AI, Edge, and IoT Smart Agriculture integrates applications of IoT, edge computing, and data analytics for sustainable agricultural development and introduces Edge of Thing-based data analytics and IoT for predictability of crop, soil, and plant disease occurrence for improved sustainability and increased profitability. The book also addresses precision irrigation, precision horticulture, greenhouse IoT, livestock monitoring, IoT ecosystem for agriculture, mobile robot for precision agriculture, energy monitoring, storage management, and smart farming. The book provides an overarching focus on sustainable environment and sustainable economic development through smart and e-agriculture.

Providing a medium for the exchange of expertise and inspiration, contributions from both smart agriculture and data mining researchers around the world provide foundational insights. The book provides practical application opportunities for the resolution of real-world problems, including contributions from the data mining, data analytics, Edge of Things, and cloud research communities working in the farming production sector. The book offers broad coverage of the concepts, themes, and instruments of this important and evolving area of IOT-based agriculture, Edge of Things and cloud-based farming, Greenhouse IOT, mobile agriculture, sustainable agriculture, and big data analytics in agriculture toward smart farming.

Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation.

The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances.

Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes.

Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR.

Lessons from COVID-19: Impact on Healthcare Systems and Technology uncovers the impact that COVID-19 has made on healthcare and technology industries. State-of-the-art case studies, empirical research, and new trends in technology-mediated solution are discussed to help inform and guide readers in understanding the effects that the COVID-19 outbreak has had across healthcare and technology industries. The book discusses challenges to identify vaccines, changes in legislation on clinical trials and re-purposing of licensed drugs, effects on primary healthcare, best practices adopted by different countries to control the pandemic, and different effects on patients within diverse age groups and comorbidities.

In addition, the book covers technology-mediated solutions and infrastructures applied, digital transformations, modeling techniques, statistical projections, and the benefits and use of cloud computing and artificial intelligence. This is a valuable resource for healthcare professionals, medical doctors, researchers and graduate students from both biomedical and technological fields who are interested in learning more about the use of new technologies to fight a pandemic.

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques.

In short, the volume :

Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India.

Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA.

Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey.

Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal.

Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques.

In short, the volume :

Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India.

Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA.

Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey.

Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal.

Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

Blockchain technology is an emerging distributed, decentralized architecture and computing paradigm, which has accelerated the development and application of cloud, fog and edge computing; artificial intelligence; cyber physical systems; social networking; crowdsourcing and crowdsensing; 5g; trust management and finance; and other many useful sectors.

Nowadays, the primary blockchain technology uses are in information systems to keep information secure and private. However, many threats and vulnerabilities are facing blockchain in the past decade such 51% attacks, double spending attacks, etc. The popularity and rapid development of blockchain brings many technical and regulatory challenges for research and academic communities. The main goal of this book is to encourage both researchers and practitioners of Blockchain technology to share and exchange their experiences and recent studies between academia and industry.

The reader will be provided with the most up-to-date knowledge of blockchain in mainstream areas of security and privacy in the decentralized domain, which is timely and essential (this is due to the fact that the distributed and p2p applications are increasing day-by-day, and the attackers adopt new mechanisms to threaten the security and privacy of the users in those environments). This book provides a detailed explanation of security and privacy with respect to blockchain for information systems, and will be an essential resource for students, researchers and scientists studying blockchain uses in information systems and those wanting to explore the current state of play.

Blockchain technology is an emerging distributed, decentralized architecture and computing paradigm, which has accelerated the development and application of cloud, fog and edge computing; artificial intelligence; cyber physical systems; social networking; crowdsourcing and crowdsensing; 5g; trust management and finance; and other many useful sectors.

Nowadays, the primary blockchain technology uses are in information systems to keep information secure and private. However, many threats and vulnerabilities are facing blockchain in the past decade such 51% attacks, double spending attacks, etc. The popularity and rapid development of blockchain brings many technical and regulatory challenges for research and academic communities. The main goal of this book is to encourage both researchers and practitioners of Blockchain technology to share and exchange their experiences and recent studies between academia and industry.

The reader will be provided with the most up-to-date knowledge of blockchain in mainstream areas of security and privacy in the decentralized domain, which is timely and essential (this is due to the fact that the distributed and p2p applications are increasing day-by-day, and the attackers adopt new mechanisms to threaten the security and privacy of the users in those environments). This book provides a detailed explanation of security and privacy with respect to blockchain for information systems, and will be an essential resource for students, researchers and scientists studying blockchain uses in information systems and those wanting to explore the current state of play.

