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Science Citation Index Expanded",{"EN":70,"VI":71},"SCIE database","Cơ sở dữ liệu SCIE","scie",[74,75],"SCIE","ISI","https:\u002F\u002Fmjl.clarivate.com\u002Fsearch-results?issn=1383-469X",[78,79],"5e7ab733-08f6-4bda-8a2e-55af36fcbd05","9115cead-3791-48e5-afe4-132081f08acd",{"id":81,"indexDatabase":82,"url":92,"indexYears":93,"academicFieldIds":94,"indexDatabaseRanking":99},"47536177-e099-4676-b230-6042302b4b4d",{"id":83,"createTime":18,"updateTime":18,"relativeEntities":84,"label":85,"description":87,"key":89,"publicationTags":90,"standard":18},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9",[],{"EN":86,"VI":86},"Scopus - Elsevier",{"EN":86,"VI":88},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[91],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F27306","1996-2025",[95,96,97,98],"499fd762-4414-45c1-b2b3-cc8d2abe204b","d5abd5bd-04fc-4adb-ad3e-286daf3b4967","f8ab36fc-bc72-48f2-a425-9f3e042f2272","b4ee5f56-1514-428f-b517-37576cea076b","SCOPUS__Q1",{"impactFactor":19,"impactFactorByYear":101,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":103,"totalPublicationByYear":104,"totalCitation":103,"totalCitationByYear":107,"totalCitationPerPublication":105,"totalCitationPerPublicationByYear":108,"hindexLast5Year":110,"hindex":110},{"2021":102},0.25,5,{"2002":105,"2020":106},1,4,{"2020":103},{"2020":109},1.25,2,"JOURNAL",{"meta":113,"data":115},{"total":114},"1869",[116,214,393,643,1011,1191,1300,1454,1664,1759],{"id":117,"createTime":118,"updateTime":119,"relativeEntities":120,"slug":121,"properties":122,"entityType":132,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":133,"viewCount":19,"primaryUrl":135,"fullTextUrl":136,"authors":137,"publicationType":154,"publisherRelationship":155,"citationCount":18,"citationInfo":18,"publishDate":18,"publishYear":18,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":212,"openAccess":18,"references":18,"isForceReanalyzing":213},"630ea022-8ca7-40ce-b725-4e9d3fe05a74","2023-12-10T18:19:50.495+00:00","2026-09-07T10:16:29.747+00:00",[],"Design-and-Implementation-of-an-Educational-Information-Management-System-Using-Deep-Learning-and-Wireless-Communication",{"abstract":123,"title":125,"references":128,"doi":130},{"EN":124},"Wireless communication and Deep Learning can enhance the capabilities of an educational information management system by optimizing physical resources, improving student learning outcomes, and reducing costs. However, the current information asymmetry in educational information management makes providing convenient information services for schools challenging. Therefore, this paper proposes an educational information management system (EIMS) that integrates wireless communication and deep learning techniques to optimize physical resources, improve student learning outcomes, and reduce costs. The current information asymmetry in educational information management makes providing convenient information services for schools challenging. The suggested design of the EIMS consists of four components: terminals, subsystems, server and database, and school website. The client\u002Fserver and the browser\u002Fserver modes are adopted to cross and utilize them under the work features of educational information management. The functional design of the system consists of a management terminal, a teacher terminal, and login and registration. The management terminal includes seven module functions: personal management function, comprehensive query function, status statistics function, attendance management function, teaching resource management function, and basic data management function. The teacher terminal is mainly divided into two parts, one is to upload the video information of the teaching status of the course, and the other is the docking of the algorithm. The system database design consists of four modules: student status management, training management, degree management, and course selection management. In addition, the designed forms include student information, teacher personal information, class schedule, cultivation plan, and student attendance sheet. The suggested EIMS is experimentally tested and analyzed using MATLAB software to educational information management system under BP neural network. The experimental results show that the BP neural network has good applicability to the educational information management system and can accurately predict the trend of educational information management, providing a reference for further improvement.",{"EN":126,"VI":127},"Design and Implementation of an Educational Information Management System Using Deep Learning and Wireless Communication","Thiết kế và triển khai hệ thống quản lý thông tin giáo dục sử dụng học sâu và truyền thông không dây",{"VOID":129},"citation_journal_title=Mob Networks Appl; citation_title=An introduction to key technology in artificial intelligence and big data driven e-learning and e-education; citation_author=P Gao, J Li, S Liu; citation_volume=26; citation_issue=5; citation_publication_date=2021; citation_pages=2123-2126; citation_doi=10.1007\u002Fs11036-021-01777-7; citation_id=CR1\ncitation_journal_title=Telematics