specom.2017.05.001",{"id":26,"text":1751,"url":26,"identifiers":1752},"Guo L, Wang L, Dang J, Liu Z, Guan H. Exploration of complementary features for speech emotion recognition based on kernel extreme learning machine. IEEE Access. 2019;7:75798–809. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2019.2921390.",{"doi":1753},"10.1109\u002FACCESS.2019.2921390",{"id":26,"text":1755,"url":26,"identifiers":1756},"Nath RK, Bajpai R, Thapliyal H. IoT based indoor location detection system for smart home environment. In: 2018 IEEE international conference on consumer electronics (ICCE), Las Vegas, NV; 2018. pp. 1–3. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICCE.2018.8326225.",{"doi":1757},"10.1109\u002FICCE.2018.8326225",{"id":26,"text":1759,"url":26,"identifiers":1760},"Greene S, Thapliyal H, Carpenter D. IoT-based fall detection for smart home environments. In: 2016 IEEE international symposium on nanoelectronic and information systems (iNIS), Gwalior; 2016. pp. 23–28. https:\u002F\u002Fdoi.org\u002F10.1109\u002FiNIS.2016.017.",{"doi":1761},"10.1109\u002FiNIS.2016.017",{"id":26,"text":1763,"url":26,"identifiers":1764},"Sun T. End-to-end speech emotion recognition with gender information. IEEE Access. 2020;8:152423–38. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2020.3017462.",{"doi":1765},"10.1109\u002FACCESS.2020.3017462",{"id":26,"text":1767,"url":26,"identifiers":1768},"Park J, Son H, Lee J, Choi J. Driving assistant companion with voice interface using long short-term memory networks. IEEE Trans Ind Inform. 2019;15(1):582–90. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTII.2018.2861739.",{"doi":1769},"10.1109\u002FTII.2018.2861739",{"id":26,"text":1771,"url":26,"identifiers":1772},"Jia J, et al. Inferring emotions from large-scale internet voice data. IEEE Trans Multimedia. 2019;21(7):1853–66. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTMM.2018.2887016.",{"doi":1773},"10.1109\u002FTMM.2018.2887016",{"id":26,"text":1775,"url":26,"identifiers":1776},"Alepis E, Patsakis C. Monkey says, monkey does: security and privacy on voice assistants. IEEE Access. 2017;5:17841–51. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2017.2747626.",{"doi":1777},"10.1109\u002FACCESS.2017.2747626",{"id":26,"text":1779,"url":26,"identifiers":1780},"Zhang R, Chen X, Wen S, Zheng X, Ding Y. Using AI to attack VA: a stealthy spyware against voice assistances in smart phones. IEEE Access. 2019;7:153542–54. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2019.2945791.",{"doi":1781},"10.1109\u002FACCESS.2019.2945791",{"id":26,"text":1783,"url":26,"identifiers":1784},"Malik KM, Javed A, Malik H, Irtaza A. A light-weight replay detection framework for voice controlled IoT devices. IEEE J Sel Top Signal Process. 2020;14(5):982–96. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJSTSP.2020.2999828.",{"doi":1785},"10.1109\u002FJSTSP.2020.2999828",{"id":26,"text":1787,"url":26,"identifiers":1788},"Yan C, Zhang G, Ji X, Zhang T, Zhang T, Xu W. The feasibility of injecting inaudible voice commands to voice assistants. IEEE Trans Dependable Secure Comput. 2019. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTDSC.2019.2906165.",{"doi":1789},"10.1109\u002FTDSC.2019.2906165",{"id":26,"text":1791,"url":26,"identifiers":1792},"Thapliyal H, Ratajczak N, Wendroth O, Labrado C. Amazon echo enabled IoT home security system for smart home environment, 2018. In: IEEE international symposium on smart electronic systems (iSES) (Formerly iNiS), Hyderabad, India; 2018. pp. 31–36. https:\u002F\u002Fdoi.org\u002F10.1109\u002FiSES.2018.00017.",{"doi":1793},"10.1109\u002FiSES.2018.00017",{"id":26,"text":1795,"url":26,"identifiers":1796},"Nguyen QN, Ta A, Prybutok V. An integrated model of voice-user interface continuance intention: the gender effect. Int J Hum-Comput Interact. 2019;35(15):1362–77. https:\u002F\u002Fdoi.org\u002F10.1080\u002F10447318.2018.1525023.",{"doi":1797},"10.1080\u002F10447318.2018.1525023",{"id":26,"text":1799,"url":26,"identifiers":1800},"Yang H, Lee H. Understanding user behavior of virtual personal assistant devices. IseB. 2019;17:65–87. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10257-018-0375-1.",{"doi":1801},"10.1007\u002Fs10257-018-0375-1",{"id":26,"text":1803,"url":26,"identifiers":1804},"Pal D, Arpnikanondt C, Funilkul S, Razzaque MA. Analyzing the adoption and diffusion of voice-enabled smart-home systems: empirical evidence from Thailand. Univers Access Inf Soc. 2020. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10209-020-00754-3.",{"doi":1805},"10.1007\u002Fs10209-020-00754-3",{"id":26,"text":1807,"url":26,"identifiers":1808},"Maguire, M. Development of a heuristic evaluation tool for voice user interfaces. In: Proc. of international conference on human-computer interaction (HCII’19), Orlando, USA; 2019. pp. 212–25. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-030-23535-2_16.",{"doi":1809},"10.1007\u002F978-3-030-23535-2_16",{"id":26,"text":1811,"url":26,"identifiers":1812},"López G, Quesada L, Guerrero LA. Alexa vs. Siri vs. Cortana vs. Google assistant: a comparison of speech-based natural user interfaces. In: Proc. of 2017 international conference on applied human factors and ergonomics (AHFE 2017), Los Angeles, USA; 2017. pp. 241–50. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-60366-7_23.",{"doi":1813},"10.1007\u002F978-3-319-60366-7_23",{"id":26,"text":1815,"url":26,"identifiers":1816},"Bogers T, et al. A study of usage and usability of intelligent personal assistants in Denmark. In: Proc. of international conference on information in contemporary society (iConference 19), Washington, USA; 2019. pp. 79–90. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-030-15742-5_7.",{"doi":1817},"10.1007\u002F978-3-030-15742-5_7",{"id":26,"text":1819,"url":26,"identifiers":1820},"Pal D, Arpnikanondt C, Funilkul S, Varadarajan V. User experience with smart voice assistants: the accent perspective. In: Proc. of 2019 10th international conference on computing, communication and networking technologies (ICCCNT), Kanpur, India; 2019, pp. 1–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICCCNT45670.2019.8944754.",{"doi":1821},"10.1109\u002FICCCNT45670.2019.8944754",{"id":26,"text":1823,"url":26,"identifiers":1824},"Ghosh D, Foong PS, Zhang S, Zhao S. Assessing the utility of the system usability scale for evaluating voice-based user interfaces. In: Proc. of the sixth international symposium of Chinese CHI (ChineseCHI '18), association for computing machinery, New York, NY, USA. pp. 11–15. https:\u002F\u002Fdoi.org\u002F10.1145\u002F3202667.3204844.",{"doi":1825},"10.1145\u002F3202667.3204844",{"id":26,"text":1827,"url":26,"identifiers":1828},"Yang C, Chen X, Xu Q, Hu L, Jin C, Wang C. A questionnaire for subjective evaluation of the intelligent speech system. In: Proc. of 2018 first Asian conference on affective computing and intelligent interaction (ACII Asia), Beijing; 2018. pp. 1–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACIIAsia.2018.8470339.",{"doi":1829},"10.1109\u002FACIIAsia.2018.8470339",{"id":26,"text":1831,"url":26,"identifiers":1832},"Brooke J. SUS: a quick and dirty usability scale. 1st ed. London: Taylor & Francis; 1996.",{},{"id":26,"text":1834,"url":26,"identifiers":1835},"Bangor A, Kortum PT, Miller JT. An empirical evaluation of the system usability scale. Int J Hum-Comput Interact. 2008;24(6):574–94. https:\u002F\u002Fdoi.org\u002F10.1080\u002F10447310802205776.",{"doi":1836},"10.1080\u002F10447310802205776",{"id":26,"text":1838,"url":26,"identifiers":1839},"Ghosh D, Foong PS, Zhao S, Chen D, Fjeld M. EDITalk: Towards designing eyes-free interactions for mobile word processing. In: Proc. of the 2018 CHI conference on human factors in computing systems—CHI’18, association for computing machinery, New York, NY, USA. pp. 1–10. https:\u002F\u002Fdoi.org\u002F10.1145\u002F3173574.3173977.",{"doi":1840},"10.1145\u002F3173574.3173977",{"id":26,"text":1842,"url":26,"identifiers":1843},"Babel M, McGuire G, King J. Towards a more nuanced view of vocal attractiveness. PLoS ONE. 2014;9(2):e88616. https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0088616.",{"doi":1844},"10.1371\u002Fjournal.pone.0088616",{"id":26,"text":1846,"url":26,"identifiers":1847},"Koreman J, Pützer M. The usability of perceptual ratings of voice quality. In: Proc. 6th international conference on advances in quantitative laryngology, voice and speech research (AQL), Hamburg, Germany; 2003.",{},{"id":26,"text":1849,"url":26,"identifiers":1850},"Pfeuffer N, Benlian A, Gimpel H, Hinz O. Anthropomorphic information systems. Bus Inf Sys Eng. 2019;61:523–33. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12599-019-00599-y.",{"doi":1851},"10.1007\u002Fs12599-019-00599-y",{"id":26,"text":1853,"url":26,"identifiers":1854},"Wei Z, Landay JA. Evaluating speech-based smart devices using new usability heuristics. IEEE Pervasive Comput. 2018;17(2):84–96. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMPRV.2018.022511249.",{"doi":1855},"10.1109\u002FMPRV.2018.022511249",{"id":26,"text":1857,"url":26,"identifiers":1858},"Bangor A, Kortum P, Miller J. Determining what Individual SUS scores mean: adding an adjective rating scale. J Usability Stud. 2009;4(3):114–23.",{},{"id":26,"text":1860,"url":26,"identifiers":1861},"Field A. Discovering statistics using IBM SPSS statistics. 4th ed. Thousand Oaks: Sage Publications; 2013.",{},{"id":1863,"createTime":1864,"updateTime":1865,"relativeEntities":1866,"slug":1867,"properties":1868,"entityType":802,"verifyStatus":25,"verifyTime":1865,"verifyNote":803,"syncStatus":28,"languages":1883,"translateLanguages":26,"viewCount":36,"primaryUrl":1884,"fullTextUrl":26,"authors":1885,"publicationType":919,"publisherRelationship":1990,"citationCount":568,"citationInfo":2012,"publishDate":2014,"publishYear":1672,"citationAnalyzeStatus":949,"lastCitationAnalyze":2015,"indexDatabases":26,"openAccess":26,"references":2016,"isForceReanalyzing":1273},"853bf54e-64b8-4f39-a78d-b3c73b6173fb","2024-04-12T04:21:11.822+00:00","2025-02-04T06:29:05.108+00:00",[],"Chest-X-ray-Classification-Using-Deep-Learning-for-Automated-COVID-19-Screening",{"mag":1869,"keywords":1871,"pmc":1872,"openalex":1874,"abstract":1876,"title":1877,"pm":1879,"doi":1881},{"VOID":1870},"3164988417",{},{"VOID":1873},"8152712",{"VOID":1875},"W3164988417",{},{"EN":1878},"Chest X-ray Classification Using Deep Learning for Automated COVID-19 Screening",{"VOID":1880},"34075355",{"VOID":1882},"10.1007\u002Fs42979-021-00695-5",[102],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-021-00695-5",[1886,1904,1918,1934,1948,1962,1976],{"id":1887,"sortIndex":36,"researcher":26,"roles":1888,"affiliations":1889,"properties":1899},"66003eb4-7bc4-47c0-bd45-a1082fb6eeb6",[],[1890],{"id":26,"sortIndex":36,"affiliation":1891,"properties":26},{"id":1892,"createTime":1893,"updateTime":1893,"relativeEntities":1894,"slug":1895,"properties":1896,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"9f2977b5-ccca-4e62-bddf-cab3c1757c99","2024-04-12T04:21:11.829+00:00",[],"K-J-Somaiya-College-of-Engineering-Vidyavihar-Mumbai-India-Maharshtra-400077",{"title":1897},{"EN":1898},"K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, India, Maharshtra, 400077",{"openalex":1900,"title":1902},{"VOID":1901},"A5031089757",{"EN":1903},"Ankita Shelke",{"id":1905,"sortIndex":162,"researcher":26,"roles":1906,"affiliations":1907,"properties":1913},"585d47d9-e9ab-4d3c-b792-afe1153a4fc0",[],[1908],{"id":26,"sortIndex":36,"affiliation":1909,"properties":26},{"id":1892,"createTime":1893,"updateTime":1893,"relativeEntities":1910,"slug":1895,"properties":1911,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1912},{"EN":1898},{"openalex":1914,"title":1916},{"VOID":1915},"A5063136533",{"EN":1917},"Talha Chafekar",{"id":1919,"sortIndex":135,"researcher":26,"roles":1920,"affiliations":1921,"properties":1927},"ce6b0d29-41fb-4745-bc11-4fc227aadbb8",[],[1922],{"id":26,"sortIndex":36,"affiliation":1923,"properties":26},{"id":1892,"createTime":1893,"updateTime":1893,"relativeEntities":1924,"slug":1895,"properties":1925,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1926},{"EN":1898},{"openalex":1928,"orcid":1930,"title":1932},{"VOID":1929},"A5051052109",{"VOID":1931},"https:\u002F\u002Forcid.org\u002F0000-0003-3037-5076",{"EN":1933},"Ninad Mehendale",{"id":1935,"sortIndex":111,"researcher":26,"roles":1936,"affiliations":1937,"properties":1943},"54400427-fc9c-4d9f-91fc-999276f97a36",[],[1938],{"id":26,"sortIndex":36,"affiliation":1939,"properties":26},{"id":1892,"createTime":1893,"updateTime":1893,"relativeEntities":1940,"slug":1895,"properties":1941,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1942},{"EN":1898},{"openalex":1944,"title":1946},{"VOID":1945},"A5087443187",{"EN":1947},"Aafiya Hussain",{"id":1949,"sortIndex":115,"researcher":26,"roles":1950,"affiliations":1951,"properties":1957},"294f06dc-e1d3-4994-b0de-6e82d70cb277",[],[1952],{"id":26,"sortIndex":36,"affiliation":1953,"properties":26},{"id":1892,"createTime":1893,"updateTime":1893,"relativeEntities":1954,"slug":1895,"properties":1955,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1956},{"EN":1898},{"openalex":1958,"title":1960},{"VOID":1959},"A5040760311",{"EN":1961},"Madhura Inamdar",{"id":1963,"sortIndex":59,"researcher":26,"roles":1964,"affiliations":1965,"properties":1971},"c36c6e76-80ca-48c6-8c9e-cc37ee58d722",[],[1966],{"id":26,"sortIndex":36,"affiliation":1967,"properties":26},{"id":1892,"createTime":1893,"updateTime":1893,"relativeEntities":1968,"slug":1895,"properties":1969,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1970},{"EN":1898},{"openalex":1972,"title":1974},{"VOID":1973},"A5047465103",{"EN":1975},"Amanshu Tiwari",{"id":1977,"sortIndex":114,"researcher":26,"roles":1978,"affiliations":1979,"properties":1985},"765ea7d6-3523-43b0-a29d-9e4b985f6abe",[],[1980],{"id":26,"sortIndex":36,"affiliation":1981,"properties":26},{"id":1892,"createTime":1893,"updateTime":1893,"relativeEntities":1982,"slug":1895,"properties":1983,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":1984},{"EN":1898},{"openalex":1986,"title":1988},{"VOID":1987},"A5024602397",{"EN":1989},"Vruddhi Shah",{"url":26,"publisher":1991,"properties":26},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":1992,"slug":663,"properties":1993,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":1997,"manageAffiliations":1998,"indexDatabases":1999,"url":755,"thumbnailPath":26,"statistic":2007,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":1994,"eissn":1995,"title":1996},{"VOID":666},{"VOID":668},{"EN":670},[],[],[2000],{"id":732,"indexDatabase":2001,"url":745,"indexYears":746,"academicFieldIds":2006,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":2002,"label":2003,"description":2004,"key":742,"publicationTags":2005,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":2008,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":2009,"totalCitation":767,"totalCitationByYear":2010,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":2011,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},{"total":568,"publishYear":26,"statisticByYear":2013},{"2020":114,"2021":116,"2022":238,"2023":260,"2024":111},"2021-07-01","2024-04-13T14:16:51.907+00:00",[2017,2021,2025,2029,2032,2035,2039,2043,2047,2051,2055,2059,2062,2066,2070,2074,2078,2082,2086,2090,2094,2098,2102,2106,2109],{"id":26,"text":2018,"url":26,"identifiers":2019},"Acter T, Uddin N, Das J, Akhter A, Choudhury TR, Kim S. Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: A global health emergency. Sci Total Environ. 2020:138996.",{"doi":2020},"10.1016\u002Fj.scitotenv.2020.138996",{"id":26,"text":2022,"url":26,"identifiers":2023},"Zhang D, Hu M, Ji Q. Financial markets under the global pandemic of COVID-19. Finance Res Lett. 2020 Oct 1;36:101528.",{"doi":2024},"10.1016\u002Fj.frl.2020.101528",{"id":26,"text":2026,"url":26,"identifiers":2027},"Ye ZW, Yuan S, Yuen KS, Fung SY, Chan CP, Jin DY. Zoonotic origins of human coronaviruses. Int J Biol Sci. 2020;16(10):1686.",{"doi":2028},"10.7150\u002Fijbs.45472",{"id":26,"text":2030,"url":26,"identifiers":2031},"King DE. Dlib-ml: A machine learning toolkit. J Mach Learn Res. 2009 Dec 1;10:1755–8.",{},{"id":26,"text":2033,"url":26,"identifiers":2034},"Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning, vol. 1. Cambridge: MIT Press; 2016.",{},{"id":26,"text":2036,"url":26,"identifiers":2037},"Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. 2020 Jun 1;121:103792.",{"doi":2038},"10.1016\u002Fj.compbiomed.2020.103792",{"id":26,"text":2040,"url":26,"identifiers":2041},"Kong W, Agarwal PP. Chest imaging appearance of COVID-19 infection. Radiol: Cardiothorac Imaging. 2020 Feb 13;2(1):e200028.",{"doi":2042},"10.1148\u002Fryct.2020200028",{"id":26,"text":2044,"url":26,"identifiers":2045},"Yoon SH, Lee KH, Kim JY, Lee YK, Ko H, Kim KH, Park CM, Kim YH. Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea. Korean J Radiol. 2020; 21(4):494.",{"doi":2046},"10.3348\u002Fkjr.2020.0132",{"id":26,"text":2048,"url":26,"identifiers":2049},"Zhao W, Zhong Z, Xie X, Yu Q, Liu J. Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: a multicenter study. American J Roentgenol. 2020;214(5):1072–7.",{"doi":2050},"10.2214\u002FAJR.20.22976",{"id":26,"text":2052,"url":26,"identifiers":2053},"Li Y, Xia L. Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. American J Roentgenol. 2020 214(6):1280–6.",{"doi":2054},"10.2214\u002FAJR.20.22954",{"id":26,"text":2056,"url":26,"identifiers":2057},"Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, Zhang LJ. Coronavirus disease 2019 (COVID-19): a perspective from China. Radiol. 2020 Aug;296(2):E15–25.",{"doi":2058},"10.1148\u002Fradiol.2020200490",{"id":26,"text":2060,"url":26,"identifiers":2061},"Hemdan EE, Shouman MA, Karar ME. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv:2003.11055. Accessed 24 Mar 2020.",{},{"id":26,"text":2063,"url":26,"identifiers":2064},"Wang L, Lin ZQ, Wong A. Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci Rep. 2020;10(1):1–2.",{"doi":2065},"10.1038\u002Fs41598-020-76550-z",{"id":26,"text":2067,"url":26,"identifiers":2068},"Apostolopoulos ID, Mpesiana TA. Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med. 2020 Jun;43(2):635–40.",{"doi":2069},"10.1007\u002Fs13246-020-00865-4",{"id":26,"text":2071,"url":26,"identifiers":2072},"Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Anal Appl. 2021 May 9:1–4.",{"doi":2073},"10.1007\u002Fs10044-021-00984-y",{"id":26,"text":2075,"url":26,"identifiers":2076},"Sethy PK, Behera SK, Ratha PK, Biswas P. Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. 2020.",{"doi":2077},"10.20944\u002Fpreprints202003.0300.v1",{"id":26,"text":2079,"url":26,"identifiers":2080},"Abbas A, Abdelsamea MM, Gaber MM. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl Intell. 2021 Feb;51(2):854–64.",{"doi":2081},"10.1007\u002Fs10489-020-01829-7",{"id":26,"text":2083,"url":26,"identifiers":2084},"Yoo SH, Geng H, Chiu TL, Yu SK, Cho DC, Heo J, Choi MS, Choi IH, Cung Van C, Nhung NV, Min BJ. Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging. Front Med. 2020 Jul 14;7:427.",{"doi":2085},"10.3389\u002Ffmed.2020.00427",{"id":26,"text":2087,"url":26,"identifiers":2088},"Ismael AM, Şengür A. Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Syst Appl. 2021;164:114054.",{"doi":2089},"10.1016\u002Fj.eswa.2020.114054",{"id":26,"text":2091,"url":26,"identifiers":2092},"Nayak SR, Nayak DR, Sinha U, Arora V, Pachori RB. Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. Biomed Signal Process Control. 2021;64:102365.",{"doi":2093},"10.1016\u002Fj.bspc.2020.102365",{"id":26,"text":2095,"url":26,"identifiers":2096},"Ouchicha C, Ammor O, Meknassi M. CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images. Chaos Solitons Fractals. 2020;140:110245.",{"doi":2097},"10.1016\u002Fj.chaos.2020.110245",{"id":26,"text":2099,"url":26,"identifiers":2100},"Tammina S. Transfer learning using VGG-16 with deep convolutional neural network for classifying images. Int J Sci Res Publ. 2019 Oct;9(10):143–50.",{"doi":2101},"10.29322\u002FIJSRP.9.10.2019.p9420",{"id":26,"text":2103,"url":26,"identifiers":2104},"Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ. Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning. Med image Anal. 2020;65:101794.",{"doi":2105},"10.1016\u002Fj.media.2020.101794",{"id":26,"text":2107,"url":26,"identifiers":2108},"Chen PH, Bak PR. Imaging Informatics for Healthcare, Research, and Applications. In Proc. of SPIE Vol 2019 (Vol. 10954, pp. 1095401–1).",{},{"id":26,"text":2110,"url":26,"identifiers":2111},"Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, Chen J, Wang R, Zhao H, Zha Y, Shen J. Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. IEEE\u002FACM Trans Comput Biol Bioinf. 2021.",{"doi":2112},"10.1109\u002FTCBB.2021.3065361",{"id":2114,"createTime":2115,"updateTime":2116,"relativeEntities":2117,"slug":2118,"properties":2119,"entityType":802,"verifyStatus":25,"verifyTime":2116,"verifyNote":803,"syncStatus":28,"languages":2131,"translateLanguages":26,"viewCount":36,"primaryUrl":2132,"fullTextUrl":26,"authors":2133,"publicationType":919,"publisherRelationship":2154,"citationCount":569,"citationInfo":2180,"publishDate":2182,"publishYear":948,"citationAnalyzeStatus":28,"lastCitationAnalyze":26,"indexDatabases":26,"openAccess":26,"references":2183,"isForceReanalyzing":1273},"550f556c-e026-4fd3-a486-e4521b287252","2024-04-17T03:22:53.916+00:00","2025-01-30T02:14:46.937+00:00",[],"Mitigating-Metaphors-A-Comprehensible-Guide-to-Recent-Nature-Inspired-Algorithms",{"mag":2120,"keywords":2122,"openalex":2123,"abstract":2125,"title":2127,"doi":2129},{"VOID":2121},"2991340219",{},{"VOID":2124},"W2991340219",{"EN":2126},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:p>In recent years, a plethora of new metaheuristic algorithms have explored different sources of inspiration within the biological and natural worlds. This nature-inspired approach to algorithm design has been widely criticised. A notable issue is the tendency for authors to use terminology that is derived from the domain of inspiration, rather than the broader domains of metaheuristics and optimisation. This makes it difficult to both comprehend how these algorithms work and understand their relationships to other metaheuristics. This paper attempts to address this issue, at least to some extent, by providing accessible descriptions of the most cited nature-inspired algorithms published in the last 20 years. It also discusses commonalities between these algorithms and more classical nature-inspired metaheuristics such as evolutionary algorithms and particle swarm optimisation, and finishes with a discussion of future directions for the field.\u003C\u002Fjats:p>",{"EN":2128},"Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms",{"VOID":2130},"10.1007\u002Fs42979-019-0050-8",[102],"http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-019-0050-8",[2134],{"id":2135,"sortIndex":36,"researcher":26,"roles":2136,"affiliations":2137,"properties":2147},"99a9f8a1-a47c-43e6-a9df-d5725f0dedad",[],[2138],{"id":2139,"sortIndex":36,"affiliation":2140,"properties":26},"ad89d11c-ea59-4639-bbeb-4accca2837e6",{"id":2141,"createTime":2142,"updateTime":2142,"relativeEntities":2143,"slug":26,"properties":2144,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"dd76afcb-65f8-41e5-ba93-abe7b3656335","2023-12-06T14:10:39.356+00:00",[],{"title":2145},{"VI":2146},"School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK",{"openalex":2148,"orcid":2150,"title":2152},{"VOID":2149},"A5049325379",{"VOID":2151},"https:\u002F\u002Forcid.org\u002F0000-0002-2745-9896",{"EN":2153},"Michael A. Lones",{"url":26,"publisher":2155,"properties":2176},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":2156,"slug":663,"properties":2157,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":2161,"manageAffiliations":2162,"indexDatabases":2163,"url":755,"thumbnailPath":26,"statistic":2171,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":2158,"eissn":2159,"title":2160},{"VOID":666},{"VOID":668},{"EN":670},[],[],[2164],{"id":732,"indexDatabase":2165,"url":745,"indexYears":746,"academicFieldIds":2170,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":2166,"label":2167,"description":2168,"key":742,"publicationTags":2169,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":2172,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":2173,"totalCitation":767,"totalCitationByYear":2174,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":2175,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},{"volume":2177,"issue":2179},{"VOID":2178},"1",{"VOID":2178},{"total":569,"publishYear":26,"statisticByYear":2181},{"2020":116,"2021":51,"2022":242,"2023":52,"2024":114},"2020-01-01",[2184,2187,2190,2194,2198,2202,2205,2209,2213,2217,2220,2224,2228,2232,2236,2239,2243,2247,2251,2255,2259,2262,2266,2269,2273,2277,2280,2283,2286,2290,2294,2298,2302,2305,2309,2313,2317,2321,2325,2329,2333,2337,2341,2345,2349,2353,2357,2361,2365,2369,2373,2377,2381,2384,2388,2392,2396,2399,2402,2405,2409,2413,2417,2420,2424,2427,2431,2434,2438,2442],{"id":26,"text":2185,"url":26,"identifiers":2186},"Abbass HA. MBO: marriage in honey bees optimization—a haplometrosis polygynous swarming approach. In: Proceedings of the 2001 congress on evolutionary computation (CEC 2001), vol 1. IEEE; 2001. , p. 207–14.",{},{"id":26,"text":2188,"url":26,"identifiers":2189},"Aranha C, Campelo F. Evolutionary computation bestiary; 2019. https:\u002F\u002Fgithub.com\u002Ffcampelo\u002FEC-Bestiary (online accessed 9 Oct 2019).",{},{"id":26,"text":2191,"url":26,"identifiers":2192},"Atashpaz-Gargari E, Lucas C. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of the 2007 congress on evolutionary computation (CEC 2007). IEEE; 2007. p. 4661–7.",{"doi":2193},"10.1109\u002FCEC.2007.4425083",{"id":26,"text":2195,"url":26,"identifiers":2196},"Blackwell T, Branke J. Multi-swarm optimization in dynamic environments. In: Workshops on applications of evolutionary computation. Springer; 2004. p. 489–500.",{"doi":2197},"10.1007\u002F978-3-540-24653-4_50",{"id":26,"text":2199,"url":26,"identifiers":2200},"Burke EK, Gendreau M, Hyde M, Kendall G, Ochoa G, Özcan E, Rong Q. Hyper-heuristics: a survey of the state of the art. J Oper Res Soc. 2013;64(12):1695–724.",{"doi":2201},"10.1057\u002Fjors.2013.71",{"id":26,"text":2203,"url":26,"identifiers":2204},"Chen J, Qin Z, Liu Y, Lu J. Particle swarm optimization with local search. In: International conference on neural networks and brain (ICNN&B’05), vol. 1. IEEE; 2005. p. 481–4.",{},{"id":26,"text":2206,"url":26,"identifiers":2207},"Chu S-C, Tsai P-W, Pan J-S. Cat swarm optimization. In: Pacific rim international conference on artificial intelligence. Springer; 2006. p. 854–8.",{"doi":2208},"10.1007\u002F978-3-540-36668-3_94",{"id":26,"text":2210,"url":26,"identifiers":2211},"Črepinšek M, Liu S-H, Mernik L. A note on teaching-learning-based optimization algorithm. Inf Sci. 2012;212:79–93.",{"doi":2212},"10.1016\u002Fj.ins.2012.05.009",{"id":26,"text":2214,"url":26,"identifiers":2215},"Črepinšek M, Liu S-H, Mernik L, Mernik M. Is a comparison of results meaningful from the inexact replications of computational experiments? Soft Comput. 2016;20(1):223–35.",{"doi":2216},"10.1007\u002Fs00500-014-1493-4",{"id":26,"text":2218,"url":26,"identifiers":2219},"Du W, Gao Y, Liu C, Zheng Z, Wang Z. Adequate is better: particle swarm optimization with limited-information. Appl Math Comput. 2015;268:832–8.",{},{"id":26,"text":2221,"url":26,"identifiers":2222},"Eberhart R, Kennedy J. Particle swarm optimization. Proc IEEE Int Conf Neural Netw. 1995;4:1942–8.",{"doi":2223},"10.1109\u002FICNN.1995.488968",{"id":26,"text":2225,"url":26,"identifiers":2226},"Epitropakis MG, Burke EK. Hyper-heuristics. Handbook of Heuristics; 2018. p. 1–57.",{"doi":2227},"10.1007\u002F978-3-319-07153-4_32-1",{"id":26,"text":2229,"url":26,"identifiers":2230},"Erol OK, Eksin I. A new optimization method: big bang-big crunch. Adv Eng Softw. 2006;37(2):106–11.",{"doi":2231},"10.1016\u002Fj.advengsoft.2005.04.005",{"id":26,"text":2233,"url":26,"identifiers":2234},"Eusuff MM, Lansey KE. Optimization of water distribution network design using the shuffled frog leaping algorithm. J Water Resour Plan Manag. 2003;129(3):210–25.",{"doi":2235},"10.1061\u002F(ASCE)0733-9496(2003)129:3(210)",{"id":26,"text":2237,"url":26,"identifiers":2238},"Fister I Jr, Yang X-S, Fister I, Brest J, Fister D. A brief review of nature-inspired algorithms for optimization. Elektrotehniški vestnik. 2013;80(3):116–22.",{},{"id":26,"text":2240,"url":26,"identifiers":2241},"Fong S, Wang X, Qiwen X, Wong R, Fiaidhi J, Mohammed S. Recent advances in metaheuristic algorithms: does the Makara dragon exist? J Supercomput. 2016;72(10):3764–86.",{"doi":2242},"10.1007\u002Fs11227-015-1592-8",{"id":26,"text":2244,"url":26,"identifiers":2245},"Gandomi AH, Alavi AH. Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul. 2012;17(12):4831–45.",{"doi":2246},"10.1016\u002Fj.cnsns.2012.05.010",{"id":26,"text":2248,"url":26,"identifiers":2249},"García-Martínez C, Gutiérrez PD, Molina D, Lozano M, Herrera F. Since CEC 2005 competition on real-parameter optimisation: a decade of research, progress and comparative analysis’s weakness. Soft Comput. 2017;21(19):5573–83.",{"doi":2250},"10.1007\u002Fs00500-016-2471-9",{"id":26,"text":2252,"url":26,"identifiers":2253},"Geem ZW, Kim JH, Loganathan GV. A new heuristic optimization. Simulation. 2001;76(2):60–8.",{"doi":2254},"10.1177\u002F003754970107600201",{"id":26,"text":2256,"url":26,"identifiers":2257},"He S, Wu QH, Saunders JR. Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Trans Evol Comput. 2009;13(5):973–90.",{"doi":2258},"10.1109\u002FTEVC.2009.2011992",{"id":26,"text":2260,"url":26,"identifiers":2261},"Holland JH. Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press; 1975.",{},{"id":26,"text":2263,"url":26,"identifiers":2264},"Joyce T, Herrmann JM. A review of no free lunch theorems, and their implications for metaheuristic optimisation. In: Nature-inspired algorithms and applied optimization. Springer; 2018. p. 27–51.",{"doi":2265},"10.1007\u002F978-3-319-67669-2_2",{"id":26,"text":2267,"url":26,"identifiers":2268},"Karaboga D. An idea based on honey bee swarm for numerical optimization. Technical report, Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department; 2005.",{},{"id":26,"text":2270,"url":26,"identifiers":2271},"Kaucic M. A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization. J Glob Optim. 2013;55(1):165–88.",{"doi":2272},"10.1007\u002Fs10898-012-9913-4",{"id":26,"text":2274,"url":26,"identifiers":2275},"Kaveh A, Talatahari S. A novel heuristic optimization method: charged system search. Acta Mech. 2010;213(3–4):267–89.",{"doi":2276},"10.1007\u002Fs00707-009-0270-4",{"id":26,"text":2278,"url":26,"identifiers":2279},"Kennedy J. The particle swarm: social adaptation of knowledge. In: IEEE international conference on evolutionary computation. IEEE; 1997. p. 303–8.",{},{"id":26,"text":2281,"url":26,"identifiers":2282},"Kennedy J. Bare bones particle swarms. In: Proceedings of the 2003 IEEE swarm intelligence symposium (SIS’03). IEEE; 2003. p. 80–7.",{},{"id":26,"text":2284,"url":26,"identifiers":2285},"Krishnanand KN, Ghose D. Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proceedings 2005 IEEE swarm intelligence symposium (SIS 2005). IEEE; 2005. p. 84–91.",{},{"id":26,"text":2287,"url":26,"identifiers":2288},"Krishnanand KN, Ghose D. Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell. 2009;3(2):87–124.",{"doi":2289},"10.1007\u002Fs11721-008-0021-5",{"id":26,"text":2291,"url":26,"identifiers":2292},"Lam AYS, Li VOK. Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput. 2010;14(3):381–99.",{"doi":2293},"10.1109\u002FTEVC.2009.2033580",{"id":26,"text":2295,"url":26,"identifiers":2296},"Lane J, Engelbrecht A, Gain J. Particle swarm optimization with spatially meaningful neighbours. In: Proceedings 2008 IEEE swarm intelligence symposium (SIS 2008). IEEE; 2008. p. 1–8.",{"doi":2297},"10.1109\u002FSIS.2008.4668281",{"id":26,"text":2299,"url":26,"identifiers":2300},"Lemke C, Budka M, Gabrys B. Metalearning: a survey of trends and technologies. Artif Intell Rev. 2015;44(1):117–30.",{"doi":2301},"10.1007\u002Fs10462-013-9406-y",{"id":26,"text":2303,"url":26,"identifiers":2304},"Li K, Malik J. Learning to optimize. In: 5th International conference on learning representations; 2017.",{},{"id":26,"text":2306,"url":26,"identifiers":2307},"Lones MA. Metaheuristics in nature-inspired algorithms. In: Proceedings of the companion publication of the 2014 annual conference on genetic and evolutionary computation. ACM; 2014. p. 1419–22.",{"doi":2308},"10.1145\u002F2598394.2609841",{"id":26,"text":2310,"url":26,"identifiers":2311},"Mehrabian AR, Lucas C. A novel numerical optimization algorithm inspired from weed colonization. Ecol Inf. 2006;1(4):355–66.",{"doi":2312},"10.1016\u002Fj.ecoinf.2006.07.003",{"id":26,"text":2314,"url":26,"identifiers":2315},"Mendes R, Kennedy J, Neves J. The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput. 2004;8(3):204–10.",{"doi":2316},"10.1109\u002FTEVC.2004.826074",{"id":26,"text":2318,"url":26,"identifiers":2319},"Mirjalili S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst. 2015a;89:228–49.",{"doi":2320},"10.1016\u002Fj.knosys.2015.07.006",{"id":26,"text":2322,"url":26,"identifiers":2323},"Mirjalili S. The ant lion optimizer. Adv Eng Softw. 2015b;83:80–98.",{"doi":2324},"10.1016\u002Fj.advengsoft.2015.01.010",{"id":26,"text":2326,"url":26,"identifiers":2327},"Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw. 2016;95:51–67.",{"doi":2328},"10.1016\u002Fj.advengsoft.2016.01.008",{"id":26,"text":2330,"url":26,"identifiers":2331},"Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw. 2014;69:46–61.",{"doi":2332},"10.1016\u002Fj.advengsoft.2013.12.007",{"id":26,"text":2334,"url":26,"identifiers":2335},"Pan W-T. A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst. 2012;26:69–74.",{"doi":2336},"10.1016\u002Fj.knosys.2011.07.001",{"id":26,"text":2338,"url":26,"identifiers":2339},"Passino KM. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 2002;22(3):52–67.",{"doi":2340},"10.1109\u002FMCS.2002.1004010",{"id":26,"text":2342,"url":26,"identifiers":2343},"Pedersen MEH, Chipperfield AJ. Simplifying particle swarm optimization. Appl Soft Comput. 2010;10(2):618–28.",{"doi":2344},"10.1016\u002Fj.asoc.2009.08.029",{"id":26,"text":2346,"url":26,"identifiers":2347},"Pham DT, Ghanbarzadeh A, Koç E, Otri S, Rahim S, Zaidi M. The bees algorithm—a novel tool for complex optimisation problems. In: Intelligent production machines and systems. Elsevier; 2006. p. 454–459.",{"doi":2348},"10.1016\u002FB978-008045157-2\u002F50081-X",{"id":26,"text":2350,"url":26,"identifiers":2351},"Piotrowski AP. Regarding the rankings of optimization heuristics based on artificially-constructed benchmark functions. Inf Sci. 2015;297:191–201.",{"doi":2352},"10.1016\u002Fj.ins.2014.11.023",{"id":26,"text":2354,"url":26,"identifiers":2355},"Rajabioun R. Cuckoo optimization algorithm. Appl Soft Comput. 2011;11(8):5508–18.",{"doi":2356},"10.1016\u002Fj.asoc.2011.05.008",{"id":26,"text":2358,"url":26,"identifiers":2359},"Rao RV, Savsani VJ, Vakharia DP. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Design. 2011;43(3):303–15.",{"doi":2360},"10.1016\u002Fj.cad.2010.12.015",{"id":26,"text":2362,"url":26,"identifiers":2363},"Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Inf Sci. 2009;179(13):2232–48.",{"doi":2364},"10.1016\u002Fj.ins.2009.03.004",{"id":26,"text":2366,"url":26,"identifiers":2367},"Ratnaweera A, Halgamuge SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput. 2004;8(3):240–55.",{"doi":2368},"10.1109\u002FTEVC.2004.826071",{"id":26,"text":2370,"url":26,"identifiers":2371},"Ray T, Liew KM. Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Trans Evol Comput. 2003;7(4):386–96.",{"doi":2372},"10.1109\u002FTEVC.2003.814902",{"id":26,"text":2374,"url":26,"identifiers":2375},"Shah-Hosseini H. The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int J Bio-Inspired Comput. 2009;1(1–2):71–9.",{"doi":2376},"10.1504\u002FIJBIC.2009.022775",{"id":26,"text":2378,"url":26,"identifiers":2379},"Shi Y. Brain storm optimization algorithm. In: International conference in swarm intelligence. Springer; 2011. p. 303–9.",{"doi":2380},"10.1007\u002F978-3-642-21515-5_36",{"id":26,"text":2382,"url":26,"identifiers":2383},"Shi Y, Eberhart RC. Empirical study of particle swarm optimization. In: Proceedings of the 1999 congress on evolutionary computation (CEC 99), vol. 3. IEEE; 1999. p. 1945–50.",{},{"id":26,"text":2385,"url":26,"identifiers":2386},"Simon D. Biogeography-based optimization. IEEE Trans Evol Comput. 2008;12(6):702–13.",{"doi":2387},"10.1109\u002FTEVC.2008.919004",{"id":26,"text":2389,"url":26,"identifiers":2390},"Sörensen K. Metaheuristics—the metaphor exposed. Int Trans Oper Res. 2015;22(1):3–18.",{"doi":2391},"10.1111\u002Fitor.12001",{"id":26,"text":2393,"url":26,"identifiers":2394},"Sörensen K, Sevaux M, Glover F. A history of metaheuristics. Handbook of heuristics; 2018. p. 1–18.",{"doi":2395},"10.1007\u002F978-3-319-07153-4_4-1",{"id":26,"text":2397,"url":26,"identifiers":2398},"Suganthan PN. Particle swarm optimiser with neighbourhood operator. In: Proceedings of the 1999 congress on evolutionary computation (CEC 99), vol. 3. IEEE; 1999. p. 1958–62.",{},{"id":26,"text":2400,"url":26,"identifiers":2401},"Sun J, Xu W, Feng B. A global search strategy of quantum-behaved particle swarm optimization. In: IEEE conference on cybernetics and intelligent systems, 2004, vol. 1. IEEE; 2004. p. 111–6.",{},{"id":26,"text":2403,"url":26,"identifiers":2404},"Swann J, Hammond K. Towards ‘metaheuristics in the large’. In: Proceedings of 11th metaheuristics international conference (MIC 2015); 2015.",{},{"id":26,"text":2406,"url":26,"identifiers":2407},"Tamura K, Yasuda K. Primary study of spiral dynamics inspired optimization. IEEJ Trans Electr Electron Eng. 2011;6(S1):1116–22.",{"doi":2408},"10.1002\u002Ftee.20628",{"id":26,"text":2410,"url":26,"identifiers":2411},"Tan Y, Zhu Y. Fireworks algorithm for optimization. In: International conference in swarm intelligence. Springer; 2010. p. 355–64.",{"doi":2412},"10.1007\u002F978-3-642-13495-1_44",{"id":26,"text":2414,"url":26,"identifiers":2415},"Weyland D. A critical analysis of the harmony search algorithm—how not to solve sudoku. Oper Res Perspect. 