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Bankruptcy Prediction through Soft Computing based Deep Learning Technique [[electronic resource] /] / by Arindam Chaudhuri, Soumya K Ghosh
| Bankruptcy Prediction through Soft Computing based Deep Learning Technique [[electronic resource] /] / by Arindam Chaudhuri, Soumya K Ghosh |
| Autore | Chaudhuri Arindam |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XVII, 102 p. 59 illus.) |
| Disciplina |
005.437
4.019 |
| Soggetto topico |
User interfaces (Computer systems)
Artificial intelligence Computer simulation Management information systems Computer science Banks and banking Statistics User Interfaces and Human Computer Interaction Artificial Intelligence Simulation and Modeling Management of Computing and Information Systems Banking Statistics for Business, Management, Economics, Finance, Insurance |
| ISBN | 981-10-6683-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Need of this Research -- Literature Review -- Bankruptcy Prediction Methodology -- Need for Risk Classification -- Experimental Framework: Bankruptcy Prediction using Soft Computing based Deep Learning Technique.- Datasets Used -- Experimental Results -- Conclusion . |
| Record Nr. | UNINA-9910254843703321 |
Chaudhuri Arindam
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Optical Character Recognition Systems for Different Languages with Soft Computing / / by Arindam Chaudhuri, Krupa Mandaviya, Pratixa Badelia, Soumya K Ghosh
| Optical Character Recognition Systems for Different Languages with Soft Computing / / by Arindam Chaudhuri, Krupa Mandaviya, Pratixa Badelia, Soumya K Ghosh |
| Autore | Chaudhuri Arindam |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIX, 248 p. 95 illus.) |
| Disciplina | 006.424 |
| Collana | Studies in Fuzziness and Soft Computing |
| Soggetto topico |
Computational intelligence
Pattern recognition Computational linguistics Artificial intelligence Computational Intelligence Pattern Recognition Computational Linguistics Artificial Intelligence |
| ISBN | 3-319-50252-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Organization of the Monograph -- 1.2 Notation -- 1.3 State of Art -- 1.4 Research Issues and Challenges -- 1.5 Figures -- 1.6 MATLAB OCR Toolbox -- References -- 2 Optical Character Recognition Systems -- Abstract -- 2.1 Introduction -- 2.2 Optical Character Recognition Systems: Background and History -- 2.3 Techniques of Optical Character Recognition Systems -- 2.3.1 Optical Scanning -- 2.3.2 Location Segmentation -- 2.3.3 Pre-processing -- 2.3.4 Segmentation -- 2.3.5 Representation -- 2.3.6 Feature Extraction -- 2.3.7 Training and Recognition -- 2.3.8 Post-processing -- 2.4 Applications of Optical Character Recognition Systems -- 2.5 Status of Optical Character Recognition Systems -- 2.6 Future of Optical Character Recognition Systems -- References -- 3 Soft Computing Techniques for Optical Character Recognition Systems -- Abstract -- 3.1 Introduction -- 3.2 Soft Computing Constituents -- 3.2.1 Fuzzy Sets -- 3.2.2 Artificial Neural Networks -- 3.2.3 Genetic Algorithms -- 3.2.4 Rough Sets -- 3.3 Hough Transform for Fuzzy Feature Extraction -- 3.4 Genetic Algorithms for Feature Selection -- 3.5 Rough Fuzzy Multilayer Perceptron -- 3.6 Fuzzy and Fuzzy Rough Support Vector Machines -- 3.7 Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 3.8 Fuzzy Markov Random Fields -- 3.9 Other Soft Computing Techniques -- References -- 4 Optical Character Recognition Systems for English Language -- Abstract -- 4.1 Introduction -- 4.2 English Language Script and Experimental Dataset -- 4.3 Challenges of Optical Character Recognition Systems for English Language -- 4.4 Data Acquisition -- 4.5 Data Pre-processing -- 4.5.1 Binarization -- 4.5.2 Noise Removal -- 4.5.3 Skew Detection and Correction -- 4.5.4 Character Segmentation -- 4.5.5 Thinning -- 4.6 Feature Extraction.
