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"This new volume, Cognitive Computing Systems: Applications and Technological Advancements, explores the emerging area of artificial intelligence that encompasses machine self-learning, human-computer interaction, natural language processing, data mining and more. It introduces cognitive computing systems, highlights their key applications, discusses the technologies used in cognitive systems, and explains underlying models and architectures. Focusing on scientific work for real-world applications, each chapter presents the use of cognitive computing and machine learning in specific application areas. These include the use of speech recognition technology, application of neural networks in construction management, elevating competency in education, comprehensive health monitoring systems, predicting type 2 diabetes, applications for smart agricultural technology, human resource management, and more. With chapters from knowledgeable researchers in the area of artificial intelligence, cognitive computing, and allied areas, this book will be an asset for researchers, faculty, advances students, and industry professionals in many fields"--
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"Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques.This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making.Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers."--Provided by publisher.
Machine learning --- Mathematical optimization --- Artificial intelligence. --- Industrial applications. --- Data processing.
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This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.
Artificial intelligence --- Medicine --- Medical applications. --- Data processing --- Computational intelligence. --- Biomedical engineering. --- Computational Intelligence. --- Biomedical Engineering and Bioengineering. --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Intelligence, Computational --- Soft computing
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This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.
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"Agriculture is one of the most fundamental human activities. It has kept humans happier and healthier and helped birth modern society as we know it. As farming has expanded, however, the usage of resources such as land, fertilizer, and water has grown exponentially. Environmental pressures from modern farming techniques have stressed our natural landscapes. Still, by some estimates, worldwide food production will need to increase 70% by 2050 to keep up with global demand. With global populations rising, it falls to technology to make farming processes more efficient and keep up with the growing demand. Fortunately, Machine Learning (ML) and the Internet of Things (IoT) can play a very promising role in the agricultural industry. Some examples include: an AI-powered drone to monitor the field, an IoT-designed automated crop watering system, sensors embedded in the field to monitor temperature and humidity, etc. The agriculture industry is the largest in the world, but when it comes to innovation there is a lot more to explore. IoT devices can be used to analyze the status of crops. For instance, with soil sensors, farmers can detect any irregular conditions such as high acidity and efficiently tackle these issues to improve their yield. In this book, we will point out the challenges facing the agro-industry that can be addressed by ML and IoT and explore the impacts of these technologies in the agriculture sector"--
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Medical informatics. --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Medicine --- Data processing --- Medical informatics
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"Applications of Blockchain and Big IoT Systems: Digital Solutions for Diverse Industries explores a plethora of blockchain-based solutions for big data and IoT applications. It explores a variety of advances in real-world applications in several sectors, including higher education, cybersecurity, agriculture, business and management, healthcare and biomedical science, construction and project management, smart city development, and others. Chapters explore emerging technology to combat the ever-increasing threat of security to computer systems and offer new architectural solutions for problems encountered in data management and security. The chapters help to provide a high level of understanding of various blockchain algorithms, along with the necessary tools and techniques. The novel architectural solutions in the deployment of blockchain presented here are the core of the book. The authors of this volume, from both research and academia, present valuable knowledge about the use of blockchain, big data, and IoT, exploring them from multiple perspectives. The chapters in this book are well written, easy to understand, and technically rich. This book will be an informative resource for data scientists, blockchain engineers, big data engineers, and analytics managers as well as for researchers, faculty, and advanced students in this field of study"--
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