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This book features high-quality research papers presented at the
2nd International Conference on Computational Intelligence in
Pattern Recognition (CIPR 2020), held at the Institute of
Engineering and Management, Kolkata, West Bengal, India, on 4-5
January 2020. It includes practical development experiences in
various areas of data analysis and pattern recognition, focusing on
soft computing technologies, clustering and classification
algorithms, rough set and fuzzy set theory, evolutionary
computations, neural science and neural network systems, image
processing, combinatorial pattern matching, social network
analysis, audio and video data analysis, data mining in dynamic
environments, bioinformatics, hybrid computing, big data analytics
and deep learning. It also provides innovative solutions to the
challenges in these areas and discusses recent developments.
This book presents practical development experiences in different
areas of data analysis and pattern recognition, focusing on soft
computing technologies, clustering and classification algorithms,
rough set and fuzzy set theory, evolutionary computations, neural
science and neural network systems, image processing, combinatorial
pattern matching, social network analysis, audio and video data
analysis, data mining in dynamic environments, bioinformatics,
hybrid computing, big data analytics and deep learning. It also
provides innovative solutions to the challenges in these areas and
discusses recent developments.
The volume contains original research findings, exchange of ideas
and dissemination of innovative, practical development experiences
in different fields of soft and advance computing. It provides
insights into the International Conference on Soft Computing in
Data Analytics (SCDA). It also concentrates on both theory and
practices from around the world in all the areas of related
disciplines of soft computing. The book provides rapid
dissemination of important results in soft computing technologies,
a fusion of research in fuzzy logic, evolutionary computations,
neural science and neural network systems and chaos theory and
chaotic systems, swarm based algorithms, etc. The book aims to
cater the postgraduate students and researchers working in the
discipline of computer science and engineering along with other
engineering branches.
This book features high-quality research papers presented at the
3rd International Conference on Computational Intelligence in
Pattern Recognition (CIPR 2021), held at the Institute of
Engineering and Management, Kolkata, West Bengal, India, on 24 - 25
April 2021. It includes practical development experiences in
various areas of data analysis and pattern recognition, focusing on
soft computing technologies, clustering and classification
algorithms, rough set and fuzzy set theory, evolutionary
computations, neural science and neural network systems, image
processing, combinatorial pattern matching, social network
analysis, audio and video data analysis, data mining in dynamic
environments, bioinformatics, hybrid computing, big data analytics
and deep learning. It also provides innovative solutions to the
challenges in these areas and discusses recent developments.
Data Analysis for Social Microblogging Platforms explores the
nature of microblog datasets, also covering the larger field which
focuses on information, data and knowledge in the context of
natural language processing. The book investigates a range of
significant computational techniques which enable data and computer
scientists to recognize patterns in these vast datasets, including
machine learning, data mining algorithms, rough set and fuzzy set
theory, evolutionary computations, combinatorial pattern matching,
clustering, summarization and classification. Chapters focus on
basic online micro blogging data analysis research methodologies,
community detection, summarization application development,
performance evaluation and their applications in big data.
This book introduces a variety of well-proven and newly developed
nature-inspired optimization algorithms solving a wide range of
real-life biomedical and healthcare problems. Few solo and hybrid
approaches are demonstrated in a lucid manner for the effective
integration and finding solution for a large-scale complex
healthcare problem. In the present bigdata-based computing
scenario, nature-inspired optimization techniques present adaptive
mechanisms that permit the understanding of complex data and
altering environments. This book is a voluminous collection for the
confront faced by the healthcare institutions and hospitals for
practical analysis, storage, and data analysis. It explores the
distinct nature-inspired optimization-based approaches that are
able to handle more accurate outcomes for the current biomedical
and healthcare problems. In addition to providing a
state-of-the-art and advanced intelligent methods, it also
enlightens an insight for solving diversified healthcare problems
such as cancer and diabetes.
This book features high-quality research papers presented at the
4th International Conference on Computational Intelligence in
Pattern Recognition (CIPR 2022), held at Indian Institute of
Engineering Science and Technology, Shibpur, Howrah, West Bengal,
India, during 23 - 24 April 2022. It includes practical development
experiences in various areas of data analysis and pattern
recognition, focusing on soft computing technologies, clustering
and classification algorithms, rough set and fuzzy set theory,
evolutionary computations, neural science and neural network
systems, image processing, combinatorial pattern matching, social
network analysis, audio and video data analysis, data mining in
dynamic environments, bioinformatics, hybrid computing, big data
analytics and deep learning. It also provides innovative solutions
to the challenges in these areas and discusses recent developments.
Handbook of Computational Intelligence in Biomedical Engineering
and Healthcare helps readers analyze and conduct advanced research
in specialty healthcare applications surrounding oncology, genomics
and genetic data, ontologies construction, bio-memetic systems,
biomedical electronics, protein structure prediction, and
biomedical data analysis. The book provides the reader with a
comprehensive guide to advanced computational intelligence,
spanning deep learning, fuzzy logic, connectionist systems,
evolutionary computation, cellular automata, self-organizing
systems, soft computing, and hybrid intelligent systems in
biomedical and healthcare applications. Sections focus on important
biomedical engineering applications, including biosensors, enzyme
immobilization techniques, immuno-assays, and nanomaterials for
biosensors and other biomedical techniques. Other sections cover
gene-based solutions and applications through computational
intelligence techniques and the impact of nonlinear/unstructured
data on experimental analysis.
Large amount of data have been collected routinely in the course of
day-to-day work in different fields. Typically, the datasets
constantly grow accumulating a large number of features, which are
not equally important in decision-making. Rough set theory
(RST)recently becomes very popular in dimensionality reduction and
feature selection of large datasets. The RST approach to feature
selection is used to determine a subset of features (or attributes)
called reduct which can predict the decision concepts. In reality,
there are multiple reducts in a given information system used for
developing classifiers, amongst which the best performer is chosen
as the final solution to the problem. Selecting a reduct with good
performance is time expensive, as there might be many reducts of a
given dataset. Therefore, obtaining a best performer classifier is
not practical rather ensemble of different classifiers may lead to
better classification accuracy. However, combining large number of
classifiers increases complexity of the system. The work trades off
between these two approaches and creates an efficient ensemble
classifier.
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