Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence
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Published By Springer International Publishing

9783030023560, 9783030023577

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Machine Learning for Enhancement Land Cover and Crop Types Classification

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_4 ◽

2018 ◽

pp. 71-87

Author(s):

Noureldin Laban ◽

Bassam Abdellatif ◽

Hala M. Ebeid ◽

Howida A. Shedeed ◽

Mohamed F. Tolba

Keyword(s):

Machine Learning ◽

Land Cover ◽

Crop Types

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

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_7 ◽

2018 ◽

pp. 135-167 ◽

Cited By ~ 1

Author(s):

Djamel Saba ◽

Fatima Zohra Laallam ◽

Houssem Eddine Degha ◽

Brahim Berbaoui ◽

Rachid Maouedj

Keyword(s):

Energy Management ◽

Design And Development

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

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_6 ◽

2018 ◽

pp. 111-134

Author(s):

Nagendra Vara Prasad Kuraku ◽

Yigang He ◽

Murad Ali

Keyword(s):

Comparative Analysis ◽

Fault Diagnosis

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Performance Evaluation of Sine-Cosine Optimization Versus Particle Swarm Optimization for Global Sequence Alignment Problem

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_18 ◽

2018 ◽

pp. 375-391 ◽

Cited By ~ 1

Author(s):

Mohamed Issa ◽

Aboul Ella Hassanien ◽

Ibrahim Ziedan

Keyword(s):

Particle Swarm Optimization ◽

Performance Evaluation ◽

Sequence Alignment ◽

Particle Swarm ◽

Swarm Optimization ◽

Alignment Problem ◽

Versus Particle

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Bio-inspired Optimization Algorithms for Segmentation and Removal of Interphase Cells from Metaphase Chromosomes Images

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_22 ◽

2018 ◽

pp. 451-474

Author(s):

Gehad Ismail Sayed ◽

Aboul Ella Hassanien

Keyword(s):

Optimization Algorithms ◽

Metaphase Chromosomes ◽

Interphase Cells

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Towards a Personalized Learning Experience Using Reinforcement Learning

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_8 ◽

2018 ◽

pp. 169-187 ◽

Cited By ~ 4

Author(s):

Doaa Shawky ◽

Ashraf Badawi

Keyword(s):

Reinforcement Learning ◽

Learning Experience ◽

Personalized Learning

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

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_16 ◽

2018 ◽

pp. 327-349

Author(s):

Ali Khater Mohamed ◽

Ali Wagdy Mohamed ◽

Ehab Zaki Elfeky ◽

Mohamed Saleh

Keyword(s):

Global Optimization ◽

Differential Evolution ◽

Optimization Problems ◽

Differential Evolution Algorithm ◽

Mixed Integer ◽

Non Linear ◽

Evolution Algorithm

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Bio-inspired Based Task Scheduling in Cloud Computing

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_14 ◽

2018 ◽

pp. 289-308 ◽

Cited By ~ 2

Author(s):

Marwa Gamal ◽

Rawya Rizk ◽

Hani Mahdi ◽

Basem Elhady

Keyword(s):

Cloud Computing ◽

Task Scheduling

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Enhancing Performance of Hybrid Named Entity Recognition for Amazighe Language

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_10 ◽

2018 ◽

pp. 211-232

Author(s):

Meryem Talha ◽

Siham Boulaknadel ◽

Driss Aboutajdine

Keyword(s):

Named Entity Recognition ◽

Entity Recognition ◽

Named Entity

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

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence ◽

10.1007/978-3-030-02357-7_15 ◽

2018 ◽

pp. 309-326 ◽

Cited By ~ 1

Author(s):

Gehad Ismail Sayed ◽

Mona Soliman ◽

Aboul Ella Hassanien

Keyword(s):

Support Vector Machine ◽

Optimal Foraging ◽

Optimal Foraging Theory ◽

Foraging Theory ◽

Parameters Optimization ◽

Support Vector

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Machine Learning Paradigms: Theory and Application - Studies in Computational IntelligenceLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Machine Learning for Enhancement Land Cover and Crop Types Classification

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

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

Performance Evaluation of Sine-Cosine Optimization Versus Particle Swarm Optimization for Global Sequence Alignment Problem

Bio-inspired Optimization Algorithms for Segmentation and Removal of Interphase Cells from Metaphase Chromosomes Images

Towards a Personalized Learning Experience Using Reinforcement Learning

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

Bio-inspired Based Task Scheduling in Cloud Computing

Enhancing Performance of Hybrid Named Entity Recognition for Amazighe Language

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

Machine Learning Paradigms: Theory and Application - Studies in Computational Intelligence
Latest Publications