Intelligent decision making through bio-inspired optimization:
"Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA)
IGI Global
c2024
|
| Schlagworte: | |
| Online-Zugang: | DE-863 |
| Zusammenfassung: | "Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with the demands of modern research and industry. This pivotal issue necessitates a shift, urging scholars to explore unconventional approaches that can transcend disciplinary boundaries and unlock new dimensions of problem-solving.In response to these pressing challenges, Intelligent Decision Making Through Bio-Inspired Optimization emerges as a beacon of ingenuity. This groundbreaking book transcends usual disciplinary boundaries, seamlessly integrating computer science, artificial intelligence, optimization, and decision science. Its multidisciplinary approach addresses the inherent complexities faced by scholars, offering a comprehensive exploration of nature-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies. The book's core mission is to empower academic scholars with the tools to overcome contemporary decision-making hurdles, providing a holistic understanding of these bio-inspired approaches and their potential to revolutionize the scholarly landscape."-- |
| Beschreibung: | 20 PDFs (xvi, 275 Seiten) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9798369320747 |
| Zugangseinschränkungen: | Restricted to subscribers or individual electronic text purchasers. |
Internformat
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| 245 | 0 | 0 | |a Intelligent decision making through bio-inspired optimization |c Ramkumar Jaganathan, Shilpa Mehta, Ram Krishan, editors. |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) |b IGI Global |c c2024 | |
| 300 | |a 20 PDFs (xvi, 275 Seiten) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Preface -- Chapter 1. Performance Evaluation of Deep Learning and Machine Learning Techniques for Opinion Mining -- Chapter 2. Swarm Intelligence-Empowered Bug Prediction Strategy for Decision Support in Software Defect Prediction -- Chapter 3. Genetic Algorithms for Decision Optimization -- Chapter 4. Acute Analysis of Bioinspired Optimization Algorithms for Diabetic Debrecen -- Chapter 5. Intelligent Decision Making Through Bio-Inspired Optimization: Ant Colony Optimization for Real-World Scenarios -- Chapter 6. Nature-Inspired Optimized Artificial Bee Colony for Decision Making in Energy-Efficient Wireless Sensor Networks -- Chapter 7. Making Healthcare Decisions: An Evolution -- Chapter 8. Bio-Inspired Design in Healthcare: The Gecko-Inspired Surgical Adhesive -- Chapter 9. Neural Network and Neural Computing -- Chapter 10. Evolutionary Computation in Artificial Intelligence: Adapting Nature's Strategies for Smart Systems -- Chapter 11. Comprehensive Study on Routing in FANET -- Chapter 12. Intelligent Routing Scheme for FANET Using Bio-Inspired Optimisation -- Chapter 13. Biospheric Reverie: Unraveling Indoor Air Quality Through Bio-Inspired Textiles, Awareness, and Decision-Making -- Compilation of References -- About the Contributors -- Index. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a "Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with the demands of modern research and industry. This pivotal issue necessitates a shift, urging scholars to explore unconventional approaches that can transcend disciplinary boundaries and unlock new dimensions of problem-solving.In response to these pressing challenges, Intelligent Decision Making Through Bio-Inspired Optimization emerges as a beacon of ingenuity. This groundbreaking book transcends usual disciplinary boundaries, seamlessly integrating computer science, artificial intelligence, optimization, and decision science. Its multidisciplinary approach addresses the inherent complexities faced by scholars, offering a comprehensive exploration of nature-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies. The book's core mission is to empower academic scholars with the tools to overcome contemporary decision-making hurdles, providing a holistic understanding of these bio-inspired approaches and their potential to revolutionize the scholarly landscape."-- |c Provided by publisher. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | |a Description based on title screen (IGI Global, viewed 04/27/2024). | ||
| 650 | 0 | |a Algorithms. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Decision making. | |
| 650 | 0 | |a Mathematical optimization. | |
| 653 | |a Bio-Inspired Decision Optimization Tools. | ||
| 653 | |a Complex Decision Scenarios. | ||
| 653 | |a Evolutionary Algorithms. | ||
| 653 | |a Genetic Algorithms. | ||
| 653 | |a Improved Efficiency of Methodologies. | ||
