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This handbook introduces the basic principles and fundamentals of
cyber security towards establishing an understanding of how to
protect computers from hackers and adversaries. The highly
informative subject matter of this handbook, includes various
concepts, models, and terminologies along with examples and
illustrations to demonstrate substantial technical details of the
field. It motivates the readers to exercise better protection and
defense mechanisms to deal with attackers and mitigate the
situation. This handbook also outlines some of the exciting areas
of future research where the existing approaches can be
implemented. Exponential increase in the use of computers as a
means of storing and retrieving security-intensive information,
requires placement of adequate security measures to safeguard the
entire computing and communication scenario. With the advent of
Internet and its underlying technologies, information security
aspects are becoming a prime concern towards protecting the
networks and the cyber ecosystem from variety of threats, which is
illustrated in this handbook. This handbook primarily targets
professionals in security, privacy and trust to use and improve the
reliability of businesses in a distributed manner, as well as
computer scientists and software developers, who are seeking to
carry out research and develop software in information and cyber
security. Researchers and advanced-level students in computer
science will also benefit from this reference.
This book addresses the difficult task of integrating computational
techniques with virtual reality and healthcare. It discusses the
use of virtual reality in various areas, such as healthcare,
cognitive and behavioural training, understanding mathematical
graphs, human-computer interaction, fluid dynamics in healthcare
industries, accurate real-time simulation, and healthcare
diagnostics. Presenting the computational techniques for virtual
reality in healthcare, it is a valuable reference resource for
professionals at educational institutes as well as researchers,
scientists, engineers and practitioners in industry.
This book aims to provide a detailed understanding of
IoMT-supported applications while engaging premium smart computing
methods and improved algorithms in the field of computer science.
It contains thirteen chapters discussing various applications under
the umbrella of the Internet of Medical Things. These applications
geared towards IoMT cloud analysis, machine learning, computer
vision and deep learning have enabled the evaluation of the
proposed solutions.
Social media sites are constantly evolving with huge amounts of
scattered data or big data, which makes it difficult for
researchers to trace the information flow. It is a daunting task to
extract a useful piece of information from the vast unstructured
big data; the disorganized structure of social media contains data
in various forms such as text and videos as well as huge real-time
data on which traditional analytical methods like statistical
approaches fail miserably. Due to this, there is a need for
efficient data mining techniques that can overcome the shortcomings
of the traditional approaches. Data Mining Approaches for Big Data
and Sentiment Analysis in Social Media encourages researchers to
explore the key concepts of data mining, such as how they can be
utilized on online social media platforms, and provides advances on
data mining for big data and sentiment analysis in online social
media, as well as future research directions. Covering a range of
concepts from machine learning methods to data mining for big data
analytics, this book is ideal for graduate students, academicians,
faculty members, scientists, researchers, data analysts, social
media analysts, managers, and software developers who are seeking
to learn and carry out research in the area of data mining for big
data and sentiment.
Since its first appearance, artificial intelligence has been
ensuring revolutionary outcomes in the context of real-world
problems. At this point, it has strong relations with biomedical
and today’s intelligent systems compete with human capabilities
in medical tasks. However, advanced use of artificial intelligence
causes intelligent systems to be black-box. That situation is not
good for building trustworthy intelligent systems in medical
applications. For a remarkable amount of time, researchers have
tried to solve the black-box issue by using modular additions,
which have led to the rise of the term: interpretable artificial
intelligence. As the literature matured (as a result of, in
particular, deep learning), that term transformed into explainable
artificial intelligence (XAI). This book provides an essential
edited work regarding the latest advancements in explainable
artificial intelligence (XAI) for biomedical applications. It
includes not only introductive perspectives but also applied
touches and discussions regarding critical problems as well as
future insights. Topics discussed in the book include: XAI for the
applications with medical images XAI use cases for alternative
medical data/task Different XAI methods for biomedical applications
Reviews for the XAI research for critical biomedical problems.
