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Journal of Medicine and Pharmacy","Tạp chí Y Dược học Cần Thơ",{"EN":487,"VI":488},"\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>",{"VOID":490},"wcQ1uqwAAAAJ","2023-05-30T08:17:21.868+00:00",[],[494],{"id":495,"createTime":28,"updateTime":28,"relativeEntities":496,"slug":28,"properties":497,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":507,"parentIds":508,"statistic":28},"6413896b-eca9-442b-a73f-182a58a0ce40",[],{"title":498,"address":501,"country":504,"abbreviation":505},{"EN":499,"VI":500},"Can Tho University of Medicine and Pharmacy","Trường Đại học Y Dược Cần Thơ",{"EN":502,"VI":503},"No 179, Nguyen Van Cu street, An Khanh ward, Ninh Kieu district, Can Tho city, Vietnam","Số 179, đường Nguyễn Văn Cừ, phường An Khánh, quận Ninh Kiều, thành phố Cần Thơ, Việt Nam",{"VOID":15},{"VOID":506},"ctump","http:\u002F\u002Fwww.ctump.edu.vn\u002F",[],[],"https:\u002F\u002Ftapchi.ctump.edu.vn\u002Findex.php\u002Fctump",{"impactFactor":32,"impactFactorByYear":512,"i10Index":32,"i10IndexLast5Year":32,"totalPublication":514,"totalPublicationByYear":515,"totalCitation":520,"totalCitationByYear":521,"totalCitationPerPublication":108,"totalCitationPerPublicationByYear":523,"hindexLast5Year":45,"hindex":45},{"2022":513,"2023":111,"2024":106},0.01,1556,{"2020":47,"2021":516,"2022":517,"2023":518,"2024":519,"2025":122},57,306,801,358,161,{"2021":146,"2022":280,"2023":522},99,{"2021":524,"2022":318,"2023":104},0.23,{"impactFactor":28,"impactFactorByYear":28,"i10Index":123,"i10IndexLast5Year":123,"totalPublication":526,"totalPublicationByYear":527,"totalCitation":526,"totalCitationByYear":528,"totalCitationPerPublication":40,"totalCitationPerPublicationByYear":531,"hindexLast5Year":49,"hindex":49},476,{"0":205,"2019":123,"2021":139,"2022":459,"2023":451,"2024":357,"2025":49,"2026":48},{"2021":42,"2022":123,"2023":161,"2024":529,"2025":360,"2026":530},136,83,{"2021":105,"2022":513,"2023":532,"2024":127,"2025":533,"2026":534},0.62,25.43,13.83,{"id":536,"createTime":537,"updateTime":382,"relativeEntities":538,"slug":539,"properties":540,"entityType":25,"verifyStatus":26,"verifyTime":28,"verifyNote":28,"languages":552,"translateLanguages":28,"viewCount":133,"subjectFields":553,"manageAffiliations":554,"indexDatabases":555,"url":556,"thumbnailPath":557,"statistic":558,"gsStatistic":594,"type":55,"analyzePriority":28},"6984a56a-db70-403b-9cc4-4013e1ceaffa","2023-05-09T06:47:40.346+00:00",[],"T%E1%BA%A1p%20ch%C3%AD%20Nghi%C3%AAn%20c%E1%BB%A9u%20n%C6%B0%E1%BB%9Bc%20ngo%C3%A0i",{"country":541,"issn":542,"title":544,"introduce":547,"gsId":550},{"VOID":15},{"VOID":543},"25252445",{"EN":545,"VI":546},"VNU Journal of Foreign Studies","Tạp chí Nghiên cứu nước ngoài",{"EN":548,"VI":549},"{\"ops\":[{\"insert\":\"\\n\\nThe \\n\"},{\"attributes\":{\"italic\":true},\"insert\":\"VNU Journal of Science\"},{\"insert\":\"\\n was established in 1985 for the publication of national and international research papers in all fields of natural sciences and technology, social sciences and humanities. 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toán không máy chủ xuất hiện như một tiêu chuẩn mới để xây dựng các ứng dụng đám mây, nơi các nhà phát triển viết các hàm ngắn gọn để phản hồi các sự kiện trong cơ sở hạ tầng đám mây. Nhiều ngành dịch vụ đám mây đã bắt đầu áp dụng mô hình không máy chủ để triển khai các ứng dụng của họ. Tuy nhiên, một giới hạn chính trong điện toán không máy chủ là nó không xem xét tầm quan trọng của dữ liệu. Trong thời đại dữ liệu lớn, khi các ứng dụng hoạt động trên một khối lượng lớn, việc chuyển dữ liệu từ phía dữ liệu sang phía tính toán để bố trí lại dữ liệu và mã, dẫn đến độ trễ cao. Tất cả các kiến trúc không máy chủ hiện có đều dựa trên kiến trúc chuyển dữ liệu. Trong bài báo này, chúng tôi trình bày một kiến trúc chuyển mã liên vùng cho điện toán không máy chủ, cho phép mã được đưa từ phía tính toán đến phía dữ liệu, nơi kích thước mã là không đáng kể so với kích thước dữ liệu. Chúng tôi đã thử nghiệm kiến trúc đề xuất của mình trên nền tảng đám mây thời gian thực Amazon Web Services với sự tích hợp của công cụ không máy chủ Fission. Đánh giá kiến trúc chuyển mã được đề xuất cho thấy đối với kích thước tệp dữ liệu là 64 MB, độ trễ trong kiến trúc chuyển mã được đề xuất là 8.36 ms và trong kiến trúc dữ liệu chuyển giao hiện tại là 16.8 ms. Do đó, kiến trúc đề xuất đạt được tốc độ gấp 2 lần trên độ trễ vòng cho các kích thước dữ liệu lớn trong một môi trường không máy chủ. Chúng tôi định nghĩa độ trễ vòng là khoảng thời gian để đọc và ghi lại dữ liệu trong bộ nhớ lưu trữ.","Serverless computing emerges as a new standard to build cloud applications, where developers write compact functions that respond to events in the cloud infrastructure. Several cloud service industries started adopting serverless for deploying their applications. But one key limitation in serverless computing is that it disregards the significance of data. In the age of big data, when applications run around a huge volume, to transfer data from the data side to the computation side to co-allocate the data and code, leads to high latency. All existing serverless architectures are based on the data shipping architecture. In this paper, we present an inter-region code shipping architecture for serverless, that enables the code to flow from computation side to the data side where the size of the code is negligible compared to the data size. We tested our proposed architecture over a real-time cloud platform Amazon Web Services with the integration of the Fission serverless tool. The evaluation of the proposed code shipping architecture shows for a data file size of 64 MB, the latency in the proposed code shipping architecture is 8.36 ms and in existing data shipped architecture is found to be 16.8 ms. Hence, the proposed architecture achieves a speedup of 2x on the round latency for high data sizes in a serverless environment. We define round latency to be the duration to read and write back the data in the storage.",{"EN":1022,"VI":1023},"Shipping code towards data in an inter-region serverless environment to leverage latency","Vận chuyển mã đến dữ liệu trong môi trường không máy chủ liên vùng để giảm thiểu độ trễ",{"VI":1025},"Điện toán không máy chủ, Kiến trúc chuyển mã, Độ trễ, Dữ liệu lớn, Nền tảng đám mây",{"VOID":1027},"Lloyd W, Vu M, Zhang B, David O, Leavesley G (2018) Improving application migration to serverless computing platforms: latency mitigation with keep-alive workloads. In: 2018 IEEE\u002FACM International Conference on Utility and Cloud Computing Companion (UCC Companion), pp 195–200, IEEE, 2018\nKjorveziroski V, Filiposka S (2022) Kubernetes distributions for the edge: serverless performance evaluation. J Supercomput, pp 1–28\nMcGrath G, Brenner PR (2017) Serverless computing: Design, implementation, and performance. In: 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), pp 405–410, IEEE\nJoseph JMF, Hellerstein M, Gonzalez J, Smith JS, Sreekanti V , Tumanov A, Wu C (2019) Serverless computing: One step forward, two steps back. In: 9th Biennial Conference on Innovative Data Systems Research, CIDR 2019, Asilomar, CA, USA, 13–16 Jan 2019, Online Proceedings, www.cidrdb.org\nYu T, Liu Q, Du D, Xia Y, Zang B, Lu Z, Yang P, Qin C, Chen H (2020) Characterizing serverless platforms with serverlessbench. In: Proceedings of the 11th ACM Symposium on Cloud Computing, pp 30–44\nAditya P, Akkus IE, Beck A, Chen R, Hilt V, Rimac I, Satzke K, Stein M (2019) Will serverless computing revolutionize nfv? Proc IEEE 107(4):667–678\nScience Advances The polar regions in a 2\\(^\\circ \\)c warmer world (2021). https:\u002F\u002Fadvances.sciencemag.org\u002Fcontent\u002F5\u002F12\u002Feaaw9883, 2019. 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In: 2018 15th IEEE Annual Consumer Communications and Networking Conference (CCNC), pp 1–7, IEEE\nKapitonov A, Lonshakov S, Bulatov V, Kia B, White J(2021) Robot-as-a-service: from cloud to peering technologies. In: Proceedings of the 4th International Conference on Information Science and Systems, pp 126–131\nZhang T, Xie D, Li F, Stutsman R (2019) Narrowing the gap between serverless and its state with storage functions. Association for Computing Machinery, New York, NY, USA\nMahmud MS, Huang JZ, Salloum S, Emara TZ, Sadatdiynov K (2020) A survey of data partitioning and sampling methods to support big data analysis. Big Data Min Anal 3(2):85–101",{"VOID":1029},"10.1007\u002Fs11227-023-05104-7","PUBLICATION","2025-01-10T03:45:43.987+00:00","Auto Verify",[30],"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-023-05104-7",[1036,1052,1067,1080],{"id":1037,"sortIndex":32,"researcher":28,"roles":1038,"affiliations":1040,"properties":1049,"displayName":1051,"givenName":28,"familyName":28},"4caa76d2-1149-469b-bb00-ffbd356b8656",[1039],"AUTHOR",[1041],{"id":1042,"sortIndex":32,"affiliation":1043,"properties":28},"3575c315-f492-4207-8a36-43e7b4f16d3b",{"id":1042,"createTime":28,"updateTime":28,"relativeEntities":1044,"slug":28,"properties":1045,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1048,"statistic":28},[],{"title":1046},{"VI":1047},"Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India",[],{"title":1050},{"VI":1051},"Biswajeet Sethi",{"id":1053,"sortIndex":40,"researcher":28,"roles":1054,"affiliations":1055,"properties":1064,"displayName":1066,"givenName":28,"familyName":28},"7cdf0f52-37eb-4e1c-bc73-ce57a1a34479",[1039],[1056],{"id":1057,"sortIndex":32,"affiliation":1058,"properties":28},"524703ae-addb-4c9d-9c72-3c855258c03b",{"id":1057,"createTime":28,"updateTime":28,"relativeEntities":1059,"slug":28,"properties":1060,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1063,"statistic":28},[],{"title":1061},{"VI":1062},"Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India",[],{"title":1065},{"VI":1066},"Sourav Kanti Addya",{"id":1068,"sortIndex":123,"researcher":28,"roles":1069,"affiliations":1070,"properties":1077,"displayName":1079,"givenName":28,"familyName":28},"2986216d-aefc-4cdb-97f1-d7598a198491",[1039],[1071],{"id":1042,"sortIndex":32,"affiliation":1072,"properties":28},{"id":1042,"createTime":28,"updateTime":28,"relativeEntities":1073,"slug":28,"properties":1074,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1076,"statistic":28},[],{"title":1075},{"VI":1047},[],{"title":1078},{"VI":1079},"Jay Bhutada",{"id":1081,"sortIndex":42,"researcher":28,"roles":1082,"affiliations":1083,"properties":1090,"displayName":1092,"givenName":28,"familyName":28},"d6152c43-dd1f-4b17-941a-1644070c162c",[1039],[1084],{"id":1042,"sortIndex":32,"affiliation":1085,"properties":28},{"id":1042,"createTime":28,"updateTime":28,"relativeEntities":1086,"slug":28,"properties":1087,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1089,"statistic":28},[],{"title":1088},{"VI":1047},[],{"title":1091},{"VI":1092},"Soumya K. 