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Phishing Detection Using Content-Based Image Classification is an
invaluable resource for any deep learning and cybersecurity
professional and scholar trying to solve various cybersecurity
tasks using new age technologies like Deep Learning and Computer
Vision. With various rule-based phishing detection techniques at
play which can be bypassed by phishers, this book provides a
step-by-step approach to solve this problem using Computer Vision
and Deep Learning techniques with significant accuracy. The book
offers comprehensive coverage of the most essential topics,
including: Programmatically reading and manipulating image data
Extracting relevant features from images Building statistical
models using image features Using state-of-the-art Deep Learning
models for feature extraction Build a robust phishing detection
tool even with less data Dimensionality reduction techniques Class
imbalance treatment Feature Fusion techniques Building performance
metrics for multi-class classification task Another unique aspect
of this book is it comes with a completely reproducible code base
developed by the author and shared via python notebooks for quick
launch and running capabilities. They can be leveraged for further
enhancing the provided models using new advancement in the field of
computer vision and more advanced algorithms.
Disruptive Trends in Computer Aided Diagnosis collates novel
techniques and methodologies in the domain of content based image
classification and deep learning/machine learning techniques to
design efficient computer aided diagnosis architecture. It is aimed
to highlight new challenges and probable solutions in the domain of
computer aided diagnosis to leverage balancing of sustainable
ecology. The volume focuses on designing efficient algorithms for
proposing CAD systems to mitigate the challenges of critical
illnesses at an early stage. State-of-the-art novel methods are
explored for envisaging automated diagnosis systems thereby
overriding the limitations due to lack of training data, sample
annotation, region of interest identification, proper segmentation
and so on. The assorted techniques addresses the challenges
encountered in existing systems thereby facilitating accurate
patient healthcare and diagnosis. Features: An integrated
interdisciplinary approach to address complex computer aided
diagnosis problems and limitations. Elucidates a rich summary of
the state-of-the-art tools and techniques related to automated
detection and diagnosis of life threatening diseases including
pandemics. Machine learning and deep learning methodologies on
evolving accurate and precise early detection and medical diagnosis
systems. Information presented in an accessible way for students,
researchers and medical practitioners. The volume would come to the
benefit of both post-graduate students and aspiring researchers in
the field of medical informatics, computer science and electronics
and communication engineering. In addition, the volume is also
intended to serve as a guiding factor for the medical practitioners
and radiologists in accurate diagnosis of diseases.
Content-Based Image Classification: Efficient Machine Learning
Using Robust Feature Extraction Techniques is a comprehensive guide
to research with invaluable image data. Social Science Research
Network has revealed that 65% of people are visual learners.
Research data provided by Hyerle (2000) has clearly shown 90% of
information in the human brain is visual. Thus, it is no wonder
that visual information processing in the brain is 60,000 times
faster than text-based information (3M Corporation, 2001).
Recently, we have witnessed a significant surge in conversing with
images due to the popularity of social networking platforms. The
other reason for embracing usage of image data is the mass
availability of high-resolution cellphone cameras. Wide usage of
image data in diversified application areas including medical
science, media, sports, remote sensing, and so on, has spurred the
need for further research in optimizing archival, maintenance, and
retrieval of appropriate image content to leverage data-driven
decision-making. This book demonstrates several techniques of image
processing to represent image data in a desired format for
information identification. It discusses the application of machine
learning and deep learning for identifying and categorizing
appropriate image data helpful in designing automated decision
support systems. The book offers comprehensive coverage of the most
essential topics, including: Image feature extraction with novel
handcrafted techniques (traditional feature extraction) Image
feature extraction with automated techniques (representation
learning with CNNs) Significance of fusion-based approaches in
enhancing classification accuracy MATLAB (R) codes for implementing
the techniques Use of the Open Access data mining tool WEKA for
multiple tasks The book is intended for budding researchers,
technocrats, engineering students, and machine learning/deep
learning enthusiasts who are willing to start their computer vision
journey with content-based image recognition. The readers will get
a clear picture of the essentials for transforming the image data
into valuable means for insight generation. Readers will learn
coding techniques necessary to propose novel mechanisms and
disruptive approaches. The WEKA guide provided is beneficial for
