Nitish Pathak – författare

This new book explores emerging new technologies for the development of business and finance sectors, to strengthen economic growth, and to facilitate the efficiency and creation of new and innovative business and finance management applications. The book covers state-of-the-art technologies such as artificial intelligence and big data analytics in fintech and finance, blockchain technology in the insurance industry, the use of AI and data science in tax administration, the growing use of 6G in industrial automation, quantum machine learning in finance, the merging of AI and blockchain in auditing, and more. Chapters explore the collaboration between humans and machines, the application of data science and artificial intelligence in the retail sector, and AI and data science in business services with a focus on enhancing efficiency and driving innovation.

This new book explores emerging new technologies for the development of business and finance sectors, to strengthen economic growth, and to facilitate the efficiency and creation of new and innovative business and finance management applications. The book covers state-of-the-art technologies such as artificial intelligence and big data analytics in fintech and finance, blockchain technology in the insurance industry, the use of AI and data science in tax administration, the growing use of 6G in industrial automation, quantum machine learning in finance, the merging of AI and blockchain in auditing, and more. Chapters explore the collaboration between humans and machines, the application of data science and artificial intelligence in the retail sector, and AI and data science in business services with a focus on enhancing efficiency and driving innovation.

A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision. Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing. Applications highlighted in the book include: diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition;computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning;methods capable of retrieving photometric and geometric transformed images;concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms;machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection;a comprehensive study of content-based image-retrieval techniques for feature extraction;machine learning approaches to understanding angiogenesis;handwritten image enhancement based on neutroscopic-fuzzy.Audience The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing.

Applications highlighted in the book include:

diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition; computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning; methods capable of retrieving photometric and geometric transformed images; concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms; machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection; a comprehensive study of content-based image-retrieval techniques for feature extraction; machine learning approaches to understanding angiogenesis; handwritten image enhancement based on neutroscopic-fuzzy.

Audience

The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

A comprehensive book providing high-quality research addressing challenges in theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Researchers are working to create new algorithms that combine the methods provided by CI approaches to solve the problems of image processing and computer vision such as image size, noise, illumination, and security. The 19 chapters in this book examine computational intelligence (CI) approaches as alternative solutions for automatic computer vision and image processing systems in a wide range of applications, using machine learning and soft computing.

Applications highlighted in the book include:

diagnostic and therapeutic techniques for ischemic stroke, object detection, tracking face detection and recognition; computational-based strategies for drug repositioning and improving performance with feature selection, extraction, and learning; methods capable of retrieving photometric and geometric transformed images; concepts of trading the cryptocurrency market based on smart price action strategies; comparative evaluation and prediction of exoplanets using machine learning methods; the risk of using failure rate with the help of MTTF and MTBF to calculate reliability; a detailed description of various techniques using edge detection algorithms; machine learning in smart houses; the strengths and limitations of swarm intelligence and computation; how to use bidirectional LSTM for heart arrhythmia detection; a comprehensive study of content-based image-retrieval techniques for feature extraction; machine learning approaches to understanding angiogenesis; handwritten image enhancement based on neutroscopic-fuzzy.

Audience

The book has been designed for researchers, engineers, graduate, and post-graduate students wanting to learn more about the theoretical and application aspects of soft computing and machine learning in image processing and computer vision.

Transform your approach to technology and sustainability with this comprehensive guide to green computing and computational intelligence. The global pursuit of sustainability has placed an urgent emphasis on developing innovative and eco-friendly technological solutions. Green computing has the potential to revolutionize the way we evaluate sustainability with the use of energy-efficient algorithms for resource optimization, sustainable hardware design, and smart resource management. Recognizing the intersection of computational intelligence and environmental stewardship, this book seeks to address the pressing challenges of integrating green practices into the realm of computational intelligence and aligning them with global sustainable development goals. Through global expertise from researchers and industry professionals, this book comprehensively covers the many applications of these innovative new technologies, as well as the challenges surrounding their implementation. Readers will find the book: Explores the convergence of environmental sustainability and advanced computational techniques, addressing the global call for energy-efficient and eco-friendly technological solutions;Integrates perspectives from computer science, engineering, environmental science, and artificial intelligence, providing a holistic view of green computing;Examines sustainable practices across diverse topics, including energy-efficient algorithms and resource optimization, sustainable hardware design, green software engineering, eco-friendly data centers, and smart resource management;Offers practical strategies for implementing sustainable computing practices while addressing theoretical and practical challenges;Highlights the role of computational intelligence in promoting sustainability, bridging the gap between technology development and environmental conservation.Audience Researchers, students, educators, and industry professionals working towards sustainable practices in and using green technology.

Transform your approach to technology and sustainability with this comprehensive guide to green computing and computational intelligence. The global pursuit of sustainability has placed an urgent emphasis on developing innovative and eco-friendly technological solutions. Green computing has the potential to revolutionize the way we evaluate sustainability with the use of energy-efficient algorithms for resource optimization, sustainable hardware design, and smart resource management. Recognizing the intersection of computational intelligence and environmental stewardship, this book seeks to address the pressing challenges of integrating green practices into the realm of computational intelligence and aligning them with global sustainable development goals. Through global expertise from researchers and industry professionals, this book comprehensively covers the many applications of these innovative new technologies, as well as the challenges surrounding their implementation. Readers will find the book: Explores the convergence of environmental sustainability and advanced computational techniques, addressing the global call for energy-efficient and eco-friendly technological solutions; Integrates perspectives from computer science, engineering, environmental science, and artificial intelligence, providing a holistic view of green computing; Examines sustainable practices across diverse topics, including energy-efficient algorithms and resource optimization, sustainable hardware design, green software engineering, eco-friendly data centers, and smart resource management; Offers practical strategies for implementing sustainable computing practices while addressing theoretical and practical challenges; Highlights the role of computational intelligence in promoting sustainability, bridging the gap between technology development and environmental conservation. Audience Researchers, students, educators, and industry professionals working towards sustainable practices in and using green technology.

Transform your approach to technology and sustainability with this comprehensive guide to green computing and computational intelligence. The global pursuit of sustainability has placed an urgent emphasis on developing innovative and eco-friendly technological solutions. Green computing has the potential to revolutionize the way we evaluate sustainability with the use of energy-efficient algorithms for resource optimization, sustainable hardware design, and smart resource management. Recognizing the intersection of computational intelligence and environmental stewardship, this book seeks to address the pressing challenges of integrating green practices into the realm of computational intelligence and aligning them with global sustainable development goals. Through global expertise from researchers and industry professionals, this book comprehensively covers the many applications of these innovative new technologies, as well as the challenges surrounding their implementation. Readers will find the book: Explores the convergence of environmental sustainability and advanced computational techniques, addressing the global call for energy-efficient and eco-friendly technological solutions; Integrates perspectives from computer science, engineering, environmental science, and artificial intelligence, providing a holistic view of green computing; Examines sustainable practices across diverse topics, including energy-efficient algorithms and resource optimization, sustainable hardware design, green software engineering, eco-friendly data centers, and smart resource management; Offers practical strategies for implementing sustainable computing practices while addressing theoretical and practical challenges; Highlights the role of computational intelligence in promoting sustainability, bridging the gap between technology development and environmental conservation. Audience Researchers, students, educators, and industry professionals working towards sustainable practices in and using green technology.