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: Using traditional design methods to enhance AI-driven decision making
  name:
    name part: Nguyen, Tien V.T.
    name part: 1987-
    role:
      role term: editor.
  name:
    name part: Vo, Nhut Thi Minh
    name part: 1986-
    role:
      role term: editor.
  name:
    name part: IGI Global Online
  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 ( xxv, 503 pages) : illustrations (some color).
  table of contents: Section 1. AI-driven decision making in healthcare and environmental sciences: nurturing wellness, sustaining nature -- navigating AI frontiers in healthcare and environmental science. Chapter 1. AI-driven decision-making applications in pharmaceutical sciences ; Chapter 2. AI clinical decision support system (AI-CDSS) for cardiovascular diseases ; Chapter 3. AI-driven IoT (AIIoT) in healthcare monitoring ; Chapter 4. Artificial intelligence and machine learning models for Alzheimer's disease ; Chapter 5. A smart healthcare diabetes prediction system using ensemble of classifiers ; Chapter 6. AI-driven powered solution selection: navigating forests and fires for a sustainable future ; Chapter 7. AI-driven solution selection: prediction of water quality using machine learning ; Chapter 8. AI-decision support system: engineering, geology, climate, and socioeconomic aspects' implications on machine learning -- Section 2. Intelligent systems from optimal-MCDM shaping tomorrow: an in-depth analysis of decision-making applications in agriculture, judiciary, education, and others. Chapter 9. AI-driven learning analytics for personalized feedback and assessment in higher education ; Chapter 10. Integrating artificial intelligence in education for sustainable development ; Chapter 11. AI-driven decision-making applications in higher education ; Chapter 12. AI-driven decision-making and optimization in modern agriculture sectors ; Chapter 13. IoT-integrated machine learning-based automated precision agriculture-indoor farming techniques ; Chapter 14. Automated plant disease detection using efficient deep ensemble learning model for smart agriculture ; Chapter 15. Exploring the power of AI-driven decision making in the judicial domain: case studies, benefits, challenges, and solutions ; Chapter 16. AI-driven decision support system for intuitionistic fuzzy assignment problems ; Chapter 17. Enhanced YOLO algorithm for robust object detection in challenging nighttime and blurry,…
  note: [Edited by] Tien V.T. Nguyen, Nhut T.M. Vo.
  note: Includes bibliographical references and index.
  subject:
    topic: Artificial intelligence
    topic: Educational applications
  subject:
    topic: Education, Higher
    topic: Decision making
  subject:
    topic: Educational leadership
  subject:
    topic: Intelligence artificielle
    topic: Applications en éducation
  subject:
    topic: Enseignement supérieur
    topic: Prise de décision
  subject:
    topic: Leadership en éducation
  subject:
    topic: Artificial intelligence
    topic: Educational applications
  subject:
    topic: Education, Higher
    topic: Decision making
  subject:
    topic: Educational leadership
  classification: LB1028.43 .U83 2024
  classification: 378.1/01
  related item:
    title info:
      title: Using traditional design methods to enhance AI-driven decision making
    origin info:
      publisher: Hershey, PA : Information Science Reference, [2024]
    identifier: (DLC) 2023038664
  related item:
    title info:
      title: Advances in media, entertainment, and the arts (AMEA) book series
  identifier: 9798369306406
  identifier: 9798369306413
  identifier: 9798369306390
  identifier: 9798369306437
  identifier: 2023038665
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DLC
    record creation date: 231019
    record change date: 20240313142111.1
    record identifier: in00004783595
    language of cataloging:
      language term: eng