Inform; citation_title=Assessing the success behind the use of education management information systems in higher education; citation_author=J Martins, F Branco, R Gonçalves, M Au-Yong-Oliveira, T Oliveira, M Naranjo-Zolotov, F Cruz-Jesus; citation_volume=38; citation_publication_date=2019; citation_pages=182-193; citation_doi=10.1016\u002Fj.tele.2018.10.001; citation_id=CR2\ncitation_journal_title=Multimedia Tools and Applications; citation_title=Students’ affective content analysis in smart classroom environment using deep learning techniques; citation_author=SK Gupta, TS Ashwin, RMR Guddeti; citation_volume=78; citation_publication_date=2019; citation_pages=25321-25348; citation_doi=10.1007\u002Fs11042-019-7651-z; citation_id=CR3\ncitation_journal_title=Australasian J Educational Technol; citation_title=Sentiment evolution with interaction levels in blended learning environments: using learning analytics and epistemic network analysis; citation_author=C Huang, Z Han, M Li, X Wang, W Zhao; citation_volume=37; citation_issue=2; citation_publication_date=2021; citation_pages=81-95; citation_doi=10.14742\u002Fajet.6749; citation_id=CR4\nLv Z, Chen D, Lv H (2022) Smart City Construction and Management by Digital Twins and BIM Big Data in COVID-19 scenario. ACM Trans Multimedia Comput Commun Appl 18(2s). \n                https:\u002F\u002Fdoi.org\u002F10.1145\u002F3529395\n                \n              \ncitation_journal_title=IEEE Access; citation_title=UTiLearn: a personalised ubiquitous teaching and learning system for smart societies; citation_author=R Mehmood, F Alam, NN Albogami, I Katib, A Albeshri, SM Altowaijri; citation_volume=5; citation_publication_date=2017; citation_pages=2615-2635; citation_doi=10.1109\u002FACCESS.2017.2668840; citation_id=CR6\nMiao H, Li A, Davis LS, Deshpande A (2017) April. Towards unified data and lifecycle management for deep learning. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (pp. 571–582). IEEE\ncitation_journal_title=Open Educ; citation_title=Organization of distance learning in school, college, university; citation_author=NV Nikulicheva, OI Dyakova, OS Glukhovskaya; citation_volume=24; citation_issue=5; citation_publication_date=2020; citation_pages=4-17; citation_doi=10.21686\u002F1818-4243-2020-5-4-17; citation_id=CR8\ncitation_journal_title=CAAI Trans Intell Technol; citation_title=The early japanese books reorganization by combining image processing and deep learning; citation_author=B Lyu, H Li, A Tanaka, L Meng; citation_volume=7; citation_issue=4; citation_publication_date=2022; citation_pages=627-643; citation_doi=10.1049\u002Fcit2.12104; citation_id=CR9\nXiang H (2021) April. Design and function of teaching incentive mechanism of university teachers under the background of Big Data. In 2021 International Conference on Internet, Education and Information Technology (IEIT) (pp. 110–113). IEEE\nKaufhold MA, Bayer M, Hartung D, Reuter C (2021) Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter. In Artificial Neural Networks and Machine Learning–ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V 30 (pp. 396–408). Springer International Publishing\ncitation_journal_title=Front Psychol; citation_title=Design and implementation of intelligent sports training system for college students’ mental health education; citation_author=T Wang, J Park; citation_volume=12; citation_publication_date=2021; citation_pages=634978; citation_doi=10.3389\u002Ffpsyg.2021.634978; citation_id=CR12\ncitation_journal_title=J Comput Cogn Eng; citation_title=Research on microvideo character perception and recognition based on target detection technology; citation_author=Y Lei; citation_volume=1; citation_issue=2; citation_publication_date=2022; citation_pages=83-87; citation_id=CR13\nLv Z, Yu Z, Xie S, Alamri A (2022) Deep learning-based Smart Predictive evaluation for interactive Multimedia-Enabled Smart Healthcare. ACM Trans Multimedia Comput Commun Appl 18(1s). \n                https:\u002F\u002Fdoi.org\u002F10.1145\u002F3468506\n                \n              \ncitation_journal_title=CAAI Trans Intell Technol; citation_title=Analysis of community question-answering issues via machine learning and deep learning: State‐of‐the‐art review; citation_author=PK Roy, S Saumya, JP Singh, S Banerjee, A Gutub; citation_volume=8; citation_issue=1; citation_publication_date=2023; citation_pages=95-117; citation_doi=10.1049\u002Fcit2.12081; citation_id=CR15\ncitation_journal_title=Educ Inform Technol; citation_title=Factors affecting the usage of learning management systems in higher education; citation_author=P Kaewsaiha, S Chanchalor; citation_volume=26; citation_publication_date=2021; citation_pages=2919-2939; citation_doi=10.1007\u002Fs10639-020-10374-2; citation_id=CR16\ncitation_journal_title=IEEE Electron Device Lett; citation_title=60-GHz compact dual-mode on-chip bandpass filter using GaAs technology; citation_author=KD Xu, YJ Guo, Y Liu, X Deng, Q Chen, Z Ma; citation_volume=42; citation_issue=8; citation_publication_date=2021; citation_pages=1120-1123; citation_doi=10.1109\u002FLED.2021.3091277; citation_id=CR17\ncitation_journal_title=Mob Networks Appl; citation_title=Introduction