Classification Algorithms for Determining Handwritten Digit

Al-Behadili, Hayder Naser Khraibet

doi:10.37917/ijeee.12.1.10

Cited by 13 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performance of the various machine learning approaches like K-Nearest-Neighbor(KNN), Neural Network(NN) and Decision Tree(DT) are compared by Hayder Naser Khraibet Al-Behadili [6]. These are used for the HWDR using MNSIT datasets.…”

Section: Related Workmentioning

confidence: 99%

Scalable Handwritten Digit Recognition Application using Neural Network and Convolutional Neural Network On Heterogeneous Architecture

Kataraki*¹,

Sumana²

2019

IJRTE

View full text Add to dashboard Cite

Recognition of handwritten digit is one of the popular problem associated with computer vision applications. The goal of our research work is to develop scalable Neural Network(NN) and Convolutional Neural Network (CNN) model that would be able to recognize and determine the handwritten digits from its image. Capability of developing the new algorithms and improve the existing algorithms is determined by the accuracy and speed factor for training and testing the models. In this context, performance of the GPUs and CPUs for handwritten digit system and effects of accelerating the training models have been analyzed. The training and testing has been conducted from publicly available MNIST handwritten database. Web based, offline and online handwritten digit recognition system is developed by using Convolutional Neural Network

show abstract

Section: Related Workmentioning

confidence: 99%

Scalable Handwritten Digit Recognition Application using Neural Network and Convolutional Neural Network On Heterogeneous Architecture

Kataraki*¹,

Sumana²

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…This research work concludes that the accuracy can be improved with the addition of more features. Hayder Naser Khraibet Al-Behadili [8] has compared the performance of the different machine learning approaches such as K-nearest neighbor (KNN), Neural Network(NN) and Decision Tree(DT) which are used for the HWDR by using the MNIST dataset which is the widely used dataset in the relevant litearature. In these approaches, the Neural Network algorithm is more precise in determining the accurate results.…”

Section: Application Of Neural Network Algorithms For Hwdrmentioning

confidence: 99%

A Survey on using Neural Network based Algorithms for Hand Written Digit Recognition

Ramzan¹,

Khan²,

Awan³

et al. 2018

ijacsa

View full text Add to dashboard Cite

The detection and recognition of handwritten content is the process of converting non-intelligent information such as images into machine edit-able text. This research domain has become an active research area due to vast applications in a number of fields such as handwritten filing of forms or documents in banks, exam form filled by students, users' authentication applications. Generally, the handwritten content recognition process consists of four steps: data preprocessing, segmentation, the feature extraction and selection, application of supervised learning algorithms. In this paper, a detailed survey of existing techniques used for Hand Written Digit Recognition(HWDR) is carried out. This review is novel as it is focused on HWDR and also it only discusses the application of Neural Network (NN) and its modified algorithms. We discuss an overview of NN and different algorithms which have been adopted from NN. In addition, this research study presents a detailed survey of the use of NN and its variants for digit recognition. Each existing work, we elaborate its steps, novelty, use of dataset and advantages and limitations as well. Moreover, we present a Scientometric analysis of HWDR which presents top journals and sources of research content in this research domain. We also present research challenges and potential future work.

show abstract

“…Conversely, supervised learning techniques use labeled data to build the data mining model [11]. Such techniques can be considered a powerful approach with an accurate and rapid result in a wide range of applications (e.g., businesses).…”

Section: Introductionmentioning

confidence: 99%

Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

Al-Behadili

Ku-Mahamud

Sagban

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

Ant colony optimization (ACO) is a well-known algorithm from swarm intelligence that plays an essential role in obtaining rich solutions to complex problems with wide search space. ACO is successfully applied to different application problems involving rules-based classification through an ant-miner classifier. However, in the ant-miner classifier, rule-pruning suffers from the problem of nesting effect origins from the method of greedy Sequential Backward Selection (SBS) in term selection, thereby depriving the opportunity of obtaining a good pruned rule by adding/removing the terms during the pruning process. This paper presents an extension to the Ant-Miner, namely the genetic algorithm Ant-Miner (GA-Ant Miner), which incorporates the use of GA as a key aspect in the design and implementation of a new rule pruning technique. This pruning technique consists of three fundamental procedures: an initial population Ant-Miner, crossover to prune the rule, and mutation to diversify the pruned classification rule. The GA-Ant Miner performance is tested and compared with the most related ant-mining classifiers, including the original Ant-Miner, ACO/ PSO2, TACO-Miner, CAnt-Miner, and Ant-Miner with a hybrid pruner, across various public available UCI datasets. These datasets are varied in terms of instance number, feature size, class number, and the application domains. Overall, the performance results indicate that the GA-Ant Miner classifier outperforms the other five classifiers in the classification accuracy and model size. Furthermore, the experimental results using statistical test prove that GA-Ant Miner is the best classifier when considering the multi objectives (i.e., accuracy and model size ranks).

show abstract

Classification Algorithms for Determining Handwritten Digit

Cited by 13 publications

References 0 publications

Scalable Handwritten Digit Recognition Application using Neural Network and Convolutional Neural Network On Heterogeneous Architecture

Scalable Handwritten Digit Recognition Application using Neural Network and Convolutional Neural Network On Heterogeneous Architecture

A Survey on using Neural Network based Algorithms for Hand Written Digit Recognition

Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

Contact Info

Product

Resources

About