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cancer is a common disease that can result in death among women. Cancer research is important because early detection of cancer facilitates clinical practice for patients. The aim of the study is to ensure that breast cancer can be diagnosed in a short time and easily. For this purpose, a dataset containing 116 samples, 9 features and 2 target variables (Breast Cancer Coimbra) from the UCI library was used during the training and testing phases. A hybrid structure was created with genetic algorithm (GA) and artificial neural network (ANN) to classify the datasets. With the established hybrid model, the feedforward backpropagation artificial neural network model and the hyperparameters in this model structure have been optimized with the genetic algorithm. The performance of the structure constructed with the most successful gene parameters obtained was compared with weighted K-nearest neighbors, decision tree, and linear support vector machine methods. In all machine learning methods used, fivefold cross-validation was applied and the dataset was divided into two groups as 50% training and 50% testing in order to test the models with different data. The hybrid model proposed in the study performed better than other machine learning methods with 100% correct classification rate. Although there are few data in this study, the accuracy is higher than other literature. In addition, an iOS–android-based application has been developed for the diagnosis and prediction of the disease with the findings obtained. Thanks to the developed application, the most important factor in the fight against the disease, time and cost spent for the diagnosis of this disease will be saved. Considering the interest in artificial intelligence techniques in cancer research, this study presents a new diagnostic method and a usable application in terms of patient decision support systems.",{"EN":1168},"Optimization of artificial neural network structure and hyperparameters in hybrid model by genetic algorithm: iOS–android application for breast cancer diagnosis\u002Fprediction",{"VOID":1170},"Santos TBd, Borges AKdM, Ferreira JD, Meira KC, Souza MCd, Guimarães RM, Jomar RT (2022) Prevalence and factors associated to advanced stage breast cancer diagnosis. Ciência & Saúde Coletiva 27:471–482\nKayikci S, Khoshgoftaar TM (2023) Breast cancer prediction using gated attentive multimodal deep learning. J Big Data 10(1):1–11\nŁuczyńska E, Pawlak M, Popiela T, Rudnicki W (2022) The role of ABUS in the diagnosis of breast cancer. J Ultrasonogr 22(89):76–85\nElbaiomy M, Akl T, Atwan N, Elsayed AA, Elzaafarany M, Shamaa S (2020) Clinical impact of breast cancer stem cells in metastatic breast cancer patients. J Oncol 2020\nLin R-H, Kujabi BK, Chuang C-L, Lin C-S, Chiu C-J (2022) Application of deep learning to construct breast cancer diagnosis model. Appl Sci 12(4):1957\nRasool A, Bunterngchit C, Tiejian L, Islam MR, Qu Q, Jiang Q (2022) Improved machine learning-based predictive models for breast cancer diagnosis. Int J Environ Res Public Health 19(6):3211\nJayandhi G, Jasmine J, Joans SM (2022) Mammogram learning system for breast cancer diagnosis using deep learning SVM. Comput Syst Sci Eng 40(2):491–503\nEscorcia-Gutierrez J, Mansour RF, Beleño K, Jiménez-Cabas J, Pérez M, Madera N, Velasquez K (2022) Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images. Comput Mater Continua 71:3–4221\nRani S, Kaur M, Kumar M (2022) Recommender system: prediction\u002Fdiagnosis of breast cancer using hybrid machine learning algorithm. Multimedia Tools Appl 81(7):9939–9948\nTarawneh O, Otair M, Husni M, Abuaddous HY, Tarawneh M, Almomani MA (2022) Breast cancer classification using decision tree algorithms. Int J Adv Comput Sci Appl 13(4)\nAslan MF, Celik Y, Sabancı K, Durdu A (2018) Breast cancer diagnosis by different machine learning methods using blood analysis data. Int J Intell Syst Appl Eng\nFijri AL, Rustam Z (2018) Comparison between fuzzy kernel c-means and sparse learning fuzzy c-means for breast cancer clustering. In: 2018 International Conference on Applied Information Technology and Innovation (ICAITI). IEEE, pp 158–161\nSilva Araújo VJ, Guimarães AJ, de Campos Souza PV, Rezende TS, Araújo VS (2019) Using resistin, glucose, age and BMI and pruning fuzzy neural network for the construction of expert systems in the prediction of breast cancer. Mach Learn Knowl Extr 1(1):466–482\nYavuz E, Eyupoglu C (2020) An effective approach for breast cancer diagnosis based on routine blood analysis features. Med Biol Eng Comput 58(7):1583–1601\nAlshutbi M, Li Z, Alrifaey M, Ahmadipour M, Othman MM (2022) A hybrid classifier based on support vector machine and Jaya algorithm for breast cancer classification. Neural Comput Appl 1–13\nBülbül MA, Öztürk C (2022) Optimization, modeling and implementation of plant water consumption control using genetic algorithm and artificial neural network in a hybrid structure. Arab J Sci Eng 47(2):2329–2343\nBülbül MA, Harirchian E, Işık MF, Aghakouchaki Hosseini SE, Işık E (2022) A hybrid ANN-GA model for an automated rapid vulnerability assessment of existing RC buildings. Appl Sci 12(10):5138\nBülbül MA, Öztürk C, Işık MF (2022) Optimization of climatic conditions affecting determination of the amount of water needed by plants in relation to their life cycle with particle swarm optimization, and determining the optimum irrigation schedule. Comput J 65(10):2654–2663\nJeyaranjani J, Devaraj D (2022) Improved genetic algorithm for optimal demand response in smart grid. Sustain Comput Inform Syst 35:100710\nNahavandi D, Alizadehsani R, Khosravi A, Acharya UR (2022) Application of artificial intelligence in wearable devices: opportunities and challenges. Comput Methods Programs Biomed 213:106541\nHasan SSU, Ghani A, Din IU, Almogren A, Altameem A (2022) Iot devices authentication using artificial neural network. Comput Mater Contin 70:3701–3716\nDudani SA (1976) The distance-weighted k-nearest-neighbor rule. IEEE Trans Syst Man Cybern 4:325–327\nMa Y, Zhao X (2021) Pod: a parallel outlier detection algorithm using weighted KNN. IEEE Access 9:81765–81777\nGeler Z, Kurbalija V, Ivanović M, Radovanović M (2020) Weighted KNN and constrained elastic distances for time-series classification. Expert Syst Appl 162:113829\nDinesh T, Rajendran T (2021) Higher classification of fake political news using decision tree algorithm over Naive Bayes algorithm. Revista Geintec-gestao Inovacao E Tecnologias 11(2):1084–1096\nQuang-Huy T, Doan PT, Yen NTH, Tran D-T (2021) Shear wave imaging and classification using extended Kalman filter and decision tree algorithm. Math Biosci Eng 18:7631–7647\nKhan MS, Khan L, Gul N, Amir M, Kim J, Kim SM (2020) Support vector machine-based classification of malicious users in cognitive radio networks. Wirel Commun Mobile Comput 2020\nFix E, Hodges J (1951) Discriminatory analysis, nonparametric discrimination: Consistency properties USAF school of aviation medicine, Randolph field. Technical report, Texas, Tech. Report 4\nReddy OY, Chatterjee S, Chakraborty AK (2021) Bilayered fault detection and classification scheme for low-voltage dc microgrid with weighted KNN and decision tree. Int J Green Energy 1–11\nAYIK YZ, Özdemir A, Yavuz U (2007) Lise türü ve lise mezuniyet başarisinin, kazanilan fakülte ile ilişkisinin veri madenciliği tekniği ile analizi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 10(2):441–454\nZhou S, Zhou W (2021) Unified SVM algorithm based on LS-DC loss. Mach Learn 1–28\nPolat K, Sentürk U (2018) A novel ml approach to prediction of breast cancer: combining of mad normalization, KMC based feature weighting and adaboostm1 classifier. In: 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, pp 1–4\nShuran C, Yian L (2020) Breast cancer diagnosis and prediction model based on improved PSO-SVM based on gray relational analysis. In: 2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, pp 231–234\nPatrício M, Pereira J, Crisóstomo J, Matafome P, Gomes M, Seiça R, Caramelo F (2018) Using resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer 18(1):1–8\nArchive.ics.uci.edu: UCI Machine Learning Repository. Last accessed 09 November 2022 (2022). https:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002Fdatasets.php\nWahyuni I, Mahmudy WF (2017) Rainfall prediction in Tengger, Indonesia using hybrid Tsukamoto Fis and genetic algorithm method. J ICT Res Appl 11(1):38–55\nLi