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Titlebook: Computational Intelligence in Pattern Recognition; Proceedings of CIPR Asit Kumar Das,Janmenjoy Nayak,Danilo Pelusi Conference proceedings

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書(shū)目名稱Computational Intelligence in Pattern Recognition
副標(biāo)題Proceedings of CIPR
編輯Asit Kumar Das,Janmenjoy Nayak,Danilo Pelusi
視頻videohttp://file.papertrans.cn/233/232527/232527.mp4
概述Provides original works presented at CIPR 2023 held in Kolkata, India.Presents research works in computational intelligence in pattern recognition.Serves as a reference for researchers and practitione
叢書(shū)名稱Lecture Notes in Networks and Systems
圖書(shū)封面Titlebook: Computational Intelligence in Pattern Recognition; Proceedings of CIPR  Asit Kumar Das,Janmenjoy Nayak,Danilo Pelusi Conference proceedings
描述.This book features high-quality research papers presented at the 5th International Conference on Computational Intelligence in Pattern Recognition (CIPR 2023), held at Department of Computer Science and Engineering, Techno Main Salt Lake, West Bengal, India, during May 27–28, 2023. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics, and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments..
出版日期Conference proceedings 2023
關(guān)鍵詞Pattern Recognition; Computational Intelligence; Intelligent computing; Intelligent Algorithms; Big Data
版次1
doihttps://doi.org/10.1007/978-981-99-3734-9
isbn_softcover978-981-99-3733-2
isbn_ebook978-981-99-3734-9Series 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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Monthly Rainfall Forecasting Using Sequential Models,n, and wind speed, which change dynamically based on the climatic conditions. Accurate forecasts of rainfall are considered important in agriculture and other industries. Therefore, there is a great need for accurate rainfall prediction, and it can be achieved by deep learning algorithms. The challe
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Method-Level Code Smells Detection Using Machine Learning Models,g wrongly developed code components and implementing them is known as code smell detection. In this study, we used two method-level code smell datasets: the long parameter list and the switch statement, for detecting the code smells. A SMOTE class balancing approach is utilized to deal with the issu
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