Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management.

Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology.

This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems.

This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning.

FEATURES

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management.

Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology.

This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems.

This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning.

FEATURES

This book presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. Written as a guide for assessing the roles that these techniques play, the book also highlights implementation strategies, lists problem-solving solutions, and paves the way for future research endeavors in smart and next-generation healthcare systems.

This book provides applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles that various techniques, such as machine learning, fuzzy logic, and statical mathematics, play in the advancements of smart healthcare systems. The book presents the basics as well as the advanced concepts to help beginners, as well as industry professionals, get up to speed on the latest developments in healthcare systems. The book examines descriptive, predictive, and social network techniques and discusses analytical tools and the important role they play in finding solutions to problems in healthcare systems. A framework of robust and novel healthcare techniques is highlighted, as well as implementation strategies and a setup for future research endeavors.

Healthcare Systems Using Soft Computing Techniques is a valuable resource for researchers and postgraduate students in healthcare systems engineering, computer science, information technology, and applied mathematics. The book introduces beginners to—and at the same time brings industry professionals up to speed with—the important role soft computing techniques play in smart healthcare systems.

This book presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. Written as a guide for assessing the roles that these techniques play, the book also highlights implementation strategies, lists problem-solving solutions, and paves the way for future research endeavors in smart and next-generation healthcare systems.

This book provides applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles that various techniques, such as machine learning, fuzzy logic, and statical mathematics, play in the advancements of smart healthcare systems. The book presents the basics as well as the advanced concepts to help beginners, as well as industry professionals, get up to speed on the latest developments in healthcare systems. The book examines descriptive, predictive, and social network techniques and discusses analytical tools and the important role they play in finding solutions to problems in healthcare systems. A framework of robust and novel healthcare techniques is highlighted, as well as implementation strategies and a setup for future research endeavors.

Healthcare Systems Using Soft Computing Techniques is a valuable resource for researchers and postgraduate students in healthcare systems engineering, computer science, information technology, and applied mathematics. The book introduces beginners to—and at the same time brings industry professionals up to speed with—the important role soft computing techniques play in smart healthcare systems.

Amazon Web Services (AWS) provides on-demand cloud computing platforms and application programming interfaces (APIs) to individuals, companies, and govern- ments, along with distributed computing processing capacity and software tools via AWS server farms. This text presents a hands-on approach for beginners to get started with Amazon Web Services (AWS) in a simple way.

Key Features

The text is primarily written for graduate students, professionals, and academic researchers working in the fields of computer science, engineering, and information technology.

Parul Dubey is currently working as an Assistant professor in the Department of Artificial Intelligence at G H Raisoni College of Engineering, Nagpur, India. She has filed for 15 Indian patents. She is responsible for about 10 publications in conference proceedings, Scopus, and journals. She has contributed book chapters in an edited book published by CRC Press and other reputed publishers. She is also an AWS Certified Cloud Practitioner.

Rohit Raja is working as an associate professor and head in the Department of Information Technology at Guru Ghasidas Vishwavidyalaya, Bilaspur, India. His research interests include facial recognition, signal processing, networking, and data mining. He has pub- lished 100 research papers in various international and national journals (including publications by the IEEE, Springer, etc.) and proceedings of reputed international and national conferences (again including publications by Springer and the IEEE).

Amazon Web Services (AWS) provides on-demand cloud computing platforms and application programming interfaces (APIs) to individuals, companies, and govern- ments, along with distributed computing processing capacity and software tools via AWS server farms. This text presents a hands-on approach for beginners to get started with Amazon Web Services (AWS) in a simple way.

Key Features

The text is primarily written for graduate students, professionals, and academic researchers working in the fields of computer science, engineering, and information technology.

Parul Dubey is currently working as an Assistant professor in the Department of Artificial Intelligence at G H Raisoni College of Engineering, Nagpur, India. She has filed for 15 Indian patents. She is responsible for about 10 publications in conference proceedings, Scopus, and journals. She has contributed book chapters in an edited book published by CRC Press and other reputed publishers. She is also an AWS Certified Cloud Practitioner.

Rohit Raja is working as an associate professor and head in the Department of Information Technology at Guru Ghasidas Vishwavidyalaya, Bilaspur, India. His research interests include facial recognition, signal processing, networking, and data mining. He has pub- lished 100 research papers in various international and national journals (including publications by the IEEE, Springer, etc.) and proceedings of reputed international and national conferences (again including publications by Springer and the IEEE).