Q, Liu S, Bai Y, He X, Yang X-S (2022) An elitism-based multi-objective evolutionary algorithm for min-cost network disintegration. Knowl-Based Syst 239:107944\nTiwari A, Dadhania AV, Ragunathrao VAB, Oliveira ER (2021) Using machine learning to develop a novel Covid-19 vulnerability index (C19VI). Sci Total Environ 773:145650\nLayer YC, Menzenbach J, Layer YL, Mayr A, Hilbert T, Velten M, Hoeft A, Wittmann M (2021) Validation of the preoperative score to predict postoperative mortality (POSPOM) in Germany. PLoS ONE 16(1):0245841",{"VOID":1172},"10.1007\u002Fs11227-023-05635-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-023-05635-z",[1175],{"id":1176,"sortIndex":32,"researcher":28,"roles":1177,"affiliations":1178,"properties":1187,"displayName":1189,"givenName":28,"familyName":28},"982501ad-4d44-4d97-ae8e-ee75d6993f06",[1039],[1179],{"id":1180,"sortIndex":32,"affiliation":1181,"properties":28},"c4ab5107-fc28-42f7-8381-9a68df1aa689",{"id":1180,"createTime":28,"updateTime":28,"relativeEntities":1182,"slug":28,"properties":1183,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1186,"statistic":28},[],{"title":1184},{"VI":1185},"Faculty of Engineering-Architecture, Computer Engineering, Nevşehir Hacı Bektaş Veli University, Nevşehir, Turkey",[],{"title":1188},{"VI":1189},"Mehmet Akif 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fusion is a very important technology which can use multisensor network data to get a better performance than single one; therefore, it is widely used in the filed of target recognition, target tracking, automatic control, decision making and so on. However, because of noise and interference, sometimes the sensors may obtain erroneous, inaccurate or heterogeneous data, which will produce the conflict information among different sensors and get the wrong result after information fusion. In this paper, based on the Dempster–Shafer (D–S) theory, we introduce how to set up the model of multisensor network information fusion. And then, we discuss the problem of conflict information fusion in the framework of evidence and several improved methods are introduced. Finally, based on Mahalanobis distance, an improved solution method is presented. The numerical simulation results prove that this new improved method can get the same result as traditional methods, beyond which it can make a reasonable decision with high conflict information. Therefore, this new improved method can be used in the filed of high noise and interference.",{"EN":1258},"A new combination method for multisensor conflict information",{"VOID":1260},"Fang Z, Liyan H (2006) A survey of multisensor information fusion technology. J Telem Track command 27(3):1–7\nLlinas J, Hall DL (1998) An introduction to multisensor data fusion. IEEE, pp I537–I540\nShafer G (1976) A mathematical theory of evidence. Princeton Univ. Press, Princeton\nZadeh LA (1984) Review of books: a mathematical theory of evidence. AI Mag 10(2):235–247\nYager RR (1987) On the Dempster–Shafer framework and new combination rules. Inf Sci 41:93–137\nSmets P (1990) The combination of evidence in the transferable belief model. IEEE Pattern Anal Mach Intell 12(5):447–458\nDubois D, Prade H (1988) Default reasoning and possibility theory. Artif Intell 35(z):243–257\nMurphy CK (2000) Combining belief functions when evidence conflicts. Decis Support Syst 29:1–9\nSun Q, Ye XQ, Gu WK (2000) A new combination rules of evidence theory. Acta Electron Sin 28(8):117–119\nLi BC, Wang B, Wei J, Qian ZB, Huang YQ (2002) An efficient combination rule of evidence theory. J Data Acquis Process 17(1):33–36\nLiang XR, Yao PY, Liang DL (2008) Improved combination rule of evidence theory and its application in fused target recognition. Electron Optics Control 15(12):37–41\nDeng Y, Shi WK, Zhu ZF, Liu Q (2004) Combining belief functions based on distance of evidence. Decis Support Syst 38(3):489–493\nLiu HY, Zhao ZG, Liu X (2008) Combination of conflict evidences in D–S theory. J Univ Electron Sci Technol China 37(5):701–704\nDempster A (1967) Upper and lower probabilities induced by multivalued mapping. Annu Math Stat 38(2):325–339\nShafer G (1976) A mathematical theory of evidence. Princeton University Press, New Jersey\nXu P, Deng Y, Su X, Mahadevan S (2013) A new method to determine basic probability assignment from training data. Knowl Based Syst 46:69–80\nJousselme AL, Grenier D, Bosse E (2001) A new distance between two bodies of evidence. Inf fusion 2(1):91–101\nWang X-X, Yang F-B (2007) A kind of evidence combination method in conflict. Danjian Yu Zhidao Xuebapo 27(8):255–257\nYong-Chao Wei (2011) An improved D–S evidence combination method based on K-L distance. Telecommun Eng 51(1):191–201\nMcLachlan GF (1999) Mahalanobis Distance. General articlewei, pp 20–26\nMitchell AFS, Krzanowski WJ (1985) The Mahalanobis distance and elliptic distributions. Biometrika 72(2):464–467\nDe Maesschalck R, Jouan-Rimbaud D, Massart DL (2000) The Mahalanobis distance[J]. Chemometrics and Intelligent Laboratory Systems, pp 1–18\nXiang S, Nie F, Zhang C (2008) Learning a Mahalanobis distance metric for data clustering and classification[J]. Pattern Recognit 41:3600–3612\nLefevre E, Colot O, Vannoorenberghe P, de Brucq D (1998) A generic framework for resolving the conflict in the combination of belief structures. In: The 3rd International Conference on Information Fusion Paris. France, pp 182–188\nYager RR (1996) On the aggregation of prioritized belief structures. IEEE Trans Syst Man Cybern Part A Syst Hum 26(6):708–717\nZhang Y, Fang K (1999) Introduction to multivariate statistical analysis. Science Press, Beijing",{"VOID":1262},"10.1007\u002Fs11227-016-1681-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-016-1681-3",[1265,1289,1302,1315,1328],{"id":1266,"sortIndex":32,"researcher":28,"roles":1267,"affiliations":1268,"properties":1286,"displayName":1288,"givenName":28,"familyName":28},"7c6ddb44-3ef7-487a-95e8-4e6c451690ac",[1039],[1269,1277],{"id":1270,"sortIndex":32,"affiliation":1271,"properties":28},"be220d81-b2d0-4ddf-bb4c-dfff745f9020",{"id":1270,"createTime":28,"updateTime":28,"relativeEntities":1272,"slug":28,"properties":1273,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1276,"statistic":28},[],{"title":1274},{"VI":1275},"College of Information and Communication Engineering, Harbin Engineering University, Harbin, People’s Republic of China",[],{"id":1278,"sortIndex":40,"affiliation":1279,"properties":1285},"2d61b168-0999-4781-a424-1b455196fdf3",{"id":1278,"createTime":28,"updateTime":28,"relativeEntities":1280,"slug":28,"properties":1281,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1284,"statistic":28},[],{"title":1282},{"VI":1283},"College of Computer Science and Technology, Harbin Engineering University, Harbin, People’s Republic of China",[],{},{"title":1287},{"VI":1288},"Yun Lin",{"id":1290,"sortIndex":40,"researcher":28,"roles":1291,"affiliations":1292,"properties":1299,"displayName":1301,"givenName":28,"familyName":28},"9630fc0b-abb9-4a5b-a471-5fb5a5cab9cd",[1039],[1293],{"id":1270,"sortIndex":32,"affiliation":1294,"properties":28},{"id":1270,"createTime":28,"updateTime":28,"relativeEntities":1295,"slug":28,"properties":1296,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1298,"statistic":28},[],{"title":1297},{"VI":1275},[],{"title":1300},{"VI":1301},"Can Wang",{"id":1303,"sortIndex":123,"researcher":28,"roles":1304,"affiliations":1305,"properties":1312,"displayName":1314,"givenName":28,"familyName":28},"0790a3fb-2b84-47a6-abb3-c214661f5297",[1039],[1306],{"id":1278,"sortIndex":32,"affiliation":1307,"properties":28},{"id":1278,"createTime":28,"updateTime":28,"relativeEntities":1308,"slug":28,"properties":1309,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1311,"statistic":28},[],{"title":1310},{"VI":1283},[],{"title":1313},{"VI":1314},"Chunguang Ma",{"id":1316,"sortIndex":42,"researcher":28,"roles":1317,"affiliations":1318,"properties":1325,"displayName":1327,"givenName":28,"familyName":28},"fd6d89ed-38fe-4dc1-b442-9a355bc0ed24",[1039],[1319],{"id":1270,"sortIndex":32,"affiliation":1320,"properties":28},{"id":1270,"createTime":28,"updateTime":28,"relativeEntities":1321,"slug":28,"properties":1322,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1324,"statistic":28},[],{"title":1323},{"VI":1275},[],{"title":1326},{"VI":1327},"Zheng Dou",{"id":1329,"sortIndex":45,"researcher":28,"roles":1330,"affiliations":1331,"properties":1340,"displayName":1342,"givenName":28,"familyName":28},"4035fb9e-ceb8-4f7e-8e24-6a6f3fbc9bf3",[1039],[1332],{"id":1333,"sortIndex":32,"affiliation":1334,"properties":28},"631bf57c-69e3-484d-b29f-2a39bf4decd9",{"id":1333,"createTime":28,"updateTime":28,"relativeEntities":1335,"slug":28,"properties":1336,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1339,"statistic":28},[],{"title":1337},{"VI":1338},"National Key Laboratory of Underwater Acoustic Science and Technology, Harbin Engineering University, Harbin, People’s Republic of China",[],{"title":1341},{"VI":1342},"Xuefei