of key problems in long-distance learning and training; citation_author=S Liu, Z Li, Y Zhang, X Cheng; citation_volume=24; citation_publication_date=2019; citation_pages=1-4; citation_doi=10.1007\u002Fs11036-018-1136-6; citation_id=CR18\ncitation_journal_title=Mob Networks Appl; citation_title=Application and exploration of artificial intelligence and edge computing in long-distance education on mobile network; citation_author=C Hou, L Hua, Y Lin, J Zhang, G Liu, Y Xiao; citation_volume=26; citation_publication_date=2021; citation_pages=2164-2175; citation_doi=10.1007\u002Fs11036-021-01773-x; citation_id=CR19\ncitation_journal_title=IEEE Trans Image Process; citation_title=Tcgl: temporal contrastive graph for self-supervised video representation learning; citation_author=Y Liu, K Wang, L Liu, H Lan, L Lin; citation_volume=31; citation_publication_date=2022; citation_pages=1978-1993; citation_doi=10.1109\u002FTIP.2022.3147032; citation_id=CR20\ncitation_journal_title=Int Arab J Inform Technol; citation_title=Rating the Crisis of Online Public Opinion using a Multi-Level Index System; citation_author=F Meng, X Xiao, J Wang; citation_volume=19; citation_issue=4; citation_publication_date=2022; citation_pages=597-608; citation_doi=10.34028\u002Fiajit\u002F19\u002F4\u002F4; citation_id=CR21\ncitation_journal_title=Comput Educ; citation_title=The influence of digital educational games on preschool children’s creative thinking; citation_author=Z Xiong, Q Liu, X Huang; citation_volume=189; citation_publication_date=2022; citation_pages=104578; citation_doi=10.1016\u002Fj.compedu.2022.104578; citation_id=CR22\nCao, K., Wang, B., Ding, H., Lv, L., Tian, J., Hu, H.,... Gong, F. (2021). Achieving Reliable and Secure Communications in Wireless-Powered NOMA Systems. IEEE transactions on vehicular technology, 70(2), 1978–1983. doi: \n                https:\u002F\u002Fdoi.org\u002F10.1109\u002FTVT.2021.3053093\n                \n              ",{"VOID":131},"10.1007\u002Fs11036-023-02171-1","PUBLICATION",[134],"VI","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11036-023-02171-1","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs11036-023-02171-1.pdf",[138],{"id":139,"sortIndex":19,"researcher":18,"roles":140,"affiliations":142,"properties":151,"displayName":153,"givenName":18,"familyName":18},"5c2a774d-69a9-4e15-b90d-4138161b0c27",[141],"AUTHOR",[143],{"id":144,"sortIndex":19,"affiliation":145,"properties":18},"3b897a97-1d72-4658-add4-820d8d81c7c7",{"id":144,"createTime":18,"updateTime":18,"relativeEntities":146,"slug":18,"properties":147,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":150,"statistic":18},[],{"title":148},{"VI":149},"School of Education of Central, China Normal University, Hubei, China",[],{"title":152},{"VI":153},"Wangang, Cai","ARTICLE",{"url":135,"publisher":156,"properties":209},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":157,"slug":10,"properties":158,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":161,"manageAffiliations":178,"indexDatabases":189,"url":18,"thumbnailPath":18,"statistic":204,"gsStatistic":18,"type":111,"analyzePriority":18},[],{"issn":159,"title":160},{"VOID":13},{"VOID":15},[162,166,170,174],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":163,"label":164,"description":165,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":167,"label":168,"description":169,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},{"id":34,"createTime":18,"updateTime":18,"relativeEntities":171,"label":172,"description":173,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":37},{},{"id":40,"createTime":18,"updateTime":18,"relativeEntities":175,"label":176,"description":177,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":43},{},[179,184],{"id":47,"createTime":18,"updateTime":18,"relativeEntities":180,"slug":18,"properties":181,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":183,"statistic":18},[],{"title":182},{"EN":51},[],{"id":54,"createTime":18,"updateTime":18,"relativeEntities":185,"slug":18,"properties":186,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":188,"statistic":18},[],{"title":187},{"EN":58},[60],[190,197],{"id":63,"indexDatabase":191,"url":76,"indexYears":18,"academicFieldIds":196,"indexDatabaseRanking":18},{"id":65,"createTime":18,"updateTime":18,"relativeEntities":192,"label":193,"description":194,"key":72,"publicationTags":195,"standard":18},[],{"EN":68,"VI":68},{"EN":70,"VI":71},[74,75],[78,79],{"id":81,"indexDatabase":198,"url":92,"indexYears":93,"academicFieldIds":203,"indexDatabaseRanking":99},{"id":83,"createTime":18,"updateTime":18,"relativeEntities":199,"label":200,"description":201,"key":89,"publicationTags":202,"standard":18},[],{"EN":86,"VI":86},{"EN":86,"VI":88},[91],[95,96,97,98],{"impactFactor":19,"impactFactorByYear":205,"i10Index":19,"i10IndexLast5Year":19,"totalPublication":103,"totalPublicationByYear":206,"totalCitation":103,"totalCitationByYear":207,"totalCitationPerPublication":105,"totalCitationPerPublicationByYear":208,"hindexLast5Year":110,"hindex":110},{"2021":102},{"2002":105,"2020":106},{"2020":103},{"2020":109},{"pages":210},{"VOID":211},"1-11",[],false,{"id":215,"createTime":216,"updateTime":217,"relativeEntities":218,"slug":219,"properties":220,"entityType":132,"verifyStatus":231,"verifyTime":232,"verifyNote":233,"languages":18,"translateLanguages":18,"viewCount":110,"primaryUrl":234,"fullTextUrl":18,"authors":235,"publicationType":154,"publisherRelationship":327,"citationCount":19,"citationInfo":386,"publishDate":389,"publishYear":387,"citationAnalyzeStatus":390,"lastCitationAnalyze":391,"indexDatabases":392,"openAccess":18,"references":18,"isForceReanalyzing":213},"3400176d-02b8-4822-8103-95536ad3cc75","2023-12-04T03:23:32.077+00:00","2026-08-19T22:57:16.654+00:00",[],"Occlusion-Aware-Detection-for-Internet-of-Vehicles-in-Urban-Traffic-Sensing-Systems",{"abstract":221,"title":223,"gsPaper":225,"references":227,"doi":229},{"EN":222},"Vehicle