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Titlebook: Cognitive Science and Artificial Intelligence; Advances and Applica Sasikumar Gurumoorthy,Bangole Narendra Kumar Rao,X Book 2018 The Author

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发表于 2025-3-21 19:06:41 | 显示全部楼层 |阅读模式
书目名称Cognitive Science and Artificial Intelligence
副标题Advances and Applica
编辑Sasikumar Gurumoorthy,Bangole Narendra Kumar Rao,X
视频videohttp://file.papertrans.cn/230/229112/229112.mp4
概述Includes supplementary material:
丛书名称SpringerBriefs in Applied Sciences and Technology
图书封面Titlebook: Cognitive Science and Artificial Intelligence; Advances and Applica Sasikumar Gurumoorthy,Bangole Narendra Kumar Rao,X Book 2018 The Author
描述.This book presents interdisciplinary research on cognition, mind and behavior from an information processing perspective. It includes chapters on Artificial Intelligence, Decision Support Systems, Machine Learning, Data Mining and Support Vector Machines, chiefly with regard to the data obtained and analyzed in Medical Informatics, Bioinformatics and related disciplines. The book reflects the state-of-the-art in Artificial Intelligence and Cognitive Science, and covers theory, algorithms, numerical simulation, error and uncertainty analysis, as well novel applications of new processing techniques in Biomedical Informatics, Computer Science and its applied areas. As such, it offers a valuable resource for students and researchers from the fields of Computer Science and Engineering in Medicine and Biology..
出版日期Book 2018
关键词Deep Learning; Data Mining; Sentiment Analysis; Machine Learning; Neural Networks; Brain-based Interactio
版次1
doihttps://doi.org/10.1007/978-981-10-6698-6
isbn_softcover978-981-10-6697-9
isbn_ebook978-981-10-6698-6Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
copyrightThe Author(s) 2018
The information of publication is updating

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Lecture Notes in Computer Sciencea environment named Machine Learning based Ensemble Analytic Approach (MLEAA) consists of two phases, namely learning phase and prediction phase. In learning phase data’s are processed by map reduce framework in hadoop and the featured attributes are working towards prediction phase. The proposed ML
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Paulo Oliveira,Kleber Padovani,Ronnie Alves the feature set to develop prediction models to extract the emotion information carried by the participant from emotional characteristics exhibited in different frequency bands. These models are evaluated on the dataset and emotions are classified using ANN into three different states such as posit
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https://doi.org/10.1007/978-981-10-6698-6Deep Learning; Data Mining; Sentiment Analysis; Machine Learning; Neural Networks; Brain-based Interactio
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Cognitive Science and Artificial Intelligence978-981-10-6698-6Series ISSN 2191-530X Series E-ISSN 2191-5318
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