Explainable Artificial Intelligence for Biomedical Applications is
ideal for academicians, researchers, students, engineers, and
experts from the fields of computer science, biomedical, medical,
and health sciences. It also welcomes all readers of different
fields to be informed about use cases of XAI in black-box
artificial intelligence. In this sense, the book can be used for
both teaching and reference source purposes.
This book covers the latest research studies regarding Explainable
Machine Learning used in multimedia-based healthcare applications.
In this context, the content includes not only introductions for
applied research efforts but also theoretical touches and
discussions targeting open problems as well as future insights. In
detail, a comprehensive topic coverage is ensured by focusing on
remarkable healthcare problems solved with Artificial Intelligence.
Because today’s conditions in medical data processing are often
associated with multimedia, the book considers research studies
with especially multimedia data processing.
This book presents research on how interpretable cognitive IoT can
work to help with the massive amount of data in the healthcare
industry. The authors give importance to IoT systems with intense
machine learning features; this ensures the scope corresponds to
use of cognitive IoT for understanding, reasoning, and learning
from medical data. The authors discuss the interpretability of an
intelligent system and its trustworthiness as a smart tool in the
context of massive healthcare applications. As a whole, book
combines three important topics: massive data, cognitive IoT, and
interpretability. Topics include health data analytics for
cognitive IoT, usability evaluation of cognitive IoT for
healthcare, interpretable cognitive IoT for health robotics, and
wearables in the context of IoT for healthcare. The book acts as a
useful reference work for a wide audience including academicians,
scientists, students, and professionals.
This book covers the latest advancements in the areas of machine
learning, computer vision, pattern recognition, computational
learning theory, big data analytics, network intelligence, signal
processing, and their applications in real world. The topics
covered in machine learning involve feature extraction, variants of
support vector machine (SVM), extreme learning machine (ELM),
artificial neural network (ANN), and other areas in machine
learning. The mathematical analysis of computer vision and pattern
recognition involves the use of geometric techniques, scene
understanding and modeling from video, 3D object recognition,
localization and tracking, medical image analysis, and so on.
Computational learning theory involves different kinds of learning
like incremental, online, reinforcement, manifold, multitask,
semi-supervised, etc. Further, it covers the real-time challenges
involved while processing big data analytics and stream processing
with the integration of smart data computing services and
interconnectivity. Additionally, it covers the recent developments
to network intelligence for analyzing the network information and
thereby adapting the algorithms dynamically to improve the
efficiency. In the last, it includes the progress in signal
processing to process the normal and abnormal categories of
real-world signals, for instance signals generated from IoT
devices, smart systems, speech, videos, etc., and involves
biomedical signal processing: electrocardiogram (ECG),
electroencephalogram (EEG), magnetoencephalography (MEG), and
electromyogram (EMG).
This book presents a comprehensive study of different tools and
techniques available to perform network forensics. Also, various
aspects of network forensics are reviewed as well as related
technologies and their limitations. This helps security
practitioners and researchers in better understanding of the
problem, current solution space, and future research scope to
detect and investigate various network intrusions against such
attacks efficiently. Forensic computing is rapidly gaining
importance since the amount of crime involving digital systems is
steadily increasing. Furthermore, the area is still underdeveloped
and poses many technical and legal challenges. The rapid
development of the Internet over the past decade appeared to have
facilitated an increase in the incidents of online attacks. There
are many reasons which are motivating the attackers to be fearless
in carrying out the attacks. For example, the speed with which an
attack can be carried out, the anonymity provided by the medium,
nature of medium where digital information is stolen without
actually removing it, increased availability of potential victims
and the global impact of the attacks are some of the aspects.
Forensic analysis is performed at two different levels: Computer
Forensics and Network Forensics. Computer forensics deals with the
collection and analysis of data from computer systems, networks,
communication streams and storage media in a manner admissible in a
court of law. Network forensics deals with the capture, recording
or analysis of network events in order to discover evidential
information about the source of security attacks in a court of law.
