Aryya Gangopadhyay

Michael Shepherd

Benno Stein

Umberto Straccia

Sumeet Dua

P. Thulasiraman

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.

6th International Conference, ICISTM 2012, Grenoble, France, March 28-30. Proceedings

Information Systems, Technology and Management

Pradeep Chowriappa

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 biological databases, it considers systems of both single and ensemble classifiers and shares effort-saving tips for model selection and performance estimation strategies.

Data Mining for Bioinformatics

Sanjay Ranka

Rajkumar Buyya

Vir V. Phoha

Rajeev Kumar

Yeh-Ching Chung

Srinivas Aluru

Sandeep Gupta

Ananth Grama

This book constitutes the refereed papers of the 2nd International Conference on Contemporary Computing, which was held in Noida (New Delhi), India, in August 2009. The 61 revised full papers presented were carefully reviewed and selected from 213 submissions and focus on topics that are of contemporary interest to computer and computational scientists and engineers. The papers are organized in topical sections on Algorithms, Applications, Bioinformatics, and Systems.

Second International Conference, IC3 2009, Noida, India, August 17-19, 2009. Proceedings

Contemporary Computing

Rajat Moona

Arunava Banerjee

Kanad Kishore Biswas

Prabhat Mishra

Sheung-Hung Poon

Cho-Li Wang

This volume constitutes the refereed proceedings of the Third International Conference on Contemporary Computing, IC3 2010, held in Noida, India, in August 2010.

Third International Conference, IC3 2010, Noida, India, August 9-11, 2010. Proceedings

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 their focus on core applications will appeal to students and practitioners in the field.

Computational Analysis of the Human Eye with Applications

Sumeet Dua

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