Ma",{"url":1263,"publisher":1344,"properties":1398},{"id":868,"createTime":869,"updateTime":870,"relativeEntities":1345,"slug":872,"properties":1346,"entityType":25,"verifyStatus":880,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":32,"subjectFields":1350,"manageAffiliations":1367,"indexDatabases":1378,"url":28,"thumbnailPath":28,"statistic":1393,"gsStatistic":28,"type":28,"analyzePriority":28},[],{"issn":1347,"title":1348,"eissn":1349},{"VOID":875},{"EN":877},{"VOID":879},[1351,1355,1359,1363],{"id":883,"createTime":28,"updateTime":28,"relativeEntities":1352,"label":1353,"description":1354,"parentId":28,"standard":28,"scholarHubFieldId":28},[],{"EN":886},{},{"id":889,"createTime":28,"updateTime":28,"relativeEntities":1356,"label":1357,"description":1358,"parentId":28,"standard":28,"scholarHubFieldId":28},[],{"EN":892},{},{"id":895,"createTime":28,"updateTime":28,"relativeEntities":1360,"label":1361,"description":1362,"parentId":28,"standard":28,"scholarHubFieldId":28},[],{"EN":898},{},{"id":901,"createTime":28,"updateTime":28,"relativeEntities":1364,"label":1365,"description":1366,"parentId":28,"standard":28,"scholarHubFieldId":28},[],{"EN":904},{},[1368,1373],{"id":908,"createTime":28,"updateTime":28,"relativeEntities":1369,"slug":28,"properties":1370,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1372,"statistic":28},[],{"title":1371},{"EN":912},[914],{"id":916,"createTime":28,"updateTime":28,"relativeEntities":1374,"slug":28,"properties":1375,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1377,"statistic":28},[],{"title":1376},{"EN":920},[],[1379,1386],{"id":924,"indexDatabase":1380,"url":930,"indexYears":931,"academicFieldIds":1385,"indexDatabaseRanking":937},{"id":775,"createTime":28,"updateTime":28,"relativeEntities":1381,"label":1382,"description":1383,"key":781,"publicationTags":1384,"standard":28},[],{"EN":778,"VI":778},{"EN":778,"VI":780},[783],[933,934,935,936],{"id":939,"indexDatabase":1387,"url":951,"indexYears":28,"academicFieldIds":1392,"indexDatabaseRanking":28},{"id":941,"createTime":28,"updateTime":28,"relativeEntities":1388,"label":1389,"description":1390,"key":948,"publicationTags":1391,"standard":28},[],{"EN":944,"VI":944},{"EN":946,"VI":947},[950,813],[953,954],{"impactFactor":32,"impactFactorByYear":1394,"i10Index":958,"i10IndexLast5Year":959,"totalPublication":960,"totalPublicationByYear":1395,"totalCitation":972,"totalCitationByYear":1396,"totalCitationPerPublication":442,"totalCitationPerPublicationByYear":1397,"hindexLast5Year":142,"hindex":142},{"2000":165,"2001":107,"2012":318,"2013":110,"2014":461,"2015":284,"2016":228,"2017":422,"2018":224,"2019":365,"2020":461,"2021":957,"2022":366,"2023":369},{"1987":49,"1988":131,"1989":199,"1990":47,"1991":146,"1992":127,"1993":205,"1994":49,"1995":199,"1996":48,"1997":135,"1998":51,"1999":130,"2000":138,"2001":202,"2002":138,"2003":137,"2004":328,"2005":436,"2006":353,"2007":157,"2008":328,"2009":156,"2010":601,"2011":211,"2012":565,"2013":962,"2014":963,"2015":359,"2016":964,"2017":965,"2018":966,"2019":967,"2020":968,"2021":969,"2022":970,"2023":971,"2024":359},{"1987":126,"1988":49,"1989":323,"1990":147,"1991":196,"1992":127,"1993":974,"1994":142,"1995":141,"1996":154,"1999":45,"2004":975,"2005":434,"2006":280,"2007":208,"2008":148,"2009":122,"2010":976,"2011":334,"2012":830,"2013":977,"2014":978,"2015":979,"2016":980,"2017":981,"2018":459,"2019":982,"2020":983,"2021":984,"2022":985,"2023":986,"2024":42},{"1987":988,"1988":320,"1989":698,"1990":989,"1991":990,"1992":40,"1993":991,"1994":992,"1995":993,"1996":994,"1999":194,"2004":465,"2005":698,"2006":995,"2007":996,"2008":997,"2009":422,"2010":998,"2011":174,"2012":697,"2013":999,"2014":1000,"2015":703,"2016":1001,"2017":1002,"2018":1003,"2019":1004,"2020":192,"2021":1005,"2022":1006,"2023":319,"2024":107},{"pages":1399,"volume":1401},{"VOID":1400},"2874-2890",{"VOID":1402},"72","2016-03-01",2016,[937,950],{"id":1407,"createTime":1408,"updateTime":1409,"relativeEntities":1410,"slug":1411,"properties":1412,"entityType":1030,"verifyStatus":880,"verifyTime":1409,"verifyNote":1421,"languages":28,"translateLanguages":28,"viewCount":32,"primaryUrl":1422,"fullTextUrl":28,"authors":1423,"publicationType":1093,"publisherRelationship":1489,"citationCount":28,"citationInfo":28,"publishDate":1549,"publishYear":1550,"citationAnalyzeStatus":880,"lastCitationAnalyze":28,"indexDatabases":1551,"openAccess":28,"references":28,"isForceReanalyzing":1157},"0044ab38-face-4d4e-aa18-abf13cd8e174","2023-12-07T00:43:03.621+00:00","2025-02-08T09:59:08.983+00:00",[],"Provably-secure-authentication-key-exchange-scheme-using-fog-nodes-in-vehicular-ad-hoc-networks",{"abstract":1413,"title":1415,"references":1417,"doi":1419},{"EN":1414},"In recent years, with the development of cloud computing, the Internet of Things (IoT), and other technologies, mobile intelligent transportation systems, particularly the vehicular ad hoc network (VANET), have been growing quickly. Researchers have attempted to use fog computing in VANETs in order to meet real-world requirements for their deployment. Fog computing is an extension of cloud computing, and thus, it inevitably inherits the same security challenges. Further, because VANETs are in an open network environment, they will also face several other potential security and privacy issues. In this study, to promote secure interaction in fog-based VANETs, a new authentication key exchange (AKE) scheme that uses fog nodes as relay nodes has been designed. The scheme completes mutual authentication and generates a session key for later confidential communication. The automatic verification tool ProVerif and the BAN (Burrows–Abadi–Needham) logic were used to formally verify the security of the scheme, and an informal analysis shows that it can resist multiple known attacks. The simulation and analysis results show that the proposed scheme is successful. Finally, performance evaluation shows the effectiveness of the approach. Compared with the previously proposed privacy protection authentication protocols, the results of the proposed scheme are more computationally efficient.\n",{"EN":1416},"Provably secure authentication key exchange scheme using fog nodes in vehicular ad hoc networks",{"VOID":1418},"Xiong H, Wu Y, Jin C, Kumari S (2020) Efficient and privacy-preserving authentication protocol for heterogeneous systems in iiot. IEEE Internet Things J. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2020.2999510\nWang EK, Yu J, Chen CM, Kumari S, Rodrigues JJ (2020) Data augmentation for internet of things dialog system. Mob Netw Appl. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11036-020-01638-9\nXiong H, Zhao Y, Hou Y, Huang X, Jin C, Wang L, Kumari S (2020) Heterogeneous signcryption with equality test for iiot environment. IEEE Internet Things J. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJIOT.2020.3008955\nStojmenovic I, Wen S (2014) The fog computing paradigm: Scenarios and security issues. In: 2014 Federated Conference on Computer Science and Information Systems, IEEE, pp 1–8\nVaquero LM, Rodero-Merino L (2014) Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput Commun Rev 44(5):27–32\nWang J, Wu W, Liao Z, Sangaiah AK, Sherratt RS (2019) An energy-efficient off-loading scheme for low latency in collaborative edge computing. IEEE Access 7:149182–149190\nWang J, Wu W, Liao Z, Sherratt RS, Kim GJ, Alfarraj O, Alzubi A, Tolba A (2020) A probability preferred priori offloading mechanism in mobile edge computing. IEEE Access 8:39758–39767\nHou X, Li Y, Chen M, Wu D, Jin D, Chen S (2016) Vehicular fog computing: a viewpoint of vehicles as the infrastructures. IEEE Trans Veh Technol 65(6):3860–3873\nHu P, Ning H, Qiu T, Song H, Wang Y, Yao X (2017) Security and privacy preservation scheme of face identification and resolution framework using fog computing in internet of things. IEEE Internet Things J 4(5):1143–1155\nHuang H, Chen X, Wu Q, Huang X, Shen J (2018) Bitcoin-based fair payments for outsourcing computations of fog devices. Fut Gener Comput Syst 78:850–858\nWang P, Chen CM, Kumari S, Shojafar M, Tafazolli R, Liu YN (2020) Hdma: hybrid d2d message authentication scheme for 5g-enabled vanets. IEEE Trans Intell Transp Syst. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTITS.2020.3013928\nZhu H, Wang X, Chen CM, Kumari S (2020) Two novel semi-quantum-reflection protocols applied in connected vehicle systems with blockchain. Comput Electr Eng 86:106714\nZhang J, Liu H, Ni L (2020) A secure energy-saving communication and encrypted storage model based on rc4 for ehr. IEEE Access 8:38995–39012\nPan JS, Lee CY, Sghaier A, Zeghid M, Xie J (2019) Novel systolization of subquadratic space complexity multipliers based on toeplitz matrix-vector product approach. IEEE Trans Very Large Scale Integr VLSI Syst 27(7):1614–1622\nBonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp 13–16\nGia TN, Jiang M, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2015) Fog computing in healthcare internet of things: a case study on ECG feature extraction. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, IEEE, pp 356–363\nOkay FY, Ozdemir S (2016) A fog computing based smart grid model. In: 2016 International Symposium on Networks, Computers and Communications (ISNCC), IEEE, pp 1–6\nHuang C, Lu R, Choo KKR (2017) Vehicular fog computing: architecture, use case, and security and forensic challenges. IEEE Commun Mag 55(11):105–111\nSookhak M, Yu FR, He Y, Talebian H, Safa NS, Zhao N, Khan MK, Kumar N (2017) Fog vehicular computing: augmentation of fog computing using vehicular cloud computing. IEEE Veh Technol Mag 12(3):55–64\nKraemer FA, Braten AE, Tamkittikhun N, Palma D (2017) Fog computing in healthcare—a review and discussion. IEEE Access 5:9206–9222\nFarahani B, Firouzi F, Chang V, Badaroglu M, Constant N, Mankodiya K (2018) Towards fog-driven iot ehealth: promises and challenges of iot in medicine and healthcare. Fut Gener Comput Syst 78:659–676\nJia X, He D, Kumar N, Choo KKR (2019) Authenticated key agreement scheme for fog-driven iot healthcare system. Wirel Netw 25(8):4737–4750\nChen CM, Huang Y, Wang KH, Kumari S, Wu ME (2020) A secure authenticated and key exchange scheme for fog computing. Enterpr Inf Syst. https:\u002F\u002Fdoi.org\u002F10.1080\u002F17517575.2020.1712746\nKhan AF, Anandharaj G (2019) A cognitive key management technique for energy efficiency and scalability in securing the sensor nodes in the iot environment: CKMT. SN Appl Sci 1(12):1575\nKhan AF, Anandharaj G (2020) Ahkm: an improved class of hash based key management mechanism with combined solution for single hop and multi hop nodes in iot. Egypt Inform J. