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Machine learning and optimization for engineering design

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning and optimization for engineering designedited by Apoorva S. Shastri, Kailash Shaw, Mangal Singh.
其他作者:	Shastri, Apoorva S.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xiv, 164 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Engineering designData processing.
電子資源:	https://doi.org/10.1007/978-981-99-7456-6
ISBN:	9789819974566$q(electronic bk.)

Machine learning and optimization for engineering design
Machine learning and optimization for engineering design[electronic resource] /edited by Apoorva S. Shastri, Kailash Shaw, Mangal Singh. - Singapore :Springer Nature Singapore :2023. - xiv, 164 p. :ill. (chiefly color), digital ;24 cm. - Engineering optimization: methods and applications,2731-4057. - Engineering optimization: methods and applications..

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.

ISBN: 9789819974566$q(electronic bk.)

Standard No.: 10.1007/978-981-99-7456-6doiSubjects--Topical Terms:

274019
Engineering design
--Data processing.

LC Class. No.: TA174

Dewey Class. No.: 620.0042028563

Machine learning and optimization for engineering design
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520 $a 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.
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