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 machine 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 to professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology.

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 machine 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 to professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology.

This book focuses on the key technologies, challenges, and research directions of the Industrial Internet of Things (IIoT). It provides a basis for discussing open principles, methods, and research problems, and provides a systematic overview of the state-of-the-art research efforts, directions, and potential challenges associated with IIoT.

Industrial Internet of Things: Technologies and Research Directions covers how industry automation is projected to be the largest and fastest-growing segment of the market. It explores the collaborative development of high-performance telecommunications, military, industrial, and general-purpose embedded computing applications, and offers a systematic overview of the state-of-the-art research efforts and new potential directions.

Researchers, academicians, and professionals working in this inter-disciplinary area will be interested in this book.

This book focuses on the key technologies, challenges, and research directions of the Industrial Internet of Things (IIoT). It provides a basis for discussing open principles, methods, and research problems, and provides a systematic overview of the state-of-the-art research efforts, directions, and potential challenges associated with IIoT.

Industrial Internet of Things: Technologies and Research Directions covers how industry automation is projected to be the largest and fastest-growing segment of the market. It explores the collaborative development of high-performance telecommunications, military, industrial, and general-purpose embedded computing applications, and offers a systematic overview of the state-of-the-art research efforts and new potential directions.

Researchers, academicians, and professionals working in this inter-disciplinary area will be interested in this book.

This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems.

Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformation of operations, as well as supply chain management. It presents sustainability and efficiency with IoT, Machine Learning, Data Analysis, and Blockchain technologies as it focuses on industrial development, design, and implementation. This book showcases the incorporation of complex processes and functions as one central component of digital systems and explores current trends that are working to accelerate industrial transformation. Case studies are also included, depicting the technologies that are influencing the transition into the fourth Industrial Revolution, such as industrial infrastructure, biodiversity, and enhanced productivity.

This book is aimed at researchers, scholars, and students that require real-time knowledge and applications where the transformation and implementation of digital systems in the manufacturing sector are needed.

This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems.

Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformation of operations, as well as supply chain management. It presents sustainability and efficiency with IoT, Machine Learning, Data Analysis, and Blockchain technologies as it focuses on industrial development, design, and implementation. This book showcases the incorporation of complex processes and functions as one central component of digital systems and explores current trends that are working to accelerate industrial transformation. Case studies are also included, depicting the technologies that are influencing the transition into the fourth Industrial Revolution, such as industrial infrastructure, biodiversity, and enhanced productivity.

This book is aimed at researchers, scholars, and students that require real-time knowledge and applications where the transformation and implementation of digital systems in the manufacturing sector are needed.

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.

Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.

Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.

Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.

Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

The purpose of designing this book is to discuss and analyze security protocols available for communication. Objective is to discuss protocols across all layers of TCP/IP stack and also to discuss protocols independent to the stack. Authors will be aiming to identify the best set of security protocols for the similar applications and will also be identifying the drawbacks of existing protocols. The authors will be also suggesting new protocols if any.

The purpose of designing this book is to discuss and analyze security protocols available for communication. Objective is to discuss protocols across all layers of TCP/IP stack and also to discuss protocols independent to the stack. Authors will be aiming to identify the best set of security protocols for the similar applications and will also be identifying the drawbacks of existing protocols. The authors will be also suggesting new protocols if any.

Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design.

The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development.

Audience

Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design.

The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development.

Audience

Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

The book’s unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work.

Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the reader’s understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide.

Among the subjects of the 12 chapters are:

Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.

The book’s unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work.

Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the reader’s understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide.

Among the subjects of the 12 chapters are:

Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.

Discusses the impact of AI on organizational transformation which is a mix of computational techniques and management practices, with in-depth analysis about the role of automation & data management, and strategic management in relation to human capital, procurement & production, finance, and marketing.