2015;2:97–105.",{"doi":2416},"10.1016\u002Fj.orp.2015.04.001",{"id":26,"text":2418,"url":26,"identifiers":2419},"Wichrowska O, Maheswaranathan N, Hoffman MW, Denil M, Colmenarejo SG, Freitas N, Sohl-Dickstein J. Learned optimizers that scale and generalize. In: Proceedings of the 34th international conference on machine learning, vol. 70; 2017.",{},{"id":26,"text":2421,"url":26,"identifiers":2422},"Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans Evol Comput. 1997;1(1):67–82.",{"doi":2423},"10.1109\u002F4235.585893",{"id":26,"text":2425,"url":26,"identifiers":2426},"Xing B, Gao W-J. Innovative computational intelligence: a rough guide to 134 clever algorithms. New York: Springer; 2016.",{},{"id":26,"text":2428,"url":26,"identifiers":2429},"Yang X-S. Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer; 2009. p. 169–78.",{"doi":2430},"10.1007\u002F978-3-642-04944-6_14",{"id":26,"text":2432,"url":26,"identifiers":2433},"Yang X-S. Nature-inspired metaheuristic algorithms. Cambridge: Luniver Press; 2010.",{},{"id":26,"text":2435,"url":26,"identifiers":2436},"Yang X-S. Flower pollination algorithm for global optimization. In: International conference on unconventional computing and natural computation. Springer; 2012. p. 240–9.",{"doi":2437},"10.1007\u002F978-3-642-32894-7_27",{"id":26,"text":2439,"url":26,"identifiers":2440},"Yang X-S, Deb S. Cuckoo search via lévy flights. In: World congress on nature and biologically inspired computing (NaBIC 2009). IEEE; 2009. p. 210–4.",{"doi":2441},"10.1109\u002FNABIC.2009.5393690",{"id":26,"text":2443,"url":26,"identifiers":2444},"Yang X-S, Gandomi AH. Bat algorithm: a novel approach for global engineering optimization. Eng Comput. 2012;29(5):464–83.",{"doi":2445},"10.1108\u002F02644401211235834",{"id":2447,"createTime":2448,"updateTime":2448,"relativeEntities":2449,"slug":2450,"properties":2451,"entityType":802,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":2462,"translateLanguages":26,"viewCount":36,"primaryUrl":2463,"fullTextUrl":26,"authors":2464,"publicationType":919,"publisherRelationship":2535,"citationCount":608,"citationInfo":2557,"publishDate":2559,"publishYear":1672,"citationAnalyzeStatus":28,"lastCitationAnalyze":26,"indexDatabases":26,"openAccess":26,"references":2560,"isForceReanalyzing":1273},"b5ca742e-326c-433f-bf76-127a06f370e7","2024-04-15T12:52:13.463+00:00",[],"Offline-Signature-Recognition-Using-Image-Processing-Techniques-and-Back-Propagation-Neuron-Network-System",{"mag":2452,"keywords":2454,"openalex":2455,"abstract":2457,"title":2458,"doi":2460},{"VOID":2453},"3156988486",{},{"VOID":2456},"W3156988486",{},{"EN":2459},"Offline Signature Recognition Using Image Processing Techniques and Back Propagation Neuron Network System",{"VOID":2461},"10.1007\u002Fs42979-021-00591-y",[102],"http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-021-00591-y",[2465,2485,2503,2521],{"id":2466,"sortIndex":115,"researcher":26,"roles":2467,"affiliations":2468,"properties":2478},"1cec76f7-ee69-4cec-b033-a94e5b72b3f2",[],[2469],{"id":26,"sortIndex":36,"affiliation":2470,"properties":26},{"id":2471,"createTime":2472,"updateTime":2472,"relativeEntities":2473,"slug":2474,"properties":2475,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"2989da60-2764-4628-93f6-105ace0b4b00","2024-04-15T12:52:13.483+00:00",[],"Deptartment-of-TCE-GSSS-Institute-of-Engineering-and-Technology-for-Women-Mysuru-India",{"title":2476},{"EN":2477},"Deptartment of TCE, GSSS Institute of Engineering and Technology for Women, Mysuru, India",{"openalex":2479,"orcid":2481,"title":2483},{"VOID":2480},"A5033495329",{"VOID":2482},"https:\u002F\u002Forcid.org\u002F0000-0002-3997-5070",{"EN":2484},"B. D. Parameshachari",{"id":2486,"sortIndex":36,"researcher":26,"roles":2487,"affiliations":2488,"properties":2498},"f0d53f77-f2d9-4cb7-af99-d62889ec176c",[],[2489],{"id":26,"sortIndex":36,"affiliation":2490,"properties":26},{"id":2491,"createTime":2492,"updateTime":2492,"relativeEntities":2493,"slug":2494,"properties":2495,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"ffddc835-d6af-4c71-a8e2-5a59809fb47a","2024-04-15T12:52:13.472+00:00",[],"Deptartment-of-ECE-Vidyavardhaka-College-of-Engineering-Mysuru-India",{"title":2496},{"EN":2497},"Deptartment of ECE, Vidyavardhaka College of Engineering, Mysuru, India",{"openalex":2499,"title":2501},{"VOID":2500},"A5067017154",{"EN":2502},"P. V. R. Sai Kiran",{"id":2504,"sortIndex":59,"researcher":26,"roles":2505,"affiliations":2506,"properties":2516},"5e4920d8-882b-44ca-a2d2-f5287d7bb03f",[],[2507],{"id":26,"sortIndex":36,"affiliation":2508,"properties":26},{"id":2509,"createTime":2510,"updateTime":2510,"relativeEntities":2511,"slug":2512,"properties":2513,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"fb73581d-8cca-49b9-9b51-6b4287328e96","2024-04-15T12:52:13.505+00:00",[],"Deptartment-of-ECE-GMIT-Mandya-India",{"title":2514},{"EN":2515},"Deptartment of ECE, GMIT, Mandya, India",{"openalex":2517,"title":2519},{"VOID":2518},"A5039811445",{"EN":2520},"K. N. Bharath",{"id":2522,"sortIndex":114,"researcher":26,"roles":2523,"affiliations":2524,"properties":2530},"5bc7dcb1-e426-49e5-bac6-7ebd49807b74",[],[2525],{"id":26,"sortIndex":36,"affiliation":2526,"properties":26},{"id":2491,"createTime":2492,"updateTime":2492,"relativeEntities":2527,"slug":2494,"properties":2528,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":2529},{"EN":2497},{"openalex":2531,"title":2533},{"VOID":2532},"A5071598890",{"EN":2534},"J. Yashwanth",{"url":26,"publisher":2536,"properties":26},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":2537,"slug":663,"properties":2538,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":2542,"manageAffiliations":2543,"indexDatabases":2544,"url":755,"thumbnailPath":26,"statistic":2552,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":2539,"eissn":2540,"title":2541},{"VOID":666},{"VOID":668},{"EN":670},[],[],[2545],{"id":732,"indexDatabase":2546,"url":745,"indexYears":746,"academicFieldIds":2551,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":2547,"label":2548,"description":2549,"key":742,"publicationTags":2550,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":2553,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":2554,"totalCitation":767,"totalCitationByYear":2555,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":2556,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},{"total":608,"publishYear":26,"statisticByYear":2558},{"2021":116,"2022":240,"2023":135,"2024":114},"2021-05-01",[2561,2565,2568,2571,2575,2579,2582,2586,2590,2594,2598,2602,2606,2610,2614],{"id":26,"text":2562,"url":26,"identifiers":2563},"Yilmaz MB, Yanikoglu B, Tirkaz C, Kholmatov A. Offline signature verification using classifier combination of HOG and LBP features. In: International Joint Conference on Biometrics (IJCB), IEEE, 2011, pp. 1–7.",{"doi":2564},"10.1109\u002FIJCB.2011.6117473",{"id":26,"text":2566,"url":26,"identifiers":2567},"Shikha P, Shailja S. Neural network based offline signature recognition and verification system. Res J Eng Sci. 2013;2278:9472.",{},{"id":26,"text":2569,"url":26,"identifiers":2570},"Shekar BH, Bharathi RK. Eigen-signature: a robust and an efficient offline signature verification algorithm. In: International Conference on Recent Trends in Information Technology (ICRTIT), IEEE, pp. 134–8.",{},{"id":26,"text":2572,"url":26,"identifiers":2573},"Kumar MM, Puhan N B. Inter-point envelope based distance moments for offline signature verification. In: International Conference on Signal Processing and Communications (SPCOM), IEEE, 2014, pp. 1–6.",{"doi":2574},"10.1109\u002FSPCOM.2014.6984008",{"id":26,"text":2576,"url":26,"identifiers":2577},"Soleimani A, Araabi BN, Fouladi K. Deep multitask metric learning for offline signature verification. Pattern Recogn Lett. 2016;80:84–90.",{"doi":2578},"10.1016\u002Fj.patrec.2016.05.023",{"id":26,"text":2580,"url":26,"identifiers":2581},"Shekar BH, Pilar B, Sunil KDS. Blockwise binary pattern: a robust and an efficient approach for offline signature verification. The International Archives of Photogrammetry. Remote Sens Spat Inform Sci. 2017;42:227.",{},{"id":26,"text":2583,"url":26,"identifiers":2584},"Bhattacharya I, Ghosh P, Biswas S. Offline signature verification using pixel matching technique. Proced Technol. 2013;10:970–7.",{"doi":2585},"10.1016\u002Fj.protcy.2013.12.445",{"id":26,"text":2587,"url":26,"identifiers":2588},"Kruthi C, Shet D C. Offline signature verification using support vector machine. In: 5th International Conference on Signal and Image Processing, IEEE, 2014, pp. 3–8.",{"doi":2589},"10.1109\u002FICSIP.2014.5",{"id":26,"text":2591,"url":26,"identifiers":2592},"Guerbai Y, Chibani Y, Hadjadji B. The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recogn. 2015;48(1):103–13.",{"doi":2593},"10.1016\u002Fj.patcog.2014.07.016",{"id":26,"text":2595,"url":26,"identifiers":2596},"Ghosh R, Kumar P, Roy PP. A dempster–shafer theory based classifier combination for online signature recognition and verification systems. Int J Mach Learn Cybern. 2019;10(9):2467–82.",{"doi":2597},"10.1007\u002Fs13042-018-0883-9",{"id":26,"text":2599,"url":26,"identifiers":2600},"Jagtap AB, Hegadi RS. Feature learning for offline handwritten signature verification using convolution neural network. Int J Technol Hum Interact (IJTHI). 2019;15:54–62.",{"doi":2601},"10.4018\u002FIJTHI.2019100105",{"id":26,"text":2603,"url":26,"identifiers":2604},"Bhunia AK, Alaei A, Roy PP. Signature verification approach using fusion of hybrid texture features. Neural Comput Appl. 2019. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00521-019-04220-x.",{"doi":2605},"10.1007\u002Fs00521-019-04220-x",{"id":26,"text":2607,"url":26,"identifiers":2608},"Dutta A, Pal U, Llados J. Compact correlated features for writer independent signature verification. In: 23Rd international conference on pattern recognition (ICPR), IEEE, 2016, pp. 3422–7.",{"doi":2609},"10.1109\u002FICPR.2016.7900163",{"id":26,"text":2611,"url":26,"identifiers":2612},"Jagtap AB, Hegadi RS. Offline handwritten signature recognition based on upper and lower envelope using eigen values. In: World congress on computing and communication technologies (WCCCT), IEEE, 2017, pp. 223–6.",{"doi":2613},"10.1109\u002FWCCCT.2016.62",{"id":26,"text":2615,"url":26,"identifiers":2616},"Amruta JB, et al. Verification of genuine and forged offline signatures using Siamese Neural Network (SNN). Multimed Tools Appl. 2020;7947:35109–23.",{},{"id":2618,"createTime":2619,"updateTime":2620,"relativeEntities":2621,"slug":2622,"properties":2623,"entityType":802,"verifyStatus":25,"verifyTime":2620,"verifyNote":803,"syncStatus":28,"languages":2638,"translateLanguages":26,"viewCount":36,"primaryUrl":2639,"fullTextUrl":26,"authors":2640,"publicationType":919,"publisherRelationship":2695,"citationCount":398,"citationInfo":2717,"publishDate":2719,"publishYear":1672,"citationAnalyzeStatus":949,"lastCitationAnalyze":2720,"indexDatabases":26,"openAccess":26,"references":2721,"isForceReanalyzing":1273},"b390e582-f13b-4212-a026-128ccdf98d59","2024-04-11T16:30:45.899+00:00","2025-01-22T19:37:08.838+00:00",[],"Detection-of-Autism-Spectrum-Disorder-in-Children-Using-Machine-Learning-Techniques",{"mag":2624,"keywords":2626,"pmc":2627,"openalex":2629,"abstract":2631,"title":2632,"pm":2634,"doi":2636},{"VOID":2625},"3184737574",{},{"VOID":2628},"8296830",{"VOID":2630},"W3184737574",{},{"EN":2633},"Detection of Autism Spectrum Disorder in Children Using Machine Learning Techniques",{"VOID":2635},"34316724",{"VOID":2637},"10.1007\u002Fs42979-021-00776-5",[102],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-021-00776-5",[2641,2659,2679],{"id":2642,"sortIndex":115,"researcher":26,"roles":2643,"affiliations":2644,"properties":2654},"945628a6-feb0-4b23-947e-0eff0a2c359f",[],[2645],{"id":26,"sortIndex":36,"affiliation":2646,"properties":26},{"id":2647,"createTime":2648,"updateTime":2648,"relativeEntities":2649,"slug":2650,"properties":2651,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"4f3a8237-20e4-4a3a-a0e2-66995f84714b","2024-04-11T16:30:45.909+00:00",[],"Computer-Engineering-Mukesh-Patel-School-of-Technology-Management-and-Engineering-NMIMS-University-Mumbai-India",{"title":2652},{"EN":2653},"Computer Engineering, Mukesh Patel School of Technology Management and Engineering, NMIMS University, Mumbai, India",{"openalex":2655,"title":2657},{"VOID":2656},"A5065767344",{"EN":2658},"Diya Purkayastha",{"id":2660,"sortIndex":114,"researcher":26,"roles":2661,"affiliations":2662,"properties":2672},"938691e2-3f56-4957-8f8d-e5804d15a2ef",[],[2663],{"id":26,"sortIndex":36,"affiliation":2664,"properties":26},{"id":2665,"createTime":2666,"updateTime":2666,"relativeEntities":2667,"slug":2668,"properties":2669,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"f775e1f4-d224-49d8-993d-7bce78425114","2024-04-11T16:30:45.915+00:00",[],"Computer-Engineering-Department-Mukesh-Patel-School-of-Technology-Management-and-Engineering-NMIMS-University-Mumbai-India",{"title":2670},{"EN":2671},"Computer Engineering Department, Mukesh Patel School of Technology Management and Engineering, NMIMS University, Mumbai, India",{"openalex":2673,"orcid":2675,"title":2677},{"VOID":2674},"A5045712912",{"VOID":2676},"https:\u002F\u002Forcid.org\u002F0000-0002-6236-0955",{"EN":2678},"Deepa Krishnan",{"id":2680,"sortIndex":36,"researcher":26,"roles":2681,"affiliations":2682,"properties":2688},"aa7a579a-a24b-4b88-9360-6b269f340f79",[],[2683],{"id":26,"sortIndex":36,"affiliation":2684,"properties":26},{"id":2647,"createTime":2648,"updateTime":2648,"relativeEntities":2685,"slug":2650,"properties":2686,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":2687},{"EN":2653},{"openalex":2689,"orcid":2691,"title":2693},{"VOID":2690},"A5090222236",{"VOID":2692},"https:\u002F\u002Forcid.org\u002F0000-0002-6844-4519",{"EN":2694},"Kaushik Vakadkar",{"url":26,"publisher":2696,"properties":26},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":2697,"slug":663,"properties":2698,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":2702,"manageAffiliations":2703,"indexDatabases":2704,"url":755,"thumbnailPath":26,"statistic":2712,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":2699,"eissn":2700,"title":2701},{"VOID":666},{"VOID":668},{"EN":670},[],[],[2705],{"id":732,"indexDatabase":2706,"url":745,"indexYears":746,"academicFieldIds":2711,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":2707,"label":2708,"description":2709,"key":742,"publicationTags":2710,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":2713,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":2714,"totalCitation":767,"totalCitationByYear":2715,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":2716,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},{"total":398,"publishYear":26,"statisticByYear":2718},{"2022":53,"2023":238,"2024":135},"2021-09-01","2024-04-11T19:14:32.859+00:00",[2722,2726,2730,2733,2737,2741,2745,2748,2751,2754,2758,2762,2765,2768,2772,2775,2778,2782,2785,2788,2792,2796,2799,2802],{"id":26,"text":2723,"url":26,"identifiers":2724},"Al Banna MH, Ghosh T, Taher KA, Kaiser MS, Mahmud M. A monitoring system for patients of autism spectrum disorder using artificial intelligence. In: International conference on brain informatics. Cham: Springer; 2020. pp. 251–62.",{"doi":2725},"10.1007\u002F978-3-030-59277-6_23",{"id":26,"text":2727,"url":26,"identifiers":2728},"Baron-Cohen S, Allen J, Gillberg C. Can autism be detected at 18 months? The needle, the haystack, and the CHAT. Br J Psychiatry. 1992;161:839–43.",{"doi":2729},"10.1192\u002Fbjp.161.6.839",{"id":26,"text":2731,"url":26,"identifiers":2732},"Dataset: https:\u002F\u002Fwww.kaggle.com\u002Ffabdelja\u002Fautism-screening-for-toddlers.  Accessed 1 Oct 2019.",{},{"id":26,"text":2734,"url":26,"identifiers":2735},"Deshpande G, Libero LE, Sreenivasan KR, Deshpande HD, Kana RK. Identification of neural connectivity signatures of autism using machine learning. Front Hum Neurosci. 2013;7:670.",{"doi":2736},"10.3389\u002Ffnhum.2013.00670",{"id":26,"text":2738,"url":26,"identifiers":2739},"Duda M, Ma R, Haber N, Wall DP. Use of machine learning for behavioral distinction of autism and ADHD. Transl Psychiatry. 2016;6: e732.",{"doi":2740},"10.1038\u002Ftp.2015.221",{"id":26,"text":2742,"url":26,"identifiers":2743},"Thabtah, F. An accessible and efficient autism screening method for behavioural data and predictive analyses. Health Informatics Journal 2019;25(4):1739–55. https:\u002F\u002Fdoi.org\u002F10.1177\u002F1460458218796636.",{"doi":2744},"10.1177\u002F1460458218796636",{"id":26,"text":2746,"url":26,"identifiers":2747},"https:\u002F\u002Fwww.autism-society.org\u002Fwhat-is\u002Ffacts-and-statistics\u002F. Accessed 25 Dec 2019.",{},{"id":26,"text":2749,"url":26,"identifiers":2750},"https:\u002F\u002Fwww.helpguide.org\u002Farticles\u002Fautism-learning-disabilities\u002Fautismspectrumdisorders.htm. Accessed 20 Dec 2019.",{},{"id":26,"text":2752,"url":26,"identifiers":2753},"“KNN Classification using Scikit-learn”, https:\u002F\u002Fwww.datacamp.com\u002Fcommunity\u002Ftutorials\u002Fk-nearest-neighbor-classification-scikit-learn. Accessed 8 Oct 2019.",{},{"id":26,"text":2755,"url":26,"identifiers":2756},"Kosmicki JA, Sochat V, Duda M, Wall DP. Searching for a minimal set of behaviors for autism detection through feature selection-based machine learning. Transl Psychiatry. 2015. https:\u002F\u002Fdoi.org\u002F10.1038\u002Ftp.2015.7.",{"doi":2757},"10.1038\u002Ftp.2015.7",{"id":26,"text":2759,"url":26,"identifiers":2760},"Li H, Parikh NA, He L. A novel transfer learning approach to enhance deep neural network classification of brain functional connectome. Front Neurosci. 2018. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffnins.2018.00491.",{"doi":2761},"10.3389\u002Ffnins.2018.00491",{"id":26,"text":2763,"url":26,"identifiers":2764},"Logistic Regression. https:\u002F\u002Fmedium.com\u002Fdatadriveninvestor\u002Flogistic-regression-18afd48779ce. Accessed 7 Oct 2019.",{},{"id":26,"text":2766,"url":26,"identifiers":2767},"Naive Bayes for Machine Learning. https:\u002F\u002Fmachinelearningmastery.com\u002Fnaive-bayes-for-machine-learning\u002F. Accessed 8 Oct 2019.",{},{"id":26,"text":2769,"url":26,"identifiers":2770},"Parikh MN, Li H, He L. Enhancing diagnosis of autism with optimized machine learning models and personal characteristic data. Front Comput Neurosci. 2019. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffncom.2019.00009.",{"doi":2771},"10.3389\u002Ffncom.2019.00009",{"id":26,"text":2773,"url":26,"identifiers":2774},"Pratap A, Kanimozhiselvi C. Soft computing models for the predictive grading of childhood Autism—a comparative study. IJSCE. 2014;4:64–7.",{},{"id":26,"text":2776,"url":26,"identifiers":2777},"Random Forests(r), Explained. https:\u002F\u002Fwww.kdnuggets.com\u002F2017\u002F10\u002Frandom-forests-explained.html. Accessed 8 Oct 2019.",{},{"id":26,"text":2779,"url":26,"identifiers":2780},"Sen B, Borle NC, Greiner R, Brown MR. A general prediction model for the detection of ADHD and Autism using structural and functional MRI. PLoS ONE. 2018;13: e0194856.",{"doi":2781},"10.1371\u002Fjournal.pone.0194856",{"id":26,"text":2783,"url":26,"identifiers":2784},"Support Vector Machine—Introduction to Machine Learning Algorithms. https:\u002F\u002Ftowardsdatascience.com\u002Fsupport-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47. Accessed 7 Oct 2019.",{},{"id":26,"text":2786,"url":26,"identifiers":2787},"Support vector machines: The linearly separable case, https:\u002F\u002Fnlp.stanford.edu\u002FIR-book\u002Fhtml\u002Fhtmledition\u002Fsupport-vector-machines-the-linearly-separable-case-1.html. Accessed 8 Oct 2019.",{},{"id":26,"text":2789,"url":26,"identifiers":2790},"Thabtah F. Machine learning in autistic spectrum disorder behavioral research: A review and ways forward. Inf Health Soc Care. 2017;44:278–97.",{"doi":2791},"10.1080\u002F17538157.2017.1399132",{"id":26,"text":2793,"url":26,"identifiers":2794},"Thabtah F, Peebles D. A new machine learning model based on induction of rules for autism detection. Health Inform J. 2020;26(1):264–86.",{"doi":2795},"10.1177\u002F1460458218824711",{"id":26,"text":2797,"url":26,"identifiers":2798},"Towle P, Patrick P. Autism spectrum disorder screening instruments for very young children: A systematic review. New York: Hindawi Publishing Corporation; 2016.",{},{"id":26,"text":2800,"url":26,"identifiers":2801},"Vaishali R, Sasikala R. A machine learning based approach to classify Autism with optimum behaviour sets. Int J Eng Technol. 2017;7:18.",{},{"id":26,"text":2803,"url":26,"identifiers":2804},"van den Bekerom B. Using machine learning for detection of autism spectrum disorder. In: 26th Twente Student Conference on IT, Feb 2017.",{},{"id":2806,"createTime":2807,"updateTime":2808,"relativeEntities":2809,"slug":2810,"properties":2811,"entityType":802,"verifyStatus":25,"verifyTime":2808,"verifyNote":803,"syncStatus":28,"languages":2822,"translateLanguages":26,"viewCount":36,"primaryUrl":2823,"fullTextUrl":26,"authors":2824,"publicationType":919,"publisherRelationship":2880,"citationCount":397,"citationInfo":2906,"publishDate":1419,"publishYear":948,"citationAnalyzeStatus":28,"lastCitationAnalyze":26,"indexDatabases":26,"openAccess":26,"references":2908,"isForceReanalyzing":1273},"956e21a3-d562-45d8-9572-8d00ae039e06","2024-04-16T20:23:02.264+00:00","2025-01-04T17:33:18.926+00:00",[],"Driver-Safety-Development-Real-Time-Driver-Drowsiness-Detection-System-Based-on-Convolutional-Neural-Network",{"mag":2812,"keywords":2814,"openalex":2815,"abstract":2817,"title":2818,"doi":2820},{"VOID":2813},"3167176781",{},{"VOID":2816},"W3167176781",{},{"EN":2819},"Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network",{"VOID":2821},"10.1007\u002Fs42979-020-00306-9",[102],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-020-00306-9",[2825,2846,2863],{"id":2826,"sortIndex":114,"researcher":26,"roles":2827,"affiliations":2828,"properties":2839},"549bd28d-0f60-4545-b42e-a71070f7ecba",[],[2829],{"id":2830,"sortIndex":36,"affiliation":2831,"properties":26},"3fe65913-e77d-46a8-b2c0-c69e0b241372",{"id":2832,"createTime":2833,"updateTime":2833,"relativeEntities":2834,"slug":2835,"properties":2836,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"2ba6e11c-7f14-4f5a-82b8-1af89f37443a","2024-04-16T20:23:02.280+00:00",[],"Iran-University-of-Science-and-Technology-Resalat-highway-Tehran-Iran",{"title":2837},{"EN":2838},"Iran University of Science and Technology, Resalat highway, Tehran, Iran",{"openalex":2840,"orcid":2842,"title":2844},{"VOID":2841},"A5051160023",{"VOID":2843},"https:\u002F\u002Forcid.org\u002F0000-0001-9603-5117",{"EN":2845},"Ali Asghar Beheshti Shirazi",{"id":2847,"sortIndex":115,"researcher":26,"roles":2848,"affiliations":2849,"properties":2856},"fbc071fc-e0b7-4fec-b9e5-1c7213318a2b",[],[2850],{"id":2851,"sortIndex":36,"affiliation":2852,"properties":26},"e730e074-8a47-4c27-99dd-b03a79e51c64",{"id":2832,"createTime":2833,"updateTime":2833,"relativeEntities":2853,"slug":2835,"properties":2854,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":2855},{"EN":2838},{"openalex":2857,"orcid":2859,"title":2861},{"VOID":2858},"A5055133450",{"VOID":2860},"https:\u002F\u002Forcid.org\u002F0000-0003-4232-3097",{"EN":2862},"Alireza Mirrashid",{"id":2864,"sortIndex":36,"researcher":26,"roles":2865,"affiliations":2866,"properties":2873},"452dc82e-8318-4ba9-b9a5-301d6926f2cb",[],[2867],{"id":2868,"sortIndex":36,"affiliation":2869,"properties":26},"fe7b0c11-9419-417c-bc2a-9e9abbcb24f2",{"id":2832,"createTime":2833,"updateTime":2833,"relativeEntities":2870,"slug":2835,"properties":2871,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":2872},{"EN":2838},{"openalex":2874,"orcid":2876,"title":2878},{"VOID":2875},"A5063161369",{"VOID":2877},"https:\u002F\u002Forcid.org\u002F0000-0002-1596-8394",{"EN":2879},"Maryam Hashemi",{"url":26,"publisher":2881,"properties":2902},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":2882,"slug":663,"properties":2883,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":2887,"manageAffiliations":2888,"indexDatabases":2889,"url":755,"thumbnailPath":26,"statistic":2897,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":2884,"eissn":2885,"title":2886},{"VOID":666},{"VOID":668},{"EN":670},[],[],[2890],{"id":732,"indexDatabase":2891,"url":745,"indexYears":746,"academicFieldIds":2896,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":2892,"label":2893,"description":2894,"key":742,"publicationTags":2895,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":2898,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":2899,"totalCitation":767,"totalCitationByYear":2900,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":2901,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},{"volume":2903,"issue":2904},{"VOID":2178},{"VOID":2905},"5",{"total":397,"publishYear":26,"statisticByYear":2907},{"2021":135,"2022":356,"2023":356,"2024":59},[2909,2912,2916,2920,2924,2928,2932,2936,2940,2944,2948,2952,2956,2960,2964,2968,2972,2976,2980,2984,2988,2992,2996,3000,3003,3007,3011,3015,3018,3022,3025,3029,3032,3036,3039,3042],{"id":26,"text":2910,"url":26,"identifiers":2911},"World Health Organization The top ten causes of death. [Online]. https:\u002F\u002Fwww.who.int\u002Fnews-room\u002Ffact-sheets\u002Fdetail\u002Fthe-top-10-causes-of-death",{},{"id":26,"text":2913,"url":26,"identifiers":2914},"Yu J, Chen Z, Zhu Y, Chen YJ, Kong L, Li M. Fine-grained abnormal driving behaviors detection and identification with smartphones. IEEE Trans Mobile Comput. 2016;16(8):2198–212.",{"doi":2915},"10.1109\u002FTMC.2016.2618873",{"id":26,"text":2917,"url":26,"identifiers":2918},"Budak U, Bajaj V, Akbulut Y, Atila O, Sengur A. An effective hybrid model for EEG-based drowsiness detection. IEEE Sens J. 2019;19(17):7624–31.",{"doi":2919},"10.1109\u002FJSEN.2019.2917850",{"id":26,"text":2921,"url":26,"identifiers":2922},"Ngxande M, Tapamo JR, Burke M. Driver drowsiness detection using behavioral measures and machine learning techniques :a review of state-of-art techniques. In: 2017 Pattern recognition association of South Africa and robotics and mechatronics. 2017;pp. 156-161.",{"doi":2923},"10.1109\u002FRoboMech.2017.8261140",{"id":26,"text":2925,"url":26,"identifiers":2926},"Sahayadhas A, Sundaraj K, Murugappan M. Detecting driver drowsiness based on sensors: a review. Sensors. 2012;12(12):16937–53.",{"doi":2927},"10.3390\u002Fs121216937",{"id":26,"text":2929,"url":26,"identifiers":2930},"Kim JY, Jeong CH, Jung MJ, Park JH, Jung DH. Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving. Int J Automotive Technol. 2013;14(6):965–70.",{"doi":2931},"10.1007\u002Fs12239-013-0106-z",{"id":26,"text":2933,"url":26,"identifiers":2934},"Zheng WL, Gao K, Li G, Liu W, Liu C, Liu J, Q., & Lu, B. L. . Vigilance estimation using a wearable EOG device in real driving environment. In: IEEE Transactions on Intelligent Transportation Systems. 2019;.",{"doi":2935},"10.1109\u002FTITS.2018.2889962",{"id":26,"text":2937,"url":26,"identifiers":2938},"Lee H, Lee J, Shin M. Using wearable ECG\u002FPPG sensors for driver drowsiness detection based on distinguishable pattern of recurrence plots. Electronics. 2019;8(2):192.",{"doi":2939},"10.3390\u002Felectronics8020192",{"id":26,"text":2941,"url":26,"identifiers":2942},"Wang MS, Jeong NT, Kim KS, Choi SB, Yang SM, You SH, Suh MW. Drowsy behavior detection based on driving information. Int J Automotive Technol. 2016;17(1):165–73.",{"doi":2943},"10.1007\u002Fs12239-016-0016-y",{"id":26,"text":2945,"url":26,"identifiers":2946},"Deng W, Wu R. Real-time driver-drowsiness detection system using facial features. IEEE Access. 2019;7:118727–38.",{"doi":2947},"10.1109\u002FACCESS.2019.2936663",{"id":26,"text":2949,"url":26,"identifiers":2950},"Zhao L, Wang Z, Wang X, Liu Q. Driver drowsiness detection using facial dynamic fusion information and a DBN. IET Intell Trans Syst. 2018;12(2):127–33.",{"doi":2951},"10.1049\u002Fiet-its.2017.0183",{"id":26,"text":2953,"url":26,"identifiers":2954},"Verma B, Choudhary A. Deep learning based real-time driver emotion monitoring. In: 2018 IEEE international conference on vehicular electronics and safety. 2018;pp 1-6.",{"doi":2955},"10.1109\u002FICVES.2018.8519595",{"id":26,"text":2957,"url":26,"identifiers":2958},"Mandal B, Li L, Wang GS, Lin J. Towards detection of bus driver fatigue based on robust visual analysis of eye state. IEEE Trans Intell Trans Syst. 2016;18(3):545–57.",{"doi":2959},"10.1109\u002FTITS.2016.2582900",{"id":26,"text":2961,"url":26,"identifiers":2962},"Massoz Q, Langohr T, François C, Verly JG. The ULg multi modality drowsiness database (called DROZY) and examples of use. In: 2016 IEEE WACV. 2016;pp. 1-7",{"doi":2963},"10.1109\u002FWACV.2016.7477715",{"id":26,"text":2965,"url":26,"identifiers":2966},"García-García M, Caplier A, Rombaut M. Sleep deprivation detection for real-time driver monitoring using deep learning. In: International conference image analysis and recognition. 2018;pp. 435-442.",{"doi":2967},"10.1007\u002F978-3-319-93000-8_49",{"id":26,"text":2969,"url":26,"identifiers":2970},"Abtahi S, Omidyeganeh M, Shirmohammadi S, Hariri B. YawDD: A yawning detection dataset. In: Proceedings of the 5th ACM multimedia systems conference. 2014;pp. 24-28.",{"doi":2971},"10.1145\u002F2557642.2563678",{"id":26,"text":2973,"url":26,"identifiers":2974},"Xing Y, Lv C, Zhang Z, Wang H, Na X, Cao D, Wang FY. Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition. IEEE Trans Comput Soc Syst. 2017;5(1):95–108.",{"doi":2975},"10.1109\u002FTCSS.2017.2766884",{"id":26,"text":2977,"url":26,"identifiers":2978},"Kim KW, Lee WO, Kim YG, Hong HG, Lee EC, Park KR. Segmentation method of eye region based on fuzzy logic system for classifying open and closed eyes. Opt Eng. 2015;54(3):033103.",{"doi":2979},"10.1117\u002F1.OE.54.3.033103",{"id":26,"text":2981,"url":26,"identifiers":2982},"Ghoddoosian R, Galib M, Athitsos V. A Realistic dataset and baseline temporal model for early drowsiness detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2019;pp. 0-0.",{"doi":2983},"10.1109\u002FCVPRW.2019.00027",{"id":26,"text":2985,"url":26,"identifiers":2986},"Song F, Tan X, Liu X, Chen S. Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recognit. 2014;47(9):2825–38.",{"doi":2987},"10.1016\u002Fj.patcog.2014.03.024",{"id":26,"text":2989,"url":26,"identifiers":2990},"Zhao L, Wang Z, Zhang G, et al. Multimed Tools Appl. 2018;77:19415. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11042-017-5380-8.",{"doi":2991},"10.1007\u002Fs11042-017-5380-8",{"id":26,"text":2993,"url":26,"identifiers":2994},"Sukrit Mehta, et al. “Real-time driver drowsiness detection system using eye aspect ratio and eye closure ratio.” In: Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), Amity University Rajasthan, Jaipur-India. 2019.",{"doi":2995},"10.2139\u002Fssrn.3356401",{"id":26,"text":2997,"url":26,"identifiers":2998},"Bamidele A, et al. Non-intrusive driver drowsiness detection based on face and eye tracking. Int J. Adv. Comput. Sci. Appl. 2019;10:",{"doi":2999},"10.14569\u002FIJACSA.2019.0100775",{"id":26,"text":3001,"url":26,"identifiers":3002},"Anitha J, Mani G, Venkata Rao K. Driver drowsiness detection using Viola Jones algorithm. In: Satapathy S, Bhateja V, Mohanty J, Udgata S, editors. Smart intelligent computing and applications.ion Smart innovation, systems and technologies, vol. 159. Singapore: Springer; 2020.",{},{"id":26,"text":3004,"url":26,"identifiers":3005},"Ji Y et al. Fatigue state detection based on multi-index fusion and state recognition network. In: IEEE Access 7, 2019.",{"doi":3006},"10.1109\u002FACCESS.2019.2917382",{"id":26,"text":3008,"url":26,"identifiers":3009},"Liu W et al. ”Convolutional two-stream network using multifacial feature fusion for driver fatigue detection. Future Internet 11.5, 2019.",{"doi":3010},"10.3390\u002Ffi11050115",{"id":26,"text":3012,"url":26,"identifiers":3013},"Ed-doughmi Y, Najlae I Driver Fatigue Detection using Recurrent Neural Networks. In: Proceedings of the 2nd international conference on networking, information systems & security. 2019.",{"doi":3014},"10.1145\u002F3320326.3320376",{"id":26,"text":3016,"url":26,"identifiers":3017},"Wanghua D, Wu R Real-time driver-drowsiness detection system using facial features. IEEE Access. 2019;7:",{"doi":2947},{"id":26,"text":3019,"url":26,"identifiers":3020},"Jabbar R et al. Real-time driver drowsiness detection for android application using deep neural networks techniques. Proc Comput Sci. 130, 2018.",{"doi":3021},"10.1016\u002Fj.procs.2018.04.060",{"id":26,"text":3023,"url":26,"identifiers":3024},"Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. 2001; 1, pp I-I.",{},{"id":26,"text":3026,"url":26,"identifiers":3027},"Kazemi V, Sullivan J. One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2014;pp. 1867-1874.",{"doi":3028},"10.1109\u002FCVPR.2014.241",{"id":26,"text":3030,"url":26,"identifiers":3031},"Intelligent Behaviour Understanding Group (iBUG). https:\u002F\u002Fibug.doc.ic.ac.uk\u002Fresources\u002Ffacial-point-annotations\u002F",{},{"id":26,"text":3033,"url":26,"identifiers":3034},"Pan G, Sun L, Wu Z, Lao S. Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: 2007 IEEE 11th international conference on computer vision. 2007;pp. 1-8.",{"doi":3035},"10.1109\u002FICCV.2007.4409068",{"id":26,"text":3037,"url":26,"identifiers":3038},"Parmar SH, Jajal M, Brijbhan YP. Drowsy Driver Warning System Using Image Processing. 2014;1.",{},{"id":26,"text":3040,"url":26,"identifiers":3041},"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 2014.",{},{"id":26,"text":3043,"url":26,"identifiers":3044},"Samiee S, Azadi S, Kazemi R, Nahvi A, Eichberger A. Data fusion to develop a driver drowsiness detection system with robustness to signal loss. Sensors. 2014;14(9):17832–47.",{"doi":3045},"10.3390\u002Fs140917832",{"id":3047,"createTime":3048,"updateTime":3049,"relativeEntities":3050,"slug":3051,"properties":3052,"entityType":802,"verifyStatus":25,"verifyTime":3049,"verifyNote":803,"syncStatus":28,"languages":3063,"translateLanguages":26,"viewCount":36,"primaryUrl":3064,"fullTextUrl":26,"authors":3065,"publicationType":919,"publisherRelationship":3101,"citationCount":397,"citationInfo":3123,"publishDate":3125,"publishYear":948,"citationAnalyzeStatus":28,"lastCitationAnalyze":26,"indexDatabases":26,"openAccess":26,"references":3126,"isForceReanalyzing":1273},"aa823838-6b3e-4435-b127-fd08108034e0","2024-04-15T21:01:02.149+00:00","2025-02-20T04:18:11.270+00:00",[],"Historical-Document-Image-Binarization-A-Review",{"mag":3053,"keywords":3055,"openalex":3056,"abstract":3058,"title":3059,"doi":3061},{"VOID":3054},"3024648667",{},{"VOID":3057},"W3024648667",{},{"EN":3060},"Historical Document Image Binarization: A Review",{"VOID":3062},"10.1007\u002Fs42979-020-00176-1",[102],"https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-020-00176-1",[3066,3084],{"id":3067,"sortIndex":36,"researcher":26,"roles":3068,"affiliations":3069,"properties":3079},"d4334854-b09a-43e8-9972-db1cd3a7706d",[],[3070],{"id":26,"sortIndex":36,"affiliation":3071,"properties":26},{"id":3072,"createTime":3073,"updateTime":3073,"relativeEntities":3074,"slug":3075,"properties":3076,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"5cbdb5d2-ee9f-401d-b823-aaf4d88129d7","2023-11-24T21:03:36.926+00:00",[],"Adobe-Research-San-Jose-USA",{"title":3077},{"VI":3078},"Adobe Research, San Jose, USA",{"openalex":3080,"title":3082},{"VOID":3081},"A5058165603",{"EN":3083},"Chris Tensmeyer",{"id":3085,"sortIndex":115,"researcher":26,"roles":3086,"affiliations":3087,"properties":3096},"e9b9bf3a-ebc0-48f0-893d-26c3e106081c",[],[3088],{"id":26,"sortIndex":36,"affiliation":3089,"properties":26},{"id":3090,"createTime":3091,"updateTime":3091,"relativeEntities":3092,"slug":26,"properties":3093,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"ba714bff-5ee7-4737-80b6-1b1fe05900f2","2024-01-08T14:30:18.783+00:00",[],{"title":3094},{"VI":3095},"Computer Science Department, Brigham Young University, Provo, USA",{"openalex":3097,"title":3099},{"VOID":3098},"A5021958184",{"EN":3100},"Tony Martinez",{"url":26,"publisher":3102,"properties":26},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":3103,"slug":663,"properties":3104,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":3108,"manageAffiliations":3109,"indexDatabases":3110,"url":755,"thumbnailPath":26,"statistic":3118,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":3105,"eissn":3106,"title":3107},{"VOID":666},{"VOID":668},{"EN":670},[],[],[3111],{"id":732,"indexDatabase":3112,"url":745,"indexYears":746,"academicFieldIds":3117,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":3113,"label":3114,"description":3115,"key":742,"publicationTags":3116,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":3119,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":3120,"totalCitation":767,"totalCitationByYear":3121,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":3122,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},{"total":397,"publishYear":26,"statisticByYear":3124},{"2020":115,"2021":116,"2022":158,"2023":103,"2024":162},"2020-05-01",[3127,3131,3135,3139,3143,3147,3151,3155,3159,3163,3167,3171,3175,3179,3183,3186,3190,3193,3197,3201,3205,3209,3213,3217,3221,3225,3229,3233,3236,3240,3243,3247,3251,3255,3259,3263,3267,3270,3274,3278,3282,3286,3290,3294,3298,3302,3306,3310,3314,3318,3322,3326,3330,3334,3338,3342,3346,3349,3352,3356,3360,3364,3367,3371,3375,3379,3383,3386,3390,3394,3398,3402,3406,3410,3414,3418,3422,3425,3428,3432,3436,3440,3444,3447,3450,3454,3458,3462,3466,3470,3474,3478,3482,3486,3490,3494,3498,3502,3506,3509,3513,3517,3520,3524,3528,3532,3535,3538,3542,3546,3550,3554,3558,3562,3566,3570,3574,3578,3582,3586,3590,3594,3598,3601,3605,3609,3613,3617,3621,3625,3629,3633,3637,3641,3645,3649,3653,3657,3661,3665,3669,3672,3676,3680,3684,3688,3692,3696,3700,3704,3708,3712,3716,3719,3723,3727,3730,3734,3737,3741,3745,3748,3752,3756,3760,3764,3768,3772,3776,3780,3784,3788,3791,3794,3798,3802,3806,3810],{"id":26,"text":3128,"url":26,"identifiers":3129},"Afzal MZ, Pastor-Pellicer J, Shafait F, Breuel TM, Dengel A, Liwicki M. Document image binarization using LSTM: a sequence learning approach. In: Workshop on historical document imaging and processing (HIP). ACM; 2015. p. 79–84.",{"doi":3130},"10.1145\u002F2809544.2809561",{"id":26,"text":3132,"url":26,"identifiers":3133},"Ahmadi E, Azimifar Z, Shams M, Famouri M, Shafiee MJ. Document image binarization using a discriminative structural classifier. Pattern Recognit Lett. 2015;63:36–42.",{"doi":3134},"10.1016\u002Fj.patrec.2015.06.008",{"id":26,"text":3136,"url":26,"identifiers":3137},"Akbari Y, Britto AS, Al-maadeed S, Oliveira LS. Binarization of degraded document images using convolutional neural networks based on predicted two-channel images. In: International conference on document analysis and recognition (ICDAR). IEEE; 2019. p. 973–8",{"doi":3138},"10.1109\u002FICDAR.2019.00160",{"id":26,"text":3140,"url":26,"identifiers":3141},"Almeida M, Lins RD, Bernardino R, Jesus D, Lima B. A new binarization algorithm for historical documents. J Imaging. 