detection is a fundamental challenge in urban traffic surveillance video. Due to the powerful representation ability of convolution neural network (CNN), CNN-based detection approaches have achieve incredible success on generic object detection. However, they can’t deal well with vehicle occlusion in complex urban traffic scene. In this paper, we present a new occlusion-aware vehicle detection CNN framework, which is an effective and efficient framework for vehicle detection. First, we concatenate the low-level and high-level feature maps to capture more robust feature representation, then we fuse the local and global feature maps for handling vehicle occlusion, the context information is also been adopted in our framework. Extensive experiments demonstrate the competitive performance of our proposed framework. Our methods achieve better effect than primal Faster R-CNN in terms of accuracy on a new urban traffic surveillance dataset (UTSD) which contains a mass of occlusion vehicles and complex scenes.",{"EN":224},"Occlusion-Aware Detection for Internet of Vehicles in Urban Traffic Sensing Systems",{"VOID":226},"[\"17905004834368935727\"]",{"VOID":228},"Hu X et al (2018) SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. arXiv 1:1–10\nChen L, Ye F, Ruan Y, Fan H, Chen Q (2018) An algorithm for highway vehicle detection based on convolutional neural network. EURASIP J. Image Video Process 2018(1):109\nFan H, Zhu H (2018) Separation of vehicle detection area using Fourier descriptor under internet of things monitoring. IEEE Access 6:47600–47609\nZhu H, Fan H, Ye F, Zhu S, Gan P (2016) A novel method for moving vehicle tracking based on horizontal edge identification and local autocorrelation images. 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In: Proceedings of the IEEE International Conference on Computer Vision, vol 2015 Inter, pp 1440–1448\nHe K, Zhang X, Ren S, Sun J (2015) Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Trans Pattern Anal Mach Intell 37(9)\nShelhamer E, Long J, Darrell T (2014) Fully Convolutional Networks for Semantic Segmentation IEEE Trans Pattern Anal Mach Intell (99):1\nAlonso Martínez A, Del Valle Soto M, Cecchini Estrada JA, Izquierdo M (2003) Asociación de la condicion física saludable y los indicadores del estado de salud (II). Arch Med del Deport 20(97):405–415\nRen S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Nips\nLiu W et al (2016) SSD: single shot MultiBox detector. In: European conference on computer vision\nRedmon J, Divvala S, Girshick R, Farhadi A (2015) You only look once: unified, real-time object detection\nZhang H, Wang K, Tian Y, Gou C, Wang FY (2018) MFR-CNN: incorporating multi-scale features and global information for traffic object detection. IEEE Trans Veh Technol 67(9):8019–8030\nWang L, Lu Y, Wang H, Zheng Y, Ye H, Xue X (2017) Evolving boxes for fast vehicle detection. In: Multimedia and Expo (ICME), 2017 IEEE International Conference on, pp 1135–1140\nSingh B, Li H, Sharma A, Davis LS (2017) R-FCN-3000 at 30fps: decoupling detection and classification\nGidaris S, Komodakis N (2015) Object Detection via a Multi-Region and Semantic Segmentation-Aware CNN Model. Iccv (1):1134–1142\nKong T, Yao A, Chen Y, Sun F (2016) HyperNet: towards accurate region proposal generation and joint object detection\nGüzel MS, Askerbeylİ İ (2018) A vehicle detection approach using deep learning methodologies\nZhang S, Wen L, Bian X, Lei Z, Li SZ (2017) Single-shot refinement neural network for object detection\nDalal N, Triggs B (2005) Histograms of Oriented Gradients for Human Detection. In: IEEE Computer Society Conference on Computer Vision & Pattern Recognition, pp 886–893\nLowe DG (2004) Distinctive Image Features from Scale-Invariant Keypoints. In: International journal of computer vision, pp 91–110\nLin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection\nUijlings JRR, Van De Sande KEA, Gevers T, Smeulders AWM (2013), “Selective search for object recognition. Int J Comput Vis 104(2)\nZitnick CL, Dollár P, Doll P (2014) Edge Boxes:Locating Object Proposals from Edges. Eur Conf Comput Vis 8693 LNCS(PART 5):1–15\nEveringham M, Eslami SMA, Van Gool L, Williams CKI, Winn J, Zisserman A (2015) The Pascal visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98–136\nLin T-Y et al (2014) Microsoft COCO: Common Objects in Context. In: Computer Vision -- ECCV 2014, pp 740–755\nDai J, Li Y, He K, Sun J (2016) R-FCN: object detection via region-based fully convolutional networks\nRussakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. 