Network forensics is not another term for network security. It is
an extended phase of network security as the data for forensic
analysis are collected from security products like firewalls and
intrusion detection systems. The results of this data analysis are
utilized for investigating the attacks. Network forensics generally
refers to the collection and analysis of network data such as
network traffic, firewall logs, IDS logs, etc. Technically, it is a
member of the already-existing and expanding the field of digital
forensics. Analogously, network forensics is defined as "The use of
scientifically proved techniques to collect, fuses, identifies,
examine, correlate, analyze, and document digital evidence from
multiple, actively processing and transmitting digital sources for
the purpose of uncovering facts related to the planned intent, or
measured success of unauthorized activities meant to disrupt,
corrupt, and or compromise system components as well as providing
information to assist in response to or recovery from these
activities." Network forensics plays a significant role in the
security of today's organizations. On the one hand, it helps to
learn the details of external attacks ensuring similar future
attacks are thwarted. Additionally, network forensics is essential
for investigating insiders' abuses that constitute the second
costliest type of attack within organizations. Finally, law
enforcement requires network forensics for crimes in which a
computer or digital system is either being the target of a crime or
being used as a tool in carrying a crime. Network security protects
the system against attack while network forensics focuses on
recording evidence of the attack. Network security products are
generalized and look for possible harmful behaviors. This
monitoring is a continuous process and is performed all through the
day. However, network forensics involves post mortem investigation
of the attack and is initiated after crime notification. There are
many tools which assist in capturing data transferred over the
networks so that an attack or the malicious intent of the
intrusions may be investigated. Similarly, various network forensic
frameworks are proposed in the literature.
Because it makes the distribution and transmission of digital
information much easier and more cost effective, multimedia has
emerged as a top resource in the modern era. In spite of the
opportunities that multimedia creates for businesses and companies,
information sharing remains vulnerable to cyber attacks and hacking
due to the open channels in which this data is being transmitted.
Protecting the authenticity and confidentiality of information is a
top priority for all professional fields that currently use
multimedia practices for distributing digital data. The Handbook of
Research on Multimedia Cyber Security provides emerging research
exploring the theoretical and practical aspects of current security
practices and techniques within multimedia information and
assessing modern challenges. Featuring coverage on a broad range of
topics such as cryptographic protocols, feature extraction, and
chaotic systems, this book is ideally designed for scientists,
researchers, developers, security analysts, network administrators,
scholars, IT professionals, educators, and students seeking current
research on developing strategies in multimedia security.
This book covers latest advancements in the areas of machine
learning, computer vision, pattern recognition, computational
learning theory, big data analytics, network intelligence, signal
processing and their applications in real world. The topics covered
in machine learning involves feature extraction, variants of
support vector machine (SVM), extreme learning machine (ELM),
artificial neural network (ANN) and other areas in machine
learning. The mathematical analysis of computer vision and pattern
recognition involves the use of geometric techniques, scene
understanding and modelling from video, 3D object recognition,
localization and tracking, medical image analysis and so on.
Computational learning theory involves different kinds of learning
like incremental, online, reinforcement, manifold, multi-task,
semi-supervised, etc. Further, it covers the real-time challenges
involved while processing big data analytics and stream processing
with the integration of smart data computing services and
interconnectivity. Additionally, it covers the recent developments
to network intelligence for analyzing the network information and
thereby adapting the algorithms dynamically to improve the
efficiency. In the last, it includes the progress in signal
processing to process the normal and abnormal categories of
real-world signals, for instance signals generated from IoT
devices, smart systems, speech, videos, etc., and involves
biomedical signal processing: electrocardiogram (ECG),
electroencephalogram (EEG), magnetoencephalography (MEG) and
electromyogram (EMG).
