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Titlebook: Machine Intelligence for Research and Innovations; Proceedings of MAiTR Om Prakash Verma,Lipo Wang,Anupam Yadav Conference proceedings 2024

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書目名稱Machine Intelligence for Research and Innovations
副標(biāo)題Proceedings of MAiTR
編輯Om Prakash Verma,Lipo Wang,Anupam Yadav
視頻videohttp://file.papertrans.cn/621/620366/620366.mp4
概述Presents research works in the field of machine intelligence.Provides original works presented at MAiTRI 2023 held at Dr B R Ambedkar National Institute of Technology Jalandhar.Serves as a reference f
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: Machine Intelligence for Research and Innovations; Proceedings of MAiTR Om Prakash Verma,Lipo Wang,Anupam Yadav Conference proceedings 2024
描述.The book is a collection of high-quality peer-reviewed research papers presented in the First International Conference on Machine Intelligence for Research and Innovations?(MAiTRI 2023 Summit), held at Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India during 1 – 3 September 2023. This book focuses on recent advancement in the theory and realization of machine intelligence (MI) and their tools and growing applications such as machine learning, deep learning, quantum machine learning, real-time computer vision, pattern recognition, natural language processing, statistical modelling, autonomous vehicles, human interfaces, computational intelligence, and robotics..
出版日期Conference proceedings 2024
關(guān)鍵詞Machine and Deep Learning; Quantum Machine Learning; Computer Vision and Pattern Recognition; Natural L
版次1
doihttps://doi.org/10.1007/978-981-99-8135-9
isbn_softcover978-981-99-8134-2
isbn_ebook978-981-99-8135-9Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Unsupervised Clustering of Asphalt Pavement Conditions Using Fuzzy C-Means Algorithm with Principalapsulates pavements in poor condition. Principal component analysis, driven by PC1 and PC2, distinctly captures group patterns, explaining over 90% of data variability. PC1 scores facilitate the two-cluster classification of pavements. The fuzzy C-means algorithm assigns membership degrees to each d
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Prediction of Heart Disease Risk in Early Ages with Boosting Techniques, and boosting classifiers and found that boosting model achieved highest accuracy and the most accurate model as compared with single classifiers in predicting heart disease risk in youngers. So a type of ensemble model called boosting model is employed to prevent overfitting and to offer the highes
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