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Multinational organizations have begun to realize that sentiment
mining plays an important role for decision making and market
strategy. The revolutionary growth of digital marketing not only
changes the market game, but also brings forth new opportunities
for skilled professionals and expertise. Currently, the
technologies are rapidly changing, and artificial intelligence (AI)
and machine learning are contributing as game-changing
technologies. These are not only trending but are also increasingly
popular among data scientists and data analysts. New Opportunities
for Sentiment Analysis and Information Processing provides
interdisciplinary research in information retrieval and sentiment
analysis including studies on extracting sentiments from textual
data, sentiment visualization-based dimensionality reduction for
multiple features, and deep learning-based multi-domain sentiment
extraction. The book also optimizes techniques used for sentiment
identification and examines applications of sentiment analysis and
emotion detection. Covering such topics as communication networks,
natural language processing, and semantic analysis, this book is
essential for data scientists, data analysts, IT specialists,
scientists, researchers, academicians, and students.
This book fosters a scientific debate for sophisticated approaches
and cognitive technologies (such as deep learning, machine learning
and advanced analytics) for enhanced healthcare services in light
of the tremendous scope in the future of intelligent systems for
healthcare. The authors discuss the proliferation of huge data
sources (e.g. genomes, electronic health records (EHRs), mobile
diagnostics, and wearable devices) and breakthroughs in artificial
intelligence applications, which have unlocked the doors for
diagnosing and treating multitudes of rare diseases. The
contributors show how the widespread adoption of intelligent health
based systems could help overcome challenges, such as shortages of
staff and supplies, accessibility barriers, lack of awareness on
certain health issues, identification of patient needs, and early
detection and diagnosis of illnesses. This book is a small yet
significant step towards exploring recent advances, disseminating
state-of-the-art techniques and deploying novel technologies in
intelligent healthcare services and applications. Describes the
advances of computing methodologies for life and medical science
data; Presents applications of artificial intelligence in
healthcare along with case studies and datasets; Provides an ideal
reference for medical imaging researchers, industry scientists and
engineers, advanced undergraduate and graduate students, and
clinicians.
This edited book is a compilation of scholarly articles on the
latest developments in the field of AI, Blockchain, ML/DL in cloud
security. This book is designed for security and risk assessment
professionals to help the students of undergraduate, postgraduate
and research scholars, academicians, and technology professionals.
Who are interested in learning practical approaches to cloud
security. It covers practical strategies or assessing the security
and privacy of cloud infrastructure and applications and shows how
to make cloud infrastructure secure to combat threats, attacks, and
prevent data breaches. The chapters are designed with a granular
framework, starting with the security concepts, followed by hand-on
assessment techniques based on real-world studies. Users will learn
detailed information on cloud computing security that-until now-has
been sorely lacking. The book has following features: 1. Includes
practical strategies for assessing the security and privacy of
cloud infrastructure and applications. 2. Covers topics such as AI,
Blockchain, ML/DL in cloud security. 3. Presents several case
studies revealing how threat actors abuse and exploit cloud
environments to spread threats. 4. Understand the privacy aspects
you need to consider in the cloud, including how they compare with
traditional computing models. 5. Examine security delivered as a
service-a different facet of cloud security.
This proposed book covers the issues associated in smart systems
due to the presence of onboard nonlinear components. It discusses
the advanced architecture of smart system for power management
unit. It explores the issues in power management and identifies the
hazardous signal in power management unit of smart devices.
Presents adaptive artificial intelligence and machine
learning-based control strategies. Discusses advanced simulations
and data synthesis for various power management issues. Showcases
solutions to the uncertainty and reliability issues in power
management units. Identifies new power quality challenges in smart
devices. Explains hybrid active power filter, shunt hybrid active
power filter, and industrial internet of things in power quality
management. The book comprehensively discusses advancements of
traditional electrical grid, benefit of smart grid to customers,
benefit of smart grid to stakeholders, properties of smart grid,
smart grid architecture, smart grid communication, and smart grid
security. It further covers architecture of advance power
management units (PMU) of smart devices, and identification of
harmonic distortions with respect to various sensor-based
technology. It will serve as an ideal reference text for senior
undergraduate, graduate students, and academic researchers in the
fields including electrical engineering, electronics, and
communications engineering, and computer engineering.
