Self-learning and adaptive algorithms for business applications :: a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions /
In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear.
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bingley, UK :
Emerald Publishing,
2019.
|
| Ausgabe: | First edition. |
| Schriftenreihe: | Emerald points.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781838671716 1838671714 9781838671730 1838671730 |
Internformat
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| 100 | 1 | |a Hu, Zhengbing, |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjxqfyQRBHgYvGjwvgrRBq |0 http://id.loc.gov/authorities/names/no2009129726 | |
| 245 | 1 | 0 | |a Self-learning and adaptive algorithms for business applications : |b a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / |c by Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko. |
| 250 | |a First edition. | ||
| 264 | 1 | |a Bingley, UK : |b Emerald Publishing, |c 2019. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Emerald points | |
| 504 | |a Includes bibliographical references. | ||
| 588 | |a Online resource; title from PDF title page (EBSCO, viewed June 13, 2019). | ||
| 505 | 0 | |a Front Cover; Self-Learning and Adaptive Algorithms for Business Applications; Copyright Page; Contents; Acknowledgment; Introduction; Chapter 1 Review of the Problem Area; 1.1. Learning and Self-learning Procedures; 1.2. Clustering; 1.2.1. Clustering Methods; 1.3. Fuzzy Sets and Fuzzy Logic; 1.3.1. Fuzzy Inference Systems and Fuzzy Control; 1.3.2. Type-2 Fuzzy Logic; 1.3.2.1. Interval Type-2 Fuzzy Sets; 1.3.2.2. Model Reduction; 1.3.2.3. Type-2 Fuzzy Clustering; 1.4. Neural Networks and Their Learning Methods; 1.4.1. Artificial Neural Networks; 1.4.2. Neural Networks' Learning | |
| 505 | 8 | |a 1.4.3. Recurrent Neural Networks1.5. Neuro-fuzzy Systems; Chapter 2 Adaptive Methods of Fuzzy Clustering; 2.1. An Objective Function for Fuzzy Clustering; 2.2. Optimization of the Objective Function; 2.3. A Linear Variable Fuzzifier; 2.3.1. Adaptive Fuzzy Clustering with a Variable Fuzzifier; 2.3.2. Possibilistic Fuzzy Clustering with a Variable Fuzzifier; 2.3.3. A Suppression Procedure for Fuzzy Clustering; 2.4. Methods Based on the Gustafson-Kessel Procedure; 2.4.1. The Basic Gustafson-Kessel Method; 2.4.2. A Possibilistic Version of the Gustafson-Kessel Method | |
| 505 | 8 | |a 2.4.3. Adaptive Versions of the Gustafson-Kessel Algorithm2.5. A Robust Fuzzy Clustering Method Based on the Cauchy Criterion; 2.5.1. The Probabilistic Approach; 2.5.2. The Possibilistic Approach; Chapter 3 Kohonen Maps and Their Ensembles for Fuzzy Clustering Tasks; 3.1. The Competitive Learning; 3.2. Kohonen Neural Networks; 3.3. Modifications of Kohonen Self-organizing Maps; 3.4. Ensembles and Their Learning Methods; 3.4.1. Reasons for Using Ensembles; 3.4.2. Basic Notions of the Theory of Collective Output Systems; 3.4.2.1. Confidence; 3.4.2.2. Diversification | |
| 505 | 8 | |a 3.4.2.3. Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; 3.4.3.1. An Algebraic Combination; 3.4.3.2. A Weighted Combination; 3.4.3.3. Complex Systems of the Collective Output; 3.5. Ensembles of Neuro-fuzzy Kohonen Networks; 3.6. Fuzzy Type-2 Clustering Using Ensembles of Modified Neuro-fuzzy Kohonen Networks; Chapter 4 Simulation Results and Solutions for Practical Tasks; 4.1. Simulation of the Adaptive Neuro-fuzzy Kohonen Network with a Variable Fuzzifier; 4.1.1. Comparative Efficiency; 4.1.2. The Fuzzifier's Influence; 4.1.3. Influence of the Suppression Parameter | |
| 505 | 8 | |a 4.2. Simulation of Adaptive Versions the Gustafson-Kessel Algorithm4.3. Simulation of the Robust Clustering Method Based on the Cauchy Criterion; 4.4. Solving the Task of Automated Cataloging of Illustrative Materials; Conclusion; References | |
