Soumya K. Ghosh's Books

Enhanced Bayesian Network Models for Spatial Time Series Prediction

Recent Research Trend in Data-Driven Predictive Analytics

By: Monidipa Das,Soumya K. Ghosh

Write a review Read reviews Take a Quiz Solve Book Puzzle

Enhanced Bayesian Network Models for Spatial Time Series Prediction

Recent Research Trend in Data-Driven Predictive Analytics

By: Monidipa Das,Soumya K. Ghosh

This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

168

Publisher

Springer Nature

Published Date

ISBN 10

3030277496

ISBN 13

9783030277499

Optical Character Recognition Systems for Different Languages with Soft Computing

By: Arindam Chaudhuri,Krupa Mandaviya,Pratixa Badelia,Soumya K Ghosh

Write a review Read reviews Take a Quiz Solve Book Puzzle

Optical Character Recognition Systems for Different Languages with Soft Computing

By: Arindam Chaudhuri,Krupa Mandaviya,Pratixa Badelia,Soumya K Ghosh

The book offers a comprehensive survey of soft-computing models for optical character recognition systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic algorithms, are tested using real texts written in different languages, such as English, French, German, Latin, Hindi and Gujrati, which have been extracted by publicly available datasets. The simulation studies, which are reported in details here, show that soft-computing based modeling of OCR systems performs consistently better than traditional models. Mainly intended as state-of-the-art survey for postgraduates and researchers in pattern recognition, optical character recognition and soft computing, this book will be useful for professionals in computer vision and image processing alike, dealing with different issues related to optical character recognition.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

260

Publisher

Springer

Published Date

ISBN 10

3319502522

ISBN 13

9783319502526

Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory

By: Arindam Chaudhuri,Soumya K. Ghosh

Write a review Read reviews Take a Quiz Solve Book Puzzle

Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory

By: Arindam Chaudhuri,Soumya K. Ghosh

This book offers a comprehensive guide to the modelling of operational risk using possibility theory. It provides a set of methods for measuring operational risks under a certain degree of vagueness and impreciseness, as encountered in real-life data. It shows how possibility theory and indeterminate uncertainty-encompassing degrees of belief can be applied in analysing the risk function, and describes the parametric g-and-h distribution associated with extreme value theory as an interesting candidate in this regard. The book offers a complete assessment of fuzzy methods for determining both value at risk (VaR) and subjective value at risk (SVaR), together with a stability estimation of VaR and SVaR. Based on the simulation studies and case studies reported on here, the possibilistic quantification of risk performs consistently better than the probabilistic model. Risk is evaluated by integrating two fuzzy techniques: the fuzzy analytic hierarchy process and the fuzzy extension of techniques for order preference by similarity to the ideal solution. Because of its specialized content, it is primarily intended for postgraduates and researchers with a basic knowledge of algebra and calculus, and can be used as reference guide for research-level courses on fuzzy sets, possibility theory and mathematical finance. The book also offers a useful source of information for banking and finance professionals investigating different risk-related aspects.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

198

Publisher

Springer

Published Date

ISBN 10

3319260391

ISBN 13

9783319260396

Bankruptcy Prediction through Soft Computing based Deep Learning Technique

By: Arindam Chaudhuri,Soumya K Ghosh

Write a review Read reviews Take a Quiz Solve Book Puzzle

Bankruptcy Prediction through Soft Computing based Deep Learning Technique

By: Arindam Chaudhuri,Soumya K Ghosh

This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured, hierarchical rough Bayesian (HRB) models. FRTDSN is formalized through TDSN and fuzzy rough sets, and HRB is formed by incorporating probabilistic rough sets in structured hierarchical Bayesian model. Then FRTDSN is integrated with HRB to form the compound FRTDSN-HRB model. HRB enhances the prediction accuracy of FRTDSN-HRB model. The experimental datasets are adopted from Korean construction companies and American and European non-financial companies, and the research presented focuses on the impact of choice of cut-off points, sampling procedures and business cycle on the accuracy of bankruptcy prediction models. The book also highlights the fact that misclassification can result in erroneous predictions leading to prohibitive costs to investors and the economy, and shows that choice of cut-off point and sampling procedures affect rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

109

Publisher

Springer

Published Date

ISBN 10

9811066833

ISBN 13

9789811066832

Book's by Soumya K. Ghosh

Enhanced Bayesian Network Models for Spatial Time Series Prediction

Enhanced Bayesian Network Models for Spatial Time Series Prediction

Optical Character Recognition Systems for Different Languages with Soft Computing

Optical Character Recognition Systems for Different Languages with Soft Computing

Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory

Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory

Bankruptcy Prediction through Soft Computing based Deep Learning Technique

Bankruptcy Prediction through Soft Computing based Deep Learning Technique

Username

Password

Username

Password

Password again

Birthday

Email