|
Showing 1 - 13 of
13 matches in All Departments
Covering theory, algorithms, and methodologies, as well as data
mining technologies, Data Mining for Bioinformatics provides a
comprehensive discussion of data-intensive computations used in
data mining with applications in bioinformatics. It supplies a
broad, yet in-depth, overview of the application domains of data
mining for bioinformatics to help readers from both biology and
computer science backgrounds gain an enhanced understanding of this
cross-disciplinary field. The book offers authoritative coverage of
data mining techniques, technologies, and frameworks used for
storing, analyzing, and extracting knowledge from large databases
in the bioinformatics domains, including genomics and proteomics.
It begins by describing the evolution of bioinformatics and
highlighting the challenges that can be addressed using data mining
techniques. Introducing the various data mining techniques that can
be employed in biological databases, the text is organized into
four sections: Supplies a complete overview of the evolution of the
field and its intersection with computational learning Describes
the role of data mining in analyzing large biological
databases-explaining the breath of the various feature selection
and feature extraction techniques that data mining has to offer
Focuses on concepts of unsupervised learning using clustering
techniques and its application to large biological data Covers
supervised learning using classification techniques most commonly
used in bioinformatics-addressing the need for validation and
benchmarking of inferences derived using either clustering or
classification The book describes the various biological databases
prominently referred to in bioinformatics and includes a detailed
list of the applications of advanced clustering algorithms used in
bioinformatics. Highlighting the challenges encountered during the
application of classification on biologica
Data mining can help pinpoint hidden information in medical data
and accurately differentiate pathological from normal data. It can
help to extract hidden features from patient groups and disease
states and can aid in automated decision making. Data Mining in
Biomedical Imaging, Signaling, and Systems provides an in-depth
examination of the biomedical and clinical applications of data
mining. It supplies examples of frequently encountered
heterogeneous data modalities and details the applicability of data
mining approaches used to address the computational challenges in
analyzing complex data. The book details feature extraction
techniques and covers several critical feature descriptors. As
machine learning is employed in many diagnostic applications, it
covers the fundamentals, evaluation measures, and challenges of
supervised and unsupervised learning methods. Both feature
extraction and supervised learning are discussed as they apply to
seizure-related patterns in epilepsy patients. Other specific
disorders are also examined with regard to the value of data mining
for refining clinical diagnoses, including depression and recurring
migraines. The diagnosis and grading of the world's fourth most
serious health threat, depression, and analysis of acoustic
properties that can distinguish depressed speech from normal are
also described. Although a migraine is a complex neurological
disorder, the text demonstrates how metabonomics can be effectively
applied to clinical practice. The authors review alignment-based
clustering approaches, techniques for automatic analysis of biofilm
images, and applications of medical text mining, including text
classification applied to medical reports. The identification and
classification of two life-threatening heart abnormalities,
arrhythmia and ischemia, are addressed, and a unique segmentation
method for mining a 3-D imaging biomarker, exemplified by
evaluation of osteoarthritis, is also present
With the rapid advancement of information discovery techniques,
machine learning and data mining continue to play a significant
role in cybersecurity. Although several conferences, workshops, and
journals focus on the fragmented research topics in this area,
there has been no single interdisciplinary resource on past and
current works and possible paths for future research in this area.
This book fills this need. From basic concepts in machine learning
and data mining to advanced problems in the machine learning
domain, Data Mining and Machine Learning in Cybersecurity provides
a unified reference for specific machine learning solutions to
cybersecurity problems. It supplies a foundation in cybersecurity
fundamentals and surveys contemporary challenges-detailing
cutting-edge machine learning and data mining techniques. It also:
Unveils cutting-edge techniques for detecting new attacks Contains
in-depth discussions of machine learning solutions to detection
problems Categorizes methods for detecting, scanning, and profiling
intrusions and anomalies Surveys contemporary cybersecurity
problems and unveils state-of-the-art machine learning and data
mining solutions Details privacy-preserving data mining methods
This interdisciplinary resource includes technique review tables
that allow for speedy access to common cybersecurity problems and
associated data mining methods. Numerous illustrative figures help
readers visualize the workflow of complex techniques and more than
forty case studies provide a clear understanding of the design and
application of data mining and machine learning techniques in
cybersecurity.
Covering theory, algorithms, and methodologies, as well as data
mining technologies, Data Mining for Bioinformatics provides a
comprehensive discussion of data-intensive computations used in
data mining with applications in bioinformatics. It supplies a
broad, yet in-depth, overview of the application domains of data
mining for bioinformatics to help readers from both biology and
computer science backgrounds gain an enhanced understanding of this
cross-disciplinary field. The book offers authoritative coverage of
data mining techniques, technologies, and frameworks used for
storing, analyzing, and extracting knowledge from large databases
in the bioinformatics domains, including genomics and proteomics.