| 520 | |a In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. | ||
| 650 | 0 | |a Electronic data processing. |0 http://id.loc.gov/authorities/subjects/sh85042288 | |
| 650 | 0 | |a Business |x Data processing. |0 http://id.loc.gov/authorities/subjects/sh85018264 | |
| 650 | 0 | |a Fuzzy systems. |0 http://id.loc.gov/authorities/subjects/sh85052628 | |
| 650 | 6 | |a Gestion |x Informatique. | |
| 650 | 6 | |a Systèmes flous. | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Industrial Management. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Management. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Management Science. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Organizational Behavior. |2 bisacsh | |
| 650 | 7 | |a Business |x Data processing |2 fast | |
| 650 | 7 | |a Electronic data processing |2 fast | |
| 650 | 7 | |a Fuzzy systems |2 fast | |
| 700 | 1 | |a Bodyanskiy, Yevgeniy V., |e author | |
| 700 | 1 | |a Tyshchenko, Oleksii, |e author. | |
| 758 | |i has work: |a Self-learning and adaptive algorithms for business applications (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFTv6ppQ4kHBCXGPcDkfYP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBU-on1104209023 |
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| _version_ | 1816796934335102976 |
| adam_text | |
| any_adam_object | |
| author | Hu, Zhengbing Bodyanskiy, Yevgeniy V. Tyshchenko, Oleksii |
| author_GND | http://id.loc.gov/authorities/names/no2009129726 |
| author_facet | Hu, Zhengbing Bodyanskiy, Yevgeniy V. Tyshchenko, Oleksii |
| author_role | aut aut aut |
| author_sort | Hu, Zhengbing |
| author_variant | z h zh y v b yv yvb o t ot |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | H - Social Science |
| callnumber-label | HD30 |
| callnumber-raw | HD30.2 |
| callnumber-search | HD30.2 |
| callnumber-sort | HD 230.2 |
| callnumber-subject | HD - Industries, Land Use, Labor |
| collection | ZDB-4-EBU |
| contents | Front Cover; Self-Learning and Adaptive Algorithms for Business Applications; Copyright Page; Contents; Acknowledgment; Introduction; Chapter 1 Review of the Problem Area; 1.1. Learning and Self-learning Procedures; 1.2. Clustering; 1.2.1. Clustering Methods; 1.3. Fuzzy Sets and Fuzzy Logic; 1.3.1. Fuzzy Inference Systems and Fuzzy Control; 1.3.2. Type-2 Fuzzy Logic; 1.3.2.1. Interval Type-2 Fuzzy Sets; 1.3.2.2. Model Reduction; 1.3.2.3. Type-2 Fuzzy Clustering; 1.4. Neural Networks and Their Learning Methods; 1.4.1. Artificial Neural Networks; 1.4.2. Neural Networks' Learning 1.4.3. Recurrent Neural Networks1.5. Neuro-fuzzy Systems; Chapter 2 Adaptive Methods of Fuzzy Clustering; 2.1. An Objective Function for Fuzzy Clustering; 2.2. Optimization of the Objective Function; 2.3. A Linear Variable Fuzzifier; 2.3.1. Adaptive Fuzzy Clustering with a Variable Fuzzifier; 2.3.2. Possibilistic Fuzzy Clustering with a Variable Fuzzifier; 2.3.3. A Suppression Procedure for Fuzzy Clustering; 2.4. Methods Based on the Gustafson-Kessel Procedure; 2.4.1. The Basic Gustafson-Kessel Method; 2.4.2. A Possibilistic Version of the Gustafson-Kessel Method 2.4.3. Adaptive Versions of the Gustafson-Kessel Algorithm2.5. A Robust Fuzzy Clustering Method Based on the Cauchy Criterion; 2.5.1. The Probabilistic Approach; 2.5.2. The Possibilistic Approach; Chapter 3 Kohonen Maps and Their Ensembles for Fuzzy Clustering Tasks; 3.1. The Competitive Learning; 3.2. Kohonen Neural Networks; 3.3. Modifications of Kohonen Self-organizing Maps; 3.4. Ensembles and Their Learning Methods; 3.4.1. Reasons for Using Ensembles; 3.4.2. Basic Notions of the Theory of Collective Output Systems; 3.4.2.1. Confidence; 3.4.2.2. Diversification 3.4.2.3. Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; 3.4.3.1. An Algebraic Combination; 3.4.3.2. A Weighted Combination; 3.4.3.3. Complex Systems of the Collective Output; 3.5. Ensembles of Neuro-fuzzy Kohonen Networks; 3.6. Fuzzy Type-2 Clustering Using Ensembles of Modified Neuro-fuzzy Kohonen Networks; Chapter 4 Simulation Results and Solutions for Practical Tasks; 4.1. Simulation of the Adaptive Neuro-fuzzy Kohonen Network with a Variable Fuzzifier; 4.1.1. Comparative Efficiency; 4.1.2. The Fuzzifier's Influence; 4.1.3. Influence of the Suppression Parameter 4.2. Simulation of Adaptive Versions the Gustafson-Kessel Algorithm4.3. Simulation of the Robust Clustering Method Based on the Cauchy Criterion; 4.4. Solving the Task of Automated Cataloging of Illustrative Materials; Conclusion; References |
| ctrlnum | (OCoLC)1104209023 |
| dewey-full | 658.4038 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4038 |
| dewey-search | 658.4038 |
| dewey-sort | 3658.4038 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | First edition. |
| format | Electronic eBook |
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| id | ZDB-4-EBU-on1104209023 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-26T14:49:34Z |
| institution | BVB |
| isbn | 9781838671716 1838671714 9781838671730 1838671730 |
| language | English |
| oclc_num | 1104209023 |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource |
| psigel | ZDB-4-EBU |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Emerald Publishing, |
| record_format | marc |
| series | Emerald points. |
| series2 | Emerald points |
| spelling | Hu, Zhengbing, author. https://id.oclc.org/worldcat/entity/E39PCjxqfyQRBHgYvGjwvgrRBq http://id.loc.gov/authorities/names/no2009129726 Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / by Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko. First edition. Bingley, UK : Emerald Publishing, 2019. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Emerald points Includes bibliographical references. Online resource; title from PDF title page (EBSCO, viewed June 13, 2019). Front Cover; Self-Learning and Adaptive Algorithms for Business Applications; Copyright Page; Contents; Acknowledgment; Introduction; Chapter 1 Review of the Problem Area; 1.1. Learning and Self-learning Procedures; 1.2. Clustering; 1.2.1. Clustering Methods; 1.3. Fuzzy Sets and Fuzzy Logic; 1.3.1. Fuzzy Inference Systems and Fuzzy Control; 1.3.2. Type-2 Fuzzy Logic; 1.3.2.1. Interval Type-2 Fuzzy Sets; 1.3.2.2. Model Reduction; 1.3.2.3. Type-2 Fuzzy Clustering; 1.4. Neural Networks and Their Learning Methods; 1.4.1. Artificial Neural Networks; 1.4.2. Neural Networks' Learning 1.4.3. Recurrent Neural Networks1.5. Neuro-fuzzy Systems; Chapter 2 Adaptive Methods of Fuzzy Clustering; 2.1. An Objective Function for Fuzzy Clustering; 2.2. Optimization of the Objective Function; 2.3. A Linear Variable Fuzzifier; 2.3.1. Adaptive Fuzzy Clustering with a Variable Fuzzifier; 2.3.2. Possibilistic Fuzzy Clustering with a Variable Fuzzifier; 2.3.3. A Suppression Procedure for Fuzzy Clustering; 2.4. Methods Based on the Gustafson-Kessel Procedure; 2.4.1. The Basic Gustafson-Kessel Method; 2.4.2. A Possibilistic Version of the Gustafson-Kessel Method 2.4.3. Adaptive Versions of the Gustafson-Kessel Algorithm2.5. A Robust Fuzzy Clustering Method Based on the Cauchy Criterion; 2.5.1. The Probabilistic Approach; 2.5.2. The Possibilistic Approach; Chapter 3 Kohonen Maps and Their Ensembles for Fuzzy Clustering Tasks; 3.1. The Competitive Learning; 3.2. Kohonen Neural Networks; 3.3. Modifications of Kohonen Self-organizing Maps; 3.4. Ensembles and Their Learning Methods; 3.4.1. Reasons for Using Ensembles; 3.4.2. Basic Notions of the Theory of Collective Output Systems; 3.4.2.1. Confidence; 3.4.2.2. Diversification 3.4.2.3. Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; 3.4.3.1. An Algebraic Combination; 3.4.3.2. A Weighted Combination; 3.4.3.3. Complex Systems of the Collective Output; 3.5. Ensembles of Neuro-fuzzy Kohonen Networks; 3.6. Fuzzy Type-2 Clustering Using Ensembles of Modified Neuro-fuzzy Kohonen Networks; Chapter 4 Simulation Results and Solutions for Practical Tasks; 4.1. Simulation of the Adaptive Neuro-fuzzy Kohonen Network with a Variable Fuzzifier; 4.1.1. Comparative Efficiency; 4.1.2. The Fuzzifier's Influence; 4.1.3. Influence of the Suppression Parameter 4.2. Simulation of Adaptive Versions the Gustafson-Kessel Algorithm4.3. Simulation of the Robust Clustering Method Based on the Cauchy Criterion; 4.4. Solving the Task of Automated Cataloging of Illustrative Materials; Conclusion; References In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Business Data processing. http://id.loc.gov/authorities/subjects/sh85018264 Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Gestion Informatique. Systèmes flous. Neural networks & fuzzy systems. bicssc BUSINESS & ECONOMICS Industrial Management. bisacsh BUSINESS & ECONOMICS Management. bisacsh BUSINESS & ECONOMICS Management Science. bisacsh BUSINESS & ECONOMICS Organizational Behavior. bisacsh Business Data processing fast Electronic data processing fast Fuzzy systems fast Bodyanskiy, Yevgeniy V., author Tyshchenko, Oleksii, author. has work: Self-learning and adaptive algorithms for business applications (Text) https://id.oclc.org/worldcat/entity/E39PCFTv6ppQ4kHBCXGPcDkfYP https://id.oclc.org/worldcat/ontology/hasWork Print version : 9781838671747 Emerald points. http://id.loc.gov/authorities/names/no2018010552 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2040570 Volltext |
| spellingShingle | Hu, Zhengbing Bodyanskiy, Yevgeniy V. Tyshchenko, Oleksii Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / Emerald points. Front Cover; Self-Learning and Adaptive Algorithms for Business Applications; Copyright Page; Contents; Acknowledgment; Introduction; Chapter 1 Review of the Problem Area; 1.1. Learning and Self-learning Procedures; 1.2. Clustering; 1.2.1. Clustering Methods; 1.3. Fuzzy Sets and Fuzzy Logic; 1.3.1. Fuzzy Inference Systems and Fuzzy Control; 1.3.2. Type-2 Fuzzy Logic; 1.3.2.1. Interval Type-2 Fuzzy Sets; 1.3.2.2. Model Reduction; 1.3.2.3. Type-2 Fuzzy Clustering; 1.4. Neural Networks and Their Learning Methods; 1.4.1. Artificial Neural Networks; 1.4.2. Neural Networks' Learning 1.4.3. Recurrent Neural Networks1.5. Neuro-fuzzy Systems; Chapter 2 Adaptive Methods of Fuzzy Clustering; 2.1. An Objective Function for Fuzzy Clustering; 2.2. Optimization of the Objective Function; 2.3. A Linear Variable Fuzzifier; 2.3.1. Adaptive Fuzzy Clustering with a Variable Fuzzifier; 2.3.2. Possibilistic Fuzzy Clustering with a Variable Fuzzifier; 2.3.3. A Suppression Procedure for Fuzzy Clustering; 2.4. Methods Based on the Gustafson-Kessel Procedure; 2.4.1. The Basic Gustafson-Kessel Method; 2.4.2. A Possibilistic Version of the Gustafson-Kessel Method 2.4.3. Adaptive Versions of the Gustafson-Kessel Algorithm2.5. A Robust Fuzzy Clustering Method Based on the Cauchy Criterion; 2.5.1. The Probabilistic Approach; 2.5.2. The Possibilistic Approach; Chapter 3 Kohonen Maps and Their Ensembles for Fuzzy Clustering Tasks; 3.1. The Competitive Learning; 3.2. Kohonen Neural Networks; 3.3. Modifications of Kohonen Self-organizing Maps; 3.4. Ensembles and Their Learning Methods; 3.4.1. Reasons for Using Ensembles; 3.4.2. Basic Notions of the Theory of Collective