Titlebook: Bankruptcy Prediction through Soft Computing based Deep Learning Technique; Arindam Chaudhuri,Soumya K Ghosh Book 2017 Springer Nature Sin

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Book 2017hat 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,

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Introduction,e institutions and public firms. It is an active research area in business and mathematical finance. The importance of bankruptcy is mainly attributed to the creditors and investors in assessing the likelihood that an organization can become bankrupt. Bankruptcy investigation is generally expressed

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Need of This Research,age lies in recognizing associated problems and appreciating the process that goes to bankruptcy and benefits from them. The research monograph addresses this issue. It is achieved by investigating bankruptcy datasets of Korean construction companies [32], American and European nonfinancial companie

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Need for Risk Classification,ons. The external rating is an expensive process. Till date most banks have sanctioned loans to small- and medium-sized companies (SME) [5] without any estimation for the associated risks. The banks base their decision-making process on several rough models. The decision to credit is taken up by the

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Experimental Framework: Bankruptcy Prediction Using Soft Computing Based Deep Learning Technique,ng machine learning technique in past few years [82]. It is deep structured learning and concerned with ANN study containing more than one hidden layer. It is based on composition of several layers with nonlinear units toward feature extraction and corresponding transformation. Here each preceding l

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