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- ItemA DYNAMIC ONTOLOGICAL FRAMEWORK FOR BUKHARI AHADITH(An - Najah National University, 2023-02-23) Areej SawwanThe 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.
- ItemAlgorithms of Optimization Techniques for Bin Packing Problem: A Comparative Study(جامعة النجاح الوطنية, 2021-09-28) EL Karmi, YasminOne of the most critical optimization problems called Bin Packing Problem (BPP) attracts researchers attention because it is an NP-Complete problem means the solution can not be found in polynomial time. It has many applications such as storage and filling container. BPP aims to pick several items with different weights and pack them in a minimum number of bins without exceeding the bin’s capacity. One dimension BPP (1D-BPP) is one of its variations. Researchers have developed and proposed many algorithms to find an optimal solution or near-optimal solution. This research aims to make a comparison between six algorithms to solve one-dimensional BPP. Two heuristic algorithms proposed by Zehmakan [?] are approximation algorithms; one of them has an approximation ratio of 3/2, called A1 and A2. Those algorithms promise to perform more efficient and much better than other algorithms. Two classical approximation algorithms First Fit Decreasing (FFD) and Best Fit Decreasing (BFD) and two meta-heuristic algorithm namely Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) with specific parameters have been compared. In this work, several data sets have been used with the known optimal solution. They vary between random and arranged. Also, they vary in size. Some groups are small such as 9, 20 items, and medium such as 50, 100, 120 items and large such as 250, 500, 1000 items. Moreover, the sets vary in difficulty between easy and medium. So the number of bins used and running time have been compared to consider these algorithms’ performance. According to the number of bins used, A2 has performed better than A1 by comparing heuristic algorithms. However, it took much more running time than A1, especially in large data sets. Nevertheless, classical heuristics (BFD FFD) outperform both A1 and A2 in easy datasets, while in hard datasets A2 outperform the classical heuristics. By comparing meta-heuristic algorithms according to the number of bins used, in small data sets, PSO has performed better than GA but in large sets it’s almost the same. Also, PSO takes double running time than GA. PSO and GA have close results by the number of bins comparison and running time comparisons in other data sets. PSO is slightly better than GA when both the heuristics and the meta-heuristics are compared. Heuristic performs more efficient according to the number of bins and running time
- ItemA Cloud Application for Smart Agricultural Irrigation Management System(An-Najah National University, 2018-09-27) يونس, مصطفى محمدAgriculture is one of the most important sectors of the Palestinian economy and it is the main consumer of fresh water. It is the broadest economic sector and plays an important role in the economic development of any nation. Several factors have a major impact on agriculture activities includes water availability, soil type, climate condition, fertilizers, and diseases. In conventional farming, farmers have to make decisions about all these factors. This include: what to grow, how to use the irrigation schedule, the type of fertilizers, and the best method to control pest and diseases. Farmer’s decisions are based solely upon their experience, which can result in wasting valuable resources such as water, fertilizers, time, labor, etc. Furthermore, conventional farming experience can result in growing plants that are not the most suitable for a particular soil and climate, which can cause less yield and profit. In this thesis, we provide a cloud-based software application that is (combined with IoT devices) able to automates the irrigation schedule based on information obtained from agricultural experts and environmental data collected from the field by using sensors technology through Wireless Sensor Network (WSN). The application can easily be extended to automate fertilization as well as provide recommendation for weed and pest control.
