Machine Intelligence and Big Data in Industry, Dominik Ry?ko; Piotr Gawrysiak; Marzena Kryszkiewi
Автор: Toyoaki Nishida; Colette Faucher Название: Modelling Machine Emotions for Realizing Intelligence ISBN: 3642126030 ISBN-13(EAN): 9783642126031 Издательство: Springer Рейтинг: Цена: 23508.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This research book presents recent progress in modelling and synthesizing emotional intelligence. It describes major concepts and issues underlying primitive machineries, and discusses how emotional engines might be incorporated into an intelligent system.
Автор: Hsu, Hui-Huang Название: Big Data Analytics for Sensor-Network Collected Intelligence ISBN: 0128093935 ISBN-13(EAN): 9780128093931 Издательство: Elsevier Science Рейтинг: Цена: 15159.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people's behaviors, and how to unobtrusively provide better services.
It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality.
In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation.
Indexing: The books of this series are submitted to EI-Compendex and SCOPUS
Contains contributions from noted scholars in computer science and electrical engineering from around the globe
Provides a broad overview of recent developments in sensor collected intelligence
Edited by a team comprised of leading thinkers in big data analytics
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Автор: Tshilidzi Marwala; Monica Lagazio Название: Militarized Conflict Modeling Using Computational Intelligence ISBN: 1447127013 ISBN-13(EAN): 9781447127017 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume offers a scientific approach to manage inter-country conflict. Readers will find that through simultaneous control of four specific aspects (democracy, dependency, allies and capacity), predicted dispute outcomes can be avoided.
Автор: A. Gomersall Название: Machine Intelligence ISBN: 3662124041 ISBN-13(EAN): 9783662124048 Издательство: Springer Рейтинг: Цена: 15672.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In 1981 Robotics Bibliography was published containing over 1,800 references on industrial robot research and development, culled from the scientific literature over the previous 12 years.
Описание: Big data is a well-trafficked subject in recent IT discourse and does not lack for current research. In fact, there is such a surfeit of material related to big data—and so much of it of questionably reliability, thanks to the high-gloss efforts of savvy tech-marketing gurus—that it can, at times, be difficult for a serious academician to navigate.The Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence cuts through the haze of glitz and pomp surrounding big data and offers a simple, straightforward reference-source of practical academic utility. Covering such topics as cloud computing, parallel computing, natural language processing, and personalized medicine, this volume presents an overview of current research, insight into recent advances, and gaps in the literature indicative of opportunities for future inquiry and is targeted toward a broad, interdisciplinary audience of students, academics, researchers, and professionals in fields of IT, networking, and data-analytics.
Автор: Witold Pedrycz; Shyi-Ming Chen Название: Information Granularity, Big Data, and Computational Intelligence ISBN: 3319082531 ISBN-13(EAN): 9783319082530 Издательство: Springer Рейтинг: Цена: 20896.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Nearest Neighbor Queries on Big Data.- Information Mining for Big Information.- Information Granules Problem: An Efficient Solution of Real-Time Fuzzy Regression Analysis.- How to Understand Connections Based on Big Data: From Cliques to Flexible Granules.- Maintain 'Omics: When e-Maintenance Enters the Big Data Era.- Incrementally Mining Frequent Patterns for Large Database.- Improved Latent Semantic Indexing-based Data Mining Methods and An Application to Big.- The Property of Different Granule and Granular Methods Based on Quotient Space.- Towards An Optimal Task-Driven Information Granulation.- Unified Framework for Construction of Rule Based Classification Systems.- Multi-granular Evaluation Model through Fuzzy Random Regression to Improve Information.- Building Fuzzy Robust Regression Model Based on Granularity and Possibility Distribution.- The Role of Cloud Computing Architectures in Big Data.- Big Data Storage Techniques for Spatial Databases: Implications of Big Data Architecture on Spatial Query Processing.- The Web KnowARR Framework: Orchestrating Computational Intelligence with Graph Databases.- Customer Relationship Management and Big Data Mining.- Performance Competition for ISCIFCM and Application of Computational Intelligence on Analysis of Air Quality Monitoring Big Data.-PEI Models under Uncontrolled Circumstances.- Rough Set Model based Knowledge Acquisition of Market Movements from Economic Data.- Deep Neural Network Modeling for Big Data Weather Forecast.- Current Knowledge and Future Challenge for Visibility Forecasting by Computational Intelligence.- Application of Computational Intelligence on Analysis of Air Quality Monitoring Big Data.
Автор: D.P. Acharjya; Satchidananda Dehuri; Sugata Sanyal Название: Computational Intelligence for Big Data Analysis ISBN: 3319165976 ISBN-13(EAN): 9783319165974 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
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