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eij.2020.05.004\nRaya M, Papadimitratos P, Hubaux JP (2006) Securing vehicular communications. IEEE Wirel Commun 13(5):8–15\nLu R, Lin X, Zhu H, Ho PH, Shen X (2008) ECPP: efficient conditional privacy preservation protocol for secure vehicular communications. In: IEEE INFOCOM 2008-the 27th Conference on Computer Communications, IEEE, pp 1229–1237\nWang NW, Huang YM, Chen WM (2008) A novel secure communication scheme in vehicular ad hoc networks. Comput Commun 31(12):2827–2837\nChuang MC, Lee JF (2014) Team: trust-extended authentication mechanism for vehicular ad hoc networks. IEEE Syst J 8(3):749–758\nZhou Y, Zhao X, Jiang Y, Shang F, Deng S, Wang X (2017) An enhanced privacy-preserving authentication scheme for vehicle sensor networks. Sensors 17(12):2854\nWu L, Sun Q, Wang X, Wang J, Yu S, Zou Y, Liu B, Zhu Z (2019) An efficient privacy-preserving mutual authentication scheme for secure v2v communication in vehicular ad hoc network. IEEE Access 7:55050–55063\nWu F, Li X, Xu L, Sangaiah AK, Rodrigues JJ (2018) Authentication protocol for distributed cloud computing: an explanation of the security situations for internet-of-things-enabled devices. IEEE Consum Electron Mag 7(6):38–44\nWazid M, Bagga P, Das AK, Shetty S, Rodrigues JJ, Park YH (2019) Akm-iov: authenticated key management protocol in fog computing-based internet of vehicles deployment. IEEE Internet Things J 6(5):8804–8817\nKai K, Cong W, Tao L (2016) Fog computing for vehicular ad-hoc networks: paradigms, scenarios, and issues. J China Univ Posts Telecommun 23(2):56–96\nKhan AA, Abolhasan M, Ni W (2018) 5G next generation vanets using SDN and fog computing framework. In: 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), IEEE, pp 1–6\nPereira J, Ricardo L, Luís M, Senna C, Sargento S (2019) Assessing the reliability of fog computing for smart mobility applications in vanets. Fut Gener Comput Syst 94:317–332\nMa M, He D, Wang H, Kumar N, Choo KKR (2019) An efficient and provably secure authenticated key agreement protocol for fog-based vehicular ad-hoc networks. 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In: ITM Web of Conferences, EDP Sciences, vol 13, p 01020\nHe D, Kumar N, Lee JH, Sherratt RS (2014) Enhanced three-factor security protocol for consumer usb mass storage devices. IEEE Trans Consum Electron 60(1):30–37\nWu JMT, Lin JCW, Tamrakar A (2019) High-utility itemset mining with effective pruning strategies. ACM Trans Knowl Discov Data TKDD 13(6):1–22\nMeng Z, Pan JS, Tseng KK (2019) Pade: an enhanced differential evolution algorithm with novel control parameter adaptation schemes for numerical optimization. Knowl-Based Syst 168:80–99\nTian AQ, Chu SC, Pan JS, Cui H, Zheng WM (2020) A compact pigeon-inspired optimization for maximum short-term generation mode in cascade hydroelectric power station. Sustainability 12(3):767\nChu SC, Xue X, Pan JS, Wu X (2020) Optimizing ontology alignment in vector space. J Internet Technology 21(1):15–22\nDu ZG, Pan JS, Chu SC, Luo HJ, Hu P (2020) Quasi-affine transformation evolutionary algorithm with communication schemes for application of RSSI in wireless sensor networks. 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networks stand out from artificial intelligence because they can complete challenging tasks, such as image classification. However, designing a neural network for a particular problem requires experience and tedious trial and error. Automating this process defines a research field usually relying on population-based meta-heuristics. This kind of optimizer generally needs numerous function evaluations, which are computationally demanding in this context as they involve building, training, and evaluating different neural networks. Fortunately, these algorithms are also well suited for parallel computing. This work describes how the teaching–learning-based optimization algorithm has been adapted for designing neural networks exploiting a multi-GPU high-performance computing environment. The optimizer, not applied before for this purpose up to the authors’ knowledge, has been selected because it lacks specific parameters and is compatible with large-scale optimization. Thus, its configuration does not result in another problem and could design architectures with many layers. The parallelization scheme is decoupled from the optimizer. It can be seen as an external evaluation service managing multiple GPUs for promising neural network designs, even at different machines, and multiple CPU’s for low-performing solutions. This strategy has been tested in designing a neural network for image classification based on the CIFAR-10 dataset. The architectures found outperform human designs, and the sequential process is accelerated 4.2 times with 4 GPUs and 96 cores thanks to parallelization, being the ideal speed up 4.39 in this case.",{"EN":1562},"Accelerating neural network architecture search using multi-GPU high-performance computing",{"VOID":1564},"Sharma N, Sharma R, Jindal N (2021) Machine learning and deep learning applications-a vision. Global Trans Proc 2(1):24–28. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.gltp.2021.01.004\nRedmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779–788. https:\u002F\u002Fdoi.org\u002F10.48550\u002FARXIV.1506.02640\nHe K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778. https:\u002F\u002Fdoi.org\u002F10.48550\u002FARXIV.1512.03385\nSimonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3\\(^{rd}\\) International Conference on Learning Representations, pp 1–14. https:\u002F\u002Fdoi.org\u002F10.48550\u002FARXIV.1409.1556\nLiu Y, Sun Y, Xue B, Zhang M, Yen GG, Tan KC (2021) A survey on evolutionary neural architecture search. IEEE Trans Neural Networks learn Syst. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTNNLS.2021.3100554\nZoph B, Le QV (2017) Neural architecture search with reinforcement learning. In: Proceedings of the 5\\(^{th}\\) International Conference on Learning Representations, pp. 1–16. https:\u002F\u002Fdoi.org\u002F10.48550\u002FARXIV.1611.01578\nReal E, Moore S, Selle A, Saxena S, Suematsu YL, Tan J, Le QV, Kurakin A (2017) Large-scale evolution of image classifiers. In: International Conference on Machine Learning, pp 2902–2911. https:\u002F\u002Fdoi.org\u002F10.48550\u002FARXIV.1703.01041. PMLR\nWang B, Sun Y, Xue B, Zhang M (2018) Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification. In: 2018 IEEE Congress on Evolutionary Computation, pp 1–8. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCEC.2018.8477735. IEEE\nByla E, Pang W (2019) Deepswarm: optimising convolutional neural networks using swarm intelligence. In: UK Workshop on Computational Intelligence, pp 119–130. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-030-29933-0_10. Springer\nRao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inform Sci 183(1):1–15. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ins.2011.08.006\nYang Z, Li K, Guo Y, Ma H, Zheng M (2018) Compact real-valued teaching-learning based optimization with the applications to neural network training. Knowl-Based Syst 159:51–62. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.knosys.2018.06.004\nJameel SM, Hashmani MA, Rehman M, Budiman A (2020) An adaptive deep learning framework for dynamic image classification in the internet of things environment. Sensors. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs20205811\nOrts F, Ortega G, Puertas AM, García I, Garzón EM (2020) On solving the unrelated parallel machine scheduling problem: active microrheology as a case study. J Supercomput 76(11):8494–8509. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11227-019-03121-z\nAugonnet C, Thibault S, Namyst R, Wacrenier P-A (2009) Starpu: A unified platform for task scheduling on heterogeneous multicore architectures. In: Sips H, Epema D, Lin H-X (eds) Euro-Par 2009 Parallel Processing. Springer, Berlin, Heidelberg, pp 863–874\nLuk C-K, Hong S, Kim H (2009) Qilin: exploiting parallelism on heterogeneous multiprocessors with adaptive mapping. In: 2009 42nd Annual IEEE\u002FACM International Symposium on Microarchitecture (MICRO), pp 45–55\nMcCormick P, Inman J, Ahrens J, Mohd-Yusof J, Roth G, Cummins S (2007) Scout: a data-parallel programming language for graphics processors. Parallel Comput 33(10):648–662. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.parco.2007.09.001\nMartinez D, Brewer W, Behm G, Strelzoff A, Wilson A, Wade D (2018) Deep learning evolutionary optimization for regression of rotorcraft vibrational spectra. In: 2018 IEEE\u002FACM Machine Learning in HPC Environments (MLHPC), pp 57–66. https:\u002F\u002Fdoi.org\u002F10.1109\u002FMLHPC.2018.8638645\nPatton RM, Johnston JT, Young SR, Schuman CD, Potok TE, Rose DC, Lim S, Chae J, Hou L, Abousamra S, Samaras D, Saltz J (2019) Exascale deep learning to accelerate cancer research. In: 2019 IEEE International Conference on Big Data (Big Data), pp 1488–1496. https:\u002F\u002Fdoi.org\u002F10.1109\u002FBigData47090.2019.9006467\nBalaprakash P, Salim M, Uram TD, Vishwanath V, Wild SM (2018) Deephyper: Asynchronous hyperparameter search for deep neural networks. In: 2018 IEEE 25th International Conference on High Performance Computing (HiPC), pp 42–51. https:\u002F\u002Fdoi.org\u002F10.1109\u002FHiPC.2018.00014\nSalim MA, Uram TD, Childers JT, Balaprakash P, Vishwanath V, Papka ME (2019) Balsam: automated scheduling and execution of dynamic, data-intensive hpc workflows. https:\u002F\u002Fdoi.org\u002F10.48550\u002FARXIV.1909.08704\nCruz NC, Redondo JL, Álvarez JD, Berenguel M, Ortigosa PM (2017) A parallel teaching-learning-based optimization procedure for automatic heliostat aiming. J Supercomput 73(1):591–606. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11227-016-1914-5\nCruz NC, Marín M, Redondo M, Ortigosa EM, Ortigosa PM (2021) A comparative study of stochastic optimizers for fitting neuron models application to the cerebellar granule cell. Informatica 32(3):477–498\nTorres-Moreno JL, Cruz NC, Álvarez JD, Redondo JL, Giménez-Fernandez A (2022) An open-source tool for path synthesis of four-bar mechanisms. Mech Mach Theory 169:104604\nBoussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inform Sci 237:82–117\nvan Geit W, De Schutter E, Achard P (2008) Automated neuron model optimization techniques: a review. Biol Cyber 99(4):241–251\nCruz NC, Álvarez JD, Redondo JL, Berenguel M, Ortigosa PM (2018) A two-layered solution for automatic heliostat aiming. Eng Appl Artif Intell 72:253–266. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2018.04.014\nYeniay Ö (2005) Penalty function methods for constrained optimization with genetic algorithms. 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autonomous driving is the future trend, and achieving it requires precise and real-time simultaneous localization and mapping (SLAM). Many practitioners are concerned about the performance of LiDAR SLAM algorithms, but there is little research work to evaluate LiDAR SLAM algorithms specifically. This paper evaluates LeGO-LOAM, SC-LeGO-LOAM, LIO-SAM, SC-LIO-SAM, and FAST-LIO2 utilizing the 05-10 sequences from KITTI dataset. The experimental results show that: firstly, there is no significant difference among the absolute trajectory error of the five SLAM algorithms. Secondly, LeGO-LAOM has the smallest relative positional error among the six sequences. Thirdly, FAST-LIO2 has the best real-time performance. Our experiments are intended to provide a reference for practitioners in selecting SLAM algorithms.",{"EN":1712},"Evaluation of 3D LiDAR SLAM algorithms based on the KITTI dataset",{"VOID":1714},"Taxonomy SAE (2018) Definitions for terms related to driving automation systems for on-road motor vehicles. SAE: Warrendale, PA, USA, 3016\nRaul Mur-Artal, Tardós Juan D (2017) Orb-slam2: an open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans Rob 33(5):1255–1262. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTRO.2017.2705103\nNewcombe Richard A, Lovegrove Steven J, Davison Andrew J (2011) DTAM: dense tracking and mapping in real-time. 2011 Int Conf Comput Vis, 2320–2327. https:\u002F\u002Fdoi.org\u002F10.1109\u002Ficcv.2011.6126513\nEngel Jakob, Schöps Thomas, Cremers Daniel (2014) LSD-SLAM: Large-scale direct monocular SLAM. Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II 13, 834–849. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-10605-2_54\nForster Christian, Pizzoli Matia, Scaramuzza Davide (2014) SVO: fast semi-direct monocular visual odometry. IEEE Int Conf Robot Autom (ICRA) 2014:15–22. https:\u002F\u002Fdoi.org\u002F10.1109\u002Ficra.2014.6906584\nWang Rui, Schworer Martin, Cremers Daniel (2017) Stereo DSO: large-scale direct sparse visual odometry with stereo cameras. Proc IEEE Int Conf Comput Vis 3903–3911. https:\u002F\u002Fdoi.org\u002F10.1109\u002Ficcv.2017.421\nCampos Carlos, Elvira Richard, Rodríguez Juan J Gómez, Montiel José MM, Tardós Juan D (2021) Orb-slam3: an accurate open-source library for visual, visual–inertial, and multimap slam. IEEE Trans Rob 37(6):1874–1890. https:\u002F\u002Fdoi.org\u002F10.1109\u002Ftro.2021.3075644\nQin Tong, Li Peiliang, Shen Shaojie (2018) Vins-mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans Rob 34(4):1004–1020. https:\u002F\u002Fdoi.org\u002F10.1109\u002Ftro.2018.2853729\nZhang Ji, Singh Sanjiv (2014) LOAM: lidar odometry and mapping in real-time. Robot Sci Syst, 2, 9, 1–9. https:\u002F\u002Fdoi.org\u002F10.15607\u002Frss.2014.x.007\nShan Tixiao, Englot Brendan (2018) Lego-loam: lightweight and ground-optimized lidar odometry and mapping on variable terrain. IEEE\u002FRSJ Int Conf Intel Robot Syst (IROS) 2018:4758–4765. https:\u002F\u002Fdoi.org\u002F10.1109\u002Firos.2018.8594299\nSC-LeGO-LOAM:real-time LiDAR SLAM. (2020), from: https:\u002F\u002Fgithub.com\u002Firapkaist\u002FSC-LeGO-LOAM\nShan Tixiao, Englot Brendan, Meyers Drew, Wang Wei, Ratti Carlo, Rus Daniela (2020) Lio-sam: tightly-coupled lidar inertial odometry via smoothing and mapping. IEEE\u002FRSJ Int Conf Intell Robot Syst (IROS) 2020:5135–5142. https:\u002F\u002Fdoi.org\u002F10.1109\u002Firos45743.2020.9341176\nSC-LIO-SAM: a real-time lidar-inertial SLAM package. (2021), from: https:\u002F\u002Fgithub.com\u002Fgisbi-kim\u002FSC-LIO-SAM\nXu Wei, Cai Yixi, He Dongjiao, Lin Jiarong, Zhang Fu (2022) Fast-lio2: fast direct lidar-inertial odometry. IEEE Trans Rob. https:\u002F\u002Fdoi.org\u002F10.1109\u002Ftro.2022.3141876\nLi Kailai, Li Meng, Hanebeck Uwe D (2021) Towards high-performance solid-state-lidar-inertial odometry and mapping. IEEE Robot Autom Lett 6(3):5167–5174. https:\u002F\u002Fdoi.org\u002F10.1109\u002Flra.2021.3070251\nQin Chao, Ye Haoyang, Pranata Christian E, Han Jun, Zhang Shuyang, Liu Ming (2020) Lins: a lidar-inertial state estimator for robust and efficient navigation. IEEE Int Conf Robot Autom (ICRA) 2020:8899–8906. https:\u002F\u002Fdoi.org\u002F10.1109\u002Ficra40945.2020.9197567\nHess Wolfgang, Kohler Damon, Rapp Holger, Andor Daniel (2016) Real-time loop closure in 2D LIDAR SLAM. IEEE Int Conf Robot Autom (ICRA) 2016:1271–1278. https:\u002F\u002Fdoi.org\u002F10.1109\u002Ficra.2016.7487258\nKim Giseop, Kim Ayoung (2018) Scan context: egocentric spatial descriptor for place recognition within 3d point cloud map. IEEE\u002FRSJ Int Conf Intel Robot Syst (IROS) 2018:4802–4809. https:\u002F\u002Fdoi.org\u002F10.1109\u002Firos.2018.8593953\nXu Wei, Zhang Fu (2021) Fast-lio: a fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter. IEEE Robot Autom Lett 6(2):3317–3324. https:\u002F\u002Fdoi.org\u002F10.1109\u002Flra.2021.3064227\nGrupp Michael (2017) evo: python package for the evaluation of odometry and SLAM, from: https:\u002F\u002Fgithub.com\u002FMichaelGrupp\u002Fevo\nGeiger Andreas, Lenz Philip, Stiller Christoph, Urtasun Raquel (2013) Vision meets robotics: the kitti dataset. Int J Robot Res 32(11):1231–1237. https:\u002F\u002Fdoi.org\u002F10.1177\u002F0278364913491297\nShan Tixiao (2020) Kitti2bag: converting raw data into rosbag, from: https:\u002F\u002Fgithub.com\u002FTixiaoShan\u002FLIO-SAM\u002Ftree\u002Fmaster\u002Fconfig\u002Fdoc\u002Fkitti2bag",{"VOID":1716},"10.1007\u002Fs11227-023-05267-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-023-05267-3",[1719,1734,1747,1760],{"id":1720,"sortIndex":32,"researcher":28,"roles":1721,"affiliations":1722,"properties":1731,"displayName":1733,"givenName":28,"familyName":28},"0463fa07-12f8-4838-8068-e944e417adf1",[1039],[1723],{"id":1724,"sortIndex":32,"affiliation":1725,"properties":28},"fd1639c7-b351-458b-9bc3-666fe219e829",{"id":1724,"createTime":28,"updateTime":28,"relativeEntities":1726,"slug":28,"properties":1727,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1730,"statistic":28},[],{"title":1728},{"VI":1729},"Department of Optoelectronic Engineering, School of Physics and Materials Science, Guangzhou University, Guangzhou, 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study presents a method to construct formal rules used to run-time verify message passing between clients in distributed systems. Rules construction is achieved in four steps: (1) Visual specification of expected behavior of the sender, receiver, and network in sending and receiving a message, (2) Extraction of properties of sender, receiver, and network from the visual specification, (3) specification of constraints that should govern message passing in distributed systems, and (4) construction of verifier rules from the properties and the constraints. The rules are used to verify actual sender, receiver, and network behavior. Expected behavior of the client (process) is one that to be and the actual one is the behavior should be verified. The rules were applied to verify the behavior of client and servers that communicated with each other in order to compute Fibonacci numbers in parallel and some violations were discovered.",{"EN":1842},"Constructing formal rules to verify message communication in distributed systems",{"VOID":1844},"Tanenbaum AS, Steen MV (2006) Distributed systems: principles and paradigms, 2nd edn. Prentice Hall, New York\nGrosso W (2002) Java RMI: designing and building distributed applications. O’Reilly and Associates, Sebastopol\nRedmond FE (1997) Dcom: microsoft distributed component object model. Wiley, New York\nBrose G, Vogel A, Duddy K (2001) JavaTM programming with CORBATM: advanced techniques for building distributed applications. Wiley, New York\nJosuttis NM (2007) SOA in practice: the art of distributed system design (theory in practice). O’Reilly Media, Sebastopol\nSen K, Vardhan A, Agha G, Rosu G (2004) Efficient decentralized monitoring of safety in distributed systems. In: Proceedings of 26th international conference on software engineering, pp 418–427\nZhang F, Qi Z, Guan H, Liu X, Yang M, Zhang Z (2009) FiLM: a runtime monitoring tool for distributed systems. In: The 3rd IEEE international conference on secure software integration and reliability improvement, pp 40–46\nKhanna G, Varadharajan P, Bagchi S (2006) Automated online monitoring of distributed applications through external monitors. IEEE Trans Dependable Secure Comput 3(2):115–129\nZulkernine M, Seviora RE (2002) A compositional approach to monitoring distributed systems. In: Proceeding of the 2002 international conference on dependable systems and networks, pp 763–772\nDrusinsky D, Shing M (2007) Verifying distributed protocols using MSC-assertions, run-time monitoring, and automatic test generation. In: 18th IEEE\u002FIFIP international workshop on rapid system prototyping (RSP’07), pp 82–88\nKruger IH, Meisinger M, Menarini M (2010) Interaction-based runtime verification for systems of systems integration. J Log Comput 20(3):725–742\nKruger IH, Meisinger M, Menarini M (2007) Runtime verification of interactions: from MSCs to aspects. In: Proceedings of the 7th international workshop on runtime verification, RV 2007. Lecture notes in computer science, vol 4839\u002F2007. Springer, Berlin, pp 63–74\nJensen K, Kristensen LM (2009) Coloured Petri Nets: modelling and validation of concurrent systems. Springer, Berlin\nDiaz M (2009) Petri Nets: fundamental models, verification and applications. Wiley, New York\nLaddad R (2009) Aspectj in action: enterprise AOP with spring applications, 2nd edn. Manning Publication, Greenwich\nBonet P, Llado CM, Puijaner R, Knottenbelt WJ (2007) PIPE v2.5: a Petri net tool for performance modeling. 