Biologically Inspired Techniques in Many-Criteria Decision Making

International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019)

Inbunden, Engelska, 2020

Av Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick, Sung-Bae Cho, Margarita N. Favorskaya

2 839 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems.

Produktinformation

Utgivningsdatum2020-01-22
Mått155 x 235 x 21 mm
Vikt582 g
FormatInbunden
SpråkEngelska
SerieLearning and Analytics in Intelligent Systems
Antal sidor258
Upplaga20001
FörlagSpringer Nature Switzerland AG
ISBN9783030390327

Tillhör följande kategorier

Artificiell intelligens inom Data och it
Databaser inom Data och it

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: Classıfıcatıon of Real Tıme Noısy Fıngerprınt Images Usıng 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 Parkinson’s 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.