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: Artificial intelligence techniques in mathematical modeling and optimization
  name:
    name part: Awasthi, Mukesh Kumar
    role:
      role term: editor.
  name:
    name part: Kumar, Sanoj
    role:
      role term: editor.
  name:
    name part: Saini, Deepika
    role:
      role term: editor.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (472 pages) : illustrations (black and white).
  abstract: Artificial Intelligence Techniques in Mathematical Modeling and Optimization offers a dynamic and comprehensive examination of the intersection between artificial intelligence and mathematical modeling. This edited volume brings together innovative research exploring how AI-driven methods revolutionize traditional approaches to complex optimization problems, enabling enhanced performance, interpretability, and real-world applicability across diverse domains.Covering foundational and advanced topics, the book introduces readers to machine learning, deep learning, and reinforcement learning as critical tools for modeling high-dimensional, nonlinear, and stochastic systems. Chapters delve into essential aspects like data pre-processing, feature engineering, neural network architectures, swarm intelligence, quantum optimization, and multi-objective decision-making. Emerging techniques such as Fire Hawk Optimization Plus (FHO+), hybrid deep learning-quantum frameworks, and explainable AI (XAI) are discussed in the context of real-world scenarios ranging from energy systems and manufacturing to disaster prediction and healthcare analytics.This volume uniquely bridges theory and application by integrating algorithmic strategies with case studies on predictive maintenance, renewable energy optimization, cyclone detection, heart disease prediction, and postpartum mental health risk assessment. It also investigates the role of circular economy principles in inventory optimization and examines future trends including neuromorphic computing and ethical AI.Key Features: Systematic exploration of AI-based optimization in mathematical modeling. In-depth coverage of ML/DL methods, quantum algorithms, and nature-inspired techniques. Practical applications in industrial manufacturing, healthcare, smart energy, and environmental resilience. Detailed discussions on model training, generalization, hyperparameter tuning, and overfitting control. Includes practical tools such as AutoML, P…
  note: edited by Mukesh Kumar Awasthi, Sanoj Kumar, Deepika Saini.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Mathematical models
    topic: Data processing
  subject:
    topic: Mathematical optimization
    topic: Data processing
  subject:
    topic: Intelligence artificielle
  subject:
    topic: artificial intelligence
  subject:
    topic: Artificial intelligence
  subject:
    topic: Mathematical models
    topic: Data processing
  subject:
    topic: Mathematical optimization
    topic: Data processing
  classification: 511.8028563
  related item:
    title info:
      title: Intelligent data-driven systems and artificial intelligence
  identifier: 9781040682524
  identifier: 1040682529
  identifier: 9781003633402
  identifier: 1003633404
  identifier: 9781040576038
  identifier: 1040576036
  identifier: 9781003633402 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: UKMGB
    record creation date: 260205
    record change date: 20260519165225.1
    record identifier: in00005782163
    language of cataloging:
      language term: eng