New prospects for biomedical and healthcare engineering are being
created by the rapid development of Robotic and Artificial
Intelligence techniques. Innovative technologies such as Artificial
Intelligence, Deep Learning, Robotics, and IoT are currently under
huge influence in today's modern world. For instance, a micro-nano
robot allows us to study the fundamental problems at a cellular
scale owing to its precise positioning and manipulation ability;
the medical robot paves a new way for the low-invasive and
high-efficient clinical operation, and rehabilitation robotics is
able to improve the rehabilitative efficacy of patients. This book
aims at exhibiting the latest research achievements, findings, and
ideas in the field of robotics in biomedical and healthcare
engineering, primarily focusing on the walking assistive robot,
telerobotic surgery, upper/lower limb rehabilitation, and
radiosurgery. As a result, a wide range of robots are being
developed to serve a variety of roles within the medical
environment. Robots specializing in human treatment include
surgical robots and rehabilitation robots. The field of assistive
and therapeutic robotic devices is also expanding rapidly. These
include robots that help patients rehabilitate from severe
conditions like strokes, empathic robots that assist in the care of
older or physically/mentally challenged individuals, and industrial
robots that take on a variety of routine tasks, such as sterilizing
rooms and delivering medical supplies and equipment, including
medications. The objectives of the book are in terms of advancing
the state-of-the-art of robotic techniques and addressing the
challenging problems in biomedical and healthcare engineering. This
book Lays a good foundation for the core concepts and principles of
robotics in biomedical and healthcare engineering, walking the
reader through the fundamental ideas with expert ease. Progresses
on the topics in a step-by-step manner and reinforces theory with a
full-fledged pedagogy designed to enhance students' understanding
and offer them a practical insight into the applications of it.
Features chapters that introduce and cover novel ideas in
healthcare engineering like Applications of Robots in Surgery,
Microrobots and Nanorobots in Healthcare Practices, Intelligent
Walker for Posture Monitoring, AI-Powered Robots in Biomedical and
Hybrid Intelligent Systems for Medical Diagnosis, and so on. Deepak
Gupta is an Assistant Professor at the Maharaja Agrasen Institute
of Technology, GGSIPU, Delhi, India. Moolchand Sharma is an
Assistant Professor at the Maharaja Agrasen Institute of
Technology, GGSIPU, Delhi, India. Vikas Chaudhary is a Professor at
the JIMS Engineering Management Technical Campus, GGSIPU, Greater
Noida, India. Ashish Khanna currently works at the Maharaja Agrasen
Institute of Technology, GGSIPU, Delhi, India.
This book discusses research in Artificial Intelligence for the
Internet of Health Things. It investigates and explores the
possible applications of machine learning, deep learning, soft
computing, and evolutionary computing techniques in design,
implementation, and optimization of challenging healthcare
solutions. This book features a wide range of topics such as AI
techniques, IoT, cloud, wearables, and secured data transmission.
Written for a broad audience, this book will be useful for
clinicians, health professionals, engineers, technology developers,
IT consultants, researchers, and students interested in the
AI-based healthcare applications. Provides a deeper understanding
of key AI algorithms and their use and implementation within the
wider healthcare sector Explores different disease diagnosis models
using machine learning, deep learning, healthcare data analysis,
including machine learning, and data mining and soft computing
algorithms Discusses detailed IoT, wearables, and cloud-based
disease diagnosis model for intelligent systems and healthcare
Reviews different applications and challenges across the design,
implementation, and management of intelligent systems and
healthcare data networks Introduces a new applications and case
studies across all areas of AI in healthcare data K. Shankar
(Member, IEEE) is a Postdoctoral Fellow of the Department of
Computer Applications, Alagappa University, Karaikudi, India.
Eswaran Perumal is an Assistant Professor of the Department of
Computer Applications, Alagappa University, Karaikudi, India. Dr.
Deepak Gupta is an Assistant Professor of the Department Computer
Science & Engineering, Maharaja Agrasen Institute of Technology
(GGSIPU), Delhi, India.