An Interdisciplinary Approach to Modern Network Security presents
the latest methodologies and trends in detecting and preventing
network threats. Investigating the potential of current and
emerging security technologies, this publication is an
all-inclusive reference source for academicians, researchers,
students, professionals, practitioners, network analysts and
technology specialists interested in the simulation and application
of computer network protection. It presents theoretical frameworks
and the latest research findings in network security technologies,
while analyzing malicious threats which can compromise network
integrity. It discusses the security and optimization of computer
networks for use in a variety of disciplines and fields. Touching
on such matters as mobile and VPN security, IP spoofing and
intrusion detection, this edited collection emboldens the efforts
of researchers, academics and network administrators working in
both the public and private sectors. This edited compilation
includes chapters covering topics such as attacks and
countermeasures, mobile wireless networking, intrusion detection
systems, next-generation firewalls, web security and much more.
Information and communication systems are an essential component of
our society, forcing us to become dependent on these
infrastructures. At the same time, these systems are undergoing a
convergence and interconnection process that has its benefits, but
also raises specific threats to user interests. Citizens and
organizations must feel safe when using cyberspace facilities in
order to benefit from its advantages. This book is
interdisciplinary in the sense that it covers a wide range of
topics like network security threats, attacks, tools and procedures
to mitigate the effects of malware and common network attacks,
network security architecture and deep learning methods of
intrusion detection.
This book aims to explain Data Analytics towards decision making in
terms of models and algorithms, theoretical concepts, applications,
experiments in relevant domains or focused on specific issues. It
explores the concepts of database technology, machine learning,
knowledge-based system, high performance computing, information
retrieval, finding patterns hidden in large datasets and data
visualization. Also, it presents various paradigms including
pattern mining, clustering, classification, and data analysis.
Overall aim is to provide technical solutions in the field of data
analytics and data mining. Features: Covers descriptive statistics
with respect to predictive analytics and business analytics.
Discusses different data analytics platforms for real-time
applications. Explain SMART business models. Includes algorithms in
data sciences alongwith automated methods and models. Explores
varied challenges encountered by researchers and businesses in the
realm of real-time analytics. This book aims at researchers and
graduate students in data analytics, data sciences, data mining,
and signal processing.
This book focuses on Artificial Intelligence and Machine Learning
technologies and how they are progressively being incorporated into
a wide range of products, including consumer gadgets, "smart"
personal assistants, cutting-edge medical diagnostic systems, and
quantum computing systems. This concise reference book offers a
broad overview of the most important trends and discusses how these
trends and technologies are being created and employed in the
applications they are finding use in. Artificial Intelligence and
Machine Learning: An Intelligent Perspective of Emerging
Technologies offers a broad package involving the incubation of ML
and AI with various emerging technologies such as IoT, Healthcare,
Smart Cities, Robotics, and more. The book discusses various data
collection and data transformation techniques and maps the legal
and ethical issues of data-driven e-healthcare systems while
covering the possible ways to resolve them. The book explores
different techniques on how AI can be used to create better virtual
reality experiences and deals with the techniques and possible ways
to merge the power of AI and IoT to create smart home appliances.
With contributions from experts in the field, this reference book
is useful to healthcare professionals, researchers, and students of
industrial engineering, systems engineering, biomedical, computer
science, electronics, and communications engineering.
Innovations in Artificial Intelligence and Human Computer
Interaction in the Digital Era investigates the interaction and
growing interdependency of the HCI and AI fields, which are not
usually addressed in traditional approaches. Chapters explore how
well AI can interact with users based on linguistics and
user-centered design processes, especially with the advances of AI
and the hype around many applications. Other sections investigate
how HCI and AI can mutually benefit from a closer association and
the how the AI community can improve their usage of HCI methods
like “Wizard of Oz” prototyping and “Thinking aloud” protocols.
Moreover, HCI can further augment human capabilities using new
technologies. This book demonstrates how an interdisciplinary team
of HCI and AI researchers can develop extraordinary applications,
such as improved education systems, smart homes, smart healthcare
and map Human Computer Interaction (HCI) for a multidisciplinary
field that focuses on the design of computer technology and the
interaction between users and computers in different domains.
