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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)

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Satchidananda Dehuri

    1. Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

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  2. Bhabani Shankar Prasad Mishra

    1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

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  3. Pradeep Kumar Mallick

    1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

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  4. Sung-Bae Cho

    1. Department of Computer Science, Yonsei University, Seoul, Korea (Republic of)

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  5. Margarita N. Favorskaya

    1. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

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  • Addresses recent challenges in optimization methods and techniques associated with the exponential growth in data production
  • Gathers the Proceedings of the International Conference on Biologically Inspired Techniques in Many-Criteria Decision Making (BITMDM-2019), held in Balasore, India, on December 19–20, 2019
  • Demonstrates how to derive optimal solutions for data-driven problems

Part of the book series: Learning and Analytics in Intelligent Systems (LAIS, volume 10)

Included in the following conference series:

Conference proceedings info: BITMDM 2019.

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Table of contents (23 papers)

  1. Front Matter

    Pages i-xv
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  2. Biologically Inspired Techniques and Their Applications

    1. Front Matter

      Pages 1-1
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    2. Classification of Arrhythmia Using Artificial Neural Network with Grey Wolf Optimization

      • Saumendra Kumar Mohapatra, Sipra Sahoo, Mihir Narayan Mohanty
      Pages 3-10

    3. Multi-objective Biogeography-Based Optimization for Influence Maximization-Cost Minimization in Social Networks

      • Sagar S. De, Satchidananda Dehuri
      Pages 11-34

    4. Classification of Credit Dataset Using Improved Particle Swarm Optimization Tuned Radial Basis Function Neural Networks

      • Trilok Nath Pandey, Parimal Kumar Giri, Alok Kumar Jagadev
      Pages 35-45

    5. Multi-Verse Optimization of Multilayer Perceptrons (MV-MLPs) for Efficient Modeling and Forecasting of Crude Oil Prices Data

      • Sarat Chandra Nayak, Ch. Sanjeev Kumar Dash, Bijan Bihari Mishra, Satchidananda Dehuri
      Pages 46-54

    6. Application of Machine Learning to Predict Diseases Based on Symptoms in Rural India

      • Suvasree S. Biswal, T. Amarnath, Prasanta K. Panigrahi, Nrusingh C. Biswal
      Pages 55-61

    7. Classification of Real Time Noisy Fingerprint Images Using Flann

      • Annapurna Mishra, Satchidananda Dehuri, Pradeep Kumar Mallick
      Pages 62-68

    8. Software Reliability Prediction with Ensemble Method and Virtual Data Point Incorporation

      • Ajit Kumar Behera, Mrutyunjaya Panda
      Pages 69-77

    9. Hyperspectral Image Classification Using Stochastic Gradient Descent Based Support Vector Machine

      • Pattem Sampurnima, Sandeep Kumar Satapathy, Shruti Mishra, Pradeep Kumar Mallick
      Pages 78-84

    10. A Survey on Ant Colony Optimization for Solving Some of the Selected NP-Hard Problem

      • Akshaya Kumar Mandal, Satchidananda Dehuri
      Pages 85-100

    11. Machine Learning Models for Stock Prediction Using Real-Time Streaming Data

      • Monalisa Jena, Ranjan Kumar Behera, Santanu Kumar Rath
      Pages 101-108

    12. Epidemiology of Breast Cancer (BC) and Its Early Identification via Evolving Machine Learning Classification Tools (MLCT)–A Study

      • Rajesh Kumar Maurya, Sanjay Kumar Yadav, Pragya Tewari
      Pages 109-119

    13. Ensemble Classification Approach for Cancer Prognosis and Prediction

      • Rajesh Kumar Maurya, Sanjay Kumar Yadav, Rishabh
      Pages 120-135

    14. Extractive Odia Text Summarization System: An OCR Based Approach

      • Priyanka Pattnaik, Debasish Kumar Mallick, Shantipriya Parida, Satya Ranjan Dash
      Pages 136-143

    15. Predicting Sensitivity of Local News Articles from Odia Dailies

      • Manoj Kumar Jena, Sanghamitra Mohanty
      Pages 144-151

    16. A Systematic Frame Work Using Machine Learning Approaches in Supply Chain Forecasting

      • K. Prahathish, J. Naren, G. Vithya, S. Akhil, K. Dinesh Kumar, S. Sai Krishna Mohan Gupta
      Pages 152-158

    17. An Intelligent System on Computer-Aided Diagnosis for Parkinson’s Disease with MRI Using Machine Learning

      • J. Naren, Praveena Ramalingam, U. Raja Rajeswari, P. Vijayalakshmi, G. Vithya
      Pages 159-165

  3. Multi-Criteria Decision Making Approaches

    1. Front Matter

      Pages 167-167
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    2. Operations on Picture Fuzzy Numbers and Their Application in Multi-criteria Group Decision Making Problems

      • Palash Dutta, Rajdeep Bora, Satya Ranjan Dash
      Pages 169-188
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  1. Biologically Inspired Techniques in Many-Criteria Decision Making

Keywords

About this book

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. 

Editors and Affiliations

  • Department of Information and Communication Technology, Fakir Mohan University, Balasore, India

    Satchidananda Dehuri

  • School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

    Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick

  • Department of Computer Science, Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  • Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

Bibliographic Information

  • Book Title: Biologically Inspired Techniques in Many-Criteria Decision Making

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

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

  • Series Title: Learning and Analytics in Intelligent Systems

  • DOI: https://doi.org/10.1007/978-3-030-39033-4

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: Springer Nature Switzerland AG 2020

  • Hardcover ISBN: 978-3-030-39032-7Published: 22 January 2020

  • Softcover ISBN: 978-3-030-39035-8Published: 22 January 2021

  • eBook ISBN: 978-3-030-39033-4Published: 21 January 2020

  • Series ISSN: 2662-3447

  • Series E-ISSN: 2662-3455

  • Edition Number: 1

  • Number of Pages: XV, 258

  • Number of Illustrations: 32 b/w illustrations, 65 illustrations in colour

  • Topics: Computational Intelligence, Data Engineering, Artificial Intelligence

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