The objective of this book is to provide the most relevant information on Human-Computer Interaction to academics, researchers, and students and for those from industry who wish to know more about the real-time application of user interface design.

Human-computer interaction (HCI) is the academic discipline, which most of us think of as UI design, that focuses on how human beings and computers interact at ever-increasing levels of both complexity and simplicity. Because of the importance of the subject, this book aims to provide more relevant information that will be useful to students, academics, and researchers in the industry who wish to know more about its real-time application. In addition to providing content on theory, cognition, design, evaluation, and user diversity, this book also explains the underlying causes of the cognitive, social and organizational problems typically devoted to descriptions of rehabilitation methods for specific cognitive processes. Also described are the new modeling algorithms accessible to cognitive scientists from a variety of different areas.

This book is inherently interdisciplinary and contains original research in computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Since machine learning research has already been carried out for a decade in various applications, the new learning approach is mainly used in machine learning-based cognitive applications. Since this will direct the future research of scientists and researchers working in neuroscience, neuroimaging, machine learning-based brain mapping, and modeling, etc., this book highlights the framework of a novel robust method for advanced cross-industry HCI technologies. These implementation strategies and future research directions will meet the design and application requirements of several modern and real-time applications for a long time to come.

Audience: A wide range of researchers, industry practitioners, and students will be interested in this book including those in artificial intelligence, machine learning, cognition, computer programming and engineering, as well as social sciences such as psychology and linguistics.

The objective of this book is to provide the most relevant information on Human-Computer Interaction to academics, researchers, and students and for those from industry who wish to know more about the real-time application of user interface design.

Human-computer interaction (HCI) is the academic discipline, which most of us think of as UI design, that focuses on how human beings and computers interact at ever-increasing levels of both complexity and simplicity. Because of the importance of the subject, this book aims to provide more relevant information that will be useful to students, academics, and researchers in the industry who wish to know more about its real-time application. In addition to providing content on theory, cognition, design, evaluation, and user diversity, this book also explains the underlying causes of the cognitive, social and organizational problems typically devoted to descriptions of rehabilitation methods for specific cognitive processes. Also described are the new modeling algorithms accessible to cognitive scientists from a variety of different areas.

This book is inherently interdisciplinary and contains original research in computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Since machine learning research has already been carried out for a decade in various applications, the new learning approach is mainly used in machine learning-based cognitive applications. Since this will direct the future research of scientists and researchers working in neuroscience, neuroimaging, machine learning-based brain mapping, and modeling, etc., this book highlights the framework of a novel robust method for advanced cross-industry HCI technologies. These implementation strategies and future research directions will meet the design and application requirements of several modern and real-time applications for a long time to come.

Audience: A wide range of researchers, industry practitioners, and students will be interested in this book including those in artificial intelligence, machine learning, cognition, computer programming and engineering, as well as social sciences such as psychology and linguistics.

The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration.

Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data.

Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth.

The book features:

Audience

Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration.

Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data.

Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth.

The book features:

Audience

Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

The book explores long-term implementation techniques and research paths of ambient intelligence and the Internet of Things that meet the design and application requirements of a variety of modern and real-time applications.

Working environments based on the emerging technologies of ambient intelligence (AmI) and the Internet of Things (IoT) are available for current and future use in the diverse field of applications. The AmI and IoT paradigms aim to help people achieve their daily goals by augmenting physical environments using networks of distributed devices, including sensors, actuators, and computational resources. Because AmI-IoT is the convergence of numerous technologies and associated research fields, it takes significant effort to integrate them to make our lives easier. It is asserted that Am I can successfully analyze the vast amounts of contextual data obtained from such embedded sensors by employing a variety of artificial intelligence (AI) techniques and that it will transparently and proactively change the environment to conform to the requirements of the user. Over time, the long-term research goals and implementation strategies could meet the design and application needs of a wide range of modern and real-time applications.

The 13 chapters in Ambient Intelligence and Internet of Things: Convergent Technologies provide a comprehensive knowledge of the fundamental structure of innovative cutting-edge AmI and IoT technologies as well as practical applications.

Audience

The book will appeal to researchers, industry engineers, and students in artificial and ambient intelligence, the Internet of Things, intelligent systems, electronics and communication, electronics instrumentations, and computer science.

The book explores long-term implementation techniques and research paths of ambient intelligence and the Internet of Things that meet the design and application requirements of a variety of modern and real-time applications.

