Titlebook: Big Data Intelligence and Computing; International Confer Ching-Hsien Hsu,Mengwei Xu,A. B. M. Shawkat Ali Conference proceedings 2023 The E

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書目名稱Big Data Intelligence and Computing影響因子(影響力)




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書目名稱Big Data Intelligence and Computing被引頻次學(xué)科排名




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Designing an AI-Driven Talent Intelligence Solution: Exploring Big Data to Extend the TOE Frameworkto identify the new requirements for developing AI-oriented artifacts to address talent management issues. Focusing on enhancing interactions between professional assessment and planning attributes, the design artifact is an intelligent employment automation solution for career guidance that is larg

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Development of Bilingual Chatbot for University Related FAQs Using Natural Language Processing and Ditions, the Universities are now offering online programmes to students. The admission process can be frustrating and time-consuming for both the students and the university. Students will have several queries and issues that need to be answered on the spot and clarified before the application proce

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Machine Learning Algorithms Performance Investigation in Fake News Detectiontaken the news industry by storm where traditionally, news media was transmitted using printed materials, radios and television, whereas over its existence it has become more effective and powerful with the use of the Internet. Social media is a very powerful tool in today’s era that is being used t

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Robust Graph Embedding Recommendation Against Data Poisoning Attacktems, matrix factoring is the most widely used technique. However, matrix factoring algorithms are very susceptible to shilling attacks (trust or espionage). The former defends methods against data poisoning attacks focused on detecting individual attack behaviors. But there are few detection method

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Privacy-Protective Distributed Machine Learning Between Rich Devices and?Edge Servers Using Confiden In most cases, a high-performance server aggregates and manages all data. However, users may refuse to pass on their personal information to an external server owing to the risk of information leakage. To address this concern, we propose a distributed machine learning model that does not extract an