New prospects for biomedical and healthcare engineering are being
created by the rapid development of Robotic and Artificial
Intelligence techniques. Innovative technologies such as Artificial
Intelligence, Deep Learning, Robotics, and IoT are currently under
huge influence in today’s modern world. For instance, a
micro-nano robot allows us to study the fundamental problems at a
cellular scale owing to its precise positioning and manipulation
ability; the medical robot paves a new way for the low-invasive and
high-efficient clinical operation, and rehabilitation robotics is
able to improve the rehabilitative efficacy of patients. This book
aims at exhibiting the latest research achievements, findings, and
ideas in the field of robotics in biomedical and healthcare
engineering, primarily focusing on the walking assistive robot,
telerobotic surgery, upper/lower limb rehabilitation, and
radiosurgery. As a result, a wide range of robots are being
developed to serve a variety of roles within the medical
environment. Robots specializing in human treatment include
surgical robots and rehabilitation robots. The field of assistive
and therapeutic robotic devices is also expanding rapidly. These
include robots that help patients rehabilitate from severe
conditions like strokes, empathic robots that assist in the care of
older or physically/mentally challenged individuals, and industrial
robots that take on a variety of routine tasks, such as sterilizing
rooms and delivering medical supplies and equipment, including
medications. The objectives of the book are in terms of advancing
the state-of-the-art of robotic techniques and addressing the
challenging problems in biomedical and healthcare engineering. This
book Lays a good foundation for the core concepts and principles of
robotics in biomedical and healthcare engineering, walking the
reader through the fundamental ideas with expert ease. Progresses
on the topics in a step-by-step manner and reinforces theory with a
full-fledged pedagogy designed to enhance students’ understanding
and offer them a practical insight into the applications of it.
Features chapters that introduce and cover novel ideas in
healthcare engineering like Applications of Robots in Surgery,
Microrobots and Nanorobots in Healthcare Practices, Intelligent
Walker for Posture Monitoring, AI-Powered Robots in Biomedical and
Hybrid Intelligent Systems for Medical Diagnosis, and so on. Deepak
Gupta is an Assistant Professor at the Maharaja Agrasen Institute
of Technology, GGSIPU, Delhi, India. Moolchand Sharma is an
Assistant Professor at the Maharaja Agrasen Institute of
Technology, GGSIPU, Delhi, India. Vikas Chaudhary is a Professor at
the JIMS Engineering Management Technical Campus, GGSIPU, Greater
Noida, India. Ashish Khanna currently works at the Maharaja Agrasen
Institute of Technology, GGSIPU, Delhi, India.
This book discusses research in Artificial Intelligence for the
Internet of Health Things. It investigates and explores the
possible applications of machine learning, deep learning, soft
computing, and evolutionary computing techniques in design,
implementation, and optimization of challenging healthcare
solutions. This book features a wide range of topics such as AI
techniques, IoT, cloud, wearables, and secured data transmission.
Written for a broad audience, this book will be useful for
clinicians, health professionals, engineers, technology developers,
IT consultants, researchers, and students interested in the
AI-based healthcare applications. Provides a deeper understanding
of key AI algorithms and their use and implementation within the
wider healthcare sector Explores different disease diagnosis models
using machine learning, deep learning, healthcare data analysis,
including machine learning, and data mining and soft computing
algorithms Discusses detailed IoT, wearables, and cloud-based
disease diagnosis model for intelligent systems and healthcare
Reviews different applications and challenges across the design,
implementation, and management of intelligent systems and
healthcare data networks Introduces a new applications and case
studies across all areas of AI in healthcare data K. Shankar
(Member, IEEE) is a Postdoctoral Fellow of the Department of
Computer Applications, Alagappa University, Karaikudi, India.
Eswaran Perumal is an Assistant Professor of the Department of
Computer Applications, Alagappa University, Karaikudi, India. Dr.
Deepak Gupta is an Assistant Professor of the Department Computer
Science & Engineering, Maharaja Agrasen Institute of Technology
(GGSIPU), Delhi, India.
This book presents the augmented reality (AR) and virtual reality
(VR) automotive applications. It unites automobile with a leading
technology i.e. augmented and virtual reality and uses the
advantages of the latter to solve the problems faced by the former.
The book highlights the reasons for the growing abundance and
complexity in this sector. Virtual and augmented reality presents a
powerful engineering tool that finds application in various
engineering fields. It brings new possibilities that result is
increasing of productivity and reliability of production, quality
of products and processes. The book further illustrates the
possible challenges in its applications and suggests ways to
overcome them. The book includes nine chapters focusing on
automobile collision avoidance, self-driving cars, autonomous
vehicles, navigation systems, and many more applications.