It begins by describing the evolution of bioinformatics and
highlighting the challenges that can be addressed using data mining
techniques. Introducing the various data mining techniques that can
be employed in biological databases, the text is organized into
four sections: Supplies a complete overview of the evolution of the
field and its intersection with computational learning Describes
the role of data mining in analyzing large biological
databases-explaining the breath of the various feature selection
and feature extraction techniques that data mining has to offer
Focuses on concepts of unsupervised learning using clustering
techniques and its application to large biological data Covers
supervised learning using classification techniques most commonly
used in bioinformatics-addressing the need for validation and
benchmarking of inferences derived using either clustering or
classification The book describes the various biological databases
prominently referred to in bioinformatics and includes a detailed
list of the applications of advanced clustering algorithms used in
bioinformatics. Highlighting the challenges encountered during the
application of classification on biologica
|
Computing, Analytics and Networks - First International Conference, ICAN 2017, Chandigarh, India, October 27-28, 2017, Revised Selected Papers (Paperback, 1st ed. 2018)
Rajnish Sharma, Archana Mantri, Sumeet Dua
|
R2,716
Discovery Miles 27 160
|
Ships in 10 - 15 working days
|
This book constitutes the revised selected papers from the First
International Conference on Computing, Analytics and Networks, ICAN
2017, held in Rajpura, India, in October 2017.The 20 revised full
papers presented in this volume were carefully reviewed and
selected from 56 submissions. They are organized in topical
sections on Mobile Cloud Computing; Big Data Analytics; Secure
Networks. Five papers in this book are available open access under
a Creative Commons Attribution 4.0 International License via
link.springer.com. For further details, please see the copyright
page.
The book is a unique effort to represent a variety of techniques
designed to represent, enhance, and empower multi-disciplinary and
multi-institutional machine learning research in healthcare
informatics. The book provides a unique compendium of current and
emerging machine learning paradigms for healthcare informatics and
reflects the diversity, complexity and the depth and breath of this
multi-disciplinary area. The integrated, panoramic view of data and
machine learning techniques can provide an opportunity for novel
clinical insights and discoveries.
The book is a unique effort to represent a variety of techniques
designed to represent, enhance, and empower multi-disciplinary and
multi-institutional machine learning research in healthcare
informatics. The book provides a unique compendium of current and
emerging machine learning paradigms for healthcare informatics and
reflects the diversity, complexity and the depth and breath of this
multi-disciplinary area. The integrated, panoramic view of data and
machine learning techniques can provide an opportunity for novel
clinical insights and discoveries.
Data mining can help pinpoint hidden information in medical data
and accurately differentiate pathological from normal data. It can
help to extract hidden features from patient groups and disease
states and can aid in automated decision making. Data Mining in
Biomedical Imaging, Signaling, and Systems provides an in-depth
examination of the biomedical and clinical applications of data
mining. It supplies examples of frequently encountered
heterogeneous data modalities and details the applicability of data
mining approaches used to address the computational challenges in
analyzing complex data. The book details feature extraction
techniques and covers several critical feature descriptors. As
machine learning is employed in many diagnostic applications, it
covers the fundamentals, evaluation measures, and challenges of
supervised and unsupervised learning methods. Both feature
extraction and supervised learning are discussed as they apply to
seizure-related patterns in epilepsy patients. Other specific
disorders are also examined with regard to the value of data mining
for refining clinical diagnoses, including depression and recurring
migraines. The diagnosis and grading of the world's fourth most
serious health threat, depression, and analysis of acoustic
properties that can distinguish depressed speech from normal are
also described. Although a migraine is a complex neurological
disorder, the text demonstrates how metabonomics can be effectively
applied to clinical practice. The authors review alignment-based
clustering approaches, techniques for automatic analysis of biofilm
images, and applications of medical text mining, including text
classification applied to medical reports. The identification and
classification of two life-threatening heart abnormalities,
arrhythmia and ischemia, are addressed, and a unique segmentation
method for mining a 3-D imaging biomarker, exemplified by
evaluation of osteoarthritis, is also present
|
Information Intelligence, Systems, Technology and Management - 5th International Conference, ICISTM 2011, Gurgaon, India, March 10-12, 2011. Proceedings (Paperback, Edition.)
Sumeet Dua, Sartaj Sahni, D. P. Goyal
|
R1,586
Discovery Miles 15 860
|
Ships in 10 - 15 working days
|
This book constitutes the refereed proceedings of the 5th
International Conference on Information Systems, Technology and
Management, ICISTM 2011, held in Gurgaon, India, in March 2011. The
35 revised full papers presented together with 4 short papers were
carefully reviewed and selected from 106 submissions. The papers
are organized in topical sections on information management,
information systems, information technology, healthcare information
management and technology, business intelligence, applications, as
well as management science and education.
|
Contemporary Computing - Third International Conference, IC3 2010, Noida, India, August 9-11, 2010. Proceedings, Part I (Paperback, Edition.)