Working environments based on the emerging technologies of ambient intelligence (AmI) and the Internet of Things (IoT) are available for current and future use in the diverse field of applications. The AmI and IoT paradigms aim to help people achieve their daily goals by augmenting physical environments using networks of distributed devices, including sensors, actuators, and computational resources. Because AmI-IoT is the convergence of numerous technologies and associated research fields, it takes significant effort to integrate them to make our lives easier. It is asserted that Am I can successfully analyze the vast amounts of contextual data obtained from such embedded sensors by employing a variety of artificial intelligence (AI) techniques and that it will transparently and proactively change the environment to conform to the requirements of the user. Over time, the long-term research goals and implementation strategies could meet the design and application needs of a wide range of modern and real-time applications.

The 13 chapters in Ambient Intelligence and Internet of Things: Convergent Technologies provide a comprehensive knowledge of the fundamental structure of innovative cutting-edge AmI and IoT technologies as well as practical applications.

Audience

The book will appeal to researchers, industry engineers, and students in artificial and ambient intelligence, the Internet of Things, intelligent systems, electronics and communication, electronics instrumentations, and computer science.

This book promotes the idea of creative thinking to those working on qualitative and quantitative analysis for reinforcing engineering designs relating to industrial and manufacturing domains.

The book focuses on the development of the theoretical foundations and discusses the utility of integration of critical components and systems engineering for effective systems design. The book presents research directions and can be used as a tool to understand in-depth possible ways related to the management of industrial and manufacturing activities, processes, actions, benchmarks, and so on. The new ideas, theories, and methods related to qualitative and quantitative research will improve manufacturing knowledge linked to the manufacturing process.

Readers will find that the book:

This book promotes the idea of creative thinking to those working on qualitative and quantitative analysis for reinforcing engineering designs relating to industrial and manufacturing domains.

The book focuses on the development of the theoretical foundations and discusses the utility of integration of critical components and systems engineering for effective systems design. The book presents research directions and can be used as a tool to understand in-depth possible ways related to the management of industrial and manufacturing activities, processes, actions, benchmarks, and so on. The new ideas, theories, and methods related to qualitative and quantitative research will improve manufacturing knowledge linked to the manufacturing process.

Readers will find that the book:

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management.

Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology.

This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems.

This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning.

FEATURES

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management.

Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology.

This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems.

This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning.

FEATURES

This book presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. Written as a guide for assessing the roles that these techniques play, the book also highlights implementation strategies, lists problem-solving solutions, and paves the way for future research endeavors in smart and next-generation healthcare systems.

This book provides applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles that various techniques, such as machine learning, fuzzy logic, and statical mathematics, play in the advancements of smart healthcare systems. The book presents the basics as well as the advanced concepts to help beginners, as well as industry professionals, get up to speed on the latest developments in healthcare systems. The book examines descriptive, predictive, and social network techniques and discusses analytical tools and the important role they play in finding solutions to problems in healthcare systems. A framework of robust and novel healthcare techniques is highlighted, as well as implementation strategies and a setup for future research endeavors.

Healthcare Systems Using Soft Computing Techniques is a valuable resource for researchers and postgraduate students in healthcare systems engineering, computer science, information technology, and applied mathematics. The book introduces beginners to—and at the same time brings industry professionals up to speed with—the important role soft computing techniques play in smart healthcare systems.

This book presents soft computing techniques and applications used in healthcare systems, along with the latest advancements. Written as a guide for assessing the roles that these techniques play, the book also highlights implementation strategies, lists problem-solving solutions, and paves the way for future research endeavors in smart and next-generation healthcare systems.

This book provides applications of soft computing techniques related to healthcare systems and can be used as a reference guide for assessing the roles that various techniques, such as machine learning, fuzzy logic, and statical mathematics, play in the advancements of smart healthcare systems. The book presents the basics as well as the advanced concepts to help beginners, as well as industry professionals, get up to speed on the latest developments in healthcare systems. The book examines descriptive, predictive, and social network techniques and discusses analytical tools and the important role they play in finding solutions to problems in healthcare systems. A framework of robust and novel healthcare techniques is highlighted, as well as implementation strategies and a setup for future research endeavors.

Healthcare Systems Using Soft Computing Techniques is a valuable resource for researchers and postgraduate students in healthcare systems engineering, computer science, information technology, and applied mathematics. The book introduces beginners to—and at the same time brings industry professionals up to speed with—the important role soft computing techniques play in smart healthcare systems.

