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Biologically Inspired Techniques in Many-Criteria Decision Making: International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019) 2020 ed. [Pehme köide]

  • Formaat: Paperback / softback, 258 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 65 Illustrations, color; 32 Illustrations, black and white; XV, 258 p. 97 illus., 65 illus. in color., 1 Paperback / softback
  • Sari: Learning and Analytics in Intelligent Systems 10
  • Ilmumisaeg: 22-Jan-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030390357
  • ISBN-13: 9783030390358
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  • Formaat: Paperback / softback, 258 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 65 Illustrations, color; 32 Illustrations, black and white; XV, 258 p. 97 illus., 65 illus. in color., 1 Paperback / softback
  • Sari: Learning and Analytics in Intelligent Systems 10
  • Ilmumisaeg: 22-Jan-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030390357
  • ISBN-13: 9783030390358
Teised raamatud teemal:

This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems.

This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing. 

Chapter 1: Classification of Arrhythmia Using Artificial Neural Network
with Grey Wolf Optimization.
Chapter 2: Multi-objective Biogeography-Based
Optimization for Influence Maximization-Cost Minimization in Social
Networks.
Chapter 3: Classification of Credit Dataset Using Improved
Particle Swarm Optimization Tuned Radial Basis Function Neural Networks.-
Chapter 4: Multi-verse Optimization of Multilayer Perceptrons (MV-MLPs) for
Efficient Modeling and Forecasting of Crude Oil Prices Data.
Chapter 5:
Application of machine learning to predict diseases based on symptoms in
rural India.
Chapter 6: Classfcaton of Real Tme Nosy Fngerprnt Images
Usng FLANN.
Chapter 7: Software Reliability Prediction with Ensemble Method
and Virtual Data Point Incorporation.
Chapter 8: Hyperspectral Image
Classification using Stochastic Gradient Descent based Support Vector
Machine.
Chapter 9: A Survey on Ant Colony Optimization for Solving Some of
the Selected NP-Hard Problem.
Chapter 10: Machine Learning Models for Stock
Prediction using Real-Time Streaming Data.
Chapter 11: Epidemiology of
Breast Cancer (BC) and its Early Identification via Evolving Machine Learning
Classification Tools (MLCT)A Study.
Chapter 12: Ensemble Classification
Approach for Cancer Prognosis and Prediction.
Chapter 13: Extractive Odia
Text Summarization System: An OCR based Approach.
Chapter 14: Predicting
sensitivity of local news articles from Odia dailies.
Chapter 15: A
systematic frame work using machine learning approaches in supply chain
forecasting.
Chapter 16: An Intelligent system on computer-aided diagnosis
for Parkinsons disease with MRI using Machine Learning.
Chapter 17:
Operations on Picture Fuzzy Numbers and their Application in Multi-Criteria
Group Decision Making Problems.
Chapter 18: Some Generalized Results on
Multi-Criteria Decision Making Model using Fuzzy TOPSIS Technique.
Chapter
19: A Survey on FP-Tree Based Incremental Frequent Pattern Mining.
Chapter
20: Improving Co-expressed Gene Pattern Finding Using Gene Ontology.
Chapter
21: Survey of Methods Used for Differential Expression Analysis on RNA Seq
Data.
Chapter 22: Adaptive Antenna Tilt for Cellular Coverage Optimization
in Suburban Scenario.
Chapter 23: A survey of the different itemset
representation for candidate.