4.7 Feature Based Classification: Sate of Art -- 4.7.1 Feature Based Classification Through Fuzzy Multilayer Perceptron -- 4.7.2 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 4.7.3 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 4.8 Experimental Results -- 4.8.1 Fuzzy Multilayer Perceptron -- 4.8.2 Rough Fuzzy Multilayer Perceptron -- 4.8.3 Fuzzy and Fuzzy Rough Support Vector Machines -- 4.9 Further Discussions -- References -- 5 Optical Character Recognition Systems for French Language -- Abstract -- 5.1 Introduction -- 5.2 French Language Script and Experimental Dataset -- 5.3 Challenges of Optical Character Recognition Systems for French Language -- 5.4 Data Acquisition -- 5.5 Data Pre-processing -- 5.5.1 Text Region Extraction -- 5.5.2 Skew Detection and Correction -- 5.5.3 Binarization -- 5.5.4 Noise Removal -- 5.5.5 Character Segmentation -- 5.5.6 Thinning -- 5.6 Feature Extraction Through Fuzzy Hough Transform -- 5.7 Feature Based Classification: Sate of Art -- 5.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 5.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 5.7.3 Feature Based Classification Through Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 5.8 Experimental Results -- 5.8.1 Rough Fuzzy Multilayer Perceptron -- 5.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 5.8.3 Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 5.9 Further Discussions -- References -- 6 Optical Character Recognition Systems for German Language -- Abstract -- 6.1 Introduction -- 6.2 German Language Script and Experimental Dataset -- 6.3 Challenges of Optical Character Recognition Systems for German Language -- 6.4 Data Acquisition -- 6.5 Data Pre-processing -- 6.5.1 Text Region Extraction. 6.5.2 Skew Detection and Correction -- 6.5.3 Binarization -- 6.5.4 Noise Removal -- 6.5.5 Character Segmentation -- 6.5.6 Thinning -- 6.6 Feature Selection Through Genetic Algorithms -- 6.7 Feature Based Classification: Sate of Art -- 6.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 6.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 6.7.3 Feature Based Classification Through Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 6.8 Experimental Results -- 6.8.1 Rough Fuzzy Multilayer Perceptron -- 6.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 6.8.3 Hierarchical Fuzzy Bidirectional Recurrent Neural Networks -- 6.9 Further Discussions -- References -- 7 Optical Character Recognition Systems for Latin Language -- Abstract -- 7.1 Introduction -- 7.2 Latin Language Script and Experimental Dataset -- 7.3 Challenges of Optical Character Recognition Systems for Latin Language -- 7.4 Data Acquisition -- 7.5 Data Pre-processing -- 7.5.1 Text Region Extraction -- 7.5.2 Skew Detection and Correction -- 7.5.3 Binarization -- 7.5.4 Noise Removal -- 7.5.5 Character Segmentation -- 7.5.6 Thinning -- 7.6 Feature Selection Through Genetic Algorithms -- 7.7 Feature Based Classification: Sate of Art -- 7.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 7.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 7.7.3 Feature Based Classification Through Hierarchical Fuzzy Rough Bidirectional Recurrent Neural Networks -- 7.8 Experimental Results -- 7.8.1 Rough Fuzzy Multilayer Perceptron -- 7.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 7.8.3 Hierarchical Fuzzy Rough Bidirectional Recurrent Neural Networks -- 7.9 Further Discussions -- References -- 8 Optical Character Recognition Systems for Hindi Language. Abstract -- 8.1 Introduction -- 8.2 Hindi Language Script and Experimental Dataset -- 8.3 Challenges of Optical Character Recognition Systems for Hindi Language -- 8.4 Data Acquisition -- 8.5 Data Pre-processing -- 8.5.1 Binarization -- 8.5.2 Noise Removal -- 8.5.3 Skew Detection and Correction -- 8.5.4 Character Segmentation -- 8.5.5 Thinning -- 8.6 Feature Extraction Through Hough Transform -- 8.7 Feature Based Classification: Sate of Art -- 8.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 8.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 8.7.3 Feature Based Classification Through Fuzzy Markov Random Fields -- 8.8 Experimental Results -- 8.8.1 Rough Fuzzy Multilayer Perceptron -- 8.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 8.8.3 Fuzzy Markov Random Fields -- 8.9 Further Discussions -- References -- 9 Optical Character Recognition Systems for Gujrati Language -- Abstract -- 9.1 Introduction -- 9.2 Gujrati Language Script and Experimental Dataset -- 9.3 Challenges of Optical Character Recognition Systems for Gujrati Language -- 9.4 Data Acquisition -- 9.5 Data Pre-processing -- 9.5.1 Binarization -- 9.5.2 Noise Removal -- 9.5.3 Skew Detection and Correction -- 9.5.4 Character Segmentation -- 9.5.5 Thinning -- 9.6 Feature Selection Through Genetic Algorithms -- 9.7 Feature Based Classification: Sate of Art -- 9.7.1 Feature Based Classification Through Rough Fuzzy Multilayer Perceptron -- 9.7.2 Feature Based Classification Through Fuzzy and Fuzzy Rough Support Vector Machines -- 9.7.3 Feature Based Classification Through Fuzzy Markov Random Fields -- 9.8 Experimental Results -- 9.8.1 Rough Fuzzy Multilayer Perceptron -- 9.8.2 Fuzzy and Fuzzy Rough Support Vector Machines -- 9.8.3 Fuzzy Markov Random Fields -- 9.9 Further Discussions -- References. 10 Summary and Future Research -- 10.1 Summary -- 10.2 Future Research -- References -- Index. |
| Record Nr. | UNINA-9910254355303321 |
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Chaudhuri Arindam
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory / / by Arindam Chaudhuri, Soumya K. Ghosh
| Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory / / by Arindam Chaudhuri, Soumya K. Ghosh |
| Autore | Chaudhuri Arindam |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XVI, 190 p. 65 illus., 53 illus. in color.) |
| Disciplina | 658.155 |
| Collana | Studies in Fuzziness and Soft Computing |
| Soggetto topico |
Computational complexity
Statistics Operations research Decision making Economics, Mathematical Complexity Statistics for Business, Management, Economics, Finance, Insurance Operations Research/Decision Theory Quantitative Finance |
| ISBN | 3-319-26039-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910254185603321 |
|
Chaudhuri Arindam
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||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks [[electronic resource] /] / by Arindam Chaudhuri
| Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks [[electronic resource] /] / by Arindam Chaudhuri |
| Autore | Chaudhuri Arindam |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIX, 98 p. 49 illus., 42 illus. in color.) |
| Disciplina | 025.04 |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Information retrieval
Database management Data mining Pattern recognition Information Storage and Retrieval Database Management Data Mining and Knowledge Discovery Pattern Recognition |
| ISBN | 981-13-7474-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter1. Introduction -- Chapter 2. Current State of Art -- Chapter 3. Literature Review -- Chapter 4. Twitter Datasets Used -- Chapter 5. Visual and Text Sentiment Analysis -- Chapter 6. Experimental Setup: Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks -- Chapter 7. Twitter Datasets Used -- Chapter 8. Experimental Results -- Chapter 9. Conclusion. |
| Record Nr. | UNINA-9910350225503321 |
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Chaudhuri Arindam
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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