Sanjay Ranka, Arunava Banerjee, Kanad Kishore Biswas, Sumeet Dua, Prabhat Mishra, …
|
R3,064
Discovery Miles 30 640
|
Ships in 10 - 15 working days
|
Welcome to the proceedings of the Third International Conference on
Contemporary Computing, which was held in Noida (outskirts of New
Delhi), India. Computing is an exciting and evolving area. This
conference, which was jointly organized by the Jaypee Institute of
Information Technology, Noida, India and the University of Fl- ida,
Gainesville, USA, focused on topics that are of contemporary
interest to computer and computational scientists and engineers.
The conference had an exciting technical program of 79 papers
submitted by - searchers and practitioners from academia, industry,
and government to advance the algorithmic, systems, applications,
and educational aspects of contemporary comp- ing. These papers
were selected from 350 submissions (with an overall acceptance rate
of around 23%). The technical program was put together by a
distinguished int- national Program Committee consisting of more
than 150 members. The Program Committee was led by the following
Track Chairs: Arunava Banerjee, Kanad Kishore Biswas, Summet Dua,
Prabhat Mishra, Rajat Moona, Sheung-Hung Poon, and Cho-Li Wang. I
would like to thank the Program Committee and the Track Chairs for
their tremendous effort. I would like to thank the General Chairs,
Prof. Sartaj Sahni and Prof. Sanjay Goel, for giving me the
opportunity to lead the technical program. Sanjay Ranka
|
Contemporary Computing - Second International Conference, IC3 2009, Noida, India, August 17-19, 2009. Proceedings (Paperback, 2009 ed.)
Sanjay Ranka, Srinivas Aluru, Rajkumar Buyya, Yeh-Ching Chung, Sandeep Gupta, …
|
R3,080
Discovery Miles 30 800
|
Ships in 10 - 15 working days
|
Welcome to the Second International Conference on Contemporary
Computing, which was held in Noida (outskirts of New Delhi), India.
Computing is an exciting and evolving area. This conference, which
was jointly organized by the Jaypee Institute of Information
Technology University, Noida, India and the University of Florida,
Gainesville, USA, focused on topics that are of contemporary
interest to computer and computational scientists and engineers.
The conference had an exciting technical program of 61 papers
submitted by - searchers and practitioners from academia, industry
and government to advance the algorithmic, systems, applications,
and educational aspects of contemporary comp- ing. These papers
were selected from 213 submissions (with an overall acceptance rate
of around 29%). The technical program was put together by a
distinguished int- national Program Committee. The Program
Committee was led by the following Track Chairs and Special Session
Chairs: Srinivas Aluru, Rajkumar Buyya, Yeh-Ching Chung, Sumeet
Dua, Ananth Grama, Sandeep Gupta, Rajeev Kumar and Vir Phoha. I
would like to thank the Program Committee, the Track Chairs and
Special Session Chairs for their tremendous effort. I would like to
thank the General Chairs, Sartaj Sahni and Sanjay Goel for giving
me the opportunity to lead the technical program. Sanjay Ranka
|
Information Systems, Technology and Management - 6th International Conference, ICISTM 2012, Grenoble, France, March 28-30. Proceedings (Paperback, 2012 ed.)
Sumeet Dua, Aryya Gangopadhyay, P. Thulasiraman, Umberto Straccia, Michael Shepherd, …
|
R1,614
Discovery Miles 16 140
|
Ships in 10 - 15 working days
|
This book constitutes the refereed proceedings of the 6th
International Conference on Information Systems, Technology and
Management, ICISTM 2012, held in Grenoble, France, in March 2012.
The 38 revised papers were carefully reviewed and selected from 85
submissions. The papers are organized in topical sections on
information systems; information technology; information
management; business intelligence; management science and
education; applications; workshop on program protection and reverse
engineering.
Advances in semi-automated high-throughput image data collection
routines, coupled with a decline in storage costs and an increase
in high-performance computing solutions have led to an exponential
surge in data collected by biomedical scientists and medical
practitioners. Interpreting this raw data is a challenging task,
and nowhere is this more evident than in the field of opthalmology.
The sheer speed at which data on cataracts, diabetic retinopathy,
glaucoma and other eye disorders are collected, makes it impossible
for the human observer to directly monitor subtle, yet critical
details.This book is a novel and well-timed endeavor to present, in
an amalgamated format, computational image modeling methods as
applied to various extrinsic scientific problems in ophthalmology.
It is self-contained and presents a highly comprehensive array of
image modeling algorithms and methodologies relevant to
ophthalmologic problems. The book is the first of its kind,
bringing eye imaging and multi-dimensional hyperspectral imaging
and data fusion of the human eye, into focus.The editors are at the
top of their fields and bring a strong multidisciplinary synergy to
this visionary volume. Their "inverted-pyramid" approach in
presenting the content, and focus on core applications, will appeal
to students and practitioners in the field.
|
|