|
Showing 1 - 8 of
8 matches in All Departments
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 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 features original papers from 25th International
Symposium on Frontiers of Research in Speech and Music (FRSM 2020),
jointly organized by National Institute of Technology, Silchar,
India, during 8-9 October 2020. The book is organized in five
sections, considering both technological advancement and
interdisciplinary nature of speech and music processing. The first
section contains chapters covering the foundations of both vocal
and instrumental music processing. The second section includes
chapters related to computational techniques involved in the speech
and music domain. A lot of research is being performed within the
music information retrieval domain which is potentially interesting
for most users of computers and the Internet. Therefore, the third
section is dedicated to the chapters related to music information
retrieval. The fourth section contains chapters on the brain signal
analysis and human cognition or perception of speech and music. The
final section consists of chapters on spoken language processing
and applications of speech processing.
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 advances in speech and music in the domain of
audio signal processing. The book begins with introductory chapters
on the basics of speech and music, and then proceeds to
computational aspects of speech and music, including music
information retrieval and spoken language processing. The authors
discuss the intersection in the field of computer science,
musicology and speech analysis, and how the multifaceted nature of
speech and music information processing requires unique algorithms,
systems using sophisticated signal processing, and machine learning
techniques that better extract useful information. The authors
discuss how a deep understanding of both speech and music in terms
of perception, emotion, mood, gesture and cognition is essential
for successful application. Also discussed is the overwhelming
amount of data that has been generated across the world that
requires efficient processing for better maintenance, retrieval,
indexing and querying and how machine learning and artificial
intelligence are most suited for these computational tasks. The
book provides both technological knowledge and a comprehensive
treatment of essential topics in speech and music processing.
Swarm Intelligence (SI) has grown significantly, both from the
perspective of algorithmic development and applications covering
almost all disciplines science and technology. This book emphasizes
the studies of existing SI techniques, their variants and
applications. The book also contains reviews of new developments in
SI techniques and hybridizations. Algorithm specific studies
covering basic introduction and analysis of key components of these
algorithms, such as convergence, balance of solution accuracy,
computational costs, tuning and control of parameters. Application
specific studies incorporating the ways of designing objective
functions, solution representation and constraint handling. The
book also includes studies on application domain specific
adaptations in the SI techniques. The book will be beneficial for
academicians and researchers from various disciplines of
engineering and science working in applications of SI and other
optimization problems.
This edited book covers ongoing research in both theory and
practical applications of using deep learning for social media
data. Social networking platforms are overwhelmed by different
contents, and their huge amounts of data have enormous potential to
influence business, politics, security, planning and other social
aspects. Recently, deep learning techniques have had many
successful applications in the AI field. The research presented in
this book emerges from the conviction that there is still much
progress to be made toward exploiting deep learning in the context
of social media data analytics. It includes fifteen chapters,
organized into four sections that report on original research in
network structure analysis, social media text analysis, user
behaviour analysis and social media security analysis. This work
could serve as a good reference for researchers, as well as a
compilation of innovative ideas and solutions for practitioners
interested in applying deep learning techniques to social media
data analytics.
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.
|
|