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: Handbook of machine learning for computational optimization
    sub title: applications and case studies
  name:
    name part: Jain, Vishal
    name part: 1983-
    role:
      role term: editor.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  genre: handbooks.
  genre: Handbooks and manuals.
  genre: Guides et manuels.
  origin info:
    place:
      place term: flu
    date issued: 2022
    copyright date: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xiv, 280 pages) : illustrations (some color).
  abstract: "Technology is moving at an exponential pace in this era of computational intelligence. Machine Learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new Machine Learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach which makes Machine Learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms which are more efficient and reliable for new dimensions in discovering other applications and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for Machine Learning based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers"--
  table of contents: Random variables in machine learning / Dr. Piratla Srihari -- Analysis of EMG signals using extreme learning machine with nature inspired feature selection techniques / A. Anitha and Bakiya Ambikapathy -- Detection of breast cancer by using various machine learning and deep learning algorithms / Yogesh Jadhav, Harsh Mathur -- Assessing the radial efficiency performance of bus transport sector using data envelopment analysis / Swati Goyal, Shivi Agarwal, Trilok Mathur and Nirbhay Mathur.
  note: edited by Vishal Jain, Sapna Juneja, Abhinav Juneja, and Ramani Kannan.
  note: Includes bibliographical references and index.
  note: Vishal Jain is an Associate Professor in Deptt. of CSE at Sharda University, Greater Noida, India. He has earlier worked with Bharati Vidyapeeth's Institute of Computer Applications and Management (BVICAM), New Delhi, India (affiliated with Guru Gobind Singh Indraprastha University, and accredited by the All India Council for Technical Education). He first joined BVICAM as Assistant Professor. Before that, he has worked for several years at the Guru Presmsukh Memorial College of Engineering, Delhi, India. He has more than 350 research citation indices with Google scholar (h-index score 9 and i-10 index 9). He has authored more than 70 research papers in reputed conferences and journals including Web of Science and Scopus. He has authored and edited more than 10 books with various reputed publishers including Springer, Apple Academic Press, Scrivener, Emerald and IGI-Global. His research areas include information retrieval, semantic web, ontology engineering, data mining, adhoc networks, and sensor networks. He has recipient a Young Active Member Award for the year 2012-13 from the Computer Society of India, Best Faculty Award for the year 2017 and Best Researcher Award for the year 2019 from BVICAM, New Delhi. Sapna Juneja is Professor in IMS, Ghaziabad, India. Earlier she has worked as Professor in Deptt. of CSE at IITM Group of Institutions and BMIET, Sonepat. She has more than 16 years of teaching experience. She completed her doctorate and masters in Computer Science and Engineering from M.D.University, Rohtak in 2018 and 2010 respectively. Her broad area of research is Software Reliability of Embedded System. Her areas of interest include Software Engineering, Computer Networks, Operating System, Database Management Systems, and Artificial Intelligence etc. She has guided several research thesis of UG and PG students in Computer Science and Engineering. She is editing book on recent technological developments. Abhinav Juneja is currently working as Professor in…
  subject:
    topic: Machine learning
    topic: Industrial applications
  subject:
    topic: Mathematical optimization
    topic: Data processing
  subject:
    topic: Artificial intelligence
  subject:
    topic: Apprentissage automatique
    topic: Applications industrielles
  subject:
    topic: Optimisation mathématique
    topic: Informatique
  subject:
    topic: Intelligence artificielle
  subject:
    topic: artificial intelligence
  subject:
    topic: TECHNOLOGY / Operations Research
  subject:
    topic: Artificial intelligence
  subject:
    topic: Machine learning
    topic: Industrial applications
  subject:
    topic: Mathematical optimization
    topic: Data processing
  classification: Q325.5 .H295 2022
  classification: 006.3/1
  related item:
    title info:
      title: Demystifying technologies for computational excellence : moving towards society 5.0
  related item:
    title info:
      title: Handbook of machine learning for computational optimization
    origin info:
      publisher: Boca Raton : CRC Press, 2021
    identifier: (DLC) 2021017098
  identifier: 9781003138020
  identifier: 1003138020
  identifier: 1000455688
  identifier: 9781000455670
  identifier: 100045567X
  identifier: 9781000455687
  identifier: 9780367685423
  identifier: 9780367685454
  identifier: 2021017099
  identifier: 9781003138020 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DLC
    record creation date: 210413
    record change date: 20241108165924.4
    record identifier: in00005524865
    language of cataloging:
      language term: eng