The impact of AI in restructuring organizational processes is a combination of management practices and computational technology. This book covers the areas like artificial intelligence & its impact on professions, as well as machine learning algorithms and technologies. The context of applications of AI in business process innovation primarily includes new business models, AI readiness and maturity at the organizational, technological, financial, and cultural levels. The book has extensive details on machine learning and the applications such as robotics, blockchain, Internet of Things. Also discussed are the influence of AI on financial strategies and policies, human skills & values, procurement innovation, production innovation, AI in marketing & sales platforms.

Audience

Readers include those working in artificial intelligence, business management studies, technology engineers, senior executives, and human resource managers in all types of business.

Discusses the impact of AI on organizational transformation which is a mix of computational techniques and management practices, with in-depth analysis about the role of automation & data management, and strategic management in relation to human capital, procurement & production, finance, and marketing.

The impact of AI in restructuring organizational processes is a combination of management practices and computational technology. This book covers the areas like artificial intelligence & its impact on professions, as well as machine learning algorithms and technologies. The context of applications of AI in business process innovation primarily includes new business models, AI readiness and maturity at the organizational, technological, financial, and cultural levels. The book has extensive details on machine learning and the applications such as robotics, blockchain, Internet of Things. Also discussed are the influence of AI on financial strategies and policies, human skills & values, procurement innovation, production innovation, AI in marketing & sales platforms.

Audience

Readers include those working in artificial intelligence, business management studies, technology engineers, senior executives, and human resource managers in all types of business.

This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications.

Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.

Audience

Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

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 machine 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 to professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology.

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 machine 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 to professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology.

This book focuses on the key technologies, challenges, and research directions of the Industrial Internet of Things (IIoT). It provides a basis for discussing open principles, methods, and research problems, and provides a systematic overview of the state-of-the-art research efforts, directions, and potential challenges associated with IIoT.

Industrial Internet of Things: Technologies and Research Directions covers how industry automation is projected to be the largest and fastest-growing segment of the market. It explores the collaborative development of high-performance telecommunications, military, industrial, and general-purpose embedded computing applications, and offers a systematic overview of the state-of-the-art research efforts and new potential directions.

Researchers, academicians, and professionals working in this inter-disciplinary area will be interested in this book.

This book focuses on the key technologies, challenges, and research directions of the Industrial Internet of Things (IIoT). It provides a basis for discussing open principles, methods, and research problems, and provides a systematic overview of the state-of-the-art research efforts, directions, and potential challenges associated with IIoT.

Industrial Internet of Things: Technologies and Research Directions covers how industry automation is projected to be the largest and fastest-growing segment of the market. It explores the collaborative development of high-performance telecommunications, military, industrial, and general-purpose embedded computing applications, and offers a systematic overview of the state-of-the-art research efforts and new potential directions.

Researchers, academicians, and professionals working in this inter-disciplinary area will be interested in this book.

This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems.

Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformation of operations, as well as supply chain management. It presents sustainability and efficiency with IoT, Machine Learning, Data Analysis, and Blockchain technologies as it focuses on industrial development, design, and implementation. This book showcases the incorporation of complex processes and functions as one central component of digital systems and explores current trends that are working to accelerate industrial transformation. Case studies are also included, depicting the technologies that are influencing the transition into the fourth Industrial Revolution, such as industrial infrastructure, biodiversity, and enhanced productivity.

This book is aimed at researchers, scholars, and students that require real-time knowledge and applications where the transformation and implementation of digital systems in the manufacturing sector are needed.

This book focuses on industrial development, design, implementation, and transformation using technologies such as Artificial Intelligence, Machine Learning, the Internet of Things (IoT), Big Data Analysis, and Blockchain. It incorporates complex processes, functions, and various other elements as one central component of digital systems.

