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This book includes selected peer-reviewed papers presented at the
International Conference on Modeling, Simulation and Optimization,
organized by National Institute of Technology, Silchar, Assam,
India, during 3-5 August 2020. The book covers topics of modeling,
simulation and optimization, including computational modeling and
simulation, system modeling and simulation, device/VLSI modeling
and simulation, control theory and applications, modeling and
simulation of energy system and optimization. The book disseminates
various models of diverse systems and includes solutions of
emerging challenges of diverse scientific fields.
This book presents innovative research works to demonstrate the
potential and the advancements of computing approaches to utilize
healthcare centric and medical datasets in solving complex
healthcare problems. Computing technique is one of the key
technologies that are being currently used to perform medical
diagnostics in the healthcare domain, thanks to the abundance of
medical data being generated and collected. Nowadays, medical data
is available in many different forms like MRI images, CT scan
images, EHR data, test reports, histopathological data and doctor
patient conversation data. This opens up huge opportunities for the
application of computing techniques, to derive data-driven models
that can be of very high utility, in terms of providing effective
treatment to patients. Moreover, machine learning algorithms can
uncover hidden patterns and relationships present in medical
datasets, which are too complex to uncover, if a data-driven
approach is not taken. With the help of computing systems, today,
it is possible for researchers to predict an accurate medical
diagnosis for new patients, using models built from previous
patient data. Apart from automatic diagnostic tasks, computing
techniques have also been applied in the process of drug discovery,
by which a lot of time and money can be saved. Utilization of
genomic data using various computing techniques is another emerging
area, which may in fact be the key to fulfilling the dream of
personalized medications. Medical prognostics is another area in
which machine learning has shown great promise recently, where
automatic prognostic models are being built that can predict the
progress of the disease, as well as can suggest the potential
treatment paths to get ahead of the disease progression.
Bloom Filter: A Data Structure for Computer Networking, Big Data,
Cloud Computing, Internet of Things, Bioinformatics, and Beyond
focuses on both the theory and practice of the most emerging areas
for Bloom filter application, including Big Data, Cloud Computing,
Internet of Things, and Bioinformatics. Sections provide in-depth
insights on structure and variants, focus on its role in computer
networking, and discuss applications in various research domains,
such as Big Data, Cloud Computing, and Bioinformatics. Since its
inception, the Bloom Filter has been extensively experimented with
and developed to enhance system performance such as web cache.
Bloom filter influences many research fields, including
Bioinformatics, Internet of Things, computer security, network
appliances, Big Data and Cloud Computing.
This book covers selected high-quality research papers presented at
the International Conference on Big Data, Machine Learning, and
Applications (BigDML 2019). It focuses on both theory and
applications in the broad areas of big data and machine learning.
It brings together the academia, researchers, developers and
practitioners from scientific organizations and industry to share
and disseminate recent research findings.
This book presents innovative research works to demonstrate the
potential and the advancements of computing approaches to utilize
healthcare centric and medical datasets in solving complex
healthcare problems. Computing technique is one of the key
technologies that are being currently used to perform medical
diagnostics in the healthcare domain, thanks to the abundance of
medical data being generated and collected. Nowadays, medical data
is available in many different forms like MRI images, CT scan
images, EHR data, test reports, histopathological data and doctor
patient conversation data. This opens up huge opportunities for the
application of computing techniques, to derive data-driven models
that can be of very high utility, in terms of providing effective
treatment to patients. Moreover, machine learning algorithms can
uncover hidden patterns and relationships present in medical
datasets, which are too complex to uncover, if a data-driven
approach is not taken. With the help of computing systems, today,
it is possible for researchers to predict an accurate medical
diagnosis for new patients, using models built from previous
patient data. Apart from automatic diagnostic tasks, computing
techniques have also been applied in the process of drug discovery,
by which a lot of time and money can be saved. Utilization of
genomic data using various computing techniques is another emerging
area, which may in fact be the key to fulfilling the dream of
personalized medications. Medical prognostics is another area in
which machine learning has shown great promise recently, where
automatic prognostic models are being built that can predict the
progress of the disease, as well as can suggest the potential
treatment paths to get ahead of the disease progression.