2018;4(2):27.",{"doi":3142},"10.3390\u002Fjimaging4020027",{"id":26,"text":3144,"url":26,"identifiers":3145},"Arruda A, Mello CA. Binarization of degraded document images based on combination of contrast images. In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2014. p. 615–20.",{"doi":3146},"10.1109\u002FICFHR.2014.108",{"id":26,"text":3148,"url":26,"identifiers":3149},"Ayatollahi SM, Nafchi HZ. Persian heritage image binarization competition (PHIBC 2012). In: Iranian conference on pattern recognition and image analysis (PRIA). IEEE; 2013. p. 1–4.",{"doi":3150},"10.1109\u002FPRIA.2013.6528442",{"id":26,"text":3152,"url":26,"identifiers":3153},"Ayyalasomayajula KR, Brun A. Document binarization using topological clustering guided Laplacian energy segmentation. In: International conference on frontiers in handwriting recognition (ICFHR); 2014. p. 523–8.",{"doi":3154},"10.1109\u002FICFHR.2014.94",{"id":26,"text":3156,"url":26,"identifiers":3157},"Ayyalasomayajula KR, Malmberg F, Brun A. PDNET: semantic segmentation integrated with a primal-dual network for document binarization. Pattern Recognit Lett. 2018;121:52–60.",{"doi":3158},"10.1016\u002Fj.patrec.2018.05.011",{"id":26,"text":3160,"url":26,"identifiers":3161},"Bag S, Bhowmick P. Adaptive-interpolative binarization with stroke preservation for restoration of faint characters in degraded documents. J Vis Commun Image Represent. 2015;31:266–81.",{"doi":3162},"10.1016\u002Fj.jvcir.2015.07.003",{"id":26,"text":3164,"url":26,"identifiers":3165},"Bataineh B, Abdullah SNHS, Omar K. An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows. Pattern Recognit Lett. 2011;32(14):1805–13.",{"doi":3166},"10.1016\u002Fj.patrec.2011.08.001",{"id":26,"text":3168,"url":26,"identifiers":3169},"Bataineh B, Abdullah SNHS, Omar K. Adaptive binarization method for degraded document images based on surface contrast variation. Pattern Anal Appl. 2017;20(3):639–52.",{"doi":3170},"10.1007\u002Fs10044-015-0520-0",{"id":26,"text":3172,"url":26,"identifiers":3173},"Bawa RK, Sethi GK. A review on binarization algorithms for camera based natural scene images. In: International conference on advances in computing, communications and informatics. ACM; 2012. p. 873–78.",{"doi":3174},"10.1145\u002F2345396.2345537",{"id":26,"text":3176,"url":26,"identifiers":3177},"Bhowmik S, Sarkar R, Das B, Doermann D. Gib: a game theory inspired binarization technique for degraded document images. IEEE Trans Image Process. 2018;28(3):1443–55.",{"doi":3178},"10.1109\u002FTIP.2018.2878959",{"id":26,"text":3180,"url":26,"identifiers":3181},"Bhunia AK, Bhunia AK, Sain A, Roy PP. Improving document binarization via adversarial noise-texture augmentation. In: IEEE international conference on image processing (ICIP). IEEE; 2019. p. 2721–25.",{"doi":3182},"10.1109\u002FICIP.2019.8803348",{"id":26,"text":3184,"url":26,"identifiers":3185},"Boiangiu CA, Olteanu A, Stefanescu A, Rosner D, Tapus N, Andreica MI. Local thresholding algorithm based on variable window size statistics. In: International conference on control systems and computer science (CSCS); 2011, vol 2. p. 647–52.",{},{"id":26,"text":3187,"url":26,"identifiers":3188},"Bolan S, Shijian L, Tan CL. A self-training learning document binarization framework. In: International conference on pattern recognition (ICPR). IEEE; 2010. p. 3187–90.",{"doi":3189},"10.1109\u002FICPR.2010.780",{"id":26,"text":3191,"url":26,"identifiers":3192},"Bouillon M, Ingold R, Liwicki M. Grayification: a meaningful grayscale conversion to improve handwritten historical documents analysis. Pattern Recognit Lett. 2018;121:4–651.",{},{"id":26,"text":3194,"url":26,"identifiers":3195},"Boykov Y, Kolmogorov V. An experimental comparison of min-cut\u002Fmax-flow algorithms for energy minimization in vision. Trans Pattern Anal Mach Intell. 2004;26(9):1124–37.",{"doi":3196},"10.1109\u002FTPAMI.2004.60",{"id":26,"text":3198,"url":26,"identifiers":3199},"Burie JC, Coustaty M, Hadi S, Kesiman MWA, Ogier JM, Paulus E, Sok K, Sunarya IMG, Valy D. ICFHR2016 Competition on the analysis of handwritten text in images of Balinese palm leaf manuscripts. In: International conference on front handwrit recognit (ICFHR) ;2016. p. 596–601.",{"doi":3200},"10.1109\u002FICFHR.2016.0114",{"id":26,"text":3202,"url":26,"identifiers":3203},"Burie JC, Kesiman MWA, Valy D, Paulus E, Suryani M, Hadi S, Verleysen M, Chhun S, Ogier JM. ICFHR2018 competition on document image analysis tasks for southeast Asian palm leaf manuscripts. In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2018.",{"doi":3204},"10.1109\u002FICFHR-2018.2018.00090",{"id":26,"text":3206,"url":26,"identifiers":3207},"Calvo-Zaragoza J, Gallego AJ. A selectional auto-encoder approach for document image binarization. Pattern Recognit. 2019;86:37–47.",{"doi":3208},"10.1016\u002Fj.patcog.2018.08.011",{"id":26,"text":3210,"url":26,"identifiers":3211},"Canny J. A computational approach to edge detection. Trans Pattern Anal Mach Intell. 1986;8:679–98.",{"doi":3212},"10.1109\u002FTPAMI.1986.4767851",{"id":26,"text":3214,"url":26,"identifiers":3215},"Chattopadhyay T, Reddy VR, Garain U. Automatic selection of binarization method for robust OCR. In: International conference on document analysis and recognition (ICDAR). IEEE; 2013. p. 1170–4.",{"doi":3216},"10.1109\u002FICDAR.2013.237",{"id":26,"text":3218,"url":26,"identifiers":3219},"Chaudhary P, Saini B. An effective and robust technique for the binarization of degraded document images. Int J Res Eng Technol (IJRET). 2014;3(06):140.",{"doi":3220},"10.15623\u002Fijret.2014.0306025",{"id":26,"text":3222,"url":26,"identifiers":3223},"Chen X, Lin L, Gao Y. Parallel nonparametric binarization for degraded document images. Neurocomputing. 2016;189:43–52.",{"doi":3224},"10.1016\u002Fj.neucom.2015.11.040",{"id":26,"text":3226,"url":26,"identifiers":3227},"Chen Y, Wang L. Broken and degraded document images binarization. Neurocomputing. 2017;237:272–80.",{"doi":3228},"10.1016\u002Fj.neucom.2016.12.058",{"id":26,"text":3230,"url":26,"identifiers":3231},"Cheriet M, Moghaddam RF, Hedjam R. A learning framework for the optimization and automation of document binarization methods. Comput Vis Image Understand. 2013;117(3):269–80.",{"doi":3232},"10.1016\u002Fj.cviu.2012.11.003",{"id":26,"text":3234,"url":26,"identifiers":3235},"Chetlur S, Woolley C, Vandermersch P, Cohen J, Tran J, Catanzaro B, Shelhamer E. CUDNN: Efficient primitives for deep learning. 2014 arXiv preprint arXiv:14100759.",{},{"id":26,"text":3237,"url":26,"identifiers":3238},"Chiu YH, Chung KL, Yang WN, Huang YH, Liao CH. Parameter-free based two-stage method for binarizing degraded document images. Pattern Recognit. 2012;45(12):4250–62.",{"doi":3239},"10.1016\u002Fj.patcog.2012.02.023",{"id":26,"text":3241,"url":26,"identifiers":3242},"Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Computer vision and pattern recognition (CVPR). IEEE; 2005, vol 1. p. 886–93.",{},{"id":26,"text":3244,"url":26,"identifiers":3245},"Deng F, Wu Z, Lu Z, Brown MS. Binarizationshop: a user-assisted software suite for converting old documents to black-and-white. In: Joint conference on digital libraries. ACM; 2010. p. 255–8.",{"doi":3246},"10.1145\u002F1816123.1816161",{"id":26,"text":3248,"url":26,"identifiers":3249},"Diem M, Hollaus F, Sablatnig R, Msio: Multispectral document image binarization. In, 12th IAPR workshop on document analysis systems (DAS). IEEE; 2016. p. 84–9.",{"doi":3250},"10.1109\u002FDAS.2016.39",{"id":26,"text":3252,"url":26,"identifiers":3253},"Elfattah MA, Hassanien AE, Abuelenin S, Bhattacharyya S. Multi-verse optimization clustering algorithm for binarization of handwritten documents. In: Recent trends in signal and image processing. Springer; 2019. p. 165–75.",{"doi":3254},"10.1007\u002F978-981-10-8863-6_17",{"id":26,"text":3256,"url":26,"identifiers":3257},"Fedorchuk M, Lamiroy B. Binary classifier evaluation without ground truth. In: International conference on advances in pattern recognition (ICAPR) 2017.",{"doi":3258},"10.1109\u002FICAPR.2017.8593175",{"id":26,"text":3260,"url":26,"identifiers":3261},"Fedorchuk M, Lamiroy B. Statistic metrics for evaluation of binary classifiers without ground-truth. In: Ukraine conference on electrical and computer engineering (UKRCON). IEEE; 2017. p. 1066–71.",{"doi":3262},"10.1109\u002FUKRCON.2017.8100414",{"id":26,"text":3264,"url":26,"identifiers":3265},"Felix R, da Silva LA, de Castro LN. Thresholding the courtesy amount of brazilian bank checks using a local methodology. In: International conference on practical applications of agents and multi-agent systems. Springer; 2015. p. 213–21.",{"doi":3266},"10.1007\u002F978-3-319-19033-4_18",{"id":26,"text":3268,"url":26,"identifiers":3269},"Fernando B, Karaoglu S, Trémeau A. Extreme value theory based text binarization in documents and natural scenes. In: International conference on machine vision (ICMV); 2010. p. 144–51.",{},{"id":26,"text":3271,"url":26,"identifiers":3272},"Gatos B, Pratikakis I, Perantonis SJ. An adaptive binarization technique for low quality historical documents. In: International workshop on document analysis systems (DAS). Springer; 2004. p. 102–13.",{"doi":3273},"10.1007\u002F978-3-540-28640-0_10",{"id":26,"text":3275,"url":26,"identifiers":3276},"Gatos B, Pratikakis I, Perantonis SJ. Adaptive degraded document image binarization. Pattern Recognit. 2006;39(3):317–27.",{"doi":3277},"10.1016\u002Fj.patcog.2005.09.010",{"id":26,"text":3279,"url":26,"identifiers":3280},"Gatos B, Ntirogiannis K, Pratikakis I. Icdar 2009 document image binarization contest (dibco 2009). In: International conference on document analysis and recognition (ICDAR). IEEE; 2009. p. 1375–82.",{"doi":3281},"10.1109\u002FICDAR.2009.246",{"id":26,"text":3283,"url":26,"identifiers":3284},"Gatos B, Ntirogiannis K, Pratikakis I. Dibco 2009: Document image binarization contest. Int J Doc Anal Recognit (IJDAR). 2011;14(1):35–44.",{"doi":3285},"10.1007\u002Fs10032-010-0115-7",{"id":26,"text":3287,"url":26,"identifiers":3288},"Hadjadj Z, Meziane A, Cherfa Y, Cheriet M, Setitra I. Isauvola: Improved sauvola’s algorithm for document image binarization. In: Image analysis and recognition. Springer; 2016. p. 737–45.",{"doi":3289},"10.1007\u002F978-3-319-41501-7_82",{"id":26,"text":3291,"url":26,"identifiers":3292},"Hadjadj Z, Cheriet M, Meziane A, Cherfa Y. A new efficient binarization method: Application to degraded historical document images. Signal Image Video Process. 2017;11(6):1155–62.",{"doi":3293},"10.1007\u002Fs11760-017-1070-2",{"id":26,"text":3295,"url":26,"identifiers":3296},"Hamza H, Smigiel E, Belaid E. Neural based binarization techniques. In: International conference on document analysis and recognition (ICDAR). IEEE; 2005. p. 317–21.",{"doi":3297},"10.1109\u002FICDAR.2005.168",{"id":26,"text":3299,"url":26,"identifiers":3300},"Hassanien AE, Elfattah MA, Aboulenin S, Schaefer G, Zhu SY, Korovin I. Historic handwritten manuscript binarisation using whale optimisation. In: International conference on systems, man, and cybernetics (SMC). IEEE; 2016. p. 3842–46.",{"doi":3301},"10.1109\u002FSMC.2016.7844833",{"id":26,"text":3303,"url":26,"identifiers":3304},"Hebert D, Nicolas S, Paquet T. Discrete crf based combination framework for document image binarization. In: International conference on document analysis and recognition (ICDAR). IEEE; 2013. p. 1165–69.",{"doi":3305},"10.1109\u002FICDAR.2013.236",{"id":26,"text":3307,"url":26,"identifiers":3308},"Hedjam R, Moghaddam RF, Cheriet M. A spatially adaptive statistical method for the binarization of historical manuscripts and degraded document images. Pattern Recognit. 2011;44(9):2184–96.",{"doi":3309},"10.1016\u002Fj.patcog.2011.02.021",{"id":26,"text":3311,"url":26,"identifiers":3312},"Hedjam R, Nafchi HZ, Kalacska M, Cheriet M. Influence of color-to-gray conversion on the performance of document image binarization: toward a novel optimization problem. Trans Image Process. 2015;24(11):3637–51.",{"doi":3313},"10.1109\u002FTIP.2015.2442923",{"id":26,"text":3315,"url":26,"identifiers":3316},"Howe NR. A Laplacian energy for document binarization. In: International conference on document analysis and recognition (ICDAR). IEEE; 2011. p. 6–10.",{"doi":3317},"10.1109\u002FICDAR.2011.11",{"id":26,"text":3319,"url":26,"identifiers":3320},"Howe NR. Document binarization with automatic parameter tuning. Int J Doc Anal Recognit (IJDAR). 2013;16(3):247–58.",{"doi":3321},"10.1007\u002Fs10032-012-0192-x",{"id":26,"text":3323,"url":26,"identifiers":3324},"Ismail SM, Abdullah SNHS, Fauzi F. Statistical binarization techniques for document image analysis. J Comput Sci. 2018;14(1):23–36.",{"doi":3325},"10.3844\u002Fjcssp.2018.23.36",{"id":26,"text":3327,"url":26,"identifiers":3328},"Jennifer Ranjani J. Bi-level thresholding for binarisation of handwritten and printed documents. IET Comput Vis. 2015;10.",{"doi":3329},"10.1049\u002Fiet-cvi.2013.0256",{"id":26,"text":3331,"url":26,"identifiers":3332},"Jia F, Shi C, He K, Wang C, Xiao B. Degraded document image binarization using structural symmetry of strokes. Pattern Recognit. 2018;74:225–40.",{"doi":3333},"10.1016\u002Fj.patcog.2017.09.032",{"id":26,"text":3335,"url":26,"identifiers":3336},"Journet N, Visani M, Mansencal B, Van-Cuong K, Billy A. Doccreator: a new software for creating synthetic ground-truthed document images. J Imaging. 2017;3(4):62.",{"doi":3337},"10.3390\u002Fjimaging3040062",{"id":26,"text":3339,"url":26,"identifiers":3340},"Kang S, Iwana BK, Uchida S. Cascading modular u-nets for document image binarization. In: International conference on document analysis and recognition (ICDAR). IEEE; 2019. p. 675–80.",{"doi":3341},"10.1109\u002FICDAR.2019.00113",{"id":26,"text":3343,"url":26,"identifiers":3344},"Kasmin F, Abdullah A, Prabuwono AS. Ensemble of steerable local neighbourhood grey-level information for binarization. Pattern Recognit Lett. 2017;98:8–15.",{"doi":3345},"10.1016\u002Fj.patrec.2017.07.014",{"id":26,"text":3347,"url":26,"identifiers":3348},"Kefali A, Sari T, Sellami M. Evaluation of several binarization techniques for old Arabic documents images. International symposium on modeling and implementing complex systems; 2010 vol 1. p. 88–99.",{},{"id":26,"text":3350,"url":26,"identifiers":3351},"Kefali A, Sari T, Bahi H. Foreground-background separation by feed-forward neural networks in old manuscripts. Informatica 2014;38(4).",{},{"id":26,"text":3353,"url":26,"identifiers":3354},"Kesiman MWA, Prum S, Burie JC, Ogier JM. An initial study on the construction of ground truth binarized images of ancient palm leaf manuscripts. In: International conference on document analysis and recognition (ICDAR). IEEE; 2015. p. 656–60.",{"doi":3355},"10.1109\u002FICDAR.2015.7333843",{"id":26,"text":3357,"url":26,"identifiers":3358},"Kesiman MWA, Prum S, Sunarya IMG, Burie JC, Ogier JM. An analysis of ground truth binarized image variability of palm leaf manuscripts. In: International conference on image processing, theory, tools and applications (IPTA) 2015.",{"doi":3359},"10.1109\u002FIPTA.2015.7367134",{"id":26,"text":3361,"url":26,"identifiers":3362},"Kesiman MWA, Burie JC, Wibawantara GNMA, Sunarya IMG, Ogier JM. Amadi\\_lontarset: the first handwritten Balinese palm leaf manuscripts dataset. In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2016. p. 168–73.",{"doi":3363},"10.1109\u002FICFHR.2016.0042",{"id":26,"text":3365,"url":26,"identifiers":3366},"Khankasikam K. Restoration of degraded historical document image: An adaptive multilayer-information binarization technique. J Inf Sci Eng. 2014;30(5):1321–38.",{},{"id":26,"text":3368,"url":26,"identifiers":3369},"Khitas M, Ziet L, Bouguezel S. Improved degraded document image binarization using median filter for background estimation. Elektronika ir Elektrotechnika. 2018;24(3):82–7.",{"doi":3370},"10.5755\u002Fj01.eie.24.3.20982",{"id":26,"text":3372,"url":26,"identifiers":3373},"Khurshid K, Siddiqi I, Faure C, Vincent N. Comparison of niblack inspired binarization methods for ancient documents. In: Document recognition and retrieval XVI. International Society for Optics and Photonics; 2009. p. 7247:72470U.",{"doi":3374},"10.1117\u002F12.805827",{"id":26,"text":3376,"url":26,"identifiers":3377},"Kligler N, Katz S, Tal A. Document enhancement using visibility detection. In: Computer vision and pattern recognition (CVPR), 2018;2374–2382.",{"doi":3378},"10.1109\u002FCVPR.2018.00252",{"id":26,"text":3380,"url":26,"identifiers":3381},"Kolmogorov V, Zabih R. What energy functions can be minimized via graph cuts? Trans Pattern Anal Mach Intell. 