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This new volume, Cognitive Computing Systems: Applications and
Technological Advancements, explores the emerging area of
artificial intelligence that encompasses machine self-learning,
human-computer interaction, natural language processing, data
mining and more. It introduces cognitive computing systems,
highlights their key applications, discusses the technologies used
in cognitive systems, and explains underlying models and
architectures. Focusing on scientific work for real-world
applications, each chapter presents the use of cognitive computing
and machine learning in specific application areas. These include
the use of speech recognition technology, application of neural
networks in construction management, elevating competency in
education, comprehensive health monitoring systems, predicting type
2 diabetes, applications for smart agricultural technology, human
resource management, and more. With chapters from knowledgeable
researchers in the area of artificial intelligence, cognitive
computing, and allied areas, this book will be an asset for
researchers, faculty, advances students, and industry professionals
in many fields.
This new volume explores a plethora of blockchain-based solutions
for big data and IoT applications, looking at advances in
real-world applications in several sectors, including higher
education, cybersecurity, agriculture, business and management,
healthcare and biomedical science, construction and project
management, smart city development, and others. Chapters explore
emerging technology to combat the ever-increasing threat of
security to computer systems and offer new architectural solutions
for problems encountered in data management and security. The
chapters help to provide a high level of understanding of various
blockchain algorithms along with the necessary tools and
techniques. The novel architectural solutions in the deployment of
blockchain presented here are the core of the book.
This book explores opportunities and challenges in the field of IoE
security and privacy under the umbrella of distributed ledger
technologies and blockchain technology including distributed
consensus mechanisms, crypto-sensors, encryption algorithms, and
fault tolerance mechanisms for devices and systems. It focusses on
the applicability of blockchain technology including architectures
and platforms for blockchain and IoE, authentication and encryption
algorithms for IoE, malicious transactions detection, blockchain
for forensics and so forth. Outlines the major benefits as well as
challenges associated with integration of blockchain with IoE
Describes detailed framework to provide security in IoE using
blockchain technology Reviews various issues while using
distributed ledger technologies for IoE Provides comprehensive
coverage of blockchain for IoE in securing information including
encryption schemes, authentication, security issues, and challenges
Includes case studies in realistic situations like healthcare
informatics, smart industry, and smart transportation This book is
aimed at researchers and graduate students in computing,
cryptography, IoT, computer engineering, and networks.
SDN-Supported Edge-Cloud Interplay for Next Generation Internet of
Things is an invaluable resource coveringa wide range of research
directions in the field of edge-cloud computing, SDN, and IoT. The
integration of SDN in edge-cloud interplay is a promising framework
for enhancing the QoS for complex IoT-driven applications. The
interplay between cloud and edge solves some of the major
challenges that arise in traditional IoT architecture. This book is
a starting point for those involved in this research domain and
explores a range of significant issues including network
congestion, traffic management, latency, QoS, scalability,
security, and controller placement problems. Features: The book
covers emerging trends, issues and solutions in the direction of
Edge-cloud interplay It highlights the research advances in on SDN,
edge, and IoT architecture for smart cities, and software-defined
internet of vehicles It includes detailed discussion has made of
performance evaluations of SDN controllers, scalable
software-defined edge computing, and AI for edge computing
Applications areas include machine learning and deep learning in
SDN-supported edge-cloud systems Different use cases covered
include smart health care, smart city, internet of drones, etc This
book is designed for scientific communities including graduate
students, academicians, and industry professionals who are
interested in exploring technologies related to the internet of
things such as cloud, SDN, edge, internet of drones, etc.
In recent years, drones have been integrated with the Internet of
Things to offer a variety of exciting new applications. Here is a
detailed exploration of adapting and implementing Internet of
Drones technologies in real-world applications, emphasizing
solutions to architectural challenges and providing a clear
overview of standardization and regulation, implementation plans,
and privacy concerns. The book discusses the architectures and
protocols for drone communications, implementing and deploying of
5G-drone setups, security issues, deep learning techniques applied
on real-time footage, and more. It also explores some of the varied
applications, such as for monitoring and analysis of troposphere
pollutants, providing services and communications in smart cities
(such as for weather forecasting, communications, transport, safety
and protection), for disaster relief management, for agricultural
crop monitoring, and more.
