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Design of intelligent applications using machine learning and deep learning techniques
Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated - bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: Fundamental models, issues and challenges in ML and DL. Comprehensive analyses and probabilistic approaches for ML and DL. Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia. Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals. This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.
BOOK
Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
Artificial Intelligence (AI) when incorporated with machine learning and deep learning algorithms can have a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems.
The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, it covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision support applications, includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications.
Academic scientists, researchers and students in the various domains of computer science engineering, electronics and communication engineering, information technology, industrial engineers, biomedical engineers, and management will find this book useful. By the end of this book, you will have understood the fundamentals of AI and various case studies that will develop your adaptive thinking to solve real time AI problems.
eBook
Empowering artificial intelligence through machine learning : new advances and applications
\"This new volume, Empowering Artificial Intelligence Through Machine Learning: New Advances and Applications, discusses various new applications of machine learning, a subset of the field of artificial intelligence. Artificial intelligence is considered to be the next big-game changer in research and technology. The volume looks at how computing has enabled machines to learn, making machines and tools become smarter in many sectors, including science and engineering, healthcare, finance, education, gaming, security, and even agriculture, plus many more areas. Topics include techniques and methods in artificial intelligence for making machines intelligent, machine learning in healthcare, using machine learning for credit card fraud detection, using artificial intelligence in education using gaming and automatization with courses and outcomes mapping, and much more. The book will be valuable for professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology\"-- Provided by publisher.
Book
Integration of Artificial Intelligence and Machine Learning Methods for Smart Internet of Things Systems and Its Applications
This book is crafted to provide a comprehensive exploration of the integration of AI and ML techniques in the context of Smart IoT systems. The editors embark on a journey through the fundamental principles, methodologies, and applications that define this dynamic field. From the basics of AI and ML to their tailored applications in the IoT domain, the chapters unfold to reveal the intricacies of this symbiotic relationship. Key features of this book include the following. Foundations of AI and ML: The book begins with a thorough examination of the foundational concepts of AI and ML, providing readers with a solid understanding of the principles that underpin these technologies; Smart IoT Systems: Delving into the world of Smart IoT systems, the book explores the architecture, components, and challenges associated with building intelligent and interconnected ecosystems; Integration Strategies: Various strategies for seamlessly integrating AI and ML into IoT systems are discussed, offering insights into how these technologies can complement each other to enhance overall system efficiency; Applications Across Industries: The practical applications of AI and ML in diverse industries are explored, showcasing real-world examples of how these technologies are reshaping sectors such as healthcare, transportation, manufacturing, and more; Challenges and Future Directions: Recognizing that every technological advancement comes with its set of challenges, the book addresses the ethical, security, and privacy concerns associated with the integration of AI and ML in Smart IoT systems. Additionally, it provides a glimpse into the future, outlining potential trends and advancements.
eBook
IoT and AI technologies for sustainable living : a practical handbook
\"This book brings together all the latest methodologies, tools and techniques related to the Internet of Things and Artificial Intelligence in a single volume to build insight into their use in the sustainable living. The applications include areas such as agriculture, smart farming, healthcare, bioinformatics, self-diagnosis system, body sensor network, multimedia mining, multimedia in forensics and security. It provides a comprehensive discussion of modeling and implementation in water resource optimization, recognizing pest pattern, traffic scheduling, web mining, cyber security and cyber forensics. It will help develop an understanding of the need of AI and IoT to have a sustainable era of human living Tools covered include genetic algorithms, cloud computing, water resource management, web mining, machine learning, block chaining, learning algorithm, sentimental analysis and NLP. It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities\"-- Provided by publisher.
Book
Advances of Machine Learning in Clean Energy and the Transportation Industry
This book presents the latest research in the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in the transport industry. The requirements and demands of problem solving have been increasing exponentially, and new artificial intelligence and machine learning technologies have reduced the scope of data coverage worldwide. Recent advances in data technology (DT) have contributed to reducing the gaps in the coverage of domains around the globe. Attention to clean energy in recent decades has been growing exponentially. This is mainly due to a decrease in the cost of both installed capacity of converters and a decrease in the cost of generated energy. Such successes were achieved thanks to the improvement of modern technologies for the production of converters, an increase in the efficiency of using incoming energy, optimization of the operation of converters and analysis of data obtained during the operation of systems with the possibility of planning production. The use of clean energy plays an important role in the transportation industry, where technologies are also being improved from year to year - the transportation industry is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex intelligent computing, machine learning optimization, planning and working with large amounts of data. The book is a valuable reference work for researchers in the fields of renewable energy, computer science and engineering with a particular focus on machine learning and intelligent optimization as well as for postgraduates, managers, economists and decision makers, policy makers, government officials, industrialists and practicing scientists and engineers as well compassionate global decision makers. Topics include: Machine learning, Quantum Optimization, Modern Technology in Transport Industry, Innovative Technologies in Transport Education, Systems Based on Renewable Energy Conversion, Business Process Models and Applications in Renewable Energy, Clean Energy, and Climate Change.
eBook
Artificial intelligence, machine learning, and data science technologies : future impact and well-being for society 5.0
\"This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, etc. and how all countries around the world are feeling this impact. The book aims at low cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and post graduate students, researchers, academicians, institutions, and professionals that are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science\"-- Provided by publisher.
Book
Machine learning for financial engineering
This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment.
eBook