Output Systems; 3.4.2.1. Confidence; 3.4.2.2. Diversification 3.4.2.3. Incremental Ensembles' Learning3.4.3. Methods for Building Ensembles; 3.4.3.1. An Algebraic Combination; 3.4.3.2. A Weighted Combination; 3.4.3.3. Complex Systems of the Collective Output; 3.5. Ensembles of Neuro-fuzzy Kohonen Networks; 3.6. Fuzzy Type-2 Clustering Using Ensembles of Modified Neuro-fuzzy Kohonen Networks; Chapter 4 Simulation Results and Solutions for Practical Tasks; 4.1. Simulation of the Adaptive Neuro-fuzzy Kohonen Network with a Variable Fuzzifier; 4.1.1. Comparative Efficiency; 4.1.2. The Fuzzifier's Influence; 4.1.3. Influence of the Suppression Parameter 4.2. Simulation of Adaptive Versions the Gustafson-Kessel Algorithm4.3. Simulation of the Robust Clustering Method Based on the Cauchy Criterion; 4.4. Solving the Task of Automated Cataloging of Illustrative Materials; Conclusion; References Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Business Data processing. http://id.loc.gov/authorities/subjects/sh85018264 Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Gestion Informatique. Systèmes flous. Neural networks & fuzzy systems. bicssc BUSINESS & ECONOMICS Industrial Management. bisacsh BUSINESS & ECONOMICS Management. bisacsh BUSINESS & ECONOMICS Management Science. bisacsh BUSINESS & ECONOMICS Organizational Behavior. bisacsh Business Data processing fast Electronic data processing fast Fuzzy systems fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85042288 http://id.loc.gov/authorities/subjects/sh85018264 http://id.loc.gov/authorities/subjects/sh85052628 |
| title | Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / |
| title_auth | Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / |
| title_exact_search | Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / |
| title_full | Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / by Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko. |
| title_fullStr | Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / by Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko. |
| title_full_unstemmed | Self-learning and adaptive algorithms for business applications : a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / by Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko. |
| title_short | Self-learning and adaptive algorithms for business applications : |
| title_sort | self learning and adaptive algorithms for business applications a guide to adaptive neuro fuzzy systems for fuzzy clustering under uncertainty conditions |
| title_sub | a guide to adaptive neuro-fuzzy systems for fuzzy clustering under uncertainty conditions / |
| topic | Electronic data processing. http://id.loc.gov/authorities/subjects/sh85042288 Business Data processing. http://id.loc.gov/authorities/subjects/sh85018264 Fuzzy systems. http://id.loc.gov/authorities/subjects/sh85052628 Gestion Informatique. Systèmes flous. Neural networks & fuzzy systems. bicssc BUSINESS & ECONOMICS Industrial Management. bisacsh BUSINESS & ECONOMICS Management. bisacsh BUSINESS & ECONOMICS Management Science. bisacsh BUSINESS & ECONOMICS Organizational Behavior. bisacsh Business Data processing fast Electronic data processing fast Fuzzy systems fast |
| topic_facet | Electronic data processing. Business Data processing. Fuzzy systems. Gestion Informatique. Systèmes flous. Neural networks & fuzzy systems. BUSINESS & ECONOMICS Industrial Management. BUSINESS & ECONOMICS Management. BUSINESS & ECONOMICS Management Science. BUSINESS & ECONOMICS Organizational Behavior. Business Data processing Electronic data processing Fuzzy systems |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2040570 |
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