1) Discusses technical details of the Machine Learning tools and
techniques in the different types of cancers 2) Machine learning
and data mining in healthcare is a very important topic and hence
there would be a demand for such a book 3) As compared to other
titles, the proposed book focuses on different types of cancer
disease and their prediction strategy using machine leaning and
data mining.
The book intends to cover various problematic aspects of emerging
smart computing and self-adapting technologies comprising of
machine learning, artificial intelligence, deep learning, robotics,
cloud computing, fog computing, data mining algorithms, including
emerging intelligent and smart applications related to these
research areas. Further coverage includes implementation of
self-adaptation architecture for smart devices, self-adaptive
models for smart cities and self-driven cars, decentralized
self-adaptive computing at the edge networks, energy-aware AI-based
systems, M2M networks, sensors, data analytics, algorithms and
tools for engineering self-adaptive systems, and so forth. Acts as
guide to Self-healing and Self-adaptation based fully automatic
future technologies Discusses about Smart Computational abilities
and self-adaptive systems Illustrates tools and techniques for data
management and explains the need to apply, and data integration for
improving efficiency of big data Exclusive chapter on the future of
self-stabilizing and self-adaptive systems of systems Covers fields
such as automation, robotics, medical sciences, biomedical and
agricultural sciences, healthcare and so forth This book is aimed
researchers and graduate students in machine learning, information
technology, and artificial intelligence.
The advanced AI techniques are essential for resolving various
problematic aspects emerging in the field of bioinformatics. This
book covers the recent approaches in artificial intelligence and
machine learning methods and their applications in Genome and Gene
editing, cancer drug discovery classification, and the protein
folding algorithms among others. Deep learning, which is widely
used in image processing, is also applicable in bioinformatics as
one of the most popular artificial intelligence approaches. The
wide range of applications discussed in this book are an
indispensable resource for computer scientists, engineers,
biologists, mathematicians, physicians, and medical informaticists.
Features: Focusses on the cross-disciplinary relation between
computer science and biology and the role of machine learning
methods in resolving complex problems in bioinformatics Provides a
comprehensive and balanced blend of topics and applications using
various advanced algorithms Presents cutting-edge research
methodologies in the area of AI methods when applied to
bioinformatics and innovative solutions Discusses the AI/ML
techniques, their use, and their potential for use in common and
future bioinformatics applications Includes recent achievements in
AI and bioinformatics contributed by a global team of researchers
This new volume, Cognitive Computing Systems: Applications and
Technological Advancements, explores the emerging area of
artificial intelligence that encompasses machine self-learning,
human-computer interaction, natural language processing, data
mining and more. It introduces cognitive computing systems,
highlights their key applications, discusses the technologies used
in cognitive systems, and explains underlying models and
architectures. Focusing on scientific work for real-world
applications, each chapter presents the use of cognitive computing
and machine learning in specific application areas. These include
the use of speech recognition technology, application of neural
networks in construction management, elevating competency in
education, comprehensive health monitoring systems, predicting type
2 diabetes, applications for smart agricultural technology, human
resource management, and more. With chapters from knowledgeable
researchers in the area of artificial intelligence, cognitive
computing, and allied areas, this book will be an asset for
researchers, faculty, advances students, and industry professionals
in many fields.
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Artificial Intelligence and Sustainable Computing for Smart City - First International Conference, AIS2C2 2021, Greater Noida, India, March 22-23, 2021, Revised Selected Papers (Paperback, 1st ed. 2021)
Arun Solanki, Sanjay Kumar Sharma, Sandhya Tarar, Pradeep Tomar, Sandeep Sharma, …
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R1,562
Discovery Miles 15 620
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Ships in 10 - 15 working days
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This book constitutes selected and revised papers of the First
International Conference on Artificial Intelligence and Sustainable
Computing for Smart City, AIS2C2 2021, held in Greater Noida,
India, in March 2021. Due to the COVID-19 pandemic the conference
was held online. The 17 full papers and 3 short papers included
were thoroughly reviewed and selected from 204 submissions. They
are organized in the following topical sections: sentimental and
emotions analysis for smart cities; smart specialization strategies
for smart cities; security in smart cities; advances applications
for future smart cities; healthcare in smart cities; machine
learning applications in smart cities.