Industrial Transformation: Implementation and Essential Components and Processes of Digital Systems discusses the industry transformation aligned with the computerization of manufacturing and the required skills needed to build a new workforce. This book covers the role that AI plays in the management of resource flow and decision-making in the transformation of operations, as well as supply chain management. It presents sustainability and efficiency with IoT, Machine Learning, Data Analysis, and Blockchain technologies as it focuses on industrial development, design, and implementation. This book showcases the incorporation of complex processes and functions as one central component of digital systems and explores current trends that are working to accelerate industrial transformation. Case studies are also included, depicting the technologies that are influencing the transition into the fourth Industrial Revolution, such as industrial infrastructure, biodiversity, and enhanced productivity.

This book is aimed at researchers, scholars, and students that require real-time knowledge and applications where the transformation and implementation of digital systems in the manufacturing sector are needed.

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.

Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.

Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.

Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.

Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.

The purpose of designing this book is to discuss and analyze security protocols available for communication. Objective is to discuss protocols across all layers of TCP/IP stack and also to discuss protocols independent to the stack. Authors will be aiming to identify the best set of security protocols for the similar applications and will also be identifying the drawbacks of existing protocols. The authors will be also suggesting new protocols if any.

The purpose of designing this book is to discuss and analyze security protocols available for communication. Objective is to discuss protocols across all layers of TCP/IP stack and also to discuss protocols independent to the stack. Authors will be aiming to identify the best set of security protocols for the similar applications and will also be identifying the drawbacks of existing protocols. The authors will be also suggesting new protocols if any.

Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design.

The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development.

Audience

Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design.

The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development.

Audience

Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

The book’s unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work.

Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the reader’s understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide.

Among the subjects of the 12 chapters are:

Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.

The book’s unified approach of balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work.

Inspired by the world around them, researchers are gathering information that can be developed for use in areas where certain practical applications of nature-inspired computation and machine learning can be applied. This book is designed to enhance the reader’s understanding of this process by portraying certain practical applications of nature-inspired algorithms (NIAs) specifically designed to solve complex real-world problems in data analytics and pattern recognition by means of domain-specific solutions. Since various NIAs and their multidisciplinary applications in the mechanical engineering and electrical engineering sectors; and in machine learning, image processing, data mining, and wireless networks are dealt with in detail in this book, it can act as a handy reference guide.

Among the subjects of the 12 chapters are:

Audience: Researchers and graduate students in mechanical engineering, electrical engineering, machine learning, image processing, data mining, and wireless networks will find this book very useful.

Discusses the impact of AI on organizational transformation which is a mix of computational techniques and management practices, with in-depth analysis about the role of automation & data management, and strategic management in relation to human capital, procurement & production, finance, and marketing.

The impact of AI in restructuring organizational processes is a combination of management practices and computational technology. This book covers the areas like artificial intelligence & its impact on professions, as well as machine learning algorithms and technologies. The context of applications of AI in business process innovation primarily includes new business models, AI readiness and maturity at the organizational, technological, financial, and cultural levels. The book has extensive details on machine learning and the applications such as robotics, blockchain, Internet of Things. Also discussed are the influence of AI on financial strategies and policies, human skills & values, procurement innovation, production innovation, AI in marketing & sales platforms.

Audience

Readers include those working in artificial intelligence, business management studies, technology engineers, senior executives, and human resource managers in all types of business.

Discusses the impact of AI on organizational transformation which is a mix of computational techniques and management practices, with in-depth analysis about the role of automation & data management, and strategic management in relation to human capital, procurement & production, finance, and marketing.

The impact of AI in restructuring organizational processes is a combination of management practices and computational technology. This book covers the areas like artificial intelligence & its impact on professions, as well as machine learning algorithms and technologies. The context of applications of AI in business process innovation primarily includes new business models, AI readiness and maturity at the organizational, technological, financial, and cultural levels. The book has extensive details on machine learning and the applications such as robotics, blockchain, Internet of Things. Also discussed are the influence of AI on financial strategies and policies, human skills & values, procurement innovation, production innovation, AI in marketing & sales platforms.

Audience

Readers include those working in artificial intelligence, business management studies, technology engineers, senior executives, and human resource managers in all types of business.

This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications.

Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.

Audience

Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.