Advanced Computing

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https://hdl.handle.net/20.500.11888/3163

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    APPLYING CENTRALITY MEASURE FOR BUKHARI AHADITH ONTOLOGY OF NARRATORS
    (An-Najah National University, 2023-10-05) Abu Rwais, Rola
    The two main sources of Islamic legislation are the Qur’an and Hadith. Ahadith are the narrations stemming from the sayings and deeds of the Prophet Mohammad (peace be upon him). The narrators transmitted hadith of the Isnad from the Prophet, and the importance of the Isnad, Muslim’s keen interest in Isnad science because it helps to differentiate between accepted and rejected hadith, or in other words, authentic and weak hadith. Islamic scholars were the first to carefully study the Isnad to know and distinguish between trusted and non-trusted Ruwah, especially with the spread of Islam in non-Arabic countries and the increased number of Muslims from different cultures. This work depends on the ontology of narrations of Jihad and Seiar Ahadith in Sahih Al Bukhari in order to apply different ontological centrality measures to generate a set of numbers related to each Rawi. These numbers were investigated to study the importance and extent of involvement of a particular narrator in the process of narrating hadith. For each centrality measure applied, we studied the calculated numbers for each Rawi in order to show how these numbers are related to the Ruwah importance in terms of their concerns in narrations, the amounts of their efforts, and their ranks of the narration process. These results are compared with the manual efforts applied by Islamic studies that rely on the manual categorization of Ruwah. We identified a list of Mokthreen narrators from the Sahaba (e.g., Abu Horaira, Ibn Abbas, Ibn Omar, etc.) as well as the Mokthreen of narrators from the second and third-generation (e.g., Shoaaba Ibn Alhajjaj, Alzohre, Sofian Ibn Aoyayna, Sofian Althori, etc.) who contribute significantly in the propagation of hadith collected in Sahih Al-Bukhari. To the best of our knowledge, this comprehensive and systematic study is based on ontology, representing narrators as a graph to analyze their contribution to the preservation and dissemination of hadith. When comparing the results of the system with the traditional results, we obtained similar results, whether in the information of the narrator or his role in the process of narrating the hadith.
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    USING ARTIFICIAL NEURAL NETWORK TO PREDICT PARTICLE TYPE IN HIGH-ENERGY PHYSICS
    (An-Najah National University, 2024-06-13) Othman, Iman
    In the realm of high-energy physics, such as particle collision experiments in particle accelerators like the Large Hadron Collider (LHC), complex collision events occur, leading to the formation of particle jets. Precisely identifying and describing these jets is crucial for understanding fundamental particles and their interactions. However, traditional jet identification algorithms face challenges in capturing subtle features and interactions within jets, especially in dense and complex environments. Thus, predicting the type of particle in high-energy physics is essential to advancing our scientific understanding of fundamental particles and their interactions. Artificial intelligence is a prominent research field that offers optimal solutions across various domains, especially in the context of deep learning. Many models have been studied, such as deep neural networks, jet classification, the use of neural networks, and recurrent neural networks. This study addresses the challenge of using neural networks to classify jets into five distinct categories (light quarks (q), gluons (g), W and Z bosons, and top quarks) with the highest possible accuracy. Using a model within the TensorFlow/Keras framework, we leveraged data from the Zenodo platform consisting of 150 particles with 16 attributes used for jet classification. The methods included building various neural network architectures in depth, including single-layer networks, two-layer networks, and three-layer networks. We explored different activation functions, the number of training epochs, and optimizers. Additionally, we adopted a strategy to control for overfitting and identify prominent features to improve classification performance. The best results were achieved by building a three-layer neural network using Softmax, Sigmoid, and Selu activation functions, with the Adamax optimizer. These results were obtained after training the model for approximately 200 epochs, achieving an accuracy of 0.7400. This research highlights the potential of neural networks to achieve high levels of jet classification accuracy and provides insights into improving neural network architectures for similar tasks in particle physics research.