Generative Adversarial Networks (GAN) have started a revolution in
Deep Learning, and today GAN is one of the most researched topics
in Artificial Intelligence. Generative Adversarial Networks for
Image-to-Image Translation provides a comprehensive overview of the
GAN (Generative Adversarial Network) concept starting from the
original GAN network to various GAN-based systems such as Deep
Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN,
Wasserstein GANs (WGAN), cyclical GANs, and many more. The book
also provides readers with detailed real-world applications and
common projects built using the GAN system with respective Python
code. A typical GAN system consists of two neural networks, i.e.,
generator and discriminator. Both of these networks contest with
each other, similar to game theory. The generator is responsible
for generating quality images that should resemble ground truth,
and the discriminator is accountable for identifying whether the
generated image is a real image or a fake image generated by the
generator. Being one of the unsupervised learning-based
architectures, GAN is a preferred method in cases where labeled
data is not available. GAN can generate high-quality images, images
of human faces developed from several sketches, convert images from
one domain to another, enhance images, combine an image with the
style of another image, change the appearance of a human face image
to show the effects in the progression of aging, generate images
from text, and many more applications. GAN is helpful in generating
output very close to the output generated by humans in a fraction
of second, and it can efficiently produce high-quality music,
speech, and images.
As technology weaves itself more tightly into everyday life,
socio-economic development has become intricately tied to these
ever-evolving innovations. Technology management is now an integral
element of sound business practices, and this revolution has opened
up many opportunities for global communication. However, such swift
change warrants greater research that can foresee and possibly
prevent future complications within and between organizations. The
Handbook of Research on Engineering Innovations and Technology
Management in Organizations is a collection of innovative research
that explores global concerns in the applications of technology to
business and the explosive growth that resulted. Highlighting a
wide range of topics such as cyber security, legal practice, and
artificial intelligence, this book is ideally designed for
engineers, manufacturers, technology managers, technology
developers, IT specialists, productivity consultants, executives,
lawyers, programmers, managers, policymakers, academicians,
researchers, and students.
Throughout the world, there is an increasing demand on diminishing
natural resources in the industrial, transport, commercial, and
residential sectors. Of these, the residential sector uses the most
energy on such needs as lighting, water heating, air conditioning,
space heating, and refrigeration. This sector alone consumes
one-third of the total primary energy resources available. By using
green building and smart automation techniques, this demand for
energy resources can be lowered. Green Building Management and
Smart Automation is an essential scholarly publication that
provides an in-depth analysis of design technologies for green
building and highlights the smart automation technologies that help
in energy conservation, along with various performance metrics that
are necessary to facilitate a building to be known as a ""Green
Smart Building."" Featuring a range of topics such as environmental
quality, energy management, and big data analytics, this book is
ideal for researchers, engineers, policymakers, government
officials, architects, and students.
Throughout the world, there is an increasing demand on diminishing
natural resources in the industrial, transport, commercial, and
residential sectors. Of these, the residential sector uses the most
energy on such needs as lighting, water heating, air conditioning,
space heating, and refrigeration. This sector alone consumes
one-third of the total primary energy resources available. By using
green building and smart automation techniques, this demand for
energy resources can be lowered. Green Building Management and
Smart Automation is an essential scholarly publication that
provides an in-depth analysis of design technologies for green
building and highlights the smart automation technologies that help
in energy conservation, along with various performance metrics that
are necessary to facilitate a building to be known as a "Green
Smart Building." Featuring a range of topics such as environmental
quality, energy management, and big data analytics, this book is
ideal for researchers, engineers, policymakers, government
officials, architects, and students.
As today's world continues to advance, Artificial Intelligence (AI)
is a field that has become a staple of technological development
and led to the advancement of numerous professional industries. An
application within AI that has gained attention is machine
learning. Machine learning uses statistical techniques and
algorithms to give computer systems the ability to understand and
its popularity has circulated through many trades. Understanding
this technology and its countless implementations is pivotal for
scientists and researchers across the world. The Handbook of
Research on Emerging Trends and Applications of Machine Learning
provides a high-level understanding of various machine learning
algorithms along with modern tools and techniques using Artificial
Intelligence. In addition, this book explores the critical role
that machine learning plays in a variety of professional fields
including healthcare, business, and computer science. While
highlighting topics including image processing, predictive
analytics, and smart grid management, this book is ideally designed
for developers, data scientists, business analysts, information
architects, finance agents, healthcare professionals, researchers,
retail traders, professors, and graduate students seeking current
research on the benefits, implementations, and trends of machine
learning.
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