Machine Learning Algorithms for Engineering Applications: Future Trends and Research Directions, Parmanand Astya, Prasenjit Chatterjee, Sudeshna Chakraborty
Старое издание
Автор: Edited By Prasenjit Chatterjee, Morteza Yazdani, F Название: Machine learning algorithms and applications in engineering / ISBN: 0367612550 ISBN-13(EAN): 9780367612559 Издательство: Taylor&Francis Цена: 7195.00 р. Наличие на складе: Есть у поставщикаПоставка под заказ. Описание: Machine Learning (ML) is a sub field of artificial intelligence that uses soft computing and algorithms to enable computers to learn on their own and identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. This book discusses various applications of ML in engineering fields and the use of ML algorithms in solving challenging engineering problems ranging from biomedical, transport, supply chain and logistics, to manufacturing and industrial. Through numerous case studies, it will assist researchers and practitioners in selecting the correct options and strategies for managing organizational tasks.
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.
Автор: Das Santosh Kumar, Das Shom Prasad, Dey Nilanjan Название: Machine Learning Algorithms for Industrial Applications ISBN: 3030506436 ISBN-13(EAN): 9783030506438 Издательство: Springer Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explores several problems and their solutions regarding data analysis and prediction for industrial applications. Machine learning is a prominent topic in modern industries: its influence can be felt in many aspects of everyday life, as the world rapidly embraces big data and data analytics.
Автор: Srinivas Mettu, Sucharitha G., Matta Anjanna Название: Machine Learning Algorithms and Applications ISBN: 1119768853 ISBN-13(EAN): 9781119768852 Издательство: Wiley Рейтинг: Цена: 25811.00 р. Наличие на складе: Поставка под заказ.
Описание: Discover the core principles of biomedical measurement design and performance evaluation with this hands-on guide. With MATLAB (R) code, problems, and a solutions manual available online, it is an essential text for advanced undergraduate and graduate students, and practicing professionals in Bioengineering and Electrical and Computer Engineering.
Описание: Artificial intelligence is at the forefront of research and implementation in many industries including healthcare and agriculture. Whether it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a platform in which they can keep up with machine learning trends and their developments in the real world.
The Handbook of Research on Applications and Implementations of Machine Learning Techniques provides innovative insights into the multi-disciplinary applications of machine learning algorithms for data analytics. The content within this publication examines disease identification, neural networks, and language support. It is designed for IT professionals, developers, data analysts, technology specialists, R&D professionals, industrialists, practitioners, researchers, academicians, and students seeking research on deep learning procedures and their enactments in the fields of medicine, engineering, and computer science.
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.
Автор: Das, S.K., Das, S.P., Dey, N., Hassanien, A.-E. Название: Machine Learning Algorithms for Industrial Applications ISBN: 3030506401 ISBN-13(EAN): 9783030506407 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explores several problems and their solutions regarding data analysis and prediction for industrial applications. Machine learning is a prominent topic in modern industries: its influence can be felt in many aspects of everyday life, as the world rapidly embraces big data and data analytics.
Описание: Provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, the book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science.
Описание: Advances in the nature-inspired swarm intelligence algorithms continue to be useful in solving complicated problems in nonlinear, non-differentiable, and un-continuous functions, as well as being applied to solve real-world applications. This title highlights the current research on swarm intelligence algorithms and its applications.
Автор: Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy Название: Deep Learning: Research and Applications ISBN: 3110670798 ISBN-13(EAN): 9783110670790 Издательство: Walter de Gruyter Цена: 20446.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will focus on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it would provide an insight of deep neural networks in action with illustrative coding examples. Moreover, the book will also provide video demonstrations on each chapter. Deep learning is a new area of machine learning research, which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non immediately related fields, for example between air pressure recordings and english words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. The unique features of this book include: • tutorials on deep learning framework with focus on tensor flow, keras etc. • video demonstration of each chapter for enabling the readers to have a good understanding of the chapter contents. • a score of worked out examples on real life applications. • illustrative diagrams • coding examples
Описание: Addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
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.
Описание: This book constitutes the refereed proceedings of the Second Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, SoMMA 2020, held in Chennai, India, in October 2020. The 12 full papers and 7 short papers presented in this volume were thoroughly reviewed and selected from 40 qualified submissions.
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