This book includes selected peer-reviewed papers presented at the
International Conference on Modeling, Simulation and Optimization,
organized by National Institute of Technology, Silchar, Assam,
India, during 3-5 August 2020. The book covers topics of modeling,
simulation and optimization, including computational modeling and
simulation, system modeling and simulation, device/VLSI modeling
and simulation, control theory and applications, modeling and
simulation of energy system and optimization. The book disseminates
various models of diverse systems and includes solutions of
emerging challenges of diverse scientific fields.
This book includes selected peer-reviewed papers presented at the
International Conference on Modeling, Simulation and Optimization
(CoMSO 2021), organized by National Institute of Technology,
Silchar, Assam, India, during December 16–18, 2021. The book
covers topics of modeling, simulation and optimization, including
computational modeling and simulation, system modeling and
simulation, device/VLSI modeling and simulation, control theory and
applications, modeling and simulation of energy systems and
optimization. The book disseminates various models of diverse
systems and includes solutions of emerging challenges of diverse
scientific fields.
Principles of Big Graph: In-depth Insight, Volume 128 in the
Advances in Computer series, highlights new advances in the field
with this new volume presenting interesting chapters on a variety
of topics, including CESDAM: Centered subgraph data matrix for
large graph representation, Bivariate, cluster and suitability
analysis of NoSQL Solutions for big graph applications, An
empirical investigation on Big Graph using deep learning, Analyzing
correlation between quality and accuracy of graph clustering,
geneBF: Filtering protein-coded gene graph data using bloom filter,
Processing large graphs with an alternative representation,
MapReduce based convolutional graph neural networks: A
comprehensive review. Fast exact triangle counting in large graphs
using SIMD acceleration, A comprehensive investigation on attack
graphs, Qubit representation of a binary tree and its operations in
quantum computation, Modified ML-KNN: Role of similarity measures
and nearest neighbor configuration in multi label text
classification on big social network graph data, Big graph based
online learning through social networks, Community detection in
large-scale real-world networks, Power rank: An interactive web
page ranking algorithm, GA based energy efficient modelling of a
wireless sensor network, The major challenges of big graph and
their solutions: A review, and An investigation on socio-cyber
crime graph.
This book presents new and innovative current discoveries in social
networking which contribute enough knowledge to the research
community. The book includes chapters presenting research advances
in social network analysis and issues emerged with diverse social
media data. The book also presents applications of the theoretical
algorithms and network models to analyze real-world large-scale
social networks and the data emanating from them as well as
characterize the topology and behavior of these networks.
Furthermore, the book covers extremely debated topics, surveys,
future trends, issues, and challenges.
This book includes selected peer-reviewed papers presented at the
International Conference on Modeling, Simulation and Optimization
(CoMSO 2021), organized by National Institute of Technology,
Silchar, Assam, India, during December 16-18, 2021. The book covers
topics of modeling, simulation and optimization, including
computational modeling and simulation, system modeling and
simulation, device/VLSI modeling and simulation, control theory and
applications, modeling and simulation of energy systems and
optimization. The book disseminates various models of diverse
systems and includes solutions of emerging challenges of diverse
scientific fields.
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Big Data, Machine Learning, and Applications - First International Conference, BigDML 2019, Silchar, India, December 16-19, 2019, Revised Selected Papers (Paperback, 1st ed. 2020)
Ripon Patgiri, Sivaji Bandyopadhyay, Malaya Dutta Borah, Dalton Meitei Thounaojam
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This book constitutes refereed proceedings of the First
International First International Conference on Big Data, Machine
Learning, and Applications, BigDML 2019, held in Silchar, India, in
December. The 6 full papers and 3 short papers were carefully
reviewed and selected from 152 submissions. The papers present
research on such topics as computing methodology; machine learning;
artificial intelligence; information systems; security and privacy.
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