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IEEE Computer Society, Los Alamitos, pp 209–306. doi:10.1109\u002FAPSEC.2008.22",{"VOID":1846},"10.1007\u002Fs11227-011-0553-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11227-011-0553-0",[1849],{"id":1850,"sortIndex":32,"researcher":28,"roles":1851,"affiliations":1852,"properties":1861,"displayName":1863,"givenName":28,"familyName":28},"a3534fe1-f062-4fe2-a1a7-e5de057104be",[1039],[1853],{"id":1854,"sortIndex":32,"affiliation":1855,"properties":28},"c2c4c753-1ccc-4a3d-8b29-daefbf4932e4",{"id":1854,"createTime":28,"updateTime":28,"relativeEntities":1856,"slug":28,"properties":1857,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1860,"statistic":28},[],{"title":1858},{"VI":1859},"Department of Computer, University of Kashan, Kashan, Iran",[],{"title":1862},{"VI":1863},"Seyed Morteza Babamir",{"url":1847,"publisher":1865,"properties":1919},{"id":868,"createTime":869,"updateTime":870,"relativeEntities":1866,"slug":872,"properties":1867,"entityType":25,"verifyStatus":880,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":32,"subjectFields":1871,"manageAffiliations":1888,"indexDatabases":1899,"url":28,"thumbnailPath":28,"statistic":1914,"gsStatistic":28,"type":28,"analyzePriority":28},[],{"issn":1868,"title":1869,"eissn":1870},{"VOID":875},{"EN":877},{"VOID":879},[1872,1876,1880,1884],{"id":883,"createTime":28,"updateTime":28,"relativeEntities":1873,"label":1874,"description":1875,"parentId":28,"standard":28,"scholarHubFieldId":28},[],{"EN":886},{},{"id":889,"createTime":28,"updateTime":28,"relativeEntities":1877,"label":1878,"description":1879,"parentId":28,"standard":28,"scholarHubFieldId":28},[],{"EN":892},{},{"id":895,"createTime":28,"updateTime":28,"relativeEntities":1881,"label":1882,"description":1883,"parentId":28,"standard":28,"scholarHubFieldId":28},[],{"EN":898},{},{"id":901,"createTime":28,"updateTime":28,"relativeEntities":1885,"label":1886,"description":1887,"parentId":28,"standard":28,"scholarHubFieldId":28},[],{"EN":904},{},[1889,1894],{"id":908,"createTime":28,"updateTime":28,"relativeEntities":1890,"slug":28,"properties":1891,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1893,"statistic":28},[],{"title":1892},{"EN":912},[914],{"id":916,"createTime":28,"updateTime":28,"relativeEntities":1895,"slug":28,"properties":1896,"entityType":28,"verifyStatus":28,"verifyTime":28,"verifyNote":28,"languages":28,"translateLanguages":28,"viewCount":28,"url":28,"parentIds":1898,"statistic":28},[],{"title":1897},{"EN":920},[],[1900,1907],{"id":924,"indexDatabase":1901,"url":930,"indexYears":931,"academicFieldIds":1906,"indexDatabaseRanking":937},{"id":775,"createTime":28,"updateTime":28,"relativeEntities":1902,"label":1903,"description":1904,"key":781,"publicationTags":1905,"standard":28},[],{"EN":778,"VI":778},{"EN":778,"VI":780},[783],[933,934,935,936],{"id":939,"indexDatabase":1908,"url":951,"indexYears":28,"academicFieldIds":1913,"indexDatabaseRanking":28},{"id":941,"createTime":28,"updateTime":28,"relativeEntities":1909,"label":1910,"description":1911,"key":948,"publicationTags":1912,"standard":28},[],{"EN":944,"VI":944},{"EN":946,"VI":947},[950,813],[953,954],{"impactFactor":32,"impactFactorByYear":1915,"i10Index":958,"i10IndexLast5Year":959,"totalPublication":960,"totalPublicationByYear":1916,"totalCitation":972,"totalCitationByYear":1917,"totalCitationPerPublication":442,"totalCitationPerPublicationByYear":1918,"hindexLast5Year":142,"hindex":142},{"2000":165,"2001":107,"2012":318,"2013":110,"2014":461,"2015":284,"2016":228,"2017":422,"2018":224,"2019":365,"2020":461,"2021":957,"2022":366,"2023":369},{"1987":49,"1988":131,"1989":199,"1990":47,"1991":146,"1992":127,"1993":205,"1994":49,"1995":199,"1996":48,"1997":135,"1998":51,"1999":130,"2000":138,"2001":202,"2002":138,"2003":137,"2004":328,"2005":436,"2006":353,"2007":157,"2008":328,"2009":156,"2010":601,"2011":211,"2012":565,"2013":962,"2014":963,"2015":359,"2016":964,"2017":965,"2018":966,"2019":967,"2020":968,"2021":969,"2022":970,"2023":971,"2024":359},{"1987":126,"1988":49,"1989":323,"1990":147,"1991":196,"1992":127,"1993":974,"1994":142,"1995":141,"1996":154,"1999":45,"2004":975,"2005":434,"2006":280,"2007":208,"2008":148,"2009":122,"2010":976,"2011":334,"2012":830,"2013":977,"2014":978,"2015":979,"2016":980,"2017":981,"2018":459,"2019":982,"2020":983,"2021":984,"2022":985,"2023":986,"2024":42},{"1987":988,"1988":320,"1989":698,"1990":989,"1991":990,"1992":40,"1993":991,"1994":992,"1995":993,"1996":994,"1999":194,"2004":465,"2005":698,"2006":995,"2007":996,"2008":997,"2009":422,"2010":998,"2011":174,"2012":697,"2013":999,"2014":1000,"2015":703,"2016":1001,"2017":1002,"2018":1003,"2019":1004,"2020":192,"2021":1005,"2022":1006,"2023":319,"2024":107},{"pages":1920,"volume":1922},{"VOID":1921},"1396-1418",{"VOID":1923},"59","2011-01-27",2011,[937,950],{"id":1928,"createTime":1929,"updateTime":1930,"relativeEntities":1931,"slug":1932,"properties":1933,"entityType":1030,"verifyStatus":26,"verifyTime":1930,"verifyNote":1032,"languages":28,"translateLanguages":28,"viewCount":32,"primaryUrl":1942,"fullTextUrl":28,"authors":1943,"publicationType":1093,"publisherRelationship":2067,"citationCount":28,"citationInfo":28,"publishDate":2125,"publishYear":1155,"citationAnalyzeStatus":880,"lastCitationAnalyze":28,"indexDatabases":2126,"openAccess":28,"references":28,"isForceReanalyzing":1157},"009be4c8-d892-4410-8b5a-63acff2b1c7b","2023-12-28T11:29:35.675+00:00","2024-12-19T02:54:05.193+00:00",[],"A-privacy-preserved-IoMT-based-mental-stress-detection-framework-with-federated-learning",{"abstract":1934,"title":1936,"references":1938,"doi":1940},{"EN":1935},"Internet of Medical Things (IoMT) can be leveraged for periodic sensing and recording of different health parameters using sensors, wireless communications, and computation platforms. Health care systems can be enhanced by using IoMT for remote patient monitoring and data-driven diagnosis powered by machine learning algorithms. In the context of IoMT, federated learning (FL) is an excellent choice to manage machine learning (ML) algorithms to drive this analysis. This is because FL models can be trained in a distributed manner on local heterogeneous datasets that all contribute to the \"collective wisdom\". The model parameters can be regulated and shared without sharing the actual health data, ensuring confidentiality and security. This paper makes a case for the viability of FL-based analysis of data acquired via IoMT by presenting some use cases and recent work in this area and proposing a novel framework for data analysis using FL specifically in the context of mental stress detection. It shows that FL-based methods can significantly reduce the required communication overhead for each local device from 10.02MB\u002Fday up to only 754B\u002Fday as compared to non-FL techniques.",{"EN":1937},"A privacy-preserved IoMT-based mental stress detection framework with federated learning",{"VOID":1939},"Khan WU, Javed MA, Nguyen TN, Khan S, Elhalawany BM (2021) Energy-efficient resource allocation for 6g backscatter-enabled noma iov networks. IEEE Trans Intell Transp Syst 5:1–11. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTITS.2021.3110942\nJaved MA, Nguyen TN, Mirza J, Ahmed J, Ali B (2022) Reliable communications for cybertwin driven 6g iovs using intelligent reflecting surfaces. IEEE Trans Indust Inform 1:1–1. https:\u002F\u002Fdoi.org\u002F10.1109\u002FTII.2022.3151773\nDash TK, Chakraborty C, Mahapatra S, Panda G (2022) Gradient boosting machine and efficient combination of features for speech-based detection of covid-19. 