Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

Al‐Jarrah, Mohammad; Ahmad, Al-Jarrah; Jarrah, Amin; AlShurbaji, Mohammad; Magableh, Sharaf K.; Al-Tamimi, Abdel-Karim; Bzoor, Nisreen; Al-Shamali, Mamoun O.

doi:10.18280/ria.360601

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…Garcia-Sanchez et al [22] observed a high linear correlation between CTDI vol and SSDE. Similar results could be obtained using clustering algorithms [32] and numerical methods [33]. Sebelego et al [34] proposed a study aiming to determine the factors that impact SSDE for chest-abdomen-pelvis and abdomen-pelvis CT exams.…”

Section: Discussionsupporting

confidence: 55%

Investigating the Effect of Patient-Related Factors on Computed Tomography Radiation Dose Using Regression and Correlation Analysis

AlShurbaji,

El Haout,

Chanchal

et al. 2024

Applied Sciences

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Computed tomography (CT) is a widely utilized diagnostic imaging modality in medicine. However, the potential risks associated with radiation exposure necessitate investigating CT exams to minimize unnecessary radiation. The objective of this study is to evaluate how patient-related parameters impact the CT dose indices for different CT exams. In this study, a dataset containing CT dose information for a cohort of 333 patients categorized into four CT exams, chest, cardiac angiogram, cardiac calcium score and abdomen/pelvis, was collected and retrospectively analyzed. Regression analysis and Pearson correlation were applied to estimate the relationships between patient-related factors, namely body mass index (BMI), weight and age as input variables, and CT dose indices, namely the volume CT dose index (CTDIvol), dose length product (DLP), patient effective dose (ED) and size-specific dose estimate (SSDE), as output variables. Moreover, the study investigated the correlation between the different CT dose indices. Using linear regression models and Pearson correlation, the study found that all CT dose indices correlate with BMI and weight in all CT exams with varying degrees as opposed to age, which did not demonstrate any significant correlation with any of the CT dose indices across all CT exams. Moreover, it was found that using multiple regression models where multiple input variables are considered resulted in a higher correlation with the output variables than when simple regression was used. Investigating the relationships between the different dose indices, statistically significant relationships were found between all dose indices. A stronger linear relationship was noticed between CTDIvol and DLP compared to the relationships between each pair of the other dose indices. The findings of this study contribute to understanding the relationships between patient-related parameters and CT dose indices, aiding in the development of optimized CT exams that ensure patient safety while maintaining the diagnostic efficacy of CT imaging.

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Section: Discussionsupporting

confidence: 55%

Investigating the Effect of Patient-Related Factors on Computed Tomography Radiation Dose Using Regression and Correlation Analysis

AlShurbaji,

El Haout,

Chanchal

et al. 2024

Applied Sciences

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“…Research on the Holy Quran improves speaker identification systems, recognizing Arabic and English speakers using 14 professional reciters' speech signals, with improved LBG-VQ algorithm matching codebook centroids with 96.43% accuracy [11]. The review highlights the effectiveness of deep learning in energy forecasting, highlighting the use of various neural networks, including simple RNN, LSTM, GRU, and bidirectional RNN [12]. The research introduces an efficient odometry method for autonomous path planning, enabling global optimal planning, mapping, and localization in static obstacles, improving computing speed and position accuracy [13].…”

Section: Opinion Miningmentioning

confidence: 99%

Fake Review Detection Using Machine Learning

Asaad,

Allami,

Ali

2023

RIA

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Online customer reviews have become an increasingly influential tool in shaping purchasing decisions. However, the growing impact of these reviews has led to a surge in the publication and promotion of fake reviews by some businesses, either to enhance their own product's reputation or to undermine their competitors. These counterfeit reviews can have an especially detrimental impact on small businesses, with even a single negative fake review capable of causing significant damage. In this context, the current study introduces a technique for classifying and identifying fake reviews using machine learning (ML) methodologies. The proposed algorithm was applied to the Yelp dataset for hotel services. The text was initially preprocessed through four stages: tokenization, normalization, stop word removal, and stemming. Subsequently, features were extracted using TFIDF techniques to leverage the benefits of sentiment analysis and to ascertain the presence of spam comments in the feature extraction approach. During the classification phase, the study employed three ML algorithms: Xgboost, a support vector classifier, and stochastic gradient descent. The proposed model was evaluated on both balanced and imbalanced datasets, using oversampling and undersampling techniques to determine its accuracy. The findings of this research hold promise for enhancing the credibility of online reviews and protecting businesses from the adverse effects of fake reviews. By unmasking fraudulent reviews, this study contributes to ensuring the integrity of online review platforms and safeguarding the interests of both businesses and consumers.

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“…Every human being has different intentions and goals in memorizing the Qur'an (Mahmoudi, 2019;Wasfey, 2022). There are people who intend because they want to get a reward from Allah and there are also those who have other intentions (Ahmad, 2019;Alhourani, 2022;Al-Jarrah, 2022). The intention to memorize the Al-Qur'an must be straightened before someone wants to memorize the Al-Qur'an.…”

Section: Introductionmentioning

confidence: 99%

Motivation and Problems of Memorizing Al-Qur'an

Sironi,

Rachele,

Anindya

2023

J. Neosantara Hybrid Learning

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Memorizers of the Qur'an often have problems in memorizing the Qur'an. The obstacles experienced were different. Age also affects the constraints experienced by memorizers of the Qur'an. Memorizers of the Koran at an early age have problems reading the Koran and cannot read the Koran. Every obstacle experienced by the memorizer of the Kalam Allah has a different solution depending on the state of the memorizer. The purpose of this study is to find solutions and answers to the difficulties experienced by memorizing the Qur'an. With the solution of this problem, it is hoped that it can help memorizers of the Qur'an to memorize more quickly and mutqin. The method used in this study is the Descriptive Qualitative method, in this study it describes ways and tips to make the process of memorizing the Qur'an easier. The results of the research are expected to be applied in memorizing and understanding the contents of the Qur'an. In this study, different methods were found for each memorizer depending on the situation and conditions of the memorizer.

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Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm

Cited by 4 publications

References 27 publications

Investigating the Effect of Patient-Related Factors on Computed Tomography Radiation Dose Using Regression and Correlation Analysis

Investigating the Effect of Patient-Related Factors on Computed Tomography Radiation Dose Using Regression and Correlation Analysis

Fake Review Detection Using Machine Learning

Motivation and Problems of Memorizing Al-Qur'an

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