- ItemA Comparative Study of the Regularization Parameter Estimation Methods for the EEG Inverse Problem(2016) Mohammed Jamil Aburidi; Dr. Adnan SalmanInvestigation of the functional neuronal activity in the human brain depends on the localization of Electroencephalographic (EEG) signals to their cortex sources, which requires solving the source localization inverse problem. The problem is ill-conditioned and under-determinate, and so it is ill-posed. To find a treatment of the ill-posed nature of the problem, a regularization scheme must be applied. A crucial issue in the application of any regularization scheme, in any domain, is the optimal selection of the regularization parameter. The selected regularization parameter has to find an optimal tradeoff between the data fitting term and the amount of regularization. Several methods exist for finding an optimal estimate of the regularization parameter of the ill-posed problems in general. In this thesis, we investigated three popular methods and applied them to the source localization problem. These methods are: L-curve, Normalized Cumulative Periodogram (NCP), and the Generalized-Cross Validation (GCV). Then we compared the performance of these methods in terms of accuracy and reliability. We opted the WMNE algorithm to solve the EEG inverse problem with the application of different noise levels and different simulated source generators. The forward solution, which maps the current source generators inside the brain to scalp potential, was computed using an efficient accurate Finite Difference Method (FDM) forward solver. Our results indicate that NCP method gives the best estimation for the regularization parameter in general. However, for some levels of noise, GCV method has similar performance. In contrast, both NCP and GCV methods outperforms the L-curve method and resulted in a better average localization error. Moreover, we compared the performance of two inverse solver algorithms, eLORETA and sLORETA. Our results indicate that eLORETA outperform sLORETA in all localization error measures that we used, which includes, the center of gravity and the spatial spreading.
- ItemDesign and Implementation of Digital System for Lung Cancer Early Detection Using Neural Network and Image Processing(جامعة النجاح الوطنية, 2021-02-16) سهراب صوالحة, ديمةيعتبر سرطان الرئة أكثر أنواع السرطانات شيوعًا بين الذكور في جميع أنحاء العالم. وهو يمثل 1 من كل 5 حالات وفاة بسبب السرطان ويحدث غالبًا بين سن 55 و65. وهذا هو الحال أيضًا في فلسطين، حيث يمثل سرطان الرئة نسبة 22.8٪ من الإصابات بين الذكور. يعد الكشف المبكر عن سرطان الرئة في المراحل الأولية خطوة حاسمة في عملية العلاج، حيث يمكن أن يزيد معدل البقاء على قيد الحياة بشكل ملحوظ. في هذه الرسالة، تم تطبيق تقنيات معالجة الصور على صور التصوير المقطعي المحوسب (CT) لسرطان الرئة للعديد من المرضى لتحديد مناطق السرطان وحجمه. كما تم إجراء دراسة مقارنة بين خوارزميات معالجة الصور المختلفة على عدة صور لتحديد الخوارزميات الأكثر دقة التي سيتم استخدامها في عملية فحص سرطان الرئة. لقد تم استخدام خوارزميات التعلم الآلي والشبكات العصبونية وعمل مقارنة دراسية بينها لتحديد إذا كان الورم سرطان ام لا.
- ItemDeveloping Wireless Sensor Network for Traffic Monitoring(جامعة النجاح الوطنية, 2019-10-16) فادي عبد الحق, محمدتلعب إدارة أنظمة المرور الذكية (ITS) دورًا مهمًا في نظام النقل الحديث حيث يعتبر تطوير انطمة مراقية حركة المرور من الابحاث الهامة في هذا المجال. أحد اهم انظمة المرور الذكية هي تلك الانظمة التي تعتمد على تطبيقات متقدمة لجمع معلومات عن حالة المرور في الزمن الحقيقي مثل عد المركبات وقياس سرعاتها وتحديد احجامها من أجل اتخاذ قرارات ذكية. تعتمد أنظمة مراقبة حركة المرور الحالية بشكل أساسي على كاميرات تصوير الفيديو أو الحلقات الاستقرائية. هذه الأنظمة لها العديد من القيود. فعلى سبيل المثال، أداء الأنظمة القائمة على كاميرات تصوير الفيديو يتأثر باحوال الطقس كالامطار الغزيرة والثلوج كذلك يحتاج نشر وصيانة الحلقات الاستقرائية إلى حفر سطح الطريق وبالتالي عرقلة لحركة المركبات، علاوة على ذلك، تكلفة كلا النوعين من أنظمة مراقبة حركة المرور عالية وليست مناسبة للنشر على نطاق واسع. في هذه الرسالة، قمنا بتطوير شبكة استشعار لاسلكية (WSN) لعد المركبات وتحديد حجمها وقياس السرعة على أساس أجهزة الاستشعار المغناطيسية التي توضع على جانب الطريق. بيانات المرور التي تم جمعها من اجهزة الاستشعار اللاسلكية يتم ارسالها الى كمبيوتر مركزي لاسلكيا لمعالجتها وتحليلها وحفظها في قاعدة بيانات وكما يمكن مشاركتها مع تطبيقات أخرى من خلال استخدام خدمات الويب. التطبيقات التي تم تطويرها في الاطروحة توفر مراقبة حركة المرور في الوقت الحقيقي، وتكلفة منخفضة، كما تضمن مراقبة مناسبة للطرق ومشاركة بشرية أقل. ينصب تركيز هذه الاطروحة على تطوير شبكة استشعار لاسلكية لادارة بيانات المرور حيث تم محاكاة البيانات لتقييم النظام بدلاً من نشر أجهزة استشعار مغناطيسية.
