Multimedia Data Processing and Computing, Swarnkar, Suman ; Patra, J P ; Tran, Tien Anh
Автор: Koichi Suzumori, Kenjiro Fukuda Название: The Science of Soft Robots ISBN: 981195173X ISBN-13(EAN): 9789811951732 Издательство: Springer Рейтинг: Цена: 10798.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The goal of this textbook is to equip readers with as structured knowledge of soft robotics as possible. Seeking to overcome the limitations of conventional robots by making them more flexible, gentle and adaptable, soft robotics has become one of the most active fields over the last decade. Soft robotics is also highly interdisciplinary, bringing together robotics, computer science, material science, biology, etc. After the introduction, the content is divided into three parts: Design of Soft Robots; Soft Materials; and Autonomous Soft Robots. Part I addresses soft mechanisms, biological mechanisms, and soft manipulation & locomotion. In Part II, the basics of polymer, biological materials, flexible & stretchable sensors, and soft actuators are discussed from a materials science standpoint. In turn, Part III focuses on modeling & control of continuum bodies, material intelligence, and information processing using soft body dynamics. In addition, the latest research results and cutting-edge research are highlighted throughout the book. Written by a team of researchers from highly diverse fields, the work offers a valuable textbook or technical guide for all students, engineers and researchers who are interested in soft robotics.
Автор: Navarro Название: Compact Data Structures ISBN: 1107152380 ISBN-13(EAN): 9781107152380 Издательство: Cambridge Academ Рейтинг: Цена: 11880.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Compact data structures are essential tools for efficiently handling massive amounts of data by exploiting the memory hierarchy and for reducing the resources needed for distributed and mobile applications. This first comprehensive book on the topic focuses on the structures that are most relevant for practical use.
Автор: Han Liu; Mihaela Cocea Название: Granular Computing Based Machine Learning ISBN: 3319888846 ISBN-13(EAN): 9783319888842 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Поставка под заказ.
Описание:
This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data.
Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries.
This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.
Автор: Xingni Zhou, Zhiyuan Ren, Yanzhuo Ma, Kai Fan, Ji Xiang Название: Non-linear Data Structures and Data Processing ISBN: 3110676052 ISBN-13(EAN): 9783110676051 Издательство: Walter de Gruyter Цена: 12078.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The systematic description starts with basic theory and applications of different kinds of data structures, including storage structures and models. It also explores on data processing methods such as sorting, index and search technologies. Due to its numerous exercises the book is a helpful reference for graduate students, lecturers.
Автор: Bhattacharyya, Siddhartha Maulik, Ujjwal Название: Soft computing for image and multimedia data processing ISBN: 3642402542 ISBN-13(EAN): 9783642402548 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Soft Computing for Image and Multimedia Data Processing
Автор: Siddhartha Bhattacharyya; Ujjwal Maulik Название: Soft Computing for Image and Multimedia Data Processing ISBN: 3662513595 ISBN-13(EAN): 9783662513590 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This full-spectrum graduate textbook starts with a tutorial on brain learning and the key soft computing techniques. It focuses on object extraction, image segmentation and edge detection, with extensive real-life applications in multimedia data processing.
Автор: Sharaff Aakanksha, Sinha G. R., Bhatia Surbhi Название: New Opportunities for Sentiment Analysis and Information Processing ISBN: 1799880621 ISBN-13(EAN): 9781799880622 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 29522.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts.
New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.
Автор: Keon Myung Lee; Seung-Jong Park; Jee-Hyong Lee Название: Soft Computing in Big Data Processing ISBN: 3319055267 ISBN-13(EAN): 9783319055268 Издательство: Springer Рейтинг: Цена: 22203.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Considering a big size of data, variety of data and frequent changes of data, the intelligent systems basically challenge new data management tasks for integration, visualization, querying and analysis.
Автор: Han Liu; Mihaela Cocea Название: Granular Computing Based Machine Learning ISBN: 331970057X ISBN-13(EAN): 9783319700571 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduction, - Traditional Machine Learning, - Semi-supervised Learning through Machine Based Labelling, - Nature Inspired Semi-heuristic Learning, - Fuzzy Classification through Generative Multi-task Learning, - Multi-granularity Semi-random Data Partitioning, - Multi-granularity Rule Learning, - Case Studies, - Con
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