A major use of practical predictive analytics in medicine has been
in the diagnosis of current diseases, particularly through medical
imaging. Now there is sufficient improvement in AI, IoT and data
analytics to deal with real time problems with an increased focus
on early prediction using machine learning and deep learning
algorithms. With the power of artificial intelligence alongside the
internet of 'medical' things, these algorithms can input the
characteristics/data of their patients and get predictions of
future diagnoses, classifications, treatment and costs. Evolving
Predictive Analytics in Healthcare: New AI techniques for real-time
interventions discusses deep learning algorithms in medical
diagnosis, including applications such as Covid-19 detection,
dementia detection, and predicting chemotherapy outcomes on breast
cancer tumours. Smart healthcare monitoring frameworks using IoT
with big data analytics are explored and the latest trends in
predictive technology for solving real-time health care problems
are examined. By using real-time data inputs to build predictive
models, this new technology can literally 'see' your future health
and allow clinicians to intervene as needed. This book is suitable
reading for researchers interested in healthcare technology, big
data analytics, and artificial intelligence.
This book fosters a scientific debate for sophisticated approaches
and cognitive technologies (such as deep learning, machine learning
and advanced analytics) for enhanced healthcare services in light
of the tremendous scope in the future of intelligent systems for
healthcare. The authors discuss the proliferation of huge data
sources (e.g. genomes, electronic health records (EHRs), mobile
diagnostics, and wearable devices) and breakthroughs in artificial
intelligence applications, which have unlocked the doors for
diagnosing and treating multitudes of rare diseases. The
contributors show how the widespread adoption of intelligent health
based systems could help overcome challenges, such as shortages of
staff and supplies, accessibility barriers, lack of awareness on
certain health issues, identification of patient needs, and early
detection and diagnosis of illnesses. This book is a small yet
significant step towards exploring recent advances, disseminating
state-of-the-art techniques and deploying novel technologies in
intelligent healthcare services and applications. Describes the
advances of computing methodologies for life and medical science
data; Presents applications of artificial intelligence in
healthcare along with case studies and datasets; Provides an ideal
reference for medical imaging researchers, industry scientists and
engineers, advanced undergraduate and graduate students, and
clinicians.
Researches and Applications of Artificial Intelligence to Mitigate
Pandemics: History, Diagnostic Tools, Epidemiology, Healthcare, and
Technology offers readers an interdisciplinary view of state-of-art
research related to the COVID-19 outbreak, with a focus on tactics
employed to model the number of cases of COVID-19 (time series
modeling), models employed to diagnostics COVID-19 based on images,
and the panoramic of COVID-19 since its discovery and up to this
book's publication. This book showcases the algorithms and models
available to manage pandemic data, the role of AI, IoT and
Mathematical Modeling, how to prevent and fight COVID-19, and the
existing medical, social and pharmaceutical support. Chapters cover
methods and protocols, the basics and history of diseases, the fast
diagnosis of disease with different automated algorithms and
artificial intelligence tools and techniques, the methods of
handling epidemiology for mitigating the spread of disease,
artificial intelligence and mathematical modeling techniques, and
how mental and physical health is affected with social media usage.
This book discusses in detail the latest trends in sentiment
analysis,focusing on "how online reviews and feedback reflect the
opinions of users and have led to a major shift in the
decision-making process at organizations." Social networking has
become essential in today's society. In the past, people's
decisions to buy certain products (and companies' efforts to sell
them) were largely based on advertisements, surveys, focus groups,
consultants, and the opinions of friends and relatives. But now
this is no longer limited to one's circle of friends, family or
small surveys;it has spread globally to online social media in the
form of blogs, posts, tweets, social networking sites, review sites
and so on. Though not always easy, the transition from surveys to
social media is certainly lucrative. Business analytical reports
have shown that many organizations have improved their sales,
marketing and strategy, setting up new policies and making
decisions based on opinion mining techniques.
Multinational organizations have begun to realize that sentiment
mining plays an important role for decision making and market
strategy. The revolutionary growth of digital marketing not only
changes the market game, but also brings forth new opportunities
for skilled professionals and expertise. Currently, the
technologies are rapidly changing, and artificial intelligence (AI)
and machine learning are contributing as game-changing
technologies. These are not only trending but are also increasingly
popular among data scientists and data analysts. New Opportunities
for Sentiment Analysis and Information Processing provides
interdisciplinary research in information retrieval and sentiment
analysis including studies on extracting sentiments from textual
data, sentiment visualization-based dimensionality reduction for
multiple features, and deep learning-based multi-domain sentiment
extraction. The book also optimizes techniques used for sentiment
identification and examines applications of sentiment analysis and
emotion detection. Covering such topics as communication networks,
natural language processing, and semantic analysis, this book is
essential for data scientists, data analysts, IT specialists,
scientists, researchers, academicians, and students.
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