Shaveta Malik – författare

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 Blockchain technology for securing data at end/server side Emerging technologies/networking, like Cloud, Edge, Fog, etc., for communicating and storing data (securely). Various open issues, challenges faced in this era towards wireless robotics, including several future research directions for the future. Several real world’s case studies are included Chapters on ethical concerns and privacy laws, i.e., laws for service providers Security and privacy challenges in wireless sensor networks and wireless roboticsThe 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 Blockchain technology for securing data at end/server side Emerging technologies/networking, like Cloud, Edge, Fog, etc., for communicating and storing data (securely). Various open issues, challenges faced in this era towards wireless robotics, including several future research directions for the future. Several real world’s case studies are included Chapters on ethical concerns and privacy laws, i.e., laws for service providers Security and privacy challenges in wireless sensor networks and wireless roboticsThe 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

Blockchain technology for securing data at end/server side

Emerging technologies/networking, like Cloud, Edge, Fog, etc., for communicating and storing data (securely).

Various open issues, challenges faced in this era towards wireless robotics, including several future research directions for the future.

Several real world’s case studies are included

Chapters on ethical concerns and privacy laws, i.e., laws for service providers

Security and privacy challenges in wireless sensor networks and wireless robotics

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

Blockchain technology for securing data at end/server side

Emerging technologies/networking, like Cloud, Edge, Fog, etc., for communicating and storing data (securely).

Various open issues, challenges faced in this era towards wireless robotics, including several future research directions for the future.

Several real world’s case studies are included

Chapters on ethical concerns and privacy laws, i.e., laws for service providers

Security and privacy challenges in wireless sensor networks and wireless robotics

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

This new volume explores how the merging of interactive multimedia with artificial intelligence has created new and advanced tools in healthcare. It looks at how the latest technologies (artificial intelligence, deep learning, machine learning, big data, IoT, smart device, etc.) help to manage health data, diagnose health issues, monitor treatment, predict pandemic diseases, and more.

The book covers several important applications of multimedia in healthcare, including for data visualization purposes, for computer vision for elder healthcare monitoring, for detection of lung nodules, for management systems using machine learning techniques, and for fusion applications in medical image processing. The chapter authors discuss using data mining and machine learning techniques for COVID-19 diagnosis and prediction, in detecting knee osteoarthritis using texture descriptor algorithms, in applying algorithms in fetal ECG enhancement using blockchain for wearable internet of things in healthcare, and more. A chapter also reviews how doctors can make good use of genomics and genetic data through advanced technology. The book concludes with discussions of open issues, challenges, and future research directions for using intelligent interactive multimedia in healthcare.

Key features:

Provides an in-depth understanding of emerging technologies and integration of artificial intelligence, deep learning, big data, IoT in healthcare Details specific applications for the use of AI, big data, and IoT in healthcare Discusses how AI technology can help in formulating protective measures for COVID-19 and other diseases Includes case studies

Intelligent Interactive Multimedia Systems for e-Healthcare Applications will be valuable to undergraduate and graduate students planning their careers in either industry or research and to software engineers for using multimedia with artificial intelligence, deep learning, big data, and IoT for healthcare applications.

This new volume explores how the merging of interactive multimedia with artificial intelligence has created new and advanced tools in healthcare. It looks at how the latest technologies (artificial intelligence, deep learning, machine learning, big data, IoT, smart device, etc.) help to manage health data, diagnose health issues, monitor treatment, predict pandemic diseases, and more.

The book covers several important applications of multimedia in healthcare, including for data visualization purposes, for computer vision for elder healthcare monitoring, for detection of lung nodules, for management systems using machine learning techniques, and for fusion applications in medical image processing. The chapter authors discuss using data mining and machine learning techniques for COVID-19 diagnosis and prediction, in detecting knee osteoarthritis using texture descriptor algorithms, in applying algorithms in fetal ECG enhancement using blockchain for wearable internet of things in healthcare, and more. A chapter also reviews how doctors can make good use of genomics and genetic data through advanced technology. The book concludes with discussions of open issues, challenges, and future research directions for using intelligent interactive multimedia in healthcare.

Key features:

Provides an in-depth understanding of emerging technologies and integration of artificial intelligence, deep learning, big data, IoT in healthcare Details specific applications for the use of AI, big data, and IoT in healthcare Discusses how AI technology can help in formulating protective measures for COVID-19 and other diseases Includes case studies

Intelligent Interactive Multimedia Systems for e-Healthcare Applications will be valuable to undergraduate and graduate students planning their careers in either industry or research and to software engineers for using multimedia with artificial intelligence, deep learning, big data, and IoT for healthcare applications.

Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.

Advanced Analytics and Deep Learning Models

The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc.

Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools.

However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc.

This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence.

Audience

Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.

Advanced Analytics and Deep Learning Models

The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc.

Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools.

However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc.

This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence.

Audience

Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.

This new volume explores how the merging of interactive multimedia with artificial intelligence has created new and advanced tools in healthcare. It looks at how the latest technologies (artificial intelligence, deep learning, machine learning, big data, IoT, smart device, etc.) help to manage health data, diagnose health issues, monitor treatment, predict pandemic diseases, and more.The book covers several important applications of multimedia in healthcare, including for data visualization purposes, for computer vision for elder healthcare monitoring, for detection of lung nodules, for management systems using machine learning techniques, and for fusion applications in medical image processing. The chapter authors discuss using data mining and machine learning techniques for COVID-19 diagnosis and prediction, in detecting knee osteoarthritis using texture descriptor algorithms, in applying algorithms in fetal ECG enhancement using blockchain for wearable internet of things in healthcare, and more. A chapter also reviews how doctors can make good use of genomics and genetic data through advanced technology. The book concludes with discussions of open issues, challenges, and future research directions for using intelligent interactive multimedia in healthcare.Key features:Provides an in-depth understanding of emerging technologies and integration of artificial intelligence, deep learning, big data, IoT in healthcareDetails specific applications for the use of AI, big data, and IoT in healthcareDiscusses how AI technology can help in formulating protective measures for COVID-19 and other diseasesIncludes case studiesIntelligent Interactive Multimedia Systems for e-Healthcare Applications will be valuable to undergraduate and graduate students planning their careers in either industry or research and to software engineers for using multimedia with artificial intelligence, deep learning, big data, and IoT for healthcare applications.