A Novel Deep Learning ArCAR System for Arabic Text Recognition with Character-Level Representation

Muaad, Abdullah Y.; Hanumanthappa, J.; Al-antari, Mugahed A.; Lee, Sungyoung

doi:10.3390/ioca2021-10903

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…An attention system applies end-to-end training via a standard back propagation (BP) technique. Muaad et al [17] suggested a method for representing and recognizing Arabic text at character phase on the basis of the ability of a deep convolutional neural network (CNN). This mechanism has been authenticated by making use of fivefold cross-validation (CV) tests for Arabic text file classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

B. Al-onazi,

Alotaibi,

S. Alzahrani

et al. 2023

Computers, Materials &Amp; Continua

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Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes. When the number of labels is limited to one, the task becomes single-label text categorization. The Arabic texts include unstructured information also like English texts, and that is understandable for machine learning (ML) techniques, the text is changed and demonstrated by numerical value. In recent times, the dominant method for natural language processing (NLP) tasks is recurrent neural network (RNN), in general, long short term memory (LSTM) and convolutional neural network (CNN). Deep learning (DL) models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection, medical image analysis, and so on. This paper introduces a Modified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition (MDFO-EMTRR) model on Arabic Corpus. The presented MDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic text. To achieve this, the MDFO-EMTRR technique encompasses data pre-processing to transform the input data into compatible format. Next, the ELM model is utilized for the representation and recognition of the Arabic text. At last, the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced 1538 CMC, 2023, vol.76, no.2 classifier results. The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.

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Section: Literature Reviewmentioning

confidence: 99%

Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

B. Al-onazi,

Alotaibi,

S. Alzahrani

et al. 2023

Computers, Materials &Amp; Continua

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“…Normalization is referred to as the transformation of a letter in the text into a canonical form or to remove the diacritics; for instance, transforms ‫ه‬ ‫ـ‬ ‫ـ‬ into ‫.ة‬ Reference [46].…”

Section: Normalizationmentioning

confidence: 99%

“…Recall/Sensitivity. Recall in information retrieval is the fraction of the items successfully retrieved that are relevant to the posted query [46,77]:…”

Section: Precisionmentioning

confidence: 99%

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

Muaad

Hanumanthappa

Guru

et al. 2022

Mathematical Problems in Engineering

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With the increasing number of online social posts, review comments, and digital documentations, the Arabic text classification (ATC) task has been hugely required for many spontaneous natural language processing (NLP) applications, especially within the coronavirus pandemics. The variations in the meaning of the same Arabic words could directly affect the performance of any AI-based framework. This work aims to identify the effectiveness of machine learning (ML) algorithms through preprocessing and representation techniques. This effectiveness is measured via different AI-based classification techniques. Basically, the ATC process is influenced by several factors such as stemming in preprocessing, method of feature extraction and selection, nature of datasets, and classification algorithm. To improve the overall classification performance, preprocessing techniques are mainly used to convert each Arabic word into its root and decrease the representation dimension among the datasets. Feature extraction and selection always play crucial roles to represent the Arabic text in a meaningful way and improve the classification accuracy rate. The selected classifiers in this study are performed based on various feature selection algorithms. The overall classification evaluation results are compared using different classifiers such as multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and Linear SVC. All of these AI classifiers are evaluated using five balanced and unbalanced benchmark datasets: BBC Arabic corpus, CNN Arabic corpus, Open-Source Arabic corpus (OSAc), ArCovidVac, and AlKhaleej. The evaluation results show that the classification performance strongly depends on the preprocessing technique, representation methods and classification technique, and the nature of datasets used. For the considered benchmark datasets, the linear SVC has outperformed other classifiers overall when prominent features are selected.

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“…The outcomes of the projected technique were compared with that of the established learning methods such as the Decision Tree (DT), k-Nearest Neighbour (KNN), Naïve Bayes (NB) and the Support Vector Machine (SVM) model. Muaad et al [19] modelled a method to recognize and represent the Arabic text at the character level with the help of a Deep Convolutional Neural Network (CNN) method. In this study, the CNN method was authenticated through a 5-fold cross-validation test for classifying the Arabic text documents.…”

Section: Introductionmentioning

confidence: 99%

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Mohsen¹,

Al-onazi²,

Nour³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though the interest in OM studies in the Arabic language is growing among researchers, it needs a vast number of investigations due to the unique morphological principles of the language. Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features. The comparative OM studies in the English language are wide and novel. But, comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage. The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text. It contains unique features such as diacritics, elongation, inflection and word length. The current study proposes a Political Optimizer with Probabilistic Neural Networkbased Comparative Opinion Mining (POPNN-COM) model for the Arabic text. The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media. Initially, the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format. Then, the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels. At last, the PO algorithm is employed for finetuning the parameters involved in this model to achieve enhanced results. The proposed POPNN-COM model was experimentally validated using two standard datasets, and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

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A Novel Deep Learning ArCAR System for Arabic Text Recognition with Character-Level Representation

Cited by 6 publications

References 14 publications

Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

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