Machine Learning and Optimization by Apoorva S. Shastri (.PDF)

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File Size: 35.5 MB

Machine Learning and Optimization for Engineering Design by Apoorva S. Shastri, Kailash Shaw, Mangal Singh
Requirements: .ePUB, .PDF reader, 35.5 MB
Overview: 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. There are several deterministic and approximation-based optimization methods that have been developed by the researchers, such as branch-and-bound techniques, simplex methods, approximation and Artificial Intelligence-based methods such as evolutionary methods, Swarm-based methods, physics-based methods, socio-inspired methods, etc. In the paper “OpenCV and MQTT Based Intelligent Management System”, a system is proposed which is intelligent and can perform identification, counting, and calculation of density of vehicles. After calculating the traffic density, the system classifies the density into low, medium, and high density with the help of a decision algorithm. This system is based on Python programming, and the libraries used in Python are Open-source Computer Vision, NumPy, Chardet, and time library.
Genre: Non-Fiction > Tech & Devices

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