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IEEE, pp 402–411",{"doi":1010},"10.1109\u002FICSE.2013.6606586",{"id":1012,"createTime":1013,"updateTime":1014,"relativeEntities":1015,"slug":1016,"properties":1017,"entityType":132,"verifyStatus":231,"verifyTime":1028,"verifyNote":233,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1029,"fullTextUrl":18,"authors":1030,"publicationType":154,"publisherRelationship":1124,"citationCount":1182,"citationInfo":1183,"publishDate":1187,"publishYear":572,"citationAnalyzeStatus":1188,"lastCitationAnalyze":1189,"indexDatabases":1190,"openAccess":18,"references":18,"isForceReanalyzing":213},"ff31f632-cab5-4b4a-8158-769bba9a9baf","2024-02-16T08:58:25.723+00:00","2026-07-25T13:05:37.805+00:00",[],"Application-and-Exploration-of-Artificial-Intelligence-and-Edge-Computing-in-Long-Distance-Education-on-Mobile-Network",{"abstract":1018,"title":1020,"gsPaper":1022,"references":1024,"doi":1026},{"EN":1019},"In response to the demand for high-quality electronic information talents in the mobile network industry, in the situation of artificial intelligence (AI) to promote technological innovation, this paper conducts an overall design in the target system, curriculum system, teaching platform, teaching mode and teaching case. The practice education mode of teaching practice, engineering practice, innovation practice, and enterprise practice, which aims to improve students’ ability to solve complex engineering problems, is constructed. The mode breaks geographical boundaries between schools and enterprises to build the through-through experimental teaching course system based on artificial intelligence and edge computing and design a medical image intelligent analysis system project case based on Mobile Edge Computing (MEC), which improves students’ practical ability, engineering design ability, scientific research innovation ability, enterprise practice ability and mobile network application capabilities. At the same time, the hardware portability of the edge computing platform provides good conditions for long-distance education and the mobile network. This method is a beneficial attempt to cultivate high-level, diversified, and creative electronic information talents.",{"EN":1021},"Application and Exploration of Artificial Intelligence and Edge Computing in Long-Distance Education on Mobile Network",{"VOID":1023},"[\"12764459253076197107\"]",{"VOID":1025},"Mshvidobadze T (2012) Evolution mobile wireless communication and LTE networks. 2012 6th international conference on application of information and communication technologies (AICT). IEEE, 1-7\nKandampully J (2003) B2B relationships and networks in the internet age. Manag Decis 41(5):443–451\nZou P, Wang C, Liu Z (2010) A cloud based SIM DRM scheme for the mobile internet. Proceedings of the 17th ACM conference on Computer and communications security, pp:759–761\nRM S P, Bhattacharya S, Maddikunta P K R (2020) Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything. Journal of Parallel and Distributed Computing 142:16–26\nLiu S, Guo C, Al-Turjman F (2020) Reliability of response region: a novel mechanism in visual tracking by edge computing for IIoT environments. Mech Syst Signal Process 138:106537.1–106537.15\nLin Y, Tu Y, Dou Z (2020) An improved neural network pruning Technology for Automatic Modulation Classification in edge devices. IEEE Trans Veh Technol 69(5):5703–5706\nSkouby K E, Lynggaard P (2014) Smart home and smart city solutions enabled by 5G, IoT, AAI and CoT services. 2014 international conference on contemporary computing and informatics (IC3I). IEEE, 874-878\nAfzal MK, Zikria YB, Mumtaz S (2018) Unlocking 5G spectrum potential for intelligent IoT: opportunities, challenges, and solutions. IEEE Commun Mag 56(10):92–93\nZhang C, Sun Y, Zhang X (2020) Research on Experimental Teaching Reform of Electronic Technology Course. International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. pp:324–333\nSandgren DL (1956) Does practice teaching change attitudes toward teaching? J Educ Res 49(9):673–680\nLin Y, Wang S, Wu Q (2019) Key technologies and solutions of remote distributed virtual laboratory for E-learning and E-education. Mobile Networks and Applications 24(1):18–24\nGong W, Tong L, Huang W (2018) The optimization of intelligent long-distance multimedia sports teaching system for IOT. Cogn Syst Res 52:678–684\nKaliannan M, Chandran SD (2012) Empowering students through outcome-based education (OBE). Res Educ 87(1):50–63\nGu PH, Hu WL, Lin P (2014) OBE engineering education model in Shantou University. Research in Higher Education of Engineering 1:27–37\nKathail V, Hwang J, Sun W (2016) SDSoC: a higher-level programming environment for Zynq SoC and Ultrascale+ MPSoC. Proceedings of the 2016 ACM\u002FSIGDA international symposium on field-programmable gate arrays 2016: 4-4\nRao A R, Clarke D, Bhdiyadra M (2018) Development of an embedded system course to teach the internet-of-things. 