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＜電子ブック＞
Machine Learning and Optimization for Engineering Design / edited by Apoorva S. Shastri, Kailash Shaw, Mangal Singh
(Engineering Optimization: Methods and Applications. ISSN:27314057)

版	1st ed. 2023.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2023
本文言語	英語
大きさ	XIV, 164 p. 79 illus., 61 illus. in color : online resource
著者標目	Shastri, Apoorva S editor Shaw, Kailash editor Singh, Mangal editor SpringerLink (Online service)
件　名	LCSH:Machine learning LCSH:Engineering design LCSH:Mathematical optimization FREE:Machine Learning FREE:Engineering Design FREE:Optimization
一般注記	Chapter 1: Development of Smart Home System Based on IoT Using a Wearable EEG -- Chapter 2: Design of Intelligent ICT Irrigation System using Crop Growth Big Data Analysis -- Chapter 3: LRBC-E: A Structurally Enhanced LRBC-Based Block Cipher for Securing Extremely Contraind IoT Devices -- Chapter 4: OpenCV and MQTT based Intelligent Traffic Management System -- Chapter 5: A Machine Learning Model for Student's Academic Success Prediction This book aims to provide a collection of state-of-the-art scientific and technical research papers related to machine learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering applications such as smart homes, ICT-based irrigation systems, academic success prediction, future agro-industry for crop production, disease classification in plants, dental problems and solutions, loan eligibility processing, etc., and their implementation with several case studies and literature reviews are included as self-contained chapters. Additionally, the book intends to highlight the importance of study and effectiveness in addressing the time and space complexity of problems and enhancing accuracy, analysis, and validations for different practical applications by acknowledging the state-of-the-art literature survey. The book targets a larger audience by exploring multidisciplinary research directions such as computer vision, machine learning, artificial intelligence, modified/newly developed machine learning algorithms, etc., to enhance engineering design applications for society. State-of-the-art research work with illustrations and exercises along with pseudo-code has been provided here HTTP:URL=https://doi.org/10.1007/978-981-99-7456-6

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