rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Metaheuristic and machine learning optimization strategies for complex systems
  name:
    name part: Mothilal, T.
    role:
      role term: editor.
  name:
    name part: S., Kaliappan
    role:
      role term: editor.
  name:
    name part: M., Suchithra
    role:
      role term: editor.
  name:
    name part: R., Thanigaivelan
    role:
      role term: editor.
  name:
    name part: IGI Global
    role:
      role term: publisher.
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: pau
    date issued: 2024
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (24 PDFs (427 pages))
  abstract: "In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges.Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more."--
  table of contents: Chapter 1. Experimental investigation and comparative analysis of an efficient machine learning algorithm for distribution system reconfiguration -- Chapter 2. Integrated approach for network intrusion detection: combining KNN algorithm, Karhunen-Loève transform, and genetic algorithm optimization -- Chapter 3. Machine learning for the detection of uniform resource locator phishing -- Chapter 4. Machine learning in industrial IoT applications for safety, security, asset localization, quality assurance, and sustainability in smart production -- Chapter 5. Machine learning in industry IoT (IIoT) -- Chapter 6. Machine learning in IoT for smart home automation -- Chapter 7. Meta-heuristics algorithm for computer communications -- Chapter 8. Meta-heuristic and machine learning modelling and optimizing complex systems -- Chapter 9. Meta-heuristic algorithms for optimal sizing of HRES -- Chapter 10. Meta-heuristic algorithms for optimal sizing of hybrid renewable energy systems -- Chapter 11. Meta-heuristic algorithms for optimal sizing of hybrid renewable energy systems -- Chapter 12. Metaheuristic algorithms for robot path planning: a comprehensive exploration -- Chapter 13. Meta-heuristic optimization for enhanced sensor-based health monitoring in cloud computing environments -- Chapter 14. Meta-heuristics and machine learning applications in complex systems -- Chapter 15. Metaheuristics and machine learning convergence: a comprehensive survey and future prospects -- Chapter 16. Optimizing optical fiber path in wavelength division multiplexing networks using particle swarm optimization -- Chapter 17. Performance evaluation of simulation-driven metaheuristic algorithms: efficiency and effectiveness measures in the search for quality solutions.
  target audience: specialized
  note: Thanigaivelan R., Suchithra M., Kaliappan S., T. Mothilal, editors.
  note: Includes bibliographical references and index.
  subject:
    topic: Algorithms
  subject:
    topic: System analysis
  subject:
    topic: Machine learning
  subject:
    topic: Internet of things
  subject:
    topic: Algorithmes
  subject:
    topic: Analyse de systèmes
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Internet des objets
  subject:
    topic: algorithms
  subject:
    topic: systems analysis
  subject:
    topic: Cloud Computing.
  subject:
    topic: Complex Systems.
  subject:
    topic: Computer Communications.
  subject:
    topic: Distribution System Reconfiguration.
  subject:
    topic: Genetic Algorithm Optimization.
  subject:
    topic: Hybrid Renewable Energy Systems.
  subject:
    topic: Internet of Things (IoT).
  subject:
    topic: Machine Learning.
  subject:
    topic: Metaheuristic Algorithms.
  subject:
    topic: Performance Evaluation.
  subject:
    topic: Sensor-Based Health Monitoring.
  subject:
    topic: Simulation-Driven Metaheuristic Algorithms.
  classification: T57.6 .M48 2024e
  classification: 003
  identifier: 9798369378441 (electronic bk.)
  identifier: 9789798369377
  identifier: 9798369378
  identifier: 9798369378427
  location:
    physical location: POD
  record info:
    record content source: IGIGL
    record creation date: 240722
    record change date: 20250810205609.9
    record identifier: in00005657867
    language of cataloging:
      language term: eng