The wide gap between the existing security solutions and the actual practical deployment in smart manufacturing, smart home, and remote environments (with respect to wireless robotics) is one of the major reasons why we require novel strategies, mechanisms, architectures, and frameworks. Furthermore, it is also important to access and understand the different level of vulnerabilities and attack vectors in Wireless Sensor Network (WSN) and Wireless Robotics. This book includes an in-depth explanation of a secure and dependable Wireless Robotics (WR) architecture, to ensure confidentiality, authenticity, and availability.

Features

The book is especially useful for academic researchers, undergraduate students, postgraduate students, and industry researchers and professionals.

The wide gap between the existing security solutions and the actual practical deployment in smart manufacturing, smart home, and remote environments (with respect to wireless robotics) is one of the major reasons why we require novel strategies, mechanisms, architectures, and frameworks. Furthermore, it is also important to access and understand the different level of vulnerabilities and attack vectors in Wireless Sensor Network (WSN) and Wireless Robotics. This book includes an in-depth explanation of a secure and dependable Wireless Robotics (WR) architecture, to ensure confidentiality, authenticity, and availability.

Features

The book is especially useful for academic researchers, undergraduate students, postgraduate students, and industry researchers and professionals.

The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.

FEATURES

The text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.

The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.

FEATURES

The text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.

AI, Edge, and IoT Smart Agriculture integrates applications of IoT, edge computing, and data analytics for sustainable agricultural development and introduces Edge of Thing-based data analytics and IoT for predictability of crop, soil, and plant disease occurrence for improved sustainability and increased profitability. The book also addresses precision irrigation, precision horticulture, greenhouse IoT, livestock monitoring, IoT ecosystem for agriculture, mobile robot for precision agriculture, energy monitoring, storage management, and smart farming. The book provides an overarching focus on sustainable environment and sustainable economic development through smart and e-agriculture.

Providing a medium for the exchange of expertise and inspiration, contributions from both smart agriculture and data mining researchers around the world provide foundational insights. The book provides practical application opportunities for the resolution of real-world problems, including contributions from the data mining, data analytics, Edge of Things, and cloud research communities working in the farming production sector. The book offers broad coverage of the concepts, themes, and instruments of this important and evolving area of IOT-based agriculture, Edge of Things and cloud-based farming, Greenhouse IOT, mobile agriculture, sustainable agriculture, and big data analytics in agriculture toward smart farming.

Artificial Intelligence for Neurological Disorders provides a comprehensive resource of state-of-the-art approaches for AI, big data analytics and machine learning-based neurological research. The book discusses many machine learning techniques to detect neurological diseases at the cellular level, as well as other applications such as image segmentation, classification and image indexing, neural networks and image processing methods. Chapters include AI techniques for the early detection of neurological disease and deep learning applications using brain imaging methods like EEG, MEG, fMRI, fNIRS and PET for seizure prediction or neuromuscular rehabilitation.

The goal of this book is to provide readers with broad coverage of these methods to encourage an even wider adoption of AI, Machine Learning and Big Data Analytics for problem-solving and stimulating neurological research and therapy advances.

Data Science for Genomics presents the foundational concepts of data science as they pertain to genomics, encompassing the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions and supporting decision-making. Sections cover Data Science, Machine Learning, Deep Learning, data analysis, and visualization techniques. The authors then present the fundamentals of Genomics, Genetics, Transcriptomes and Proteomes as basic concepts of molecular biology, along with DNA and key features of the human genome, as well as the genomes of eukaryotes and prokaryotes.

Techniques that are more specifically used for studying genomes are then described in the order in which they are used in a genome project, including methods for constructing genetic and physical maps. DNA sequencing methodology and the strategies used to assemble a contiguous genome sequence and methods for identifying genes in a genome sequence and determining the functions of those genes in the cell. Readers will learn how the information contained in the genome is released and made available to the cell, as well as methods centered on cloning and PCR.

Lessons from COVID-19: Impact on Healthcare Systems and Technology uncovers the impact that COVID-19 has made on healthcare and technology industries. State-of-the-art case studies, empirical research, and new trends in technology-mediated solution are discussed to help inform and guide readers in understanding the effects that the COVID-19 outbreak has had across healthcare and technology industries. The book discusses challenges to identify vaccines, changes in legislation on clinical trials and re-purposing of licensed drugs, effects on primary healthcare, best practices adopted by different countries to control the pandemic, and different effects on patients within diverse age groups and comorbidities.

