Advanced Computing

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Now showing 1 - 5 of 9
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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
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    (جامعة النجاح الوطنية, 2022-03-29) Mukhaimer, Eman
    During 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%.
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    Algorithms of Optimization Techniques for Bin Packing Problem: A Comparative Study
    (جامعة النجاح الوطنية, 2021-09-28) EL Karmi, Yasmin
    One 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
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    Wireless Sensor Network for Smart Irrigation System
    (جامعة النجاح الوطنية, 2019-02-17) زياد ناجي, عرين
    مع 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.