| 653 | |a Innovative Algorithmic Approaches. | ||
| 653 | |a Interdisciplinary Problem Solving. | ||
| 653 | |a Multidisciplinary Empowerment. | ||
| 653 | |a Nature Inspired Problem Solving. | ||
| 653 | |a Overcoming Contemporary Challenges. | ||
| 653 | |a Practical Theoretical Foundations. | ||
| 653 | |a Swarm Intelligence Applications. | ||
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Jaganathan, Ramkumar |d 1986- |e editor. | |
| 700 | 1 | |a Krishan, Ram |e editor. | |
| 700 | 1 | |a Mehta, Shilpa |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
| 776 | 0 | 8 | |i Print version: |z 9798369320730 |
| 966 | 4 | 0 | |l DE-863 |p ZDB-98-IGB |q FWS_PDA_IGB |u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-2073-0 |3 Volltext |
| 912 | |a ZDB-98-IGB | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-98-IGB-00331690 |
|---|---|
| _version_ | 1825936823232233472 |
| adam_text | |
| any_adam_object | |
| author2 | Jaganathan, Ramkumar 1986- Krishan, Ram Mehta, Shilpa |
| author2_role | edt edt edt |
| author2_variant | r j rj r k rk s m sm |
| author_facet | Jaganathan, Ramkumar 1986- Krishan, Ram Mehta, Shilpa |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | QA402 |
| callnumber-raw | QA402.5 .I683 2024e |
| callnumber-search | QA402.5 .I683 2024e |
| callnumber-sort | QA 3402.5 I683 42024E |
| callnumber-subject | QA - Mathematics |
| collection | ZDB-98-IGB |
| contents | Preface -- Chapter 1. Performance Evaluation of Deep Learning and Machine Learning Techniques for Opinion Mining -- Chapter 2. Swarm Intelligence-Empowered Bug Prediction Strategy for Decision Support in Software Defect Prediction -- Chapter 3. Genetic Algorithms for Decision Optimization -- Chapter 4. Acute Analysis of Bioinspired Optimization Algorithms for Diabetic Debrecen -- Chapter 5. Intelligent Decision Making Through Bio-Inspired Optimization: Ant Colony Optimization for Real-World Scenarios -- Chapter 6. Nature-Inspired Optimized Artificial Bee Colony for Decision Making in Energy-Efficient Wireless Sensor Networks -- Chapter 7. Making Healthcare Decisions: An Evolution -- Chapter 8. Bio-Inspired Design in Healthcare: The Gecko-Inspired Surgical Adhesive -- Chapter 9. Neural Network and Neural Computing -- Chapter 10. Evolutionary Computation in Artificial Intelligence: Adapting Nature's Strategies for Smart Systems -- Chapter 11. Comprehensive Study on Routing in FANET -- Chapter 12. Intelligent Routing Scheme for FANET Using Bio-Inspired Optimisation -- Chapter 13. Biospheric Reverie: Unraveling Indoor Air Quality Through Bio-Inspired Textiles, Awareness, and Decision-Making -- Compilation of References -- About the Contributors -- Index. |
| ctrlnum | (CaBNVSL)slc00005826 (OCoLC)1412136833 |
| dewey-full | 519.3 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.3 |
| dewey-search | 519.3 |
| dewey-sort | 3519.3 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| genre | Electronic books. |
| genre_facet | Electronic books. |
| id | ZDB-98-IGB-00331690 |
| illustrated | Not Illustrated |
| indexdate | 2025-03-07T12:04:11Z |
| institution | BVB |
| isbn | 9798369320747 |
| language | English |
| oclc_num | 1412136833 |
| open_access_boolean | |
| owner | DE-863 DE-BY-FWS |
| owner_facet | DE-863 DE-BY-FWS |
| physical | 20 PDFs (xvi, 275 Seiten) Also available in print. |
| psigel | ZDB-98-IGB FWS_PDA_IGB ZDB-98-IGB |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | IGI Global |
| record_format | marc |
| spelling | Intelligent decision making through bio-inspired optimization Ramkumar Jaganathan, Shilpa Mehta, Ram Krishan, editors. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) IGI Global c2024 20 PDFs (xvi, 275 Seiten) text rdacontent electronic isbdmedia online resource rdacarrier Includes bibliographical references and index. Preface -- Chapter 1. Performance Evaluation of Deep Learning and Machine Learning Techniques for Opinion Mining -- Chapter 2. Swarm Intelligence-Empowered Bug Prediction Strategy for Decision Support in Software Defect Prediction -- Chapter 3. Genetic Algorithms for Decision Optimization -- Chapter 4. Acute Analysis of Bioinspired Optimization Algorithms for Diabetic Debrecen -- Chapter 5. Intelligent Decision Making Through Bio-Inspired Optimization: Ant Colony Optimization for Real-World Scenarios -- Chapter 6. Nature-Inspired Optimized Artificial Bee Colony for Decision Making in Energy-Efficient Wireless Sensor Networks -- Chapter 7. Making Healthcare Decisions: An Evolution -- Chapter 8. Bio-Inspired Design in Healthcare: The Gecko-Inspired Surgical Adhesive -- Chapter 9. Neural Network and Neural Computing -- Chapter 10. Evolutionary Computation in Artificial