2018 IEEE integrated STEM education conference (ISEC). IEEE, 154-160\nLiu S, Bai W, Liu G (2018) Parallel fractal compression method for big video data. Complexity 2016976\nJain AK, Pham KD, Cui J (2014) Virtualized execution and management of hardware tasks on a hybrid ARM-FPGA platform. Journal of Signal Processing Systems 77(1–2):61–76\nLiu S, Wang S, Liu X (2020) Fuzzy detection aided real-time and robust visual tracking under complex environments. IEEE transactions on fuzzy systems, pp(99):1-1\nRehman B, Ong WH, Tan ACH (2020) Face detection and tracking using hybrid margin-based ROI techniques. Vis Comput 36(3):633–647\nMliki H, Hammami M (2018) Face analysis in video: face detection and tracking with pose estimation. International Journal of Biometrics 10(2):121\nAhlawat S, Choudhary A, Nayyar A (2020) Improved handwritten digit recognition using convolutional neural networks (CNN). Sensors 20(12):334\nLin Y, Wang M, Zhou X, Ding G, Mao S (2020) Dynamic Spectrum interaction of UAV flight formation communication with priority: a deep reinforcement learning approach. IEEE Transactions on Cognitive Communications and Networking 6(3):892–903\nHe K, Zhang X, Ren S (2016) Deep residual learning for image recognition. Computer Vision & Pattern Recognition, pp:770–778",{"VOID":1027},"10.1007\u002Fs11036-021-01773-x","2024-06-25T00:10:33.836+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11036-021-01773-x",[1031,1055,1068,1083,1096,1111],{"id":1032,"sortIndex":19,"researcher":18,"roles":1033,"affiliations":1034,"properties":1052,"displayName":1054,"givenName":18,"familyName":18},"5fd721c2-a4af-43ba-a241-8a76bd4c45c8",[141],[1035,1043],{"id":1036,"sortIndex":19,"affiliation":1037,"properties":18},"98cf18ad-a429-4af2-907c-cccec694dfef",{"id":1036,"createTime":18,"updateTime":18,"relativeEntities":1038,"slug":18,"properties":1039,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1042,"statistic":18},[],{"title":1040},{"VI":1041},"College of Information and Communication Engineering, Harbin Engineering University, Harbin, China",[],{"id":1044,"sortIndex":105,"affiliation":1045,"properties":1051},"e4f0db44-805b-4062-9701-4ba04e0d8b3d",{"id":1044,"createTime":18,"updateTime":18,"relativeEntities":1046,"slug":18,"properties":1047,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1050,"statistic":18},[],{"title":1048},{"EN":1049},"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, China",[],{},{"title":1053},{"VI":1054},"Changbo 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paper presents a simple network layer protocol that integrates routing and connectionless transfer of data in a wireless environment. The protocol is specifically geared towards supporting transfer of signalling in mobile networks based on a rooted tree topology. Exploiting the special characteristics of such a topology allows the specification of a very simple and processing efficient routing function. Using the routing function, a connectionless message transport service is implemented. The connectionless transport service is comparable to that of typical network layer protocols of existing data networks. The protocol has originally been specified to carry signalling messages in the control plane of mobile, cellular systems but has the potential to be used also in other environments.",{"EN":1201},"A simple and efficient routing protocol for the UMTSA Access network",{"VOID":657},{"VOID":1204},"E. Buitenwerf et al., UMTS: Fixed network issues and design options, IEEE Personal Commun. Mag. 1 (1995) 30–37.\nM.S. Carson and A. Ephremides, A distributed routing algorithm for mobile wireless networks, Wireless Networks 1 (1995), 61–81.\nCEC deliverable R2066\u002FVTT\u002FGA4\u002FDS\u002FP\u002F065\u002Fb1, evaluation of supporting protocol stacks (1994).\nCCITT Recommendation Q.711, Functional description of the signalling connection control part of signalling system No. 7 (CCITT, Geneva, 1989).\nCCITT Recommendations Q.711–Q.775, Transaction capabilities application part for signalling system No. 7 (CCITT, Geneva, 1992).\nR. Ghai, S. Singh, An architecture and communication protocol for picocellular networks, IEEE Personal Commun. 3 (1994) 36–46.\nISO IS-8473, Information processing systems — Data communication — Protocol for providing the connectionlessmode network service and provision of underlying service.\nISO IS-10589, Information Technology—Telecommunications and information exchange between systems—Intermediate system to intermediate system intradomain routing information exchange protocol for use in conjunction with the Protocol for providing the connectionless-mode network service (IS 8473).\nK. Keeton et al., Providing connection-oriented network services to mobile hosts, in:USENIX symposium on Mobile and Location-Independent Computing, Cambridge, Mass. (1993).\nA.R. Modarressi and R.A. Skoog, Signalling system No. 7: A tutorial, IEEE Commun. Mag. 7 (1990) 19–35.\nJ. Moy, OSPF Version 2, RFC 1247 (1991).\nC. Perkins, IP mobility support, IETF Internet Draft (1995).\nJ. Postel, Internet Protocol, Protocol Specification, RFC791 (1981).\nR. Tanaka and M. Tsukamoto, A CLNP-based protocol for end systems within an area,Proc. Int. Conf. on Network Protocols (IEEE, Los Alamitos, California, 1993) pp. 64–71.