This book brings together the latest research in smart sensors
technology and exposes the reader to myriad industrial applications
that this technology has enabled. The book emphasizes several
topics in the area of smart sensors in industrial real-world
applications. The contributions in this book give a broader view on
the usage of smart sensor devices covering a wide range of
interdisciplinary areas like Intelligent Transport Systems,
Healthcare, Agriculture, Drone communications and Security. By
presenting an insight into Smart Sensors for Industrial IoT, this
book directs the readers to explore the utility and advancement in
smart sensors and their applications into numerous research fields.
Lastly, the book aims to reach through a mass number of industry
experts, researchers, scientists, engineers, and practitioners and
help them guide and evolve to advance research practices.
This book aims to provide a detailed understanding of
IoMT-supported applications while engaging premium smart computing
methods and improved algorithms in the field of computer science.
It contains thirteen chapters discussing various applications under
the umbrella of the Internet of Medical Things. These applications
geared towards IoMT cloud analysis, machine learning, computer
vision and deep learning have enabled the evaluation of the
proposed solutions.
This handbook introduces the basic principles and fundamentals of
cyber security towards establishing an understanding of how to
protect computers from hackers and adversaries. The highly
informative subject matter of this handbook, includes various
concepts, models, and terminologies along with examples and
illustrations to demonstrate substantial technical details of the
field. It motivates the readers to exercise better protection and
defense mechanisms to deal with attackers and mitigate the
situation. This handbook also outlines some of the exciting areas
of future research where the existing approaches can be
implemented. Exponential increase in the use of computers as a
means of storing and retrieving security-intensive information,
requires placement of adequate security measures to safeguard the
entire computing and communication scenario. With the advent of
Internet and its underlying technologies, information security
aspects are becoming a prime concern towards protecting the
networks and the cyber ecosystem from variety of threats, which is
illustrated in this handbook. This handbook primarily targets
professionals in security, privacy and trust to use and improve the
reliability of businesses in a distributed manner, as well as
computer scientists and software developers, who are seeking to
carry out research and develop software in information and cyber
security. Researchers and advanced-level students in computer
science will also benefit from this reference.
This book addresses the difficult task of integrating computational
techniques with virtual reality and healthcare. It discusses the
use of virtual reality in various areas, such as healthcare,
cognitive and behavioural training, understanding mathematical
graphs, human-computer interaction, fluid dynamics in healthcare
industries, accurate real-time simulation, and healthcare
diagnostics. Presenting the computational techniques for virtual
reality in healthcare, it is a valuable reference resource for
professionals at educational institutes as well as researchers,
scientists, engineers and practitioners in industry.
This book includes original unpublished contributions presented at
the International Conference on Data Analytics and Management
(ICDAM 2020), held at Jan Wyzykowski University, Poland, during
June 2020. The book covers the topics in data analytics, data
management, big data, computational intelligence, and communication
networks. The book presents innovative work by leading academics,
researchers, and experts from industry which is useful for young
researchers and students.
This book gathers high-quality research papers presented at the
Second International Conference on Innovative Computing and
Communication (ICICC 2019), which was held at the VSB - Technical
University of Ostrava, Czech Republic, on 21-22 March 2019.
Highlighting innovative papers by scientists, scholars, students,
and industry experts in the fields of computing and communication,
the book promotes the transformation of fundamental research into
institutional and industrialized research, and the translation of
applied research into real-world applications.
The book includes high-quality research papers presented at the
International Conference on Innovative Computing and Communication
(ICICC 2018), which was held at the Guru Nanak Institute of
Management (GNIM), Delhi, India on 5-6 May 2018. Introducing the
innovative works of scientists, professors, research scholars,
students and industrial experts in the field of computing and
communication, the book promotes the transformation of fundamental
research into institutional and industrialized research and the
conversion of applied exploration into real-time applications.
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