Soumya K. Ghosh - Böcker
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10 produkter
10 produkter
Enhanced Bayesian Network Models for Spatial Time Series Prediction
Recent Research Trend in Data-Driven Predictive Analytics
Inbunden, Engelska, 2019
1 578 kr
Skickas inom 10-15 vardagar
This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. 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.
Enhanced Bayesian Network Models for Spatial Time Series Prediction
Recent Research Trend in Data-Driven Predictive Analytics
Häftad, Engelska, 2020
1 578 kr
Skickas inom 10-15 vardagar
This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. 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.
1 733 kr
Skickas inom 10-15 vardagar
For low latency and high bandwidth services, edge computing assisted IoT (Internet of Things) has become the pillar for the development of smart environments and their applications such as smart home, smart health, smart traffic management, smart agriculture, and smart city.
1 733 kr
Skickas inom 10-15 vardagar
For low latency and high bandwidth services, edge computing assisted IoT (Internet of Things) has become the pillar for the development of smart environments and their applications such as smart home, smart health, smart traffic management, smart agriculture, and smart city.
Statistical and Machine Learning Models for Remote Sensing Data Mining
Recent Advancements
Inbunden, Engelska, 2022
521 kr
Skickas inom 5-8 vardagar
Del 331 - Studies in Fuzziness and Soft Computing
Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory
Inbunden, Engelska, 2015
1 064 kr
Skickas inom 10-15 vardagar
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.
Del 331 - Studies in Fuzziness and Soft Computing
Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory
Häftad, Engelska, 2016
1 064 kr
Skickas inom 10-15 vardagar
This book offers a comprehensive guide to the modelling of operational risk using possibility theory. 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.
Del 352 - Studies in Fuzziness and Soft Computing
Optical Character Recognition Systems for Different Languages with Soft Computing
Inbunden, Engelska, 2017
1 625 kr
Skickas inom 10-15 vardagar
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.
Del 352 - Studies in Fuzziness and Soft Computing
Optical Character Recognition Systems for Different Languages with Soft Computing
Häftad, Engelska, 2018
1 625 kr
Skickas inom 10-15 vardagar
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.
552 kr
Skickas inom 10-15 vardagar
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 bookalso 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.