Knowledge Discovery in the Hadith According to the Reliability and Memory of the Reporters Using Machine Learning Techniques

Abdelaal, Hammam M.; Ahmed, Abdelmoty M.; Ghribi, Wade; Alansary, Hassan A. Youness

doi:10.1109/access.2019.2944118

Cited by 15 publications

(9 citation statements)

References 15 publications

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“…Abdelaal et al [45] proposed an automatic classification model for Hadith. The proposed model was used to organize Arabic text Hadith into related categories: Sahih, Hasan, Da'if, and Maudu.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

et al. 2022

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We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of features alongside the classifier. Although several text classification methods have been proposed for the Arabic language using different techniques, such as feature selection methods, an ensemble of classifiers, and discriminative features, choosing the optimal method becomes an NP-hard problem considering the huge search space. Therefore, we propose a method, called Optimal Configuration Determination for Arabic text Classification (OCATC), which utilized the Particle Swarm Optimization (PSO) algorithm to find the optimal solution (configuration) from this space. The proposed OCATC method extracts and converts the features from the textual documents into a numerical vector using the Term Frequency-Inverse Document Frequency (TF–IDF) approach. Finally, the PSO selects the best architecture from a set of classifiers to feature selection methods with an optimal number of features. Extensive experiments were carried out to evaluate the performance of the OCATC method using six datasets, including five publicly available datasets and our proposed dataset. The results obtained demonstrate the superiority of OCATC over individual classifiers and other state-of-the-art methods.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

et al. 2022

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“…Tabel 6. Analisis Metode Representasi Teks dalam Arabic Natural Language Processing Metode Untuk Representasi Teks Peneliti Jumlah TF-IDF [4], [9], [48], [30], [23], [24], [29], [53], [54], [7], [38], [55], [32], [56], [25], [31], [59], [1], [51], [60] 20 Word2Vec [34], [24], [49], [7], [57], [39], [41], [43], [45], [46] 10 AraVec [34], [35], [37], [40], [41], [42] 6 FastText [34], [47], [35], [57], [41], [42], [50], [46] 8 mBERT [27], [22], [41], [46] 4 AraBERT [36],…”

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

“…Untuk lebih mempertimbangkan konteks kata dan makna kalimat dapat diatasi dengan dynamic atau contextual embedding yang mempelajari representasi kalimat secara universal [27]. Contextual embedding yang pertama kali diusulkan adalah Embeddings from Language Model (ELMO) [68] [33], [34], [47], [48], [30], [23], [24], [54], [38], [27], [56], [31], [57], [39], [1], [42], [51], [46], [9] 20…”

Section: Tahap Pembuatan Rencana Awalunclassified

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Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.

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“…The study detailed in [2] proposed an automation model that could classify Al-hadeeth features into Sahih, Hasan, Da'if, and Maudu, using machine learning techniques (LinearSVC, SGDClassifier, and LogisticRegression). The author of [3] built a machine-learning model using an algorithm (KNN, SVM, and Naïve Bayes) classification model to annotate labels for the Quranic verses.…”

Section: Related Workmentioning

confidence: 99%

The Multi-Class Classification for the First Six Surats of the Holy Quran

Elmitwally¹,

Alsayat²

2020

IJACSA

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The Holy Quran is one of the holy books revealed to the prophet Muhammad in the form of separate verses. These verses were written on tree leaves, stones, and bones during his life; as such, they were not arranged or grouped into one book until later. There is no intelligent system that is able to distinguish the verses of Quran chapters automatically. Accordingly, in this study we propose a model that can recognize and categorize Quran verses automatically and conclusion the essential features through Quran chapters classification for the first six Surat of the Holy Quran chapters, based on machine learning techniques. The classification of the Quran verses into chapters using machine learning classifiers is considered an intelligent task. Classification algorithms like Naïve Bayes, SVM, KNN, and decision tree J48 help to classify texts into categories or classes. The target of this research is using machine learning algorithms for the text classification of the Holy Quran verses. As the Quran texts consists of 114 chapters, we are only working with the first six chapters. In this paper, we build a multi-class classification model for the chapter names of the Quranic verses using Support Vector Classifier (SVC) and GaussianNB. The results show the best overall accuracy is 80% for the SVC and 60% for the Gaussian Naïve Bayes.

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Knowledge Discovery in the Hadith According to the Reliability and Memory of the Reporters Using Machine Learning Techniques

Cited by 15 publications

References 15 publications

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

The Multi-Class Classification for the First Six Surats of the Holy Quran

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