Описание: Presents innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is designed for engineers, academics, researchers, practitioners, and students.
Автор: Rik Das, Siddhartha Bhattacharyya, Sudarshan Nandy Название: Machine Learning Applications: Emerging Trends ISBN: 3110608537 ISBN-13(EAN): 9783110608533 Издательство: Walter de Gruyter Цена: 18586.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.
Описание: This book constitutes the refereed proceedings of the First Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, held in Trivandrum, India, in December 2019. The 17 full papers and 6 short papers presented in this volume were thoroughly reviewed and selected from 53 qualified submissions.
This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations.
The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems.
Subjects covered in detail include:
Mathematical foundations of machine learning with various examples.
An empirical study of supervised learning algorithms like Naпve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview.
Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth.
Hands-on machine leaning open source tools viz. Apache Mahout, H2O.
Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning.
Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on.
Описание: This monograph provides a comprehensive overview of methods for searching, evaluating, and optimizing highway location and alignments using genetic algorithms (GAs), a powerful Artificial Intelligence (AI) technique. It presents a two-level programming structure to deal with the effects of varying highway location on traffic level changes in surrounding road networks within the highway location search and alignment optimization process. In addition, the proposed method evaluates environmental impacts as well as all relevant highway costs associated with its construction, operation, and maintenance. The monograph first covers various search methods, relevant cost functions, constraints, computational efficiency, and solution quality issues arising from optimizing the highway alignment optimization (HAO) problem. It then focuses on applications of a special-purpose GA in the HAO problem where numerous highway alignments are generated and evaluated, and finally the best ones are selected based on costs, traffic impacts, safety, energy, and environmental considerations. A review of other promising optimization methods for the HAO problem is also provided in this monograph.
Описание: This book provides a broad overview of the available machine learning techniques for solving civil engineering problems including drought forecasting, river flow forecasting, precipitation forecasting, and significant wave height forecasting. Fundamentals of both theoretical and practical aspects are discussed in varied domains.
Описание: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics.
Описание: Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
Описание: In today's developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing. Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is ideally designed for engineers, academicians, researchers, practitioners, and students seeking current research on solving engineering problems using smart technology.
Автор: Mirjalili Seyedali, Faris Hossam, Aljarah Ibrahim Название: Evolutionary Machine Learning Techniques: Algorithms and Applications ISBN: 9813299924 ISBN-13(EAN): 9789813299924 Издательство: Springer Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book mainly analyzes the major issues at all phases of the transition of urban-rural relation, as well as measures adopted by the transition launcher in face of such issues, including not only the system and policy design of the national and local government, but the countermeasures of basic-level units at urban and rural areas and the people.
Автор: Saifullah Khalid Название: Applications of Artificial Intelligence in Electrical Engineering ISBN: 1799827186 ISBN-13(EAN): 9781799827184 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 42451.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. The prevalence of AI in industry from stock market trading to manufacturing makes it easy to forget how complex artificial intelligence has become. Engineering provides various current and prospective applications of these new and complex artificial intelligence technologies.
Applications of Artificial Intelligence in Electrical Engineering is a critical research book that examines the advancing developments in artificial intelligence with a focus on theory and research and their implications. Highlighting a wide range of topics such as evolutionary computing, image processing, and swarm intelligence, this book is essential for engineers, manufacturers, technology developers, IT specialists, managers, academicians, researchers, computer scientists, and students.
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