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 IoT-sensor networks
  name:
    name part: Elhoseny, Mohamed
    role:
      role term: editor.
  name:
    name part: Shankar, K.
    role:
      role term: editor.
  name:
    name part: Abdel-Basset, Mohamed
    name part: 1985-
    role:
      role term: editor.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    date issued: 2021
    edition: First edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (x, 221 pages).
  abstract: "Artificial Intelligence Techniques in IoT-Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in Artificial Intelligence (AI), Sensor Networks and Internet of Things (IoT). This book intends to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications, and their implications on the design of solutions for sensor networks"--
  table of contents: PrefaceChapter 1Adaptive Regularized Gaussian Kernel FCM for the Segmentation of Medical Images -- An Artificial Intelligence Based IoT Implementation for Teleradiology Network 1.1 Introduction1.2 Proposed Methodology 1.2.1 Fuzzy C Means Clustering1.3 Results and Discussion1.4 ConclusionReferencesChapter 2Artificial Intelligence Based Fuzzy Logic with Modified Particle Swarm Optimization Algorithm for Internet of Things Enabled Logistic Transportation Planning 2.1. Introduction2.2. Related works2.3. Proposed Method 2.3.1. Package Partitioning 2.3.2. Planning of delivery path using HFMPSO algorithm 2.3.3. Inserting Pickup Packages2.4. Experimental Validation 2.4.1. Performance analysis under varying package count 2.4.2. Performance analysis under varying vehicle capacities 2.4.3. Computation Time (CT) analysis2.5. ConclusionReferences Chapter 3Butterfly Optimization based Feature Selection with Gradient Boosting Tree for Big Data Analytics in Social Internet of Things 3.1. Introduction3.2. Related works3.3. The Proposed Method 3.3.1. Hadoop Ecosystem 3.3.2. BOA based FS process 3.3.3. GBT based Classification3.4. Experimental Analysis 3.4.1. FS Results analysis 3.4.2. Classification Results Analysis 3.4.3. Energy Consumption Analysis 3.4.4. Throughput Analysis3.5. ConclusionReferencesChapter 4An Energy Efficient Fuzzy Logic based Clustering with Data Aggregation Protocol for WSN assisted IoT system 4. 1. Introduction4. 2. Background Information 4. 2.1. Clustering objective 4. 2. 2. Clustering characteristics4. 3. Proposed Fuzzy based Clustering and Data Aggregation (FC-DR) protocol 4. 3. 1. Fuzzy based Clustering process 4. 3. 2. Data aggregation process 4. 4. Performance Validation4. 5. ConclusionReferencesChapter 5Analysis of Smart Home Recommendation system from Natural Language Processing Services with Clustering Technique 5. 1. Introduction5. 2. Review of Literatures5. 3. Smart Home- Cloud Backend Services 5. 3.1 Internet of Things (IoT)5. 4. Our Proposed Approa…
  note: edited by Mohamed Elhoseny, K. Shankar, Mohamed Abdel-Basset.
  note: Includes bibliographical references and index.
  subject:
    topic: Sensor networks
  subject:
    topic: Internet of things
  subject:
    topic: Réseaux de capteurs
  subject:
    topic: Internet des objets
  subject:
    topic: COMPUTERS
    topic: Computer Engineering
  subject:
    topic: COMPUTERS
    topic: Machine Theory
  subject:
    topic: Internet of things
  subject:
    topic: Sensor networks
  classification: TK7872.D48
  classification: 006.3
  related item:
    title info:
      title: Chapman & Hall/CRC distributed sensing and intelligent systems series
  related item:
    title info:
      title: Artificial intelligence techniques in IoT-sensor networks
    origin info:
      publisher: Boca Raton : CRC Press/Taylor & Francis Group, 2021.
    identifier: (DLC) 2020037221
  identifier: 9781000318821
  identifier: 1000318826
  identifier: 9780367439255
  identifier: 9781003007265
  identifier: 1003007260
  identifier: 9781000318708
  identifier: 1000318702
  identifier: 9781000318760
  identifier: 1000318761
  identifier: 9780367681456
  identifier: 0367439255
  identifier: 2021702057
  identifier: 9781003007265 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: DLC
    record creation date: 200914
    record change date: 20241108170231.1
    record identifier: in00005526423
    language of cataloging:
      language term: eng