Amazon Web Services (AWS) provides on-demand cloud computing platforms and application programming interfaces (APIs) to individuals, companies, and govern- ments, along with distributed computing processing capacity and software tools via AWS server farms. This text presents a hands-on approach for beginners to get started with Amazon Web Services (AWS) in a simple way.

Key Features

The text is primarily written for graduate students, professionals, and academic researchers working in the fields of computer science, engineering, and information technology.

Parul Dubey is currently working as an Assistant professor in the Department of Artificial Intelligence at G H Raisoni College of Engineering, Nagpur, India. She has filed for 15 Indian patents. She is responsible for about 10 publications in conference proceedings, Scopus, and journals. She has contributed book chapters in an edited book published by CRC Press and other reputed publishers. She is also an AWS Certified Cloud Practitioner.

Rohit Raja is working as an associate professor and head in the Department of Information Technology at Guru Ghasidas Vishwavidyalaya, Bilaspur, India. His research interests include facial recognition, signal processing, networking, and data mining. He has pub- lished 100 research papers in various international and national journals (including publications by the IEEE, Springer, etc.) and proceedings of reputed international and national conferences (again including publications by Springer and the IEEE).

Amazon Web Services (AWS) provides on-demand cloud computing platforms and application programming interfaces (APIs) to individuals, companies, and govern- ments, along with distributed computing processing capacity and software tools via AWS server farms. This text presents a hands-on approach for beginners to get started with Amazon Web Services (AWS) in a simple way.

Key Features

The text is primarily written for graduate students, professionals, and academic researchers working in the fields of computer science, engineering, and information technology.

Parul Dubey is currently working as an Assistant professor in the Department of Artificial Intelligence at G H Raisoni College of Engineering, Nagpur, India. She has filed for 15 Indian patents. She is responsible for about 10 publications in conference proceedings, Scopus, and journals. She has contributed book chapters in an edited book published by CRC Press and other reputed publishers. She is also an AWS Certified Cloud Practitioner.

Rohit Raja is working as an associate professor and head in the Department of Information Technology at Guru Ghasidas Vishwavidyalaya, Bilaspur, India. His research interests include facial recognition, signal processing, networking, and data mining. He has pub- lished 100 research papers in various international and national journals (including publications by the IEEE, Springer, etc.) and proceedings of reputed international and national conferences (again including publications by Springer and the IEEE).

The objective of this book is to provide the most relevant information on Human-Computer Interaction to academics, researchers, and students and for those from industry who wish to know more about the real-time application of user interface design.

Human-computer interaction (HCI) is the academic discipline, which most of us think of as UI design, that focuses on how human beings and computers interact at ever-increasing levels of both complexity and simplicity. Because of the importance of the subject, this book aims to provide more relevant information that will be useful to students, academics, and researchers in the industry who wish to know more about its real-time application. In addition to providing content on theory, cognition, design, evaluation, and user diversity, this book also explains the underlying causes of the cognitive, social and organizational problems typically devoted to descriptions of rehabilitation methods for specific cognitive processes. Also described are the new modeling algorithms accessible to cognitive scientists from a variety of different areas.

This book is inherently interdisciplinary and contains original research in computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Since machine learning research has already been carried out for a decade in various applications, the new learning approach is mainly used in machine learning-based cognitive applications. Since this will direct the future research of scientists and researchers working in neuroscience, neuroimaging, machine learning-based brain mapping, and modeling, etc., this book highlights the framework of a novel robust method for advanced cross-industry HCI technologies. These implementation strategies and future research directions will meet the design and application requirements of several modern and real-time applications for a long time to come.

Audience: A wide range of researchers, industry practitioners, and students will be interested in this book including those in artificial intelligence, machine learning, cognition, computer programming and engineering, as well as social sciences such as psychology and linguistics.

The objective of this book is to provide the most relevant information on Human-Computer Interaction to academics, researchers, and students and for those from industry who wish to know more about the real-time application of user interface design.

Human-computer interaction (HCI) is the academic discipline, which most of us think of as UI design, that focuses on how human beings and computers interact at ever-increasing levels of both complexity and simplicity. Because of the importance of the subject, this book aims to provide more relevant information that will be useful to students, academics, and researchers in the industry who wish to know more about its real-time application. In addition to providing content on theory, cognition, design, evaluation, and user diversity, this book also explains the underlying causes of the cognitive, social and organizational problems typically devoted to descriptions of rehabilitation methods for specific cognitive processes. Also described are the new modeling algorithms accessible to cognitive scientists from a variety of different areas.

