Описание: This book presents the latest advances in computational intelligence and data analytics for sustainable future smart cities. It focuses on computational intelligence and data analytics to bring together the smart city and sustainable city endeavors.
Описание: THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35897.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 27027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Описание: This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis.
Описание: 1. Introduction.- 2. Data Engineering and Exploratory Data Analysis Techniques.- 3. Agricultural Economy and ML Models.- 4. Commodity Markets - Machine Learning Techniques.- 5. Weather Patterns and Machine Learning.- 6. Agriculture Employment and the Role of AI in improving Productivity.- 7. Role of Government and the AI Readiness.- 8. Future.
Описание: This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis.
Автор: Fa–Long Luo Название: Machine Learning for Future Wireless Communications ISBN: 1119562252 ISBN-13(EAN): 9781119562252 Издательство: Wiley Рейтинг: Цена: 18683.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A comprehensive review to the theory, application and research of machine learning for future wireless communications
In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities.
Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author - a noted expert on the topic - covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource:
Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks
Covers a range of topics from architecture and optimization to adaptive resource allocations
Reviews state-of-the-art machine learning based solutions for network coverage
Includes an overview of the applications of machine learning algorithms in future wireless networks
Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing
Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
Описание: This volume offers a wealth of interdisciplinary approaches to artificial intelligence, machine learning and optimization tools, which contribute to the optimization of urban features towards forming smart, sustainable, and livable future cities. Special features include: * New research on the design of city elements and smart systems with respect to new technologies and scientific thinking * Discussions on the theoretical background that lead to smart cities for the future * New technologies and principles of research that can promote ideas of artificial intelligence and machine learning in optimized urban environments The book engages students and researchers in the subjects of artificial intelligence, machine learning, and optimization tools in smart sustainable cities as eminent international experts contribute their research results and thinking in its chapters. Overall, its audience can benefit from a variety of disciplines including, architecture, engineering, physics, mathematics, computer science, and related fields.
Описание: The integration of machine intelligence and IoT technologies can greatly help in devising cutting edge solutions to very recent issues of industrial applications. Machine intelligence is the most appropriate set of techniques for constructing prediction models due to its capability in handling large-scale and complex datasets.
Описание: Introduction: The Evolving Data-Driven Smart Sustainable Approach to Urbanism for Tackling the Challenges of Sustainability and Urbanization.- Smart Sustainable Urbanism and Big Data Computing: A Topical Literature Review.- Conceptual, Theoretical, and Disciplinary Foundations: An Interdisciplinary and Transdisciplinary Perspective.- The Data-Driven Smart Sustainable Paradigm of Urbanism: A Qualitative Analysis of Long-lasting Trends.- The Anatomy of the Data-Driven Smart Sustainable City: Instrumentation, Datafication, Computerization, and Technologization.- Paradigmatic, Scientific, Scholarly, Epistemic, and Discursive Shifts in Light of Big Data Science and Analytics.- On the Unsustainability and Sustainability of Smart Urbanism in the Era of Big Data.- Advancing Sustainable Urbanism Processes: The Key Practical and Analytical Applications of Big Data Computing for Urban Systems and Domains.- The Unfolding and Soaring Data Deluge for Advancing Smart Sustainable Urbanism: Data-Driven Urban Studies and Analytics.- Data-Driven Smart Sustainable Urbanism: Decision-Making, Intelligence Functions, Simulation Models, Optimization Methods, and their Synergy in Complex City Systems.- Towards a Novel Model Integrating the Data-Driven City, the Eco-city, and the Compact City: A Scholarly and Planning Approach to Future Vision Construction.
During the last decade, developments in smart cars, mobile devices, Internet of Things and vehicular communications have transformed the future of smart cities. With the rapid integration of these smart devices into our surroundings, as we enter into a new era of a highly connected and environmentally friendly ecosystem.
This book offers a unique opportunity for the reader to explore state-of-the-art developments in applications, technologies (such as Big Data and Artificial Intelligence), services and research trends in smart mobility for smart cities. It also provides a reference for professionals and researchers in the areas of Smart Mobility (e.g. autonomous valet parking, passenger trajectory data, smart traffic control systems) and recent technical trends on their enabling technologies. The materials have been carefully selected to reflect the latest developments in the field with many novel contributions from academics and industry experts from around the world.
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