those uncomfortable coding for machine learning algorithms. The
WEKA tool assists the learner in implementing machine learning
algorithms with the click of a button. Thus, this book will be a
stepping-stone for your machine learning journey. Please visit the
author's website for any further guidance at
https://www.rikdas.com/
Applied Smart Health Care Informatics Explores how intelligent
systems offer new opportunities for optimizing the acquisition,
storage, retrieval, and use of information in healthcare Applied
Smart Health Care Informatics explores how health information
technology and intelligent systems can be integrated and deployed
to enhance healthcare management. Edited and authored by leading
experts in the field, this timely volume introduces modern
approaches for managing existing data in the healthcare sector by
utilizing artificial intelligence (AI), meta-heuristic algorithms,
deep learning, the Internet of Things (IoT), and other smart
technologies. Detailed chapters review advances in areas including
machine learning, computer vision, and soft computing techniques,
and discuss various applications of healthcare management systems
such as medical imaging, electronic medical records (EMR), and drug
development assistance. Throughout the text, the authors propose
new research directions and highlight the smart technologies that
are central to establishing proactive health management, supporting
enhanced coordination of care, and improving the overall quality of
healthcare services. Provides an overview of different deep
learning applications for intelligent healthcare informatics
management Describes novel methodologies and emerging trends in
artificial intelligence and computational intelligence and their
relevance to health information engineering and management Proposes
IoT solutions that disseminate essential medical information for
intelligent healthcare management Discusses mobile-based healthcare
management, content-based image retrieval, and computer-aided
diagnosis using machine and deep learning techniques Examines the
use of exploratory data analysis in intelligent healthcare
informatics systems Applied Smart Health Care Informatics: A
Computational Intelligence Perspective is an invaluable text for
graduate students, postdoctoral researchers, academic lecturers,
and industry professionals working in the area of healthcare and
intelligent soft computing.
Interdisciplinary approaches using Machine Learning and Deep
Learning techniques are smartly addressing real life challenges and
have emerged as an inseparable element of disruption in current
times. Applications of Disruptive Technology in Management
practices are an ever interesting domain for researchers and
professionals. This volume entitled Emerging Trends in Disruptive
Technology Management for Sustainable Development has attempted to
collate five different interesting research approaches that have
innovatively reflected diverse potential of disruptive trends in
the era of 4th. Industrial Revolution. The uniqueness of the volume
is going to cater the entrepreneurs and professionals in the domain
of artificial intelligence, machine learning, deep learning etc.
with its unique propositions in each of the chapters. The volume is
surely going to be a significant source of knowledge and
inspiration to those aspiring minds endeavouring to shape their
futures in the area of applied research in machine learning and
computer vision. The expertise and experiences of the contributing
authors to this volume is encompassing different fields of
proficiencies. This has set an excellent prelude to discover the
correlation among multidisciplinary approaches of innovation.
Covering a broad range of topics initiating from IoT based
sustainable development to crowd sourcing concepts with a blend of
applied machine learning approaches has made this volume a must
read to inquisitive wits. Features Assorted approaches to
interdisciplinary research using disruptive trends Focus on
application of disruptive technology in technology management Focus
on role of disruptive technology on sustainable development
Promoting green IT with disruptive technology The book is meant to
benefit several categories of students and researchers. At the
students' level, this book can serve as a treatise/reference book
for the special papers at the masters level aimed at inspiring
possibly future researchers. Newly inducted PhD aspirants would
also find the contents of this book useful as far as their
compulsory course-works are concerned. At the researchers' level,
those interested in interdisciplinary research would also be
benefited from the book. After all, the enriched interdisciplinary
contents of the book would always be a subject of interest to the
faculties, existing research communities and new research aspirants
from diverse disciplines of the concerned departments of premier
institutes across the globe. This is expected to bring different
research backgrounds (due to its cross platform characteristics)
close to one another to form effective research groups all over the
world. Above all, availability of the book should be ensured to as
much universities and research institutes as possible through
whatever graceful means it may be. Hope this volume will cater as a
ready reference to your quest for diving deep into the ocean of
technology management for 4th. Industrial Revolution.
The publication is attempted to address emerging trends in machine
learning applications. Recent trends in information identification
have identified huge scope in applying machine learning techniques
for gaining meaningful insights. Random growth of unstructured data
poses new research challenges to handle this huge source of
information. Efficient designing of machine learning techniques is
the need of the hour. Recent literature in machine learning has
emphasized on single technique of information identification. Huge
scope exists in developing hybrid machine learning models with
reduced computational complexity for enhanced accuracy of
information identification. This book will focus on techniques to
reduce feature dimension for designing light weight techniques for
real time identification and decision fusion. Key Findings of the
book will be the use of machine learning in daily lives and the
applications of it to improve livelihood. However, it will not be
able to cover the entire domain in machine learning in its limited
scope. This book is going to benefit the research scholars,
entrepreneurs and interdisciplinary approaches to find new ways of
applications in machine learning and thus will have novel research
contributions. The lightweight techniques can be well used in real
time which will add value to practice.
The publication is attempted to address emerging trends in machine
learning applications. Recent trends in information identification
have identified huge scope in applying machine learning techniques
for gaining meaningful insights. Random growth of unstructured data
poses new research challenges to handle this huge source of
information. Efficient designing of machine learning techniques is
the need of the hour. Recent literature in machine learning has
emphasized on single technique of information identification. Huge
scope exists in developing hybrid machine learning models with
reduced computational complexity for enhanced accuracy of
information identification. This book will focus on techniques to
reduce feature dimension for designing light weight techniques for
real time identification and decision fusion. Key Findings of the
book will be the use of machine learning in daily lives and the
applications of it to improve livelihood. However, it will not be
able to cover the entire domain in machine learning in its limited
scope. This book is going to benefit the research scholars,
entrepreneurs and interdisciplinary approaches to find new ways of
applications in machine learning and thus will have novel research
contributions. The lightweight techniques can be well used in real
time which will add value to practice.
This volume comprises eight well-versed contributed chapters
devoted to report the latest findings on the intelligent approaches
to multimedia data analysis. Multimedia data is a combination of
different discrete and continuous content forms like text, audio,
images, videos, animations and interactional data. At least a
single continuous media in the transmitted information generates
multimedia information. Due to these different types of varieties,
multimedia data present varied degrees of uncertainties and
imprecision, which cannot be easy to deal by the conventional
computing paradigm. Soft computing technologies are quite efficient
to handle the imprecision and uncertainty of the multimedia data
and they are flexible enough to process the real-world information.
Proper analysis of multimedia data finds wide applications in
medical diagnosis, video surveillance, text annotation etc. This
volume is intended to be used as a reference by undergraduate and
post graduate students of the disciplines of computer science,
electronics and telecommunication, information science and
electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL
INTELLIGENCE The series Frontiers In Computational Intelligence is
envisioned to provide comprehensive coverage and understanding of
cutting edge research in computational intelligence. It intends to
augment the scholarly discourse on all topics relating to the
advances in artifi cial life and machine learning in the form of
metaheuristics, approximate reasoning, and robotics. Latest
research fi ndings are coupled with applications to varied domains
of engineering and computer sciences. This field is steadily
growing especially with the advent of novel machine learning
algorithms being applied to different domains of engineering and
technology. The series brings together leading researchers that
intend to continue to advance the fi eld and create a broad
knowledge about the most recent state of the art.
Image data has portrayed immense potential as a foundation of
information for numerous applications. Recent trends in multimedia
computing have witnessed a rapid growth in digital image
collections, resulting in a need for increased image data
management. Feature Dimension Reduction for Content-Based Image
Identification is a pivotal reference source that explores the
contemporary trends and techniques of content-based image
recognition. Including research covering topics such as feature
extraction, fusion techniques, and image segmentation, this book
explores different theories to facilitate timely identification of
image data and managing, archiving, maintaining, and extracting
information. This book is ideally designed for engineers, IT
specialists, researchers, academicians, and graduate-level students
seeking interdisciplinary research on image processing and
analysis.
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