This book is inherently interdisciplinary and contains original research in computing, engineering, artificial intelligence, psychology, linguistics, and social and system organization as applied to the design, implementation, application, analysis, and evaluation of interactive systems. Since machine learning research has already been carried out for a decade in various applications, the new learning approach is mainly used in machine learning-based cognitive applications. Since this will direct the future research of scientists and researchers working in neuroscience, neuroimaging, machine learning-based brain mapping, and modeling, etc., this book highlights the framework of a novel robust method for advanced cross-industry HCI technologies. These implementation strategies and future research directions will meet the design and application requirements of several modern and real-time applications for a long time to come.

Audience: A wide range of researchers, industry practitioners, and students will be interested in this book including those in artificial intelligence, machine learning, cognition, computer programming and engineering, as well as social sciences such as psychology and linguistics.

The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration.

Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data.

Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth.

The book features:

Audience

Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration.

Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data.

Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth.

The book features:

Audience

Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

The book explores long-term implementation techniques and research paths of ambient intelligence and the Internet of Things that meet the design and application requirements of a variety of modern and real-time applications.

Working environments based on the emerging technologies of ambient intelligence (AmI) and the Internet of Things (IoT) are available for current and future use in the diverse field of applications. The AmI and IoT paradigms aim to help people achieve their daily goals by augmenting physical environments using networks of distributed devices, including sensors, actuators, and computational resources. Because AmI-IoT is the convergence of numerous technologies and associated research fields, it takes significant effort to integrate them to make our lives easier. It is asserted that Am I can successfully analyze the vast amounts of contextual data obtained from such embedded sensors by employing a variety of artificial intelligence (AI) techniques and that it will transparently and proactively change the environment to conform to the requirements of the user. Over time, the long-term research goals and implementation strategies could meet the design and application needs of a wide range of modern and real-time applications.

The 13 chapters in Ambient Intelligence and Internet of Things: Convergent Technologies provide a comprehensive knowledge of the fundamental structure of innovative cutting-edge AmI and IoT technologies as well as practical applications.

Audience

The book will appeal to researchers, industry engineers, and students in artificial and ambient intelligence, the Internet of Things, intelligent systems, electronics and communication, electronics instrumentations, and computer science.

The book explores long-term implementation techniques and research paths of ambient intelligence and the Internet of Things that meet the design and application requirements of a variety of modern and real-time applications.

Working environments based on the emerging technologies of ambient intelligence (AmI) and the Internet of Things (IoT) are available for current and future use in the diverse field of applications. The AmI and IoT paradigms aim to help people achieve their daily goals by augmenting physical environments using networks of distributed devices, including sensors, actuators, and computational resources. Because AmI-IoT is the convergence of numerous technologies and associated research fields, it takes significant effort to integrate them to make our lives easier. It is asserted that Am I can successfully analyze the vast amounts of contextual data obtained from such embedded sensors by employing a variety of artificial intelligence (AI) techniques and that it will transparently and proactively change the environment to conform to the requirements of the user. Over time, the long-term research goals and implementation strategies could meet the design and application needs of a wide range of modern and real-time applications.

The 13 chapters in Ambient Intelligence and Internet of Things: Convergent Technologies provide a comprehensive knowledge of the fundamental structure of innovative cutting-edge AmI and IoT technologies as well as practical applications.

Audience

The book will appeal to researchers, industry engineers, and students in artificial and ambient intelligence, the Internet of Things, intelligent systems, electronics and communication, electronics instrumentations, and computer science.

This book promotes the idea of creative thinking to those working on qualitative and quantitative analysis for reinforcing engineering designs relating to industrial and manufacturing domains.

The book focuses on the development of the theoretical foundations and discusses the utility of integration of critical components and systems engineering for effective systems design. The book presents research directions and can be used as a tool to understand in-depth possible ways related to the management of industrial and manufacturing activities, processes, actions, benchmarks, and so on. The new ideas, theories, and methods related to qualitative and quantitative research will improve manufacturing knowledge linked to the manufacturing process.

Readers will find that the book:

This book promotes the idea of creative thinking to those working on qualitative and quantitative analysis for reinforcing engineering designs relating to industrial and manufacturing domains.

The book focuses on the development of the theoretical foundations and discusses the utility of integration of critical components and systems engineering for effective systems design. The book presents research directions and can be used as a tool to understand in-depth possible ways related to the management of industrial and manufacturing activities, processes, actions, benchmarks, and so on. The new ideas, theories, and methods related to qualitative and quantitative research will improve manufacturing knowledge linked to the manufacturing process.

Readers will find that the book: