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.

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

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.

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.

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.

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

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.

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.

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

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

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

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.