Intelligence: Adapting Nature's Strategies for Smart Systems -- Chapter 11. Comprehensive Study on Routing in FANET -- Chapter 12. Intelligent Routing Scheme for FANET Using Bio-Inspired Optimisation -- Chapter 13. Biospheric Reverie: Unraveling Indoor Air Quality Through Bio-Inspired Textiles, Awareness, and Decision-Making -- Compilation of References -- About the Contributors -- Index. Restricted to subscribers or individual electronic text purchasers. "Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with the demands of modern research and industry. This pivotal issue necessitates a shift, urging scholars to explore unconventional approaches that can transcend disciplinary boundaries and unlock new dimensions of problem-solving.In response to these pressing challenges, Intelligent Decision Making Through Bio-Inspired Optimization emerges as a beacon of ingenuity. This groundbreaking book transcends usual disciplinary boundaries, seamlessly integrating computer science, artificial intelligence, optimization, and decision science. Its multidisciplinary approach addresses the inherent complexities faced by scholars, offering a comprehensive exploration of nature-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies. The book's core mission is to empower academic scholars with the tools to overcome contemporary decision-making hurdles, providing a holistic understanding of these bio-inspired approaches and their potential to revolutionize the scholarly landscape."-- Provided by publisher. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 04/27/2024). Algorithms. Artificial intelligence. Decision making. Mathematical optimization. Bio-Inspired Decision Optimization Tools. Complex Decision Scenarios. Evolutionary Algorithms. Genetic Algorithms. Improved Efficiency of Methodologies. Innovative Algorithmic Approaches. Interdisciplinary Problem Solving. Multidisciplinary Empowerment. Nature Inspired Problem Solving. Overcoming Contemporary Challenges. Practical Theoretical Foundations. Swarm Intelligence Applications. Electronic books. Jaganathan, Ramkumar 1986- editor. Krishan, Ram editor. Mehta, Shilpa editor. IGI Global, publisher. Print version: 9798369320730 |
| spellingShingle | Intelligent decision making through bio-inspired optimization Preface -- Chapter 1. Performance Evaluation of Deep Learning and Machine Learning Techniques for Opinion Mining -- Chapter 2. Swarm Intelligence-Empowered Bug Prediction Strategy for Decision Support in Software Defect Prediction -- Chapter 3. Genetic Algorithms for Decision Optimization -- Chapter 4. Acute Analysis of Bioinspired Optimization Algorithms for Diabetic Debrecen -- Chapter 5. Intelligent Decision Making Through Bio-Inspired Optimization: Ant Colony Optimization for Real-World Scenarios -- Chapter 6. Nature-Inspired Optimized Artificial Bee Colony for Decision Making in Energy-Efficient Wireless Sensor Networks -- Chapter 7. Making Healthcare Decisions: An Evolution -- Chapter 8. Bio-Inspired Design in Healthcare: The Gecko-Inspired Surgical Adhesive -- Chapter 9. Neural Network and Neural Computing -- Chapter 10. Evolutionary Computation in Artificial Intelligence: Adapting Nature's Strategies for Smart Systems -- Chapter 11. Comprehensive Study on Routing in FANET -- Chapter 12. Intelligent Routing Scheme for FANET Using Bio-Inspired Optimisation -- Chapter 13. Biospheric Reverie: Unraveling Indoor Air Quality Through Bio-Inspired Textiles, Awareness, and Decision-Making -- Compilation of References -- About the Contributors -- Index. Algorithms. Artificial intelligence. Decision making. Mathematical optimization. |
| title | Intelligent decision making through bio-inspired optimization |
| title_auth | Intelligent decision making through bio-inspired optimization |
| title_exact_search | Intelligent decision making through bio-inspired optimization |
| title_full | Intelligent decision making through bio-inspired optimization Ramkumar Jaganathan, Shilpa Mehta, Ram Krishan, editors. |
| title_fullStr | Intelligent decision making through bio-inspired optimization Ramkumar Jaganathan, Shilpa Mehta, Ram Krishan, editors. |
| title_full_unstemmed | Intelligent decision making through bio-inspired optimization Ramkumar Jaganathan, Shilpa Mehta, Ram Krishan, editors. |
| title_short | Intelligent decision making through bio-inspired optimization |
| title_sort | intelligent decision making through bio inspired optimization |
| topic | Algorithms. Artificial intelligence. Decision making. Mathematical optimization. |
| topic_facet | Algorithms. Artificial intelligence. Decision making. Mathematical optimization. Electronic books. |
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