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: Meta Heuristic Techniques in Software Engineering and Its Applications
    sub title: METASOFT 2022
  name:
    name part: Mohanty, Mihir Narayan.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Das, Swagatam.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Ray, Mitrabinda.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Patra, Bichitrananda.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2022
    edition: 1st ed. 2022.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (X, 358 pages 209 illustrations, 129 illustrations in color)
  abstract: This book discusses an integration of machine learning with metaheuristic techniques that provide more robust and efficient ways to address traditional optimization problems. Modern metaheuristic techniques, along with their main characteristics and recent applications in artificial intelligence, software engineering, data mining, planning and scheduling, logistics and supply chains, are discussed in this book and help global leaders in fast decision making by providing quality solutions to important problems in business, engineering, economics and science. Novel ways are also discovered to attack unsolved problems in software testing and machine learning. The discussion on foundations of optimization and algorithms leads beginners to apply current approaches to optimization problems. The discussed metaheuristic algorithms include genetic algorithms, simulated annealing, ant algorithms, bee algorithms and particle swarm optimization. New developments on metaheuristics attract researchers and practitioners to apply hybrid metaheuristics in real scenarios.
  table of contents: Performance analysis of Heuristic optimization algorithms for Transportation problem -- Source Code Features Based Branch Coverage Prediction using Ensemble Technique -- Implicit Methods of Multi-Factor Authentication -- Comparative Analysis of different Classifiers Using Machine Learning Algorithm for Diabetes Mellitus -- Survey on Machine Learning Techniques for Software Reliability Accuracy Prediction -- Classification of Pest in Tomato Plants using CNN -- Deep Neural Network Approach For Identifying Good Answers in Community Platforms -- Time Series Analysis of SAR-Cov-2 virus in India using Facebook's Prophet -- Model-Based Smoke Testing Approach of Service Oriented Architecture (SOA) -- Role of Hybrid Evolutionary Approaches for Feature Selection in Classification: A Review -- Evaluation of Deep Learning Models for Detecting Breast Cancer using Mammograms -- Evaluation of Crop Yield Prediction using arsenal and Ensemble Machine learning algorithms -- Notification Based Multichannel MAC (NM-MAC) Protocol for Wireless Body Area Network -- A multi Brain Tumor Classification using a Deep Reinforcement Learning Model -- A Brief Analysis on Security in Healthcare Data using Blockchain -- A Review on Test Case Selection, Prioritization and Minimization in Regression Testing -- Artificial Intelligence Advancement in Pandemic Era -- Predictive technique for Identification of Diabetes using Machine Learning -- Prognosis of Prostate Cancer Using Machine Learning -- Sign language Detection Using Tensorflow Object Detection -- Automated Test Case Prioritization using Machine Learning -- A New Approach To Solve Linear Fuzzy Stochastic Differential Equation -- An Improved Software Reliability Prediction Model by Using Feature Selection and Extreme Learning Machine -- Signal Processing Approaches for Encoded Protein Sequences in Gynaecological Cancer Hotspot Prediction: A Review -- DepNet: Deep Neural Network based model for Estimating the Crowd Count -- Dynamic Stability enh…
  note: edited by Mihir Narayan Mohanty, Swagatam Das, Mitrabinda Ray, Bichitrananda Patra.
  subject:
    topic: Computational intelligence
  subject:
    topic: Software engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational Intelligence
  subject:
    topic: Software Engineering
  subject:
    topic: Artificial Intelligence
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Artificial Intelligence-Enhanced Software and Systems Engineering, 1
  identifier: 9978-3-031-11713-8
  identifier: 9783031117138
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 221017
    record change date: 20240805190131.3
    record identifier: in00004658599