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are often inconsistent in pervasive computing environments, owing to many heterogeneous devices with limited processing capabilities, imperfect measurement techniques, and user movement. A variety of schemes have been proposed to check context inconsistency. However, they implicitly require central control. This requirement inhibits their effectiveness in some pervasive computing environments (e.g., transport systems) where all nodes are resource-constrained and cannot act as a centralized node. To this end, we propose in this paper DCCI—a scheme of Decentralized Checking of Context Inconsistency in pervasive computing environments. DCCI exploits a simple, yet efficient, preference-based locality that denotes nodes requiring that the same context can check the inconsistency on this type of contexts. According to this locality, DCCI constructs a preference-based shortcut structure such that it checks context inconsistency within the shortcut structure. Extensive experiments show that DCCI can accurately and efficiently check context inconsistency in the presence of node churns and heterogeneity.",{"EN":2137},"Decentralized checking of context inconsistency in pervasive computing environments",{"EN":2139},"",{"VOID":2141},"Baldauf M, Dustdar S, Rosenberg F (2007) A survey on context-aware systems. Int J Ad Hoc Ubiq Comput 2(4):263–277\nBikakis A, Antoniou F (2008a) Distributed reasoning with conflicts in a multi-context framework. In: Fox D, Gomes CP (eds) Proceedings of the 23rd AAAI conference on artificial intelligence (AAAI ’08), pp 1778–1779\nBikakis A, Antoniou G (2008b) Local and distributed defeasible reasoning in multi-context systems. In: Proceedings of the international RuleML symposium on rule interchange and applications (RuleML ’08). Springer, Berlin, pp 135–149\nBradley NA, Dunlop MD (2009) Toward a multidisciplinary model of context to support context-aware computing. Hum-Comput Interact 20(4):403–446\nBu Y, Chen S, Tao X, Li J, Lu J (2006a) Context consistency management using ontology based model. In: International conference on extending database technology, pp 741–755\nBu Y, Gu T, Tao X, Li J, Chen S, Lu J (2006b) Managing quality of context in pervasive computing. In: Proceedings of the 6th international conference on quality software (QSIC ’06), pp 193–200\nCapra L, Emmerich W, Mascolo C (2003) Carisma: Context-aware reflective middleware system for mobile applications. IEEE Trans Softw Eng 29(10):929–945\nDey AK, Abowd GD, Salber D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Int J Hum-Comput Interact 16(2):97–166\nElnahrawy E, Nath B (2003) Cleaning and querying noisy sensors. In: Proceedings of the 2nd ACM international conference on wireless sensor networks and applications (WSNA ’03), pp 78–87\nErramilli V, Crovella M, Chaintreau A, Diot C (2008) Delegation forwarding. In: Proceedings of the 9th ACM international symposium on mobile ad hoc networking and computing (MobiHoc ’08). ACM, New York, pp 251–260\nGarlan D, Siewiorek DP, Steenkiste P (2002) Project aura: toward distraction-free pervasive computing. IEEE Pervasive Comput 1:22–31\nHarter A, Hopper A, Steggles P, Ward A, Webster P (1999) The anatomy of a context-aware application. In: Proceedings of the 5th annual ACM\u002FIEEE international conference on mobile computing and networking (MobiCom ’99), pp 59–68\nHuang Y, Ma X, Cao J, Tao X, Lu J (2009) Concurrent event detection for asynchronous consistency checking of pervasive context. In: Proceedings of the 7th annual IEEE international conference on pervasive computing and communications (Percom ’09), pp 131–139\nIBM (2010) InfoSphere Streams. http:\u002F\u002Fwww-01.ibm.com\u002Fsoftware\u002Fdata\u002Finfosphere\u002Fstreams\u002F\nJeffery SR, Garofalakis M, Franklin MJ (2006) Adaptive cleaning for RFID data streams. In: Proceedings of the 32nd international conference on very large data bases (VLDB ’06), pp 163–174\nJulien C, Roman GC (2006) Egospaces: facilitating rapid development of context-aware mobile applications. IEEE Trans Softw Eng 32(5):281–298\nKabadayi S, Julien C, O’Brien W, Stovall D (2007) Virtual sensors: a demonstration. In: The 26th international conference on computer communications: demonstrations track (Infocom), pp 10–12\nLiu K, Chen L, Liu Y, Li M (2008) Robust and efficient aggregate query processing in wireless sensor networks. Mob Netw Appl 13(1–2):212–227\nLu H, Chan W, Tse T (2008) Testing pervasive software in the presence of context inconsistency resolution services. In: Proceedings of the 30th international conference on software engineering (ICSE ’08), New York, NY, USA, pp 61–70\nPark I, Lee D Hyun S (2005) A dynamic context-conflict management scheme for group-aware ubiquitous computing environments. In: Proceedings of the 29th annual international computer software and applications conference (COMPSAC ’05), vol 1\nPu C, Schwan K, Walpole J (2001) Infosphere project: system support for information flow applications. SIGMOD Rec 30:25–34\nRanganathan A, Campbell RH (2003) An infrastructure for context-awareness based on first order logic. Pers Ubiquitous Comput 7(6):353–364\nRanganathan A, Campbell R, Ravi A, Mahajan A (2002) Conchat: a context-aware chat program. IEEE Pervasive Comput 1(3):51–57\nRatnasamy S, Francis P, Handley M, Karp R, Schenker S (2001) A scalable content-addressable network. In: Proceedings of the 2001 conference on applications, technologies, architectures, and protocols for computer communications (SIGCOMM ’01), New York, NY, USA, pp 161–172\nRomán M, Hess C, Cerqueira R, Ranganathan A, Campbell RH, Nahrstedt K (2002) A middleware infrastructure for active spaces. IEEE Pervasive Comput 1(4):74–83\nSatoh I (2009) A context-aware service framework for large-scale ambient computing environments. In: Proceedings of the 2009 international conference on pervasive services (ICPS ’09), New York, NY, USA, pp 199–208\nStoica I, Morris R, Karger D, Kaashoek MF, Balakrishnan H (2001) Chord: a scalable peer-to-peer lookup service for Internet applications. In: Proceedings of the 2001 conference on applications, technologies, architectures, and protocols for computer communications (SIGCOMM ’01), New York, NY, USA, pp 149–160\nStrang T, Popien C (2004) A context modeling survey. In: Proc of the workshop on advanced context modelling, reasoning and management\nWeiser M (1991) The computer for the 21st century. Sci Am 265:66–75\nXu C, Cheung SC (2005) Inconsistency detection and resolution for context-aware middleware support. In: Proceedings of the 10th European software engineering conference held jointly with the 13th ACM SIGSOFT international symposium on foundations of software engineering (SIGSOFT ’05), pp 336–345\nXu C, Cheung SC, Chan WK (2006) Incremental consistency checking for pervasive context. In: Proceedings of the 28th international conference on software engineering (ICSE ’06), pp 292–301\nXu C, Cheung SC, Chan WK, Ye C (2008) Heuristics-based strategies for resolving context inconsistencies in pervasive computing applications. In: Proceedings of the 28th IEEE international conference on distributed computing systems (ICDCS ’08), pp 713–721\nXue W, Pung H, Palmes PP, Gu T (2008) Schema matching for context-aware computing. In: Proceedings of the 11th international conference on ubiquitous computing (Ubicomp ’08), pp 292–301\nYau SS, Karim F (2004) An adaptive middleware for context-sensitive communications for real-time applications in ubiquitous computing environments. Real-Time Syst 26(1):29–61\nZhang D, Cao J, Zhou J, Guo M (2009) Extended Dempster–Shafer theory in context reasoning for ubiquitous computing environments. In: Proceedings of the 7th IEEE\u002FIFIP international conference on embedded and ubiquitous computing, pp 205–212\nZhang D, Guo M, Zhou J, Kang D, Cao J (2010) Context reasoning using extended evidence theory in pervasive computing environments. Future Gener Comput Syst 26(2):207–216\nZhang D, Zhou J, Guo M, Cao J, Li T (2011) Tasa: tag-free activity sensing using rfid tag arrays. 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compilers for high-level languages are increasingly recognised to be the key to reducing the productivity gap for advanced circuit development in general, and for reconfigurable designs in particular. 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A programmable ANSI C transformation engine. In Proc. Int. Conf. on Compiler Construction, LNCS 1575, Springer, 1999.\nCeloxica, http:\u002F\u002Fwww.celoxica.com\nJ. G. F. Coutinho and W. Luk. Source-directed transformations for hardware compilation. In Proc. Int. Conf. on Field-Programmable Technology, IEEE, 2003.\nJ. G. F. Coutinho, W. Luk, and M. Weinhardt. Optimizing parallel programs for hardware implementation. In Proc. SPIE ITCom, 2002.\nG. De Micheli. Synthesis and Optimization of Digital Circuits. McGraw-Hill, 1994.\nE. Gamma, R. Helm, R. Johnson, and J. Vlissides. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley, 1995.\nM. B. Gokhale et al. Stream-oriented FPGA computing in the Streams-C high level language. In IEEE Symp. on Field-Programmable Custom Computing Machines, IEEE Computer Society Press, 2000.\nS. Gupta et al. SPARK: A high-level synthesis framework for applying parallelizing compiler transformations. In Proc. Int. Conf. on VLSI Design, Jan. 2003.\nT. K. Lee et al. Compiling policy descriptions into reconfigurable firewall processors. In Proc. Symp. on Field-Programmable Custom Computing Machines, IEEE Computer Society Press, 2003.\nW. Luk and S. W. McKeever. Pebble: A language for parametrised and reconfigurable hardware design. Field-Programmable Logic and Applications, LNCS 1482, Springer, 1998.\nS. W. McKeever and W. Luk. Towards provably-correct hardware compilation tools based on pass separation techniques. Correct Hardware Design and Verification Methods, LNCS 2144, Springer, 2001.\nO. Mencer et al. Design space exploration with A Stream Compiler. In Proc. Int. Conf. on Field Programmable Technology, IEEE, 2003.\nS. Meyers. Effective C++, 2nd ed. Addison-Wesley, 1998.\nI. Page and W. Luk. Compiling occam into FPGAs. FPGAs, Abingdon EE&CS Books, 1991.\nN. Shirazi, W. Luk, and P. Y. K. Cheung. Framework and tools for run-time reconfigurable designs. 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