A Minimal Subset of Features Using Feature Selection for Handwritten Digit Recognition

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Journal of Intelligent Learning Systems and Applications

ISSN Print: 2150-8402
ISSN Online: 2150-8410
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"A Minimal Subset of Features Using Feature Selection for Handwritten Digit Recognition"
written by Areej Alsaafin, Ashraf Elnagar,
published by Journal of Intelligent Learning Systems and Applications, Vol.9 No.4, 2017

has been cited by the following article(s):

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[4]	Chapter-5 Empirical Study of Ensemble Classifiers for Handwritten Recognition
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[9]	Atom Search Optimization with Simulated Annealing--a Hybrid Metaheuristic Approach for Feature Selection
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[10]	Assessing the Efficacy of Machine Learning Techniques for Handwritten Digit Recognition
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[11]	Handwritten digit recognition using various machine learning algorithms and models
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[12]	Recognition of Handwritten Characters based on Deep Learning with Tensor Flow
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[13]	Handwritten digits recognition with decision tree classification: a machine learning approach.
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[14]	M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification
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[15]	BIFFOA: A novel binary improved fruit fly algorithm for feature selection
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[16]	Handwritten digits recognition with decision tree classification: a machine learning approach
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[17]	Binary grasshopper optimisation algorithm approaches for feature selection problems
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[18]	Grafik işleme ünitesi (GPU) tabanlı öğrenme kullanarak otonom araçlar için algılama sisteminin geliştirilmesi/Development of perception system for autonomous …
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[19]	Automatic laboratory marks entry for student assessment system (SAS)
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[20]	Grafik işleme ünitesi (GPU) tabanlı öğrenme kullanarak otonom araçlar için algılama sisteminin geliştirilmesi
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[21]	Comparisons of deep learning algorithms for MNIST in real-time environment
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[1]	Intelligent System Design Lecture Notes in Networks and Systems, 2023 DOI:10.1007/978-981-19-4863-3_22

[2]	An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application Applied Computational Intelligence and Soft Computing, 2023 DOI:10.1155/2023/3140270

[3]	Using random forest for brain tissue identification by Raman spectroscopy Machine Learning: Science and Technology, 2023 DOI:10.1088/2632-2153/ad1349

[4]	Influence of Various ML-Based Binary Classifiers on the Performance on Handwritten Digit Recognition 2023 10th International Conference on Future Internet of Things and Cloud (FiCloud), 2023 DOI:10.1109/FiCloud58648.2023.00049

[5]	Evolutionary Computing and Mobile Sustainable Networks Lecture Notes on Data Engineering and Communications Technologies, 2022 DOI:10.1007/978-981-16-9605-3_8

[6]	Image Classification using Deep Learning: An Experimental Study on Handwritten Digit Recognition 2022 IEEE 7th International conference for Convergence in Technology (I2CT), 2022 DOI:10.1109/I2CT54291.2022.9824910

[7]	M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification Journal of Intelligent Systems, 2019 DOI:10.1515/jisys-2019-0064

[8]	Binary grasshopper optimisation algorithm approaches for feature selection problems Expert Systems with Applications, 2019 DOI:10.1016/j.eswa.2018.09.015

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