- ItemFeature Extraction of EEG Signal to Classify Epileptic Signal Using Neural Network(جامعة النجاح الوطنية, 2020-09-07) Jazzar, Isam MutasemElectroencephalogram (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
- ItemOptimization of Traffic Signals Timing Using Parameter-less Metaheuristic Optimization Algorithms(جامعة النجاح الوطنية, 2018-07-22) Thaher, ThaerTraffic 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
- ItemPANDEMIC-BASED SCHOOL CLASSES SCHEDULING AUTOMATION ALGORITHM: PALESTINIAN SCHOOLS AS A TESTING CASE(جامعة النجاح الوطنية, 2022-03-29) Mukhaimer, EmanDuring the COVID-19 pandemic, the distance learning was proposed as a vital solution to go on with the teaching/learning process and to keep both students and teachers (elementary and/or higher education) in contact with the avoidance of possible infection between them. However, despite the distance education offers and despite its role in eliminating the amounts of infections, it suffers from several drawbacks (in some communities): the lack of distance learning experience for both students and teachers, the need of high students’ motivation and the need for sufficient number of devices especially if the family has more than one school/college student. The purpose of this study is to propose a solution for the last problem (considering elementary education) by the proper scheduling of school classes’ sessions considering all of the affecting parameters like the number of lessons per teacher, the number of brothers students and the number of devices per family. The study is applied for 4 different school subjects: Arabic, English, Science and Mathematics and the study considers 4 elementary Palestinian schools to be involved in the study. The problem is modelled as an Integer Programming problem, and it is implemented using Gurobi. Comprehensive experimental tests are executed to compare between our work and the manual preparation of lessons scheduling in which a promising result are achieved. The IP algorithm decreased the number of conflicts by 40%.
- ItemWireless Sensor Network for Smart Irrigation System(جامعة النجاح الوطنية, 2019-02-17) زياد ناجي, عرينمع التطورات الحديثة في تقنيات الحوسبة وأجهزة الاستشعار اللاسلكية ، أصبح بالامكان رصد ومراقبة البيئة من حولنا. كما يعتبر نظام الري الزراعي التقليدي المستخدم حالياً مستهلكاً رئيسياً للمياه حيث يتم استهلاك كمية كبيرة من المياه من خلال التبديد والصرف. علاوة على ذلك ، يمكن أن يؤدي نهج الري التقليدي إلى المبالغه او الجحاف في الري, والذي يمكن أن يكون له تأثير سلبي على جودة المحاصيل وإنتاجية المحاصيل. بما أن جدولة الري تعتمد بشكل كبير على حالة الطقس ، وخصائص التربة ، ونوع النبات ، فإن نظام الري التلقائي الذكي والمراقبة ، الذي يأخذ هذه العوامل في الاعتبار ، يمكن أن يؤدي إلى توفير كمية كبيرة من المياه ، وزيادة غلة المحاصيل ، وتحسين جودة المحاصيل. . في هذه الأطروحة ، نقدم نظام الري الذكي الذي يستخدم شبكة الاستشعار اللاسلكية WSN ، لمراقبة الظروف الزراعية ، والتحكم في رطوبة التربة ، لتحقيق زراعة تلقائية أفضل.