In addition, the book covers technology-mediated solutions and infrastructures applied, digital transformations, modeling techniques, statistical projections, and the benefits and use of cloud computing and artificial intelligence. This is a valuable resource for healthcare professionals, medical doctors, researchers and graduate students from both biomedical and technological fields who are interested in learning more about the use of new technologies to fight a pandemic.

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques.

In short, the volume :

Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India.

Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA.

Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey.

Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal.

Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques.

In short, the volume :

Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India.

Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA.

Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey.

Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal.

Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.

Blockchain technology is an emerging distributed, decentralized architecture and computing paradigm, which has accelerated the development and application of cloud, fog and edge computing; artificial intelligence; cyber physical systems; social networking; crowdsourcing and crowdsensing; 5g; trust management and finance; and other many useful sectors.

Nowadays, the primary blockchain technology uses are in information systems to keep information secure and private. However, many threats and vulnerabilities are facing blockchain in the past decade such 51% attacks, double spending attacks, etc. The popularity and rapid development of blockchain brings many technical and regulatory challenges for research and academic communities. The main goal of this book is to encourage both researchers and practitioners of Blockchain technology to share and exchange their experiences and recent studies between academia and industry.

The reader will be provided with the most up-to-date knowledge of blockchain in mainstream areas of security and privacy in the decentralized domain, which is timely and essential (this is due to the fact that the distributed and p2p applications are increasing day-by-day, and the attackers adopt new mechanisms to threaten the security and privacy of the users in those environments). This book provides a detailed explanation of security and privacy with respect to blockchain for information systems, and will be an essential resource for students, researchers and scientists studying blockchain uses in information systems and those wanting to explore the current state of play.

Blockchain technology is an emerging distributed, decentralized architecture and computing paradigm, which has accelerated the development and application of cloud, fog and edge computing; artificial intelligence; cyber physical systems; social networking; crowdsourcing and crowdsensing; 5g; trust management and finance; and other many useful sectors.

Nowadays, the primary blockchain technology uses are in information systems to keep information secure and private. However, many threats and vulnerabilities are facing blockchain in the past decade such 51% attacks, double spending attacks, etc. The popularity and rapid development of blockchain brings many technical and regulatory challenges for research and academic communities. The main goal of this book is to encourage both researchers and practitioners of Blockchain technology to share and exchange their experiences and recent studies between academia and industry.

The reader will be provided with the most up-to-date knowledge of blockchain in mainstream areas of security and privacy in the decentralized domain, which is timely and essential (this is due to the fact that the distributed and p2p applications are increasing day-by-day, and the attackers adopt new mechanisms to threaten the security and privacy of the users in those environments). This book provides a detailed explanation of security and privacy with respect to blockchain for information systems, and will be an essential resource for students, researchers and scientists studying blockchain uses in information systems and those wanting to explore the current state of play.

The wide gap between the existing security solutions and the actual practical deployment in smart manufacturing, smart home, and remote environments (with respect to wireless robotics) is one of the major reasons why we require novel strategies, mechanisms, architectures, and frameworks. Furthermore, it is also important to access and understand the different level of vulnerabilities and attack vectors in Wireless Sensor Network (WSN) and Wireless Robotics. This book includes an in-depth explanation of a secure and dependable Wireless Robotics (WR) architecture, to ensure confidentiality, authenticity, and availability.

Features

The book is especially useful for academic researchers, undergraduate students, postgraduate students, and industry researchers and professionals.

The wide gap between the existing security solutions and the actual practical deployment in smart manufacturing, smart home, and remote environments (with respect to wireless robotics) is one of the major reasons why we require novel strategies, mechanisms, architectures, and frameworks. Furthermore, it is also important to access and understand the different level of vulnerabilities and attack vectors in Wireless Sensor Network (WSN) and Wireless Robotics. This book includes an in-depth explanation of a secure and dependable Wireless Robotics (WR) architecture, to ensure confidentiality, authenticity, and availability.

Features

The book is especially useful for academic researchers, undergraduate students, postgraduate students, and industry researchers and professionals.

The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.

FEATURES

The text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.

The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.

FEATURES

The text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.