2004;26:147–59.",{"doi":3382},"10.1109\u002FTPAMI.2004.1262177",{"id":26,"text":3384,"url":26,"identifiers":3385},"Krähenbühl P, Koltun V. Efficient inference in fully connected crfs with Gaussian edge potentials. In: Advances in neural information processing systems (NIPS), 2011;109–117.",{},{"id":26,"text":3387,"url":26,"identifiers":3388},"Krantz A, Westphal F. Cluster-based sample selection for document image binarization. In: 2019 International conference on document analysis and recognition workshops (ICDARW). IEEE; 2019. p. 5:47–52.",{"doi":3389},"10.1109\u002FICDARW.2019.40080",{"id":26,"text":3391,"url":26,"identifiers":3392},"Kuk JG, Cho NI. Feature based binarization of document images degraded by uneven light condition. In: International conference on document analysis and recognition (ICDAR). IEEE; 2009. p. 748–52.",{"doi":3393},"10.1109\u002FICDAR.2009.157",{"id":26,"text":3395,"url":26,"identifiers":3396},"Le THN, Bui TD, Suen CY. Ternary entropy-based binarization of degraded document images using morphological operators. In: International conference on document analysis and recognition (ICDAR). IEEE; 2011. p. 114–8.",{"doi":3397},"10.1109\u002FICDAR.2011.32",{"id":26,"text":3399,"url":26,"identifiers":3400},"LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.",{"doi":3401},"10.1109\u002F5.726791",{"id":26,"text":3403,"url":26,"identifiers":3404},"Lelore T, Bouchara F. Document image binarisation using Markov field model. In: International conference on document analysis and recognition (ICDAR). IEEE; 2009. p. 551–5.",{"doi":3405},"10.1109\u002FICDAR.2009.117",{"id":26,"text":3407,"url":26,"identifiers":3408},"Lelore T, Bouchara F. Fair: a fast algorithm for document image restoration. Trans Pattern Anal Mach Intell. 2013;35(8):2039–48.",{"doi":3409},"10.1109\u002FTPAMI.2013.63",{"id":26,"text":3411,"url":26,"identifiers":3412},"Liang Y, Lin Z, Sun L, Cao J. Document image binarization via optimized hybrid thresholding. In: International symposium on circuits and systems (ISCAS). IEEE; 2017. p. 1–4.",{"doi":3413},"10.1109\u002FISCAS.2017.8050993",{"id":26,"text":3415,"url":26,"identifiers":3416},"Likforman-Sulem L, Darbon J, Smith EHB. Enhancement of historical printed document images by combining total variation regularization and non-local means filtering. Image Vision Comput. 2011;29(5):351–63.",{"doi":3417},"10.1016\u002Fj.imavis.2011.01.001",{"id":26,"text":3419,"url":26,"identifiers":3420},"Lins RD, de Almeida MM, Bernardino RB, Jesus D, Oliveira JM. Assessing binarization techniques for document images. In: Symposium on document engineering, ACM, 2017;183–192.",{"doi":3421},"10.1145\u002F3103010.3103021",{"id":26,"text":3423,"url":26,"identifiers":3424},"Lins RD, Bernardino R, Jesus DM. A quality and time assessment of binarization algorithms. In: International conference on document analysis and recognition (ICDAR). IEEE. 2019. p. 1444–50.",{},{"id":26,"text":3426,"url":26,"identifiers":3427},"Lins RD, Kavallieratou E, Smith EB, Bernardino RB, de Jesus DM. Icdar 2019 time-quality binarization competition. In: 2019 International conference on document analysis and recognition (ICDAR). IEEE; 2019. p. 1539–46.",{},{"id":26,"text":3429,"url":26,"identifiers":3430},"Liu N, Zhang D, Xu X, Liu W, Ke D, Guo L, Shi S, Liu H, Chen L. An iterative refinement framework for image document binarization with Bhattacharyya similarity measure. In: International conference on document analysis and recognition (ICDAR). IEEE; 2017. p. 1:93–98.",{"doi":3431},"10.1109\u002FICDAR.2017.24",{"id":26,"text":3433,"url":26,"identifiers":3434},"Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Computer vision and pattern recognition (CVPR), 2015;3431–40.",{"doi":3435},"10.1109\u002FCVPR.2015.7298965",{"id":26,"text":3437,"url":26,"identifiers":3438},"Lu H, Kot AC, Shi YQ. Distance-reciprocal distortion measure for binary document images. Signal Process Lett. 2004;11(2):228–31.",{"doi":3439},"10.1109\u002FLSP.2003.821748",{"id":26,"text":3441,"url":26,"identifiers":3442},"Lu S, Su B, Tan CL. Document image binarization using background estimation and stroke edges. Int J Doc Anal Recognit (IJDAR). 2010;13(4):303–14.",{"doi":3443},"10.1007\u002Fs10032-010-0130-8",{"id":26,"text":3445,"url":26,"identifiers":3446},"Lu Z, Wu Z, Brown MS. Directed assistance for ink-bleed reduction in old documents. In: Computer vision and pattern recognition (CVPR). IEEE; 2009. p. 88–95.",{},{"id":26,"text":3448,"url":26,"identifiers":3449},"Lu Z, Wu Z, Brown MS. Interactive degraded document binarization: An example (and case) for interactive computer vision. In: Workshop on applications of computer vision (WACV). IEEE; 2009. p. 1–8.",{},{"id":26,"text":3451,"url":26,"identifiers":3452},"Mesquita RG, Mello CA, Almeida L. A new thresholding algorithm for document images based on the perception of objects by distance. Integr Comput Aided Eng. 2014;21(2):133–46.",{"doi":3453},"10.3233\u002FICA-130453",{"id":26,"text":3455,"url":26,"identifiers":3456},"Mesquita RG, Silva RM, Mello CA, Miranda PB. Parameter tuning for document image binarization using a racing algorithm. Expert Syst Appl. 2015;42(5):2593–603.",{"doi":3457},"10.1016\u002Fj.eswa.2014.10.039",{"id":26,"text":3459,"url":26,"identifiers":3460},"Messaoud IB, El Abed H, Amiri H, Märgner V. New method for the selection of binarization parameters based on noise features of historical documents. In: Joint workshop on multilingual OCR and analytics for noisy unstructured text data. ACM; 2011. p. 1.",{"doi":3461},"10.1145\u002F2034617.2034619",{"id":26,"text":3463,"url":26,"identifiers":3464},"Messaoud IB, El Abed H, Märgner V, Amiri H. A design of a preprocessing framework for large database of historical documents. In: Workshop on historical document imaging and processing (HIP). ACM; 2011. p. 177–83.",{"doi":3465},"10.1145\u002F2037342.2037372",{"id":26,"text":3467,"url":26,"identifiers":3468},"Messaoud IB, Amiri H, El Abed H, Margner V. Document preprocessing system-automatic selection of binarization. In: International workshop on document analysis systems (DAS). IEEE; 2012. p. 85–9.",{"doi":3469},"10.1109\u002FDAS.2012.31",{"id":26,"text":3471,"url":26,"identifiers":3472},"Messaoud IB, Amiri H, El Abed H, Märgner V. Region based local binarization approach for handwritten ancient documents. In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2012. p. 633–8.",{"doi":3473},"10.1109\u002FICFHR.2012.261",{"id":26,"text":3475,"url":26,"identifiers":3476},"Mishra A, Alahari K, Jawahar C. Unsupervised refinement of color and stroke features for text binarization. Int J Doc Anal Recognit (IJDAR). 2017;20(2):105–21.",{"doi":3477},"10.1007\u002Fs10032-017-0283-9",{"id":26,"text":3479,"url":26,"identifiers":3480},"Mitianoudis N, Papamarkos N. Local co-occurrence and contrast mapping for document image binarization. In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2014. p. 609–14.",{"doi":3481},"10.1109\u002FICFHR.2014.107",{"id":26,"text":3483,"url":26,"identifiers":3484},"Mitianoudis N, Papamarkos N. Document image binarization using local features and gaussian mixture modeling. Image Vis Comput. 2015;38:33–51.",{"doi":3485},"10.1016\u002Fj.imavis.2015.04.003",{"id":26,"text":3487,"url":26,"identifiers":3488},"Moghaddam RF, Cheriet M. A multi-scale framework for adaptive binarization of degraded document images. Pattern Recognit. 2010;43(6):2186–98.",{"doi":3489},"10.1016\u002Fj.patcog.2009.12.024",{"id":26,"text":3491,"url":26,"identifiers":3492},"Moghaddam RF, Cheriet M. Adotsu: an adaptive and parameterless generalization of otsu’s method for document image binarization. Pattern Recognit. 2012;45(6):2419–31.",{"doi":3493},"10.1016\u002Fj.patcog.2011.12.013",{"id":26,"text":3495,"url":26,"identifiers":3496},"Moghaddam RF, Moghaddam FF, Cheriet M. Unsupervised ensemble of experts (eoe) framework for automatic binarization of document images. In: International conference on document analysis and recognition (ICDAR). IEEE; 2013. p. 703–7.",{"doi":3497},"10.1109\u002FICDAR.2013.144",{"id":26,"text":3499,"url":26,"identifiers":3500},"Mollah AF, Basu S, Nasipuri M. Computationally efficient implementation of convolution-based locally adaptive binarization techniques. In: Wireless networks and computational intelligence. Springer; 2012, p. 159–168.",{"doi":3501},"10.1007\u002F978-3-642-31686-9_18",{"id":26,"text":3503,"url":26,"identifiers":3504},"Mondal R, Chakraborty D, Chanda B. Learning 2D morphological network for old document image binarization. In: International conference on document analysis and recognition (ICDAR). IEEE; 2019. p. 65–70.",{"doi":3505},"10.1109\u002FICDAR.2019.00020",{"id":26,"text":3507,"url":26,"identifiers":3508},"More PK, Dighe D. A review on document image binarization technique for degraded document images. Int Res J Eng Technol. 2016;3:1132–8.",{},{"id":26,"text":3510,"url":26,"identifiers":3511},"Mustafa WA, Aziz H, Khairunizam W, Ibrahim Z, Shahriman A, Razlan ZM. Review of different binarization approaches on degraded document images. In: International conference on computational approach in smart systems design and applications (ICASSDA). IEEE; 2018. p. 1–8.",{"doi":3512},"10.1109\u002FICASSDA.2018.8477621",{"id":26,"text":3514,"url":26,"identifiers":3515},"Nafchi HZ, Kanan HR. A phase congruency based document binarization. In: International conference on image and signal processing. Springer; 2012. p. 113–21.",{"doi":3516},"10.1007\u002F978-3-642-31254-0_13",{"id":26,"text":3518,"url":26,"identifiers":3519},"Nafchi HZ, Moghaddam RF, Cheriet M. Historical document binarization based on phase information of images. In: Asian conference on computer vision (ACCV). Springer; 2012. p. 1–12.",{},{"id":26,"text":3521,"url":26,"identifiers":3522},"Nafchi HZ, Ayatollahi SM, Moghaddam RF, Cheriet M. An efficient ground truthing tool for binarization of historical manuscripts. In: International conference on document analysis and recognition (ICDAR). IEEE; 2013. p. 807–11.",{"doi":3523},"10.1109\u002FICDAR.2013.165",{"id":26,"text":3525,"url":26,"identifiers":3526},"Nafchi HZ, Moghaddam RF, Cheriet M. Phase-based binarization of ancient document images: Model and applications. Trans Image Process. 2014;23(7):2916–30.",{"doi":3527},"10.1109\u002FTIP.2014.2322451",{"id":26,"text":3529,"url":26,"identifiers":3530},"Natarajan J, Sreedevi I. Enhancement of ancient manuscript images by log based binarization technique. AEU Int J Electron Commun. 2017;75:15–22.",{"doi":3531},"10.1016\u002Fj.aeue.2017.03.002",{"id":26,"text":3533,"url":26,"identifiers":3534},"Niblack W. An introduction to digital image processing. Birkerod: Strandberg Publishing Company; 1985.",{},{"id":26,"text":3536,"url":26,"identifiers":3537},"Nicolaou A, Liwicki M, Ingolf R. Investigating the power of integral histograms for document images, a binarization case study. 2013. http:\u002F\u002Fnicolaou.homouniversalis.org\u002Fassets\u002Flof\u002Fnicolaou2013binarization_lowres.pdf. Accessed 15 Jan 2018.",{},{"id":26,"text":3539,"url":26,"identifiers":3540},"Nina O, Morse B, Barrett W. A recursive otsu thresholding method for scanned document binarization. In: Workshop on applications of computer vision (WACV). IEEE; 2011. p. 307–14.",{"doi":3541},"10.1109\u002FWACV.2011.5711519",{"id":26,"text":3543,"url":26,"identifiers":3544},"Ntirogiannis K, Gatos B, Pratikakis I. An objective evaluation methodology for document image binarization techniques. In: International workshop on document analysis systems (DAS). IEEE; 2008. p. 217–24.",{"doi":3545},"10.1109\u002FDAS.2008.41",{"id":26,"text":3547,"url":26,"identifiers":3548},"Ntirogiannis K, Gatos B, Pratikakis I. Performance evaluation methodology for historical document image binarization. Trans Image Process. 2013;22(2):595–609.",{"doi":3549},"10.1109\u002FTIP.2012.2219550",{"id":26,"text":3551,"url":26,"identifiers":3552},"Ntirogiannis K, Gatos B, Pratikakis I. A combined approach for the binarization of handwritten document images. Pattern Recognit Lett. 2014;35:3–15.",{"doi":3553},"10.1016\u002Fj.patrec.2012.09.026",{"id":26,"text":3555,"url":26,"identifiers":3556},"Ntirogiannis K, Gatos B, Pratikakis I. ICFHR2014 competition on handwritten document image binarization (H-DIBCO 2014). In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2014. p. 809–13.",{"doi":3557},"10.1109\u002FICFHR.2014.141",{"id":26,"text":3559,"url":26,"identifiers":3560},"Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Trans Pattern Anal Mach Intell. 2002;24(7):971–87.",{"doi":3561},"10.1109\u002FTPAMI.2002.1017623",{"id":26,"text":3563,"url":26,"identifiers":3564},"Otsu N. A threshold selection method from gray-level histograms. Trans Syst Man Cybern. 1979;9(1):62–6.",{"doi":3565},"10.1109\u002FTSMC.1979.4310076",{"id":26,"text":3567,"url":26,"identifiers":3568},"Ouafek N, Kholladi MK. A binarization method for degraded document image using artificial neural network and interpolation inpainting. In: International conference on optimization and applications (ICOA). IEEE; 2018. p. 1–5.",{"doi":3569},"10.1109\u002FICOA.2018.8370550",{"id":26,"text":3571,"url":26,"identifiers":3572},"Paredes R, Kavallieratou E, Lins RD. ICFHR 2010 contest: quantitative evaluation of binarization algorithms. In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2010. p. 733–6.",{"doi":3573},"10.1109\u002FICFHR.2010.119",{"id":26,"text":3575,"url":26,"identifiers":3576},"Parker J, Frieder O, Frieder G. Robust binarization of degraded document images using heuristics. In: Document recognition and retrieval XXI. International Society for Optics and Photonics; 2014. p. 9021:90210U.",{"doi":3577},"10.1117\u002F12.2042581",{"id":26,"text":3579,"url":26,"identifiers":3580},"Pastor-Pellicer J, Zamora-Martínez F, Espa na-Boquera S, Castro-Bleda MJ. F-measure as the error function to train neural networks. In: International work-conference on artificial neural networks. Springer; 2013. p. 376–84.",{"doi":3581},"10.1007\u002F978-3-642-38679-4_37",{"id":26,"text":3583,"url":26,"identifiers":3584},"Pastor-Pellicer J, Espa na-Boquera S, Zamora-Martínez F, Afzal MZ, Castro-Bleda MJ. Insights on the use of convolutional neural networks for document image binarization. In: International work-conference on artificial neural networks. Springer; 2015. p. 115–26.",{"doi":3585},"10.1007\u002F978-3-319-19222-2_10",{"id":26,"text":3587,"url":26,"identifiers":3588},"Peng X, Setlur S, Govindaraju V, Sitaram R. Markov random field based binarization for hand-held devices captured document images. In: Indian conference on computer vision, graphics and image processing. ACM; 2010. p. 71–6.",{"doi":3589},"10.1145\u002F1924559.1924569",{"id":26,"text":3591,"url":26,"identifiers":3592},"Peng X, Cao H, Natarajan P. Using convolutional encoder-decoder for document image binarization. In: International conference on document analysis and recognition (ICDAR). IEEE; 2017. p. 1:708–13.",{"doi":3593},"10.1109\u002FICDAR.2017.121",{"id":26,"text":3595,"url":26,"identifiers":3596},"Peng X, Wang C, Cao H. Document binarization via multi-resolutional attention model with DRD loss. In: International conference on document analysis and recognition (ICDAR). IEEE; 2019. p. 45–50.",{"doi":3597},"10.1109\u002FICDAR.2019.00017",{"id":26,"text":3599,"url":26,"identifiers":3600},"Perret B, Lefèvre S, Collet C, Slezak E. From hyperconnections to hypercomponent tree: application to document image binarization. In: WADGMM 2010; 2010. p. 62.",{},{"id":26,"text":3602,"url":26,"identifiers":3603},"Pratikakis I, Gatos B, Ntirogiannis K. H-DIBCO 2010-handwritten document image binarization competition. In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2010. p. 727–32.",{"doi":3604},"10.1109\u002FICFHR.2010.118",{"id":26,"text":3606,"url":26,"identifiers":3607},"Pratikakis I, Gatos B, Ntirogiannis K. ICDAR 2011 document image binarization contest (DIBCO 2011). In: International conference on document analysis and recognition (ICDAR). IEEE; 2011. p. 1506–10.",{"doi":3608},"10.1109\u002FICDAR.2011.299",{"id":26,"text":3610,"url":26,"identifiers":3611},"Pratikakis I, Gatos B, Ntirogiannis K. ICFHR 2012 competition on handwritten document image binarization (H-DIBCO 2012). In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2012. p. 817–22.",{"doi":3612},"10.1109\u002FICFHR.2012.216",{"id":26,"text":3614,"url":26,"identifiers":3615},"Pratikakis I, Gatos B, Ntirogiannis K. ICDAR 2013 document image binarization contest (DIBCO 2013). In: International conference on document analysis and recognition (ICDAR). IEEE; 2013. p. 1471–76.",{"doi":3616},"10.1109\u002FICDAR.2013.219",{"id":26,"text":3618,"url":26,"identifiers":3619},"Pratikakis I, Zagoris K, Barlas G, Gatos B. ICFHR2016 handwritten document image binarization contest (H-DIBCO 2016). In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2016. p. 619–23.",{"doi":3620},"10.1109\u002FICFHR.2016.0118",{"id":26,"text":3622,"url":26,"identifiers":3623},"Pratikakis I, Zagoris K, Barlas G, Gatos B. ICDAR2017 competition on document image binarization (DIBCO 2017). In: International conference on document analysis and recognition (ICDAR). IEEE; 2017. p. 1395–403.",{"doi":3624},"10.1109\u002FICDAR.2017.228",{"id":26,"text":3626,"url":26,"identifiers":3627},"Pratikakis I, Zagoris K, Kaddas P, Gatos B. ICFHR2018 competition on handwritten document image binarization contest (H-DIBCO 2018). In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2018. p. 1–1.",{"doi":3628},"10.1109\u002FICFHR-2018.2018.00091",{"id":26,"text":3630,"url":26,"identifiers":3631},"Pratikakis I, Zagoris K, Karagiannis X, Tsochatzidis L, Mondal T, ICDAR Marthot-Santaniello I. Competition on document image binarization (DIBCO 2019). In: International conference on document analysis and recognition (ICDAR); 2019, p. 1547–56.",{"doi":3632},"10.1109\u002FICDAR.2019.00249",{"id":26,"text":3634,"url":26,"identifiers":3635},"Rabelo JC, Zanchettin C, Mello CA, Bezerra BL. A multi-layer perceptron approach to threshold documents with complex background. In: International conference on systems, man, and cybernetics (SMC). IEEE; 2011. p. 2523–30.",{"doi":3636},"10.1109\u002FICSMC.2011.6084056",{"id":26,"text":3638,"url":26,"identifiers":3639},"Ramírez-Ortegón MA, Tapia E, Ramírez-Ramírez LL, Rojas R, Cuevas E. Transition pixel: a concept for binarization based on edge detection and gray-intensity histograms. Pattern Recognit. 2010;43(4):1233–43.",{"doi":3640},"10.1016\u002Fj.patcog.2009.11.006",{"id":26,"text":3642,"url":26,"identifiers":3643},"Ramírez-Ortegón MA, Tapia E, Rojas R, Cuevas E. Transition thresholds and transition operators for binarization and edge detection. Pattern Recognit. 2010;43(10):3243–54.",{"doi":3644},"10.1016\u002Fj.patcog.2010.04.028",{"id":26,"text":3646,"url":26,"identifiers":3647},"Ramírez-Ortegón MA, Duénez-Guzmán EA, Rojas R, Cuevas E. Unsupervised measures for parameter selection of binarization algorithms. Pattern Recognit. 2011;44(3):491–502.",{"doi":3648},"10.1016\u002Fj.patcog.2010.09.018",{"id":26,"text":3650,"url":26,"identifiers":3651},"Ramírez-Ortegón MA, Märgner V, Cuevas E, Rojas R. An optimization for binarization methods by removing binary artifacts. Pattern Recognit Lett. 2013;34(11):1299–306.",{"doi":3652},"10.1016\u002Fj.patrec.2013.04.007",{"id":26,"text":3654,"url":26,"identifiers":3655},"Ramírez-Ortegón MA, Ramírez-Ramírez LL, Märgner V, Messaoud IB, Cuevas E, Rojas R. An analysis of the transition proportion for binarization in handwritten historical documents. Pattern Recognit. 2014;47(8):2635–51.",{"doi":3656},"10.1016\u002Fj.patcog.2014.02.003",{"id":26,"text":3658,"url":26,"identifiers":3659},"Ramírez-Ortegón MA, Ramírez-Ramírez LL, Messaoud IB, Märgner V, Cuevas E, Rojas R. A model for the gray-intensity distribution of historical handwritten documents and its application for binarization. Int J Doc Anal Recognit (IJDAR). 2014;17(2):139–60.",{"doi":3660},"10.1007\u002Fs10032-013-0212-5",{"id":26,"text":3662,"url":26,"identifiers":3663},"Rivest-Hénault D, Moghaddam RF, Cheriet M. A local linear level set method for the binarization of degraded historical document images. Int J Doc Anal Recognit (IJDAR). 2012;15(2):101–24.",{"doi":3664},"10.1007\u002Fs10032-011-0157-5",{"id":26,"text":3666,"url":26,"identifiers":3667},"Roe E, Mello CA. Thresholding color images of historical documents with preservation of the visual quality of graphical elements. Integr Comput Aided Eng. 2018;25(3):261–72.",{"doi":3668},"10.3233\u002FICA-180562",{"id":26,"text":3670,"url":26,"identifiers":3671},"Romero A, Ballas N, Kahou SE, Chassang A, Gatta C, Bengio Y. Fitnets: hints for thin deep nets. In: International conference on learning representations (ICLR); 2015.",{},{"id":26,"text":3673,"url":26,"identifiers":3674},"Rowley-Brooke R, Pitié F, Kokaram A. A ground truth bleed-through document image database. In: International conference on theory and practice of digital libraries. Springer; 2012. p. 185–96.",{"doi":3675},"10.1007\u002F978-3-642-33290-6_21",{"id":26,"text":3677,"url":26,"identifiers":3678},"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al. Imagenet large scale visual recognition challenge. Int J Comput Vis (IJCV). 2015;115(3):211–52.",{"doi":3679},"10.1007\u002Fs11263-015-0816-y",{"id":26,"text":3681,"url":26,"identifiers":3682},"Saddami K, Munadi K, Muchallil S, Arnia F. Improved thresholding method for enhancing jawi binarization performance. In: International conference on document analysis and recognition (ICDAR). IEEE; 2017. p. 1:1108–13.",{"doi":3683},"10.1109\u002FICDAR.2017.183",{"id":26,"text":3685,"url":26,"identifiers":3686},"Saund E, Lin J, Sarkar P. Pixlabeler: User interface for pixel-level labeling of elements in document images. In: International conference on document analysis and recognition (ICDAR). IEEE; 2009. p. 646–50.",{"doi":3687},"10.1109\u002FICDAR.2009.250",{"id":26,"text":3689,"url":26,"identifiers":3690},"Sauvola J, Pietikäinen M. Adaptive document image binarization. Pattern Recognit. 2000;33(2):225–36.",{"doi":3691},"10.1016\u002FS0031-3203(99)00055-2",{"id":26,"text":3693,"url":26,"identifiers":3694},"Sehad A, Chibani Y, Hedjam R, Cheriet M. Gabor filter-based texture for ancient degraded document image binarization. Pattern Anal Appl. 2018;1–22.",{"doi":3695},"10.1007\u002Fs10044-018-0747-7",{"id":26,"text":3697,"url":26,"identifiers":3698},"Şekeroğlu B, Khashman A. Performance evaluation of binarization methods for document images. In: International conference on advances in image processing. ACM; 2017, p. 96–102.",{"doi":3699},"10.1145\u002F3133264.3133272",{"id":26,"text":3701,"url":26,"identifiers":3702},"Seuret M, Chen K, Eichenbergery N, Liwicki M, Ingold R. Gradient-domain degradations for improving historical documents images layout analysis. In: International conference on document analysis and recognition (ICDAR). IEEE; 2015. p. 1006–10.",{"doi":3703},"10.1109\u002FICDAR.2015.7333913",{"id":26,"text":3705,"url":26,"identifiers":3706},"Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. J Electronic Imaging. 2004;13(1):146–66.",{"doi":3707},"10.1117\u002F1.1631315",{"id":26,"text":3709,"url":26,"identifiers":3710},"Shafait F, Keysers D, Breuel TM. Efficient implementation of local adaptive thresholding techniques using integral images. In: Document recognition and retrieval XV. International Society for Optics and Photonics; 2008. p. 6815:681510.",{"doi":3711},"10.1117\u002F12.767755",{"id":26,"text":3713,"url":26,"identifiers":3714},"Shaus A, Sober B, Turkel E, Piasetzky E. Beyond the ground truth: Alternative quality measures of document binarizations. In: International conference on frontiers in handwriting recognition (ICFHR). IEEE; 2016. p. 495–500.",{"doi":3715},"10.1109\u002FICFHR.2016.0097",{"id":26,"text":3717,"url":26,"identifiers":3718},"Simard PY, Steinkraus D, Platt JC. Best practices for convolutional neural networks applied to visual document analysis. In: International conference on document analysis and recognition (ICDAR). IEEE; 2003. p. 958.",{},{"id":26,"text":3720,"url":26,"identifiers":3721},"Smith EHB. An analysis of binarization ground truthing. In: International workshop on document analysis systems (DAS). ACM, 2010. p. 27–34.",{"doi":3722},"10.1145\u002F1815330.1815334",{"id":26,"text":3724,"url":26,"identifiers":3725},"Smith EHB, An C. Effect of “ground truth” on image binarization. In: International workshop on document analysis systems (DAS). IEEE; 2012. p. 250–4.",{"doi":3726},"10.1109\u002FDAS.2012.32",{"id":26,"text":3728,"url":26,"identifiers":3729},"Smith EHB, Likforman-Sulem L, Darbon J. Effect of pre-processing on binarization. In: Document recognition and retrieval XVII. International Society for Optics and Photonics; 2010. p. 7534:75340H.",{},{"id":26,"text":3731,"url":26,"identifiers":3732},"Sokratis V, Kavallieratou E. A tool for tuning binarization techniques. In: International conference on document analysis and recognition (ICDAR). IEEE; 2011. p. 1–5.",{"doi":3733},"10.1109\u002FICDAR.2011.10",{"id":26,"text":3735,"url":26,"identifiers":3736},"Stathis P, Kavallieratou E, Papamarkos N. An evaluation technique for binarization algorithms. J Univ Comput Sci. 2008;14(18):3011–30.",{},{"id":26,"text":3738,"url":26,"identifiers":3739},"Stommel M, Frieder G. Automatic estimation of the legibility of binarised historic documents for unsupervised parameter tuning. In: International conference on document analysis and recognition (ICDAR). IEEE; 2011. p. 104–8",{"doi":3740},"10.1109\u002FICDAR.2011.30",{"id":26,"text":3742,"url":26,"identifiers":3743},"Su B, Lu S, Tan CL. Combination of document image binarization techniques. In: International conference on document analysis and recognition (ICDAR). IEEE; 2011. p. 22–6.",{"doi":3744},"10.1109\u002FICDAR.2011.14",{"id":26,"text":3746,"url":26,"identifiers":3747},"Su B, Lu S, Tan CL. A learning framework for degraded document image binarization using markov random field. In: International conference on pattern recognition (ICPR). IEEE; 2012. p. 3200–3.",{},{"id":26,"text":3749,"url":26,"identifiers":3750},"Su B, Lu S, Tan CL. Robust document image binarization technique for degraded document images. Trans Image Process. 2013;22(4):1408–17.",{"doi":3751},"10.1109\u002FTIP.2012.2231089",{"id":26,"text":3753,"url":26,"identifiers":3754},"Su B, Tian S, Lu S, Dinh TA, Tan CL. Self learning classification for degraded document images by sparse representation. In: International conference on document analysis and recognition (ICDAR). IEEE; 2013. p. 155–9.",{"doi":3755},"10.1109\u002FICDAR.2013.38",{"id":26,"text":3757,"url":26,"identifiers":3758},"Tensmeyer C, Martinez T. Document image binarization with fully convolutional neural networks. In: International conference on document analysis and recognition (ICDAR). IEEE; 2017. p. 1:99–104.",{"doi":3759},"10.1109\u002FICDAR.2017.25",{"id":26,"text":3761,"url":26,"identifiers":3762},"Tensmeyer C, Brodie M, Saunders D, Martinez T. Generating realistic binarization data with generative adversarial networks. In: International conference on document analysis and recognition (ICDAR). IEEE; 2019. p. 172–7.",{"doi":3763},"10.1109\u002FICDAR.2019.00036",{"id":26,"text":3765,"url":26,"identifiers":3766},"Valizadeh M, Kabir E. An adaptive water flow model for binarization of degraded document images. Int J Doc Anal Recognit (IJDAR). 2013;16(2):165–76.",{"doi":3767},"10.1007\u002Fs10032-012-0182-z",{"id":26,"text":3769,"url":26,"identifiers":3770},"Vats E, Hast A, Singh P. Automatic document image binarization using Bayesian optimization. In: Workshop on historical document imaging and processing (HIP). ACM; 2017. p. 89–94.",{"doi":3771},"10.1145\u002F3151509.3151520",{"id":26,"text":3773,"url":26,"identifiers":3774},"Vo GD, Park C. Robust regression for image binarization under heavy noise and nonuniform background. Pattern Recognit. 2018;81:224–39.",{"doi":3775},"10.1016\u002Fj.patcog.2018.04.005",{"id":26,"text":3777,"url":26,"identifiers":3778},"Vo QN, Kim SH, Yang HJ, Lee G. Binarization of degraded document images based on hierarchical deep supervised network. Pattern Recognit. 2018;74:568–86.",{"doi":3779},"10.1016\u002Fj.patcog.2017.08.025",{"id":26,"text":3781,"url":26,"identifiers":3782},"Westphal F, Grahn H, Lavesson N. Efficient document image binarization using heterogeneous computing and parameter tuning. Int J Doc Anal Recognit (IJDAR). 2018;21(1–2):41–58.",{"doi":3783},"10.1007\u002Fs10032-017-0293-7",{"id":26,"text":3785,"url":26,"identifiers":3786},"Westphal F, Lavesson N, Grahn H. Document image binarization using recurrent neural networks. In: International workshop on document analysis systems (DAS). IEEE; 2018. p. 263–8.",{"doi":3787},"10.1109\u002FDAS.2018.71",{"id":26,"text":3789,"url":26,"identifiers":3790},"Wiener N. Extrapolation, interpolation, and smoothing of stationary time series. Cambridge: The MIT Press; 1964.",{},{"id":26,"text":3792,"url":26,"identifiers":3793},"Wolf C, Jolion JM, Chassaing F. Text localization, enhancement and binarization in multimedia documents. In: International conference on pattern recognition (ICPR). IEEE; 2002, vol 2. p. 1037–40.",{},{"id":26,"text":3795,"url":26,"identifiers":3796},"Wu Y, Natarajan P, Rawls S, AbdAlmageed W. Learning document image binarization from data. In: International conference on image processing (ICIP). IEEE; 2016. p. 3763–67.",{"doi":3797},"10.1109\u002FICIP.2016.7533063",{"id":26,"text":3799,"url":26,"identifiers":3800},"Würsch M, Liwicki M, Ingold R. Web services in document image analysis-recent developments on DIVAservices and the importance of building an ecosystem. In: International workshop on document analysis systems (DAS). IEEE; 2018. p. 334–9.",{"doi":3801},"10.1109\u002FDAS.2018.40",{"id":26,"text":3803,"url":26,"identifiers":3804},"Xiong W, Xu J, Xiong Z, Wang J, Liu M. Degraded historical document image binarization using local features and support vector machine (SVM). Optik. 2018;164:218–23.",{"doi":3805},"10.1016\u002Fj.ijleo.2018.02.072",{"id":26,"text":3807,"url":26,"identifiers":3808},"Zemouri ET, Chibani Y, Brik Y. Restoration based contourlet transform for historical document image binarization. In: International conference on multimedia computing and systems (ICMCS). IEEE; 2014. p. 309–13.",{"doi":3809},"10.1109\u002FICMCS.2014.6911321",{"id":26,"text":3811,"url":26,"identifiers":3812},"Zhao J, Shi C, Jia F, Wang Y, Xiao B. Document image binarization with cascaded generators of conditional generative adversarial networks. Pattern Recognit. 2019;96:106968.",{"doi":3813},"10.1016\u002Fj.patcog.2019.106968",{"id":3815,"createTime":3816,"updateTime":3817,"relativeEntities":3818,"slug":3819,"properties":3820,"entityType":802,"verifyStatus":25,"verifyTime":3817,"verifyNote":803,"syncStatus":28,"languages":3831,"translateLanguages":26,"viewCount":36,"primaryUrl":3832,"fullTextUrl":26,"authors":3833,"publicationType":919,"publisherRelationship":3929,"citationCount":285,"citationInfo":3955,"publishDate":3957,"publishYear":948,"citationAnalyzeStatus":28,"lastCitationAnalyze":26,"indexDatabases":26,"openAccess":26,"references":3958,"isForceReanalyzing":1273},"4923bc6f-7d43-4344-9155-5ec984dcf3d3","2024-04-16T17:46:13.823+00:00","2024-12-28T21:20:23.288+00:00",[],"Literature-Review-on-Transfer-Learning-for-Human-Activity-Recognition-Using-Mobile-and-Wearable-Devices-with-Environmental-Technology",{"mag":3821,"keywords":3823,"openalex":3824,"abstract":3826,"title":3827,"doi":3829},{"VOID":3822},"3004834163",{},{"VOID":3825},"W3004834163",{},{"EN":3828},"Literature Review on Transfer Learning for Human Activity Recognition Using Mobile and Wearable Devices with Environmental Technology",{"VOID":3830},"10.1007\u002Fs42979-020-0070-4",[102],"http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs42979-020-0070-4",[3834,3853,3873,3894,3912],{"id":3835,"sortIndex":36,"researcher":26,"roles":3836,"affiliations":3837,"properties":3848},"8c74d64e-495d-470f-ab78-009e63b5455d",[],[3838],{"id":3839,"sortIndex":36,"affiliation":3840,"properties":26},"35f4f92d-e42c-4962-a353-72552dd9b3b1",{"id":3841,"createTime":3842,"updateTime":3842,"relativeEntities":3843,"slug":3844,"properties":3845,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"232e2cfc-cee5-4bc9-8110-8a893ca7c90a","2024-04-16T17:46:13.875+00:00",[],"Ulster-University-Newtownabbey-Belfast-Northern-Ireland-UK",{"title":3846},{"EN":3847},"Ulster University, Newtownabbey, Belfast, Northern Ireland, UK",{"openalex":3849,"title":3851},{"VOID":3850},"A5039943096",{"EN":3852},"Netzahualcóyotl Hernández",{"id":3854,"sortIndex":111,"researcher":26,"roles":3855,"affiliations":3856,"properties":3866},"007da97e-39e7-4d08-b9fb-d06932536fea",[],[3857],{"id":3858,"sortIndex":36,"affiliation":3859,"properties":26},"791be2e6-90dc-41b2-8095-3d5d9fd56712",{"id":3860,"createTime":3861,"updateTime":3861,"relativeEntities":3862,"slug":26,"properties":3863,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"1c005666-38cb-4abc-8726-2f8c8159dc56","2024-01-17T10:42:23.471+00:00",[],{"title":3864},{"VI":3865},"Hasso Plattner Institute, University of Potsdam, Potsdam, Germany",{"openalex":3867,"orcid":3869,"title":3871},{"VOID":3868},"A5058391738",{"VOID":3870},"https:\u002F\u002Forcid.org\u002F0000-0001-8380-7667",{"EN":3872},"Bert Arnrich",{"id":3874,"sortIndex":115,"researcher":26,"roles":3875,"affiliations":3876,"properties":3887},"bd841a30-eec3-4da8-969e-99d8b774b24b",[],[3877],{"id":3878,"sortIndex":36,"affiliation":3879,"properties":26},"6b564200-3d8d-43b8-90d1-8469d51cd4a0",{"id":3880,"createTime":3881,"updateTime":3881,"relativeEntities":3882,"slug":3883,"properties":3884,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"5e361013-e896-4417-8675-7a739cd74c25","2024-04-16T17:46:13.850+00:00",[],"Raytelligence-AB-Klammerdammsgatan-Halmstad-Sweden",{"title":3885},{"EN":3886},"Raytelligence AB, Klammerdammsgatan, Halmstad, Sweden",{"openalex":3888,"orcid":3890,"title":3892},{"VOID":3889},"A5076881250",{"VOID":3891},"https:\u002F\u002Forcid.org\u002F0000-0001-8804-5884",{"EN":3893},"Jens Lundström",{"id":3895,"sortIndex":114,"researcher":26,"roles":3896,"affiliations":3897,"properties":3907},"2ee10027-9eef-48e5-b1b0-c664a41330a4",[],[3898],{"id":3899,"sortIndex":36,"affiliation":3900,"properties":26},"302d7197-6540-4770-9e53-b77fe1c0a682",{"id":3901,"createTime":3902,"updateTime":3902,"relativeEntities":3903,"slug":26,"properties":3904,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},"e13e2576-8ea6-44f8-aa47-880f2a936b0b","2024-02-07T18:11:48.510+00:00",[],{"title":3905},{"VI":3906},"CICESE, Ensenada, Baja California Mexico",{"openalex":3908,"title":3910},{"VOID":3909},"A5002190612",{"EN":3911},"Jesús Favela",{"id":3913,"sortIndex":59,"researcher":26,"roles":3914,"affiliations":3915,"properties":3922},"174858a0-19c9-4045-9159-e406e514c6b8",[],[3916],{"id":3917,"sortIndex":36,"affiliation":3918,"properties":26},"46ae1c6d-65d6-48c4-8e99-480223e052cc",{"id":3841,"createTime":3842,"updateTime":3842,"relativeEntities":3919,"slug":3844,"properties":3920,"entityType":98,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36},[],{"title":3921},{"EN":3847},{"openalex":3923,"orcid":3925,"title":3927},{"VOID":3924},"A5002181537",{"VOID":3926},"https:\u002F\u002Forcid.org\u002F0000-0002-5202-5360",{"EN":3928},"Ian McChesney",{"url":26,"publisher":3930,"properties":3951},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":3931,"slug":663,"properties":3932,"entityType":24,"verifyStatus":28,"verifyTime":26,"verifyNote":26,"syncStatus":28,"languages":26,"translateLanguages":26,"viewCount":36,"subjectFields":3936,"manageAffiliations":3937,"indexDatabases":3938,"url":755,"thumbnailPath":26,"statistic":3946,"gsStatistic":26,"type":175,"analyzePriority":26},[],{"issn":3933,"eissn":3934,"title":3935},{"VOID":666},{"VOID":668},{"EN":670},[],[],[3939],{"id":732,"indexDatabase":3940,"url":745,"indexYears":746,"academicFieldIds":3945,"indexDatabaseRanking":754},{"id":734,"createTime":735,"updateTime":736,"relativeEntities":3941,"label":3942,"description":3943,"key":742,"publicationTags":3944,"standard":26},[],{"EN":739,"VI":739},{"EN":739,"VI":741},[744],[748,749,750,751,752,753],{"impactFactor":36,"impactFactorByYear":3947,"i10Index":533,"i10IndexLast5Year":258,"totalPublication":760,"totalPublicationByYear":3948,"totalCitation":767,"totalCitationByYear":3949,"totalCitationPerPublication":772,"totalCitationPerPublicationByYear":3950,"hindexLast5Year":396,"hindex":396},{"2021":758,"2022":759,"2023":520},{"2019":46,"2020":762,"2021":763,"2022":764,"2023":765,"2024":766},{"2020":769,"2021":770,"2022":771},{"2020":774,"2021":775,"2022":776},{"volume":3952,"issue":3953},{"VOID":2178},{"VOID":3954},"2",{"total":285,"publishYear":26,"statisticByYear":3956},{"2020":162,"2021":298,"2022":158,"2023":135,"2024":114},"2020-03-01",[3959,3963,3967,3971,3975,3979,3983,3987,3991,3995,3999,4003,4007,4011,4015,4019,4023,4027,4031,4035,4039,4043,4047,4051,4055,4059,4063,4067,4071,4075,4079,4083,4087,4091,4095,4099,4103,4107,4111,4115,4119,4123,4127,4131,4134,4138,4142,4145,4149,4153,4157,4161,4165,4169,4173,4177,4181,4185,4189,4193,4196,4200,4204,4208],{"id":26,"text":3960,"url":26,"identifiers":3961},"Aggarwal JK, Xia L. Human activity recognition from 3D data: a review. Pattern Recognit Lett. 2014;. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.patrec.2014.04.011.",{"doi":3962},"10.1016\u002Fj.patrec.2014.04.011",{"id":26,"text":3964,"url":26,"identifiers":3965},"Alam MAU, Roy N. Unseen activity recognitions: a hierarchical active transfer learning approach. Proc Int Conf Distrib Comput Syst. 2017;. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICDCS.2017.264.",{"doi":3966},"10.1109\u002FICDCS.2017.264",{"id":26,"text":3968,"url":26,"identifiers":3969},"Appelboom G, Camacho E, Abraham ME, Bruce SS, Dumont ELP, Zacharia BE, D’Amico R, Slomian J, Reginster JY, Bruyère O, Sander CE. Smart wearable body sensors for patient self-assessment and monitoring. Arch Public Health. 2014;. https:\u002F\u002Fdoi.org\u002F10.1186\u002F2049-3258-72-28.",{"doi":3970},"10.1186\u002F2049-3258-72-28",{"id":26,"text":3972,"url":26,"identifiers":3973},"Bashir SA, Petrovski A, Doolan D. UDetect: unsupervised concept change detection for mobile activity recognition. In: Proceedings of the 14th international conference on advances in mobile computing and multi media (MoMM ’16). New York: ACM; 2016. p. 20–27. https:\u002F\u002Fdoi.org\u002F10.1145\u002F3007120.3007144.",{"doi":3974},"10.1145\u002F3007120.3007144",{"id":26,"text":3976,"url":26,"identifiers":3977},"Benedetti F, Carlino E, Piedimonte A. Increasing uncertainty in CNS clinical trials: the role of placebo, nocebo, and Hawthorne effects. Lancet Neurol. 