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    A DYNAMIC ONTOLOGICAL FRAMEWORK FOR BUKHARI AHADITH
    (An - Najah National University, 2023-02-23) Areej Sawwan
    The honorable Sunnah is the second source of Islamic legislation after the Holy Qur’an. Muslim scholars have been interested in preserving and codifying the hadith because of its role in the statement of the Holy Qur’an by allocating the general, restricting the absolute, and clarifying the total. The Prophet’s Sunnah was transmitted orally with isnad(Isnad, from Arabic “sanad” (support), in Islam, a list of authorities who have transmitted a report(hadith) of Prophet Mohammad(PBUH) or his companions. Its reliability determines the validity of a hadith), which are the most dominant, and in writing for those who mastered writing in the time of the Prophet and the Companions, then it was written down and the hadiths were compiled with their chains of narrators until today they became Resources books of hadith(hadith, everything that was narrated from the Prophet Muhammad (PBUH) in terms of saying, acting, or reporting), Mustadrakat and other books that specialize in collecting and classifying hadiths. Enriching Islamic electronic content is a great challenge for researchers. Although Arabic is a global language and ranks as the sixth most used language around the world and is spoken by more than 400 million people, it does not have a sufficient presence on the internet, compared to other languages. Ontology is defined as a knowledge representation way, and it is data model that reflects a set of concepts inside a domain and the relationships between those concepts. This study aimed to build a dynamic ontological framework through which specialists in hadith science will be able to provide sufficient information about hadith, so that the ontology is able to save hadith, taking into account linking it with the relevant hadiths and narrators present in the framework, which makes the process of retrieval of information easy and fruitful. The study was conducted on the “Ablution Book” from Sahih Al-Bukhari. The researcher evaluated the system by executing several queries about narrators, hadiths, and the chain of isnad, and when comparing the results of the system with the traditional results and other ways of knowledge representation, the outcome was much better in search, retrieval, or in drawing hadith isnad trees, or in execution time in searching for information.
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    Optimization of Traffic Signals Timing Using Parameter-less Metaheuristic Optimization Algorithms
    (جامعة النجاح الوطنية, 2018-07-22) Thaher, Thaer
    Traffic congestion is a common challenge in urban areas, so several methods are used to reduce it. A powerful solution that can reduce the congestion problem is by developing a real-time traffic light control system with an optimization technique to minimize the overall traffic delay through optimizing the traffic signals timing. Researchers have proposed several simulation models and used various techniques to optimize the traffic signals timing. The purpose of this research is to evaluate and compare the performance of several meta-heuristic techniques in tackling the Traffic Signals Optimization Problem (TSOP). In this work, recently published algorithms that do not have specific parameters (the parameter-less) such as Teaching-Learning-Based Optimization (TLBO) and Jaya are applied to solve the traffic signals optimization problem. These algorithms have not been applied to the considered problem yet. A stochastic micro-simulator called 'Simulation of Urban Mobility' (SUMO) is used as a tool to implement and evaluate the performance and convergence speed of each algorithm. Three road networks of different sizes: small, medium and large containing 13, 34 and 141 phases respectively are simulated to study the scalability of algorithms. The performance of TLBO and Jaya algorithms are compared to three algorithms that have some parameters that need to be set such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Weighted Teaching-Learning-Based Optimization (WTLBO). The study also considers the effect of common controlling parameters (i.e. the population size) on the performance of the evaluated algorithms. After conducting many experiments, the comparisons and discussions have shown that TLBO and Jaya outperformed WTLBO, GA, and PSO for small and medium-sized networks. Moreover, TLBO achieved the best performance and scalability for the complex network
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    Feature Extraction of EEG Signal to Classify Epileptic Signal Using Neural Network
    (جامعة النجاح الوطنية, 2020-09-07) Jazzar, Isam Mutasem
    Electroencephalogram (EEG) is the electrical signal associated with the communication of the brain neural cells. It is used to evaluate and test the electrical activity of the brain. Consequently, it can be used to detect abnormalities associated with this activity such as epilepsy. Epilepsy, characterized by recurrent seizures, is one of the most common neurological disorder that affect people at all ages. It is associated with abnormal electrical activity in the brain. One way to detect and diagnose epilepsy is by using electroencephalogram (EEG) signal since it contains enough information to characterize the disease. We designed an algorithm capable of automate the process of identifying epileptic seizures and classifying it into three classes: normal, interictal, and ictal. The four-stage pipeline consists of a preprocessing stage, a wavelet transformation stage, a feature extraction stage, and a classification stage. The wavelet transformation stage is used to process the signals in order to prepare them for feature extraction stage. Then, statistical features are extracted from the coefficients of the wavelet transformation. Nine features were extracted and used in the classification of the signals using the Artificial Neural Network. To evaluate the performance of our model we used several measures includes: accuracy, sensitivity, and specificity. Using 300 brain signals and carrying proper calculations, we identified 144 epilepsy cases, and 156 non-epileptic cases. The accuracy, specificity, and sensitivity of our model are 81%, 80%, 84% respectively. The project provided a method to solve problems resulted from epilepsy diagnosis