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Machine Learning Paradigms: Theory and Application

  • Book
  • © 2019

Overview

Editors:
  1. Aboul Ella Hassanien

    1. Faculty of Computers and Information, Cairo University, Giza, Egypt

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  • Presents machine learning paradigms
  • Focuses on recent theory and applications
  • Written by experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 801)

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Table of contents (22 chapters)

  1. Front Matter

    Pages i-ix
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  2. Machine Learning in Feature Selection

    1. Front Matter

      Pages 1-1
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    2. Hybrid Feature Selection Method Based on the Genetic Algorithm and Pearson Correlation Coefficient

      • Rania Saidi, Waad Bouaguel, Nadia Essoussi
      Pages 3-24

    3. Weighting Attributes and Decision Rules Through Rankings and Discretisation Parameters

      • Urszula StaÅ„czyk
      Pages 25-43

    4. Greedy Selection of Attributes to Be Discretised

      • Grzegorz Baron
      Pages 45-67

  3. Machine Learning in Classification and Ontology

    1. Front Matter

      Pages 69-69
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    2. Machine Learning for Enhancement Land Cover and Crop Types Classification

      • Noureldin Laban, Bassam Abdellatif, Hala M. Ebeid, Howida A. Shedeed, Mohamed F. Tolba
      Pages 71-87

    3. An Optimal Machine Learning Classification Model for Flash Memory Bit Error Prediction

      • Barry Fitzgerald, Conor Ryan, Joe Sullivan
      Pages 89-110

    4. Comparative Analysis of the Fault Diagnosis in CHMLI Using k-NN Classifier Based on Different Feature Extractions

      • Nagendra Vara Prasad Kuraku, Yigang He, Murad Ali
      Pages 111-134

    5. Design and Development of an Intelligent Ontology-Based Solution for Energy Management in the Home

      • Djamel Saba, Fatima Zohra Laallam, Houssem Eddine Degha, Brahim Berbaoui, Rachid Maouedj
      Pages 135-167

    6. Towards a Personalized Learning Experience Using Reinforcement Learning

      • Doaa Shawky, Ashraf Badawi
      Pages 169-187

    7. Towards Objective-Dependent Performance Analysis on Online Sentiment Review

      • Doaa Mohey El-Din, Mohamed Hamed N. Taha, Nour Eldeen M. Khalifa
      Pages 189-210

    8. Enhancing Performance of Hybrid Named Entity Recognition for Amazighe Language

      • Meryem Talha, Siham Boulaknadel, Driss Aboutajdine
      Pages 211-232

    9. A Real-Time Aspect-Based Sentiment Analysis System of YouTube Cooking Recipes

      • Randa Benkhelifa, Nasria Bouhyaoui, Fatima Zohra Laallam
      Pages 233-251

    10. Detection of Palm Tree Pests Using Thermal Imaging: A Review

      • Ali Ahmed, Abdelhameed Ibrahim, Sherif Hussein
      Pages 253-270

    11. Unleashing Machine Learning onto Big Data: Issues, Challenges and Trends

      • Roheet Bhatnagar
      Pages 271-286

  4. Bio-inspiring Optimization and Applications

    1. Front Matter

      Pages 287-287
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    2. Bio-inspired Based Task Scheduling in Cloud Computing

      • Marwa Gamal, Rawya Rizk, Hani Mahdi, Basem Elhady
      Pages 289-308

    3. Parameters Optimization of Support Vector Machine Based on the Optimal Foraging Theory

      • Gehad Ismail Sayed, Mona Soliman, Aboul Ella Hassanien
      Pages 309-326

    4. Solving Constrained Non-linear Integer and Mixed-Integer Global Optimization Problems Using Enhanced Directed Differential Evolution Algorithm

      • Ali Khater Mohamed, Ali Wagdy Mohamed, Ehab Zaki Elfeky, Mohamed Saleh
      Pages 327-349
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Keywords

About this book

The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms.
 
The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today’s world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.

Editors and Affiliations

  • Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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