\nA. Urie et al., An advanced TDMA mobile access system for UMTS, IEEE Personal Commun. Mag. 1 (1995) 38–47.",{"VOID":1206},"10.1007\u002FBF01193335","2024-06-25T16:07:13.934+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF01193335",[1210,1225],{"id":1211,"sortIndex":19,"researcher":18,"roles":1212,"affiliations":1213,"properties":1222,"displayName":1224,"givenName":18,"familyName":18},"80d53b22-a30a-4a9d-bf0a-027edddbe5af",[141],[1214],{"id":1215,"sortIndex":19,"affiliation":1216,"properties":18},"20f7cfd2-cac8-415e-8676-110bc7061ecb",{"id":1215,"createTime":18,"updateTime":18,"relativeEntities":1217,"slug":18,"properties":1218,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1221,"statistic":18},[],{"title":1219},{"VI":1220},"VTT Information Technology, Finland",[],{"title":1223},{"VI":1224},"Håkan 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System is a hot topic of Artificial Intelligence, and it is extensively used to complete some tasks among different agents. While voting is often used for this purpose because it aggregates individual preferences into a collective decision. However, the winner determination problem has seriously hindered the development of the voting theory, then we innovatively introduce the concept of “satisfaction degree” to solve the problem. In this paper, we propose a formula for agents to express satisfaction degree of candidates, which we call Social Satisfaction Degree (SSD). To find the winners from candidates, we then design Single-winner Determination Algorithm (SWDA) and Multi-winner Determination Algorithm (MWDA) for single-winner and multi-winner based on SSD, respectively. The empirical results from the PrefLib data set show that our new algorithms can produce the winner set with optimal SSD more accurately than other voting rules.",{"EN":1310},"A Voting Aggregation Algorithm for Optimal Social Satisfaction",{"VOID":657},{"VOID":1313},"Lu H, Li Y, Mu S et al (2017) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning[J]. IEEE Internet Things J PP(99):1–1\nLu H, Li B, Zhu J et al (2016) Wound intensity correction and segmentation with convolutional neural networks[J]. Concurr Comput Pract Exper 29(6):1–10\nGuo H, Cao L (2011) Improvement of Borda voting method in meta search engine [J]. Comput Eng 37 (1):81–83\nXia LR (2013) Introduction of computational social choice theory [J]. Commun CCF 9(10):8–14\nEndriss U (2014) Social choice theory as a foundation for multi-agent systems[M]. Springer International Publishing, Berlin, pp 1–6\nPitt J, Kamara L, Sergot M et al (2006) Voting in multi-agent systems[J]. Comput J 49(2):156–170\nMao A, Procaccia AD, Chen Y (2013) Better human computation through principled voting[C]. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence. AAAI Press, 2013:1142–1148\nNurmi H (1987) Comparing voting systems[M]. Springer Netherlands 3(4393):187–189\nSoufiani HA, Parkes DC, Xia L (2012) Random utility theory for social choice[J]. Adv Neural Inf Process Syst 1:126–134\nBartholdi JJ III, Orlin JB (1991) Single transferable vote resists strategic voting. Soc Choice Welfare 8 (4):341– 354\nLagerspetz E (2016) Plurality, approval, or Borda? A nineteenth century dispute on voting rules[J]. Publ Choice 168 (3-4):1–13\nRichie R (2004) Instant runoff voting: what mexico (and others) could learn. Elect Law J 3:501C512\nBrams SJ, Kilgour DM (2014) Satisfaction Approval Voting. In: Fara R., Leech D., Salles M. (eds) Voting Power and Procedures. Studies in Choice and Welfare. Springer, Cham. https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-05158-1_18\nChen J (2015) The game theory of the classic voting model-theory and practice of Borda Count [J]. Admin Forum 4:75– 79\nAziz H, Walsh T (2015) Algorithms for two variants of Satisfaction Approval Voting. CoRR arXiv:1501.02144\nFalmagne JC, Regenwetter M (1996) A random utility model for approval voting[J]. J Math Psychol 40 (2):152– 159\nSingh SP (2014) Not all election winners are equal: satisfaction with democracy and the nature of the vote[J]. 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the development of social information technology and the increasing of information data in big data era, how to query the required data accurately is becoming more and more important, the purpose of this paper is to establish a model of data mining technology. In this paper, we use the Bayesian network learning model to study the data mining technology. In this paper, a Bayesian network learning model is established, then, the parameters of the recognition and the selection of coefficients are analyzed in detail, after that, the data mining model based on Bayesian computation is deduced, and the reliability of the model is verified by the example of the students. The probability distribution pattern used by Bayes has many advantages in data mining. It further proves the applicability of Bayesian formula, and provides a reference for data mining technology.",{"EN":1464},"Research and Citation Analysis of Data Mining Technology Based on Bayes 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(2014) Machine learning approach for text and document mining[J]. arXiv preprint