2016;. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS1474-4422(16)00066-1.",{"doi":3978},"10.1016\u002FS1474-4422(16)00066-1",{"id":26,"text":3980,"url":26,"identifiers":3981},"Benini L, Farella E, Guiducci C. Wireless sensor networks: enabling technology for ambient intelligence. Microelectron J. 2006;37(12):1639–49. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.mejo.2006.04.021.",{"doi":3982},"10.1016\u002Fj.mejo.2006.04.021",{"id":26,"text":3984,"url":26,"identifiers":3985},"Berlin E, Van Laerhoven K. Detecting leisure activities with dense motif discovery. In: UbiComp’12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing. 2012. https:\u002F\u002Fdoi.org\u002F10.1145\u002F2370216.2370257.",{"doi":3986},"10.1145\u002F2370216.2370257",{"id":26,"text":3988,"url":26,"identifiers":3989},"Bieber, G. et al. Sensor requirements for activity recognition on smart watches. In: ACM International Conference Proceeding Series. 2013. https:\u002F\u002Fdoi.org\u002F10.1145\u002F2504335.2504407.",{"doi":3990},"10.1145\u002F2504335.2504407",{"id":26,"text":3992,"url":26,"identifiers":3993},"Boualia SN, Essoukri AN. Pose-based human activity recognition: a review. In: 2019 15th international wireless communications mobile computing conference (IWCMC). 2019. p. 1468–1475. https:\u002F\u002Fdoi.org\u002F10.1109\u002FIWCMC.2019.8766694.",{"doi":3994},"10.1109\u002FIWCMC.2019.8766694",{"id":26,"text":3996,"url":26,"identifiers":3997},"Campbell JP, Maxey VA, Watson WA. Hawthorne effect: implications for prehospital research. Ann Emerg Med. 1995;. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0196-0644(95)70009-9.",{"doi":3998},"10.1016\u002FS0196-0644(95)70009-9",{"id":26,"text":4000,"url":26,"identifiers":4001},"Cao L, Liu Z, Huang TS. Cross-dataset action detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. 2010. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCVPR.2010.5539875.",{"doi":4002},"10.1109\u002FCVPR.2010.5539875",{"id":26,"text":4004,"url":26,"identifiers":4005},"Capponi A, Fiandrino C, Kantarci B, Foschini L, Kliazovich D, Bouvry P. A survey on mobile crowdsensing systems: challenges, solutions, and opportunities. IEEE Commun Surv Tutor. 2019;21(3):2419–65. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCOMST.2019.2914030.",{"doi":4006},"10.1109\u002FCOMST.2019.2914030",{"id":26,"text":4008,"url":26,"identifiers":4009},"Carpenter GA, Grossberg S, Reynolds JH. ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network. Neural Netw. 1991;. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0893-6080(91)90012-T.",{"doi":4010},"10.1016\u002F0893-6080(91)90012-T",{"id":26,"text":4012,"url":26,"identifiers":4013},"Chen L, Nugent CD, Wang H. A knowledge-driven approach to activity recognition in smart homes. IEEE Trans Knowl Data Eng. 2012;. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTKDE.2011.51.",{"doi":4014},"10.1109\u002FTKDE.2011.51",{"id":26,"text":4016,"url":26,"identifiers":4017},"Chikhaoui B, Wang S, Pigot H. ADR-SPLDA: activity discovery and recognition by combining sequential patterns and latent Dirichlet allocation. Pervasive Mob Comput. 2012;8(6):845–62. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.pmcj.2012.08.004.",{"doi":4018},"10.1016\u002Fj.pmcj.2012.08.004",{"id":26,"text":4020,"url":26,"identifiers":4021},"Cook D, Feuz KD, Krishnan NC. Transfer learning for activity recognition: a survey. Knowl Inf Syst. 2013;36(3):537–56. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10115-013-0665-3.",{"doi":4022},"10.1007\u002Fs10115-013-0665-3",{"id":26,"text":4024,"url":26,"identifiers":4025},"Dhillon JK, Chandni, Kushwaha AKS. A recent survey for human activity recognition based on deep learning approach. In: 2017 fourth international conference on image information processing (ICIIP). 2017. p. 1–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICIIP.2017.8313715.",{"doi":4026},"10.1109\u002FICIIP.2017.8313715",{"id":26,"text":4028,"url":26,"identifiers":4029},"Dimitrievski A, Zdravevski E, Lameski P, Trajkovik V. A survey of ambient assisted living systems: challenges and opportunities. In: 2016 IEEE 12th international conference on intelligent computer communication and processing (ICCP). 2016. p. 49–53. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICCP.2016.7737121.",{"doi":4030},"10.1109\u002FICCP.2016.7737121",{"id":26,"text":4032,"url":26,"identifiers":4033},"Ding R, Li X, Nie L, Li J, Si X, Chu D, Liu G, Zhan D. Empirical study and improvement on deep transfer learning for human activity recognition. Sensors. 2019;19:1. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs19010057.",{"doi":4034},"10.3390\u002Fs19010057",{"id":26,"text":4036,"url":26,"identifiers":4037},"Du X, Farrahi K, Niranjan M. Transfer learning across human activities using a cascade neural network architecture. In: Proceedings of the 23rd international symposium on wearable computers (ISWC ’19). New York: Association for Computing Machinery; 2019. p. 35–44. https:\u002F\u002Fdoi.org\u002F10.1145\u002F3341163.3347730.",{"doi":4038},"10.1145\u002F3341163.3347730",{"id":26,"text":4040,"url":26,"identifiers":4041},"Feuz KD, Cook DJ. Collegial activity learning between heterogeneous sensors. Knowl Inf Syst. 2017;. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10115-017-1043-3.",{"doi":4042},"10.1007\u002Fs10115-017-1043-3",{"id":26,"text":4044,"url":26,"identifiers":4045},"Fortin-Simard D, Bilodeau JS, Bouchard K, Gaboury S, Bouchard B, Bouzouane A. Exploiting passive RFID technology for activity recognition in smart homes. IEEE Intell Syst. 2015;. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMIS.2015.18.",{"doi":4046},"10.1109\u002FMIS.2015.18",{"id":26,"text":4048,"url":26,"identifiers":4049},"Francis L. Unsupervised learning. In: Predictive Modeling Applications in Actuarial Science: Volume I: Predictive Modeling Techniques. 2014. https:\u002F\u002Fdoi.org\u002F10.1017\u002FCBO9781139342674.012.",{"doi":4050},"10.1017\u002FCBO9781139342674.012",{"id":26,"text":4052,"url":26,"identifiers":4053},"Gama J, Žliobaitė I, Bifet A, Pechenizkiy M, Bouchachia A. A survey on concept drift adaptation. Comput Surv. 2014;. https:\u002F\u002Fdoi.org\u002F10.1145\u002F2523813.",{"doi":4054},"10.1145\u002F2523813",{"id":26,"text":4056,"url":26,"identifiers":4057},"Gupta A, Priyadarshini J. Smart monitoring of automatic teller machine—a survey. In: Nath V, Mandal JK, editors. Proceedings of the third international conference on microelectronics, computing and communication systems. Singapore: Springer Singapore; 2019. p. 109–15.",{"doi":4058},"10.1007\u002F978-981-13-7091-5_10",{"id":26,"text":4060,"url":26,"identifiers":4061},"Hachiya H, Sugiyama M, Ueda N. Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition. Neurocomputing. 2012;. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neucom.2011.09.016.",{"doi":4062},"10.1016\u002Fj.neucom.2011.09.016",{"id":26,"text":4064,"url":26,"identifiers":4065},"Hu DH, Yang Q. Transfer learning for activity recognition via sensor mapping. In: IJCAI international joint conference on artificial intelligence. 2011. https:\u002F\u002Fdoi.org\u002F10.5591\u002F978-1-57735-516-8\u002FIJCAI11-328.",{"doi":4066},"10.5591\u002F978-1-57735-516-8\u002FIJCAI11-328",{"id":26,"text":4068,"url":26,"identifiers":4069},"Hu L, Chen Y, Wang S, Wang J, Shen J, Jiang X, Shen Z. Less annotation on personalized activity recognition using context data. In: Proceedings—13th IEEE international conference on ubiquitous intelligence and computing, 13th IEEE international conference on advanced and trusted computing, 16th IEEE international conference on scalable computing and communications, IEEE international conference on cloud and big data computing, IEEE international conference on internet of people and IEEE smart world congress and workshops, UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld 2016. 2017. https:\u002F\u002Fdoi.org\u002F10.1109\u002FUIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0066.",{"doi":4070},"10.1109\u002FUIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0066",{"id":26,"text":4072,"url":26,"identifiers":4073},"Huynh T, Fritz M, Schiele B. Discovery of activity patterns using topic models. In: UbiComp 2008 - Proceedings of the 10th International Conference on Ubiquitous Computing. 2008. https:\u002F\u002Fdoi.org\u002F10.1145\u002F1409635.1409638.",{"doi":4074},"10.1145\u002F1409635.1409638",{"id":26,"text":4076,"url":26,"identifiers":4077},"Jain AK, Murty MN, Flynn PJ. Data clustering: a review. Comput Surv. 1999. https:\u002F\u002Fdoi.org\u002F10.1145\u002F331499.331504.",{"doi":4078},"10.1145\u002F331499.331504",{"id":26,"text":4080,"url":26,"identifiers":4081},"Jiang H, Cai C, Ma X, Yang Y, Liu J. Smart home based on WiFi sensing: a survey. IEEE Access. 2018;2018(6):13317–25. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2018.2812887.",{"doi":4082},"10.1109\u002FACCESS.2018.2812887",{"id":26,"text":4084,"url":26,"identifiers":4085},"Jobanputra C, Bavishi J, Doshi N. Human activity recognition: a survey. Proc Comput Sci. 2019;155(2019):698–703. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.procs.2019.08.100. The 16th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2019), The 14th International Conference on Future Networks and Communications (FNC-2019),The 9th International Conference on Sustainable Energy Information Technology.",{"doi":4086},"10.1016\u002Fj.procs.2019.08.100",{"id":26,"text":4088,"url":26,"identifiers":4089},"Kazemi L, Shahabi C. GeoCrowd: enabling query answering with spatial crowdsourcing. In: Proc. 20th ACM int. conf. adv. geogr. inf. syst. (SIGSPATIAL). 2012. https:\u002F\u002Fdoi.org\u002F10.1145\u002F2424321.2424346.",{"doi":4090},"10.1145\u002F2424321.2424346",{"id":26,"text":4092,"url":26,"identifiers":4093},"Kwon Y, Kang K, Bae C. Unsupervised learning for human activity recognition using smartphone sensors. Expert Syst Appl. 2014;. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eswa.2014.04.037.",{"doi":4094},"10.1016\u002Fj.eswa.2014.04.037",{"id":26,"text":4096,"url":26,"identifiers":4097},"Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT. A survey of mobile phone sensing. IEEE Commun Mag. 2010;48(9):140–50. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMCOM.2010.5560598.",{"doi":4098},"10.1109\u002FMCOM.2010.5560598",{"id":26,"text":4100,"url":26,"identifiers":4101},"Lara ÓD, Labrador MA. A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor. 2013;. https:\u002F\u002Fdoi.org\u002F10.1109\u002FSURV.2012.110112.00192.",{"doi":4102},"10.1109\u002FSURV.2012.110112.00192",{"id":26,"text":4104,"url":26,"identifiers":4105},"Falk Leichsenring. Randomized controlled versus naturalistic studies: a new research agenda. Bull Menninger Clinic. 2004;. https:\u002F\u002Fdoi.org\u002F10.1521\u002Fbumc.68.2.137.35952.",{"doi":4106},"10.1521\u002Fbumc.68.2.137.35952",{"id":26,"text":4108,"url":26,"identifiers":4109},"Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing. 2017;. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neucom.2016.12.038.",{"doi":4110},"10.1016\u002Fj.neucom.2016.12.038",{"id":26,"text":4112,"url":26,"identifiers":4113},"Maekawa T, Nakai D, Ohara K, Namioka Y. Toward practical factory activity recognition: unsupervised understanding of repetitive assembly work in a factory. In: UbiComp 2016. 2016. https:\u002F\u002Fdoi.org\u002F10.1145\u002F2971648.2971721.",{"doi":4114},"10.1145\u002F2971648.2971721",{"id":26,"text":4116,"url":26,"identifiers":4117},"Michalski RS. A theory and methodology of inductive learning. Artif Intell. 1983;. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0004-3702(83)90016-4.",{"doi":4118},"10.1016\u002F0004-3702(83)90016-4",{"id":26,"text":4120,"url":26,"identifiers":4121},"Morales FJO, Roggen D. Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations. In: Proceedings of the 2016 ACM international symposium on wearable computers (ISWC ’16). New York: Association for Computing Machinery; 2016. p. 92–9. https:\u002F\u002Fdoi.org\u002F10.1145\u002F2971763.2971764.",{"doi":4122},"10.1145\u002F2971763.2971764",{"id":26,"text":4124,"url":26,"identifiers":4125},"Okoli C, Schabram K. A guide to conducting a systematic literature review of information systems research. SSRN Electron. J. 2012. https:\u002F\u002Fdoi.org\u002F10.2139\u002Fssrn.1954824.",{"doi":4126},"10.2139\u002Fssrn.1954824",{"id":26,"text":4128,"url":26,"identifiers":4129},"Pan SJ, Yang Q. A survey on transfer learning. 2010. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTKDE.2009.191.",{"doi":4130},"10.1109\u002FTKDE.2009.191",{"id":26,"text":4132,"url":26,"identifiers":4133},"Qiang Y, Pan SJ. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22(10):1345–59.",{"doi":4130},{"id":26,"text":4135,"url":26,"identifiers":4136},"Rawassizadeh R, Price BA, Petre M. Wearables: has the age of smartwatches finally arrived? Commun ACM. 2014;. https:\u002F\u002Fdoi.org\u002F10.1145\u002F2629633.",{"doi":4137},"10.1145\u002F2629633",{"id":26,"text":4139,"url":26,"identifiers":4140},"Riboni D et al. Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning. In: UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 2016. https:\u002F\u002Fdoi.org\u002F10.1145\u002F2971648.2971691.",{"doi":4141},"10.1145\u002F2971648.2971691",{"id":26,"text":4143,"url":26,"identifiers":4144},"Rohrbach M, Ebert S, Schiele B. Transfer learning in a transductive setting. In: Advances in Neural Information Processing Systems. 2013.",{},{"id":26,"text":4146,"url":26,"identifiers":4147},"Saeedi R, Ghasemzadeh H, Gebremedhin AH. Transfer learning algorithms for autonomous reconfiguration of wearable systems. In: Proceedings—2016 IEEE international conference on big data, big data 2016. 2016. https:\u002F\u002Fdoi.org\u002F10.1109\u002FBigData.2016.7840648.",{"doi":4148},"10.1109\u002FBigData.2016.7840648",{"id":26,"text":4150,"url":26,"identifiers":4151},"Sánchez D, Tentori M, Favela J. Activity recognition for the smart hospital. IEEE Intell Syst. 2008;. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMIS.2008.18.",{"doi":4152},"10.1109\u002FMIS.2008.18",{"id":26,"text":4154,"url":26,"identifiers":4155},"Seiter J, Chiu WC, Fritz M, Amft O, Tröster G. Joint segmentation and activity discovery using semantic and temporal priors. In: 2015 IEEE international conference on pervasive computing and communications, PerCom 2015. 2015. https:\u002F\u002Fdoi.org\u002F10.1109\u002FPERCOM.2015.7146511.",{"doi":4156},"10.1109\u002FPERCOM.2015.7146511",{"id":26,"text":4158,"url":26,"identifiers":4159},"Sjøberg DIK, Hannay JE, Hansen O, Kampenes VB, Karahasanović A, Liborg NK, Rekdal AC. A survey of controlled experiments in software engineering. IEEE Trans Softw Eng. 2005;. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTSE.2005.97.",{"doi":4160},"10.1109\u002FTSE.2005.97",{"id":26,"text":4162,"url":26,"identifiers":4163},"Stikic M, Larlus D, Schiele B. Multi-graph based semi-supervised learning for activity recognition. In: 2009 international symposium on wearable computers. 2009. p. 85–92. https:\u002F\u002Fdoi.org\u002F10.1109\u002FISWC.2009.24.",{"doi":4164},"10.1109\u002FISWC.2009.24",{"id":26,"text":4166,"url":26,"identifiers":4167},"Stikic M, Van Laerhoven K, Schiele B. Exploring semi-supervised and active learning for activity recognition. In: Proceedings—international symposium on wearable computers, ISWC. 2008. https:\u002F\u002Fdoi.org\u002F10.1109\u002FISWC.2008.4911590.",{"doi":4168},"10.1109\u002FISWC.2008.4911590",{"id":26,"text":4170,"url":26,"identifiers":4171},"Storm FA, Heller BW, Mazzà C. Step detection and activity recognition accuracy of seven physical activity monitors. PLoS One. 2015;. https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0118723.",{"doi":4172},"10.1371\u002Fjournal.pone.0118723",{"id":26,"text":4174,"url":26,"identifiers":4175},"Su X, Tong H, Ji P. Activity recognition with smartphone sensors. Tsinghua Sci Technol. 2014;. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTST.2014.6838194.",{"doi":4176},"10.1109\u002FTST.2014.6838194",{"id":26,"text":4178,"url":26,"identifiers":4179},"Suh MK, Chen CA, Woodbridge J, Tu MK, Kim JI, Nahapetian A, Evangelista LS, Sarrafzadeh M. A remote patient monitoring system for congestive heart failure. J Med Syst. 2011;. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10916-011-9733-y.",{"doi":4180},"10.1007\u002Fs10916-011-9733-y",{"id":26,"text":4182,"url":26,"identifiers":4183},"Sun S. A survey of multi-view machine learning. Neural Comput Appl. 2013;. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00521-013-1362-6.",{"doi":4184},"10.1007\u002Fs00521-013-1362-6",{"id":26,"text":4186,"url":26,"identifiers":4187},"Taraldsen K, Chastin SFM, Riphagen II, Vereijken B, Helbostad JL. Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: a systematic literature review of current knowledge and applications. Maturitas. 2012;. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.maturitas.2011.11.003.",{"doi":4188},"10.1016\u002Fj.maturitas.2011.11.003",{"id":26,"text":4190,"url":26,"identifiers":4191},"Vahdatpour A, Amini N, Sarrafzadeh M, On-body device localization for health and medical monitoring applications. In: 2011 IEEE international conference on pervasive computing and communications, PerCom 2011. 2011. https:\u002F\u002Fdoi.org\u002F10.1109\u002FPERCOM.2011.5767593.",{"doi":4192},"10.1109\u002FPERCOM.2011.5767593",{"id":26,"text":4194,"url":26,"identifiers":4195},"van Kasteren T, Englebienne G, Kröse BJA. Recognizing activities in multiple contexts using transfer learning. In: AAAI AI in eldercare symposium. 2008.",{},{"id":26,"text":4197,"url":26,"identifiers":4198},"Wang J, Chen Y, Gu Y, Xiao Y, Pan H. SensoryGANs: an effective generative adversarial framework for sensor-based human activity recognition. In: Proceedings of the international joint conference on neural networks. 2018. https:\u002F\u002Fdoi.org\u002F10.1109\u002FIJCNN.2018.8489106.",{"doi":4199},"10.1109\u002FIJCNN.2018.8489106",{"id":26,"text":4201,"url":26,"identifiers":4202},"Yang CC, Hsu YL. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors. 2010;. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs100807772.",{"doi":4203},"10.3390\u002Fs100807772",{"id":26,"text":4205,"url":26,"identifiers":4206},"Yin XC, Yang C, Pei WY, Hao HW. Shallow classification or deep learning: an experimental study. In: Proceedings—international conference on pattern recognition. 2014. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICPR.2014.333.",{"doi":4207},"10.1109\u002FICPR.2014.333",{"id":26,"text":4209,"url":26,"identifiers":4210},"Zhu S, Xu J, Guo H, Liu Q, Wu S, Wang H. Indoor human activity recognition based on ambient radar with signal processing and machine learning. In: 2018 IEEE international conference on communications (ICC). 2018. p. 1–6. https:\u002F\u002Fdoi.org\u002F10.1109\u002FICC.2018.8422107.",{"doi":4211},"10.1109\u002FICC.2018.8422107"]