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Titlebook: Advances in Machine Learning/Deep Learning-based Technologies; Selected Papers in H George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain Book

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发表于 2025-3-21 19:03:49 | 显示全部楼层 |阅读模式
期刊全称Advances in Machine Learning/Deep Learning-based Technologies
期刊简称Selected Papers in H
影响因子2023George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain
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发行地址Presents recent research on Machine Learning/Deep Learning-based Technologies,.Presents Selected Papers in Honour of Professor Nikolaos G. Bourbakis – Vol. 2.Written by experts in the field
学科分类Learning and Analytics in Intelligent Systems
图书封面Titlebook: Advances in Machine Learning/Deep Learning-based Technologies; Selected Papers in H George A. Tsihrintzis,Maria Virvou,Lakhmi C. Jain Book
影响因子.As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “.Society 5.0.”, the field of .Machine Learning. (and its sub-field of .Deep Learning.) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society... ..The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for
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Advances in Machine Learning/Deep Learning-based Technologies978-3-030-76794-5Series ISSN 2662-3447 Series E-ISSN 2662-3455
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Energy Efficiency Projects that Move Slowly, on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent a
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A Representative Energy Efficiency Project,r equipment damages. Such a task becomes even harder for universities offering distance education, as their students visit less often their lab facilities and may have limited opportunities to become familiar with the respective instruments and equipment. For this purpose, the Hellenic Open Universi
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https://doi.org/10.1007/978-1-4471-4516-5from educational data and enhancing the quality of learning. Predicting student learning outcomes is one of the most significant problems facing these fields. Addressing effectively a predictive problem comprises the training of a supervised learning algorithm on a given set of labeled data. The dif
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A Representative Energy Efficiency Project,hnologies that can detect hidden nuclear material before its use. The process of detecting and identifying nuclear materials for non-reported purposes is under the umbrella of nuclear security. Among several areas that contribute to the security and safeguards of nuclear materials, radiation data an
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