arXiv:1406.1580",{"doi":582},{"id":1629,"text":1630,"url":1631,"identifiers":1632},"73248066-8300-4db2-b4ae-f1c52db410c9","Xing W, Guo R, Petakovic E et al (2015) Participation-based student final performance prediction model through interpretable genetic programming: integrating learning analytics, educational data mining and theory. Comput Hum Behav 47:168–181","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0747563214004865",{"doi":1633},"10.1016\u002Fj.chb.2014.09.034",{"id":578,"text":1635,"url":580,"identifiers":1636},"Okazaki S, Díaz-Martín AM, Rozano M et al (2015) Using twitter to engage with customers: a data mining approach. Internet Res 25(3):1066–2243",{"doi":582},{"id":578,"text":1638,"url":580,"identifiers":1639},"Bounhas M, Hamed MG, Prade H et al (2014) Naive possibilistic classifiers for imprecise or uncertain numerical data. 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These findings could be useful for water shortage assessment and allocation planning in this area in the climate change context in the Dong Nai River Basin.",{"EN":1769},"Meteorological and Hydrological Drought Assessment for Dong Nai River Basin, Vietnam under Climate Change",{"VOID":1771},"[\"9259577461029594317\"]",{"VOID":1773},"10.1007\u002Fs11036-021-01757-x","2024-05-02T23:57:13.929+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11036-021-01757-x",[1777,1792,1809,1824,1837,1852,1867,1882,1896],{"id":1778,"sortIndex":19,"researcher":18,"roles":1779,"affiliations":1780,"properties":1789,"displayName":1791,"givenName":18,"familyName":18},"03de8e9c-6594-4d0a-8a34-13eb159cc1f6",[141],[1781],{"id":1782,"sortIndex":19,"affiliation":1783,"properties":18},"229ce06c-6c6d-4166-8675-45eab2b8ba45",{"id":1782,"createTime":18,"updateTime":18,"relativeEntities":1784,"slug":18,"properties":1785,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1788,"statistic":18},[],{"title":1786},{"VI":1787},"Department of Natural Resources and Environment, Ho Chi Minh City, Vietnam",[],{"title":1790},{"VI":1791},"Vu Thuy Linh",{"id":1793,"sortIndex":105,"researcher":18,"roles":1794,"affiliations":1795,"properties":1804,"displayName":1806,"givenName":18,"familyName":18},"65b9aaf7-9ca6-46f5-93ab-05e3d0297dca",[141],[1796],{"id":1797,"sortIndex":19,"affiliation":1798,"properties":18},"00c3f2ca-0bcf-41c2-882c-71b5e3f680e9",{"id":1797,"createTime":18,"updateTime":18,"relativeEntities":1799,"slug":18,"properties":1800,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1803,"statistic":18},[],{"title":1801},{"EN":1802},"Research Center for Climate Change, Nong Lam University Ho Chi Minh City, Ho Chi Minh City, Vietnam",[],{"title":1805,"gsAuthor":1807},{"VI":1806},"Vo Ngoc Quynh Tram",{"VOID":1808},"[\"_NGM1CUAAAAJ\"]",{"id":1810,"sortIndex":110,"researcher":18,"roles":1811,"affiliations":1812,"properties":1821,"displayName":1823,"givenName":18,"familyName":18},"77aa9160-ea59-487e-af43-0d076c341528",[141],[1813],{"id":1814,"sortIndex":19,"affiliation":1815,"properties":18},"2762041b-e010-47a3-a570-94d13f0e5186",{"id":1814,"createTime":18,"updateTime":18,"relativeEntities":1816,"slug":18,"properties":1817,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1820,"statistic":18},[],{"title":1818},{"EN":1819},"Institute of Computational Science and Technology, Ho Chi Minh City, Vietnam",[],{"title":1822},{"VI":1823},"Ho Minh Dung",{"id":1825,"sortIndex":289,"researcher":18,"roles":1826,"affiliations":1827,"properties":1834,"displayName":1836,"givenName":18,"familyName":18},"6c7cb527-63fb-4ea0-aa1b-42703977f8c8",[141],[1828],{"id":1797,"sortIndex":19,"affiliation":1829,"properties":18},{"id":1797,"createTime":18,"updateTime":18,"relativeEntities":1830,"slug":18,"properties":1831,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1833,"statistic":18},[],{"title":1832},{"EN":1802},[],{"title":1835},{"VI":1836},"Dang Nguyen Dong Phuong",{"id":1838,"sortIndex":106,"researcher":18,"roles":1839,"affiliations":1840,"properties":1849,"displayName":1851,"givenName":18,"familyName":18},"64f3adc5-3ced-4417-8633-0fde98b1992c",[141],[1841],{"id":1842,"sortIndex":19,"affiliation":1843,"properties":18},"8e76de75-fd05-4ae7-95b7-28119b28b87a",{"id":1842,"createTime":18,"updateTime":18,"relativeEntities":1844,"slug":18,"properties":1845,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1848,"statistic":18},[],{"title":1846},{"VI":1847},"Faculty of Environment and Natural Resources, Nong Lam University Ho Chi Minh City, Ho Chi Minh City, Vietnam",[],{"title":1850},{"VI":1851},"Nguyen Duy Liem",{"id":1853,"sortIndex":103,"researcher":18,"roles":1854,"affiliations":1855,"properties":1864,"displayName":1866,"givenName":18,"familyName":18},"1af62cd6-42e9-40e3-b1c3-c2244d4de237",[141],[1856],{"id":1857,"sortIndex":19,"affiliation":1858,"properties":18},"be7e7eb4-77c7-402e-8897-37983a5934b9",{"id":1857,"createTime":18,"updateTime":18,"relativeEntities":1859,"slug":18,"properties":1860,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1863,"statistic":18},[],{"title":1861},{"VI":1862},"Institute of Research and Development, Duy Tan University, Da Nang City, Vietnam",[],{"title":1865},{"VI":1866},"Long D. 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