A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application

Das, Nibaran; Sarkar, Ram; Basu, Subhadip; Kundu, Mahantapas; Nasipuri, Mita; Basu, Dipak Kumar

doi:10.1016/j.asoc.2011.11.030

Cited by 151 publications

(70 citation statements)

References 14 publications

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“…GA has been widely used to solve various optimization problems in different branches of engineering, and it is known as a successful tool to deal with complex real-world problems. Das et al (2012) employed GA to sample the optimal set of local regions from which an optimal feature set can be extracted. The proposed method has been evaluated using data set of handwritten Bangla digits, and the results certified that the GA-based region sampling outperforms several existing methods.…”

Section: Applications Of Genetic Algorithm (Ga)mentioning

confidence: 99%

A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

Abtahi

Bijari

2016

J Ind Eng Int

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In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several metaheuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.

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Section: Applications Of Genetic Algorithm (Ga)mentioning

confidence: 99%

A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

Abtahi

Bijari

2016

J Ind Eng Int

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“…The past work on OCR of handwritten alphabets and numerals has mostly found to concentrate on Roman script related to English and some other European languages, and scripts related to Asian languages like Chinese [2], Korean, Japanese. Among Indian scripts, Devnagari, Tamil, Oriya and Bangle have started to receive attention for OCR related research in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…In spite of that, OCR of handwritten Arabic script including numerals has not so far received sufficient attention. Majority of the past work related to OCR of Arabic script was done with the printed characters [2].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis on Bangla and Arabic Numeral Recognition

2017

IJSR

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Abstract:Handwritten numeral recognition is a challenging work at this time. In this paper we deal with accuracy comparison among Arabic numerals and Bangla numeral recognition. This paper composed with four main phases pre-processing, feature extraction, classification and result comparison with both numerals. After pre-processing image are divided into different equal parts and then applied feature extraction techniques. Numerical feature extracted contain more details about image that provide improved recognition accuracy. These numerals recognition is carried out by SVM classifier.

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“…Therefore, it is important to find the best validity measures that could be instantaneously attain good clustering results. In order to evaluate the quality of the clustering, external measures like Jaccard-index, Minkowskiindex, Rand-index, and so on can be utilized to optimize the multi-objective problem [5]. This measure are used to identify the intra-cluster similarity or compactness and the inter-cluster separation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Algorithm to the Analyses of Diabetes Disease Diagnosis

Anusha¹,

Sathiaseelan²

2016

ijacsa

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Abstract-There is huge amount of data available in health industry which is found difficult in handing, hence mining of data is necessary to innovate the hidden patterns and their relevant features. Recently, many researchers have devoted to the study of using data mining on disease diagnosis. Mining biomedical data is one of the predominant research area where evolutionary algorithms and clustering techniques are emphasized in diabetes disease diagnosis. Therefore, this research focuses on application of evolution clustering multiobjective optimization algorithm (ECMO) to analyze the data of patients suffering from diabetes disease. The main objective of this work is to maximize the prediction accuracy of cluster and computation efficiency along with minimum cost for data clustering. The experimental results prove that this application has attained maximum accuracy for dataset of Pima Indians Diabetes from UCI repository. In this way, by analyzing the three objectives, ECMO could achieve best Pareto fronts.

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A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application

Cited by 151 publications

References 14 publications

A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems

Performance Analysis on Bangla and Arabic Numeral Recognition

Multi-Objective Optimization Algorithm to the Analyses of Diabetes Disease Diagnosis

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