Fuzzy Expert System In Determining Hadith1 Validity

Ghazizadeh, Mohammad Sadegh; Zahedi, Mohammad Hadi; Kahani, Mohsen; Minaei-Bidgoli, Behrouz

doi:10.1007/978-1-4020-8741-7_64

Cited by 23 publications

(7 citation statements)

References 5 publications

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“…Vector space model, cosine, and TF-IDF are used in this study, it used 60 Hadiths to perform the experiments, and the precision and recall were 66% and 80%, respectively. In [11], the authors introduced a classification system of Hadiths' authenticity depending on the fuzzy technique, the experiments were conducted on Al-Kafi book, the authors did not mention the number of Hadiths they used in the study, and the accuracy of the system was 94%. In [12], the researchers introduced a system to classify Hadiths depending on the artificial neural network (ANN) and singular value decomposition (SVD), the experiments were conducted on 453 Hadiths, and the F-measures were 85.75% and 88.33% for ANN and ANN with SVD, respectively.…”

Section: Related Workmentioning

confidence: 99%

A Hidden Markov Model-Based Tagging Approach for Arabic Isnads of Hadiths

Najeeb

2022

Mathematical Problems in Engineering

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Hadith judgment implies checking the validity of Hadith to decide whether it is correct (trustworthy) or false (bogus). “Matn” and “Isnad” are the main constituents of Hadith; “Matn” is the sayings of the prophet, whereas “Isnad” represents the narrators’ series. The first step of Hadith judgment is the extraction of narrators’ names, after that, the rules of judgment, which were set out by Hadith’s scientists, could be implemented, three of these rules are particularly related to the narrators’ series, and these rules are continuity of the transmission chain, the trustworthiness of the narrators, and the preciseness of the narrators. Therefore, to check the authenticity of Hadiths, the three conditions must be satisfied, and to do so, the narrators’ names must be extracted first. Isnad contains many words and phrases called “Isnad-Phrases”; these phrases have many types or categories called part of Isnads (POIs) like Narrator-Name, Prophet-Name, and Received-Method. A lot of computational research studies suggest serving Hadith sciences by extracting the narrators’ names and other POIs using various approaches. This study presents a new hybrid approach founded on the hidden Markov model (HMM) and gazetteer lists to process “Isnad.” The approach objective is to expect all POIs in the Isnad including narrators’ names. The experiments carried on 1,000 Hadiths from “Sahih Muslim”: 900 Hadiths as training dataset and 100 Hadiths as testing dataset, and the results show a noteworthy accuracy for the proposed hybrid approach.

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Section: Related Workmentioning

confidence: 99%

A Hidden Markov Model-Based Tagging Approach for Arabic Isnads of Hadiths

Najeeb

2022

Mathematical Problems in Engineering

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“…Over a decade ago, Zahedi et al [41] presented a fuzzy expert system with an ambition to authenticate Hadiths with its rate of validity. The system initiates with domain experts' inputs for developing a knowledge base with some essential rules taking into account the narrators' names, particularly the Sanad, as a main focus for the rating process.…”

Section: Related Workmentioning

confidence: 99%

Fabricated Hadith Detection: A Novel Matn-Based Approach With Transformer Language Models

Gaanoun¹,

Alsuhaibani

2022

IEEE Access

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Muslims rely primarily on the Quran and the Hadiths in all their spiritual life and consider them as sacred sources. If the Quran is God's word, then the Hadiths are God's instructions in the words of the Prophet Muhammad. Since Hadiths are transmitted through multiple narrators, they have been extensively studied to ensure their authenticity. The purpose of this study is to detect fabricated Hadiths, or Mawdu, which is the type of Hadith most rejected by Muslim scholars. The study utilises the central text and content of Hadith, Matn, rather than solely focusing on Hadith chain of narrators, Sanad. In order to accomplish this, we create and release the first dataset dedicated to Mawdu Hadiths, called MAHADDAT. Furthermore, we set up a Mawdu Hadith (MH) detection system based on a transformer language model, BERT, achieving a 92.47% F1 MH score.

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“…Ghazizadeh [26] utilize Fuzzy Logic to fabricate a specialist framework which may assist in deciding the accuracy of Hadith. In [27], Hyder and Ghazanfer speak to the storytellers' chain as a diagram, and fabricated a Database to store the data of storytellers in a manner which may assist in following the recorded occasions and true to life data, they gave loads for circular segments among hubs and assess the different ways of storytellers, at that point they gave an evaluation for every one of them.…”

Section: Related Workmentioning

confidence: 99%

Towards a Deep Leaning-based Approach for Hadith Classification

Najeeb

2021

EJENG

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The exploration of Hadith sciences gains significant consideration over the most recent couple of years. Hadith is mostly the sayings of Prophet Mohammad. The Holy Quran represents the first origin of law in Islam then Hadith takes the second role. Many research efforts manage Hadith with respect to the “Isnad” and “Matn”; which are the main two pieces of Hadith. In this paper, we examine the chance of utilizing Deep Learning to process Isnad of Hadiths. Consequently, a definitive objective of our framework is to help in the systematic classification of Hadiths and differentiate among the correct (“Sahih”) Hadiths and the not accurate (“Da'ief”) Hadiths.

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Fuzzy Expert System In Determining Hadith1 Validity

Cited by 23 publications

References 5 publications

A Hidden Markov Model-Based Tagging Approach for Arabic Isnads of Hadiths

A Hidden Markov Model-Based Tagging Approach for Arabic Isnads of Hadiths

Fabricated Hadith Detection: A Novel Matn-Based Approach With Transformer Language Models

Towards a Deep Leaning-based Approach for Hadith Classification

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