Solving Traveling Salesman Problem Using Parallel River Formation Dynamics Optimization Algorithm on Multi-core Architecture Using Apache Spark Esra’a Alhenawi, Ruba Abu Khurma, Robertas Damaševic̆ius, Abdelazim G. Hussien International Journal of Computational Intelligence Systems, 2024 According to Moore’s law, computer processing hardware technology performance is doubled every year. To make effective use of this technological development, the algorithmic solutions have to be developed at the same speed. Consequently, it is necessary to design parallel algorithms to be implemented on parallel machines. This helps to exploit the multi-core environment by executing multiple instructions simultaneously on multiple processors. Traveling Salesman (TSP) is a challenging non-deterministic-hard optimization problem that has exponential running time using brute-force methods. TSP is concerned with finding the shortest path starting with a point and returning to that point after visiting the list of points, provided that these points are visited only once. Meta-heuristic optimization algorithms have been used to tackle TSP and find near-optimal solutions in a reasonable time. This paper proposes a parallel River Formation Dynamics Optimization Algorithm (RFD) to solve the TSP problem. The parallelization technique depends on dividing the population into different processors using the Map-Reduce framework in Apache Spark. The experiments are accomplished in three phases. The first phase compares the speedup, running time, and efficiency of RFD on 1 (sequential RFD), 4, 8, and 16 cores. The second phase compares the proposed parallel RFD with three parallel water-based algorithms, namely the Water Flow algorithm, Intelligent Water Drops, and the Water Cycle Algorithm. To achieve fairness, all algorithms are implemented using the same system specifications and the same values for shared parameters. The third phase compares the proposed parallel RFD with the reported results of metaheuristic algorithms that were used to solve TSP in the literature. The results demonstrate that the RFD algorithm has the best performance for the majority of problem instances, achieving the lowest running times across different core counts. Our findings highlight the importance of selecting the most suitable algorithm and core count based on the problem characteristics to achieve optimal performance in parallel optimization.
Predictive factors and adverse perinatal outcomes associated with maternal smoking status Shereen Hamadneh, Jehan Hamadneh, Esraa Alhenawi, Ruba Abu Khurma, Abdelazim G. Hussien Scientific Reports, 2024 To identify risk factors for smoking among pregnant women, and adverse perinatal outcomes among pregnant women. A case–control study of singleton full-term pregnant women who gave birth at a university hospital in Jordan in June 2020. Pregnant women were divided into three groups according to their smoking status, active, passive, and non-smokers. They were interviewed using a semi-structured questionnaire that included demographic data, current pregnancy history, and neonatal outcomes. Low-level maternal education, unemployment, secondary antenatal care, and having a smoking husband were identified as risk factors for smoke exposure among pregnant women. The risk for cesarean section was ninefold higher in nulliparous smoking women. Women with low family income, those who did not receive information about the hazards of smoking, unemployed passive smoking women, and multiparty raised the risk of neonatal intensive care unit admission among active smoking women. This risk increased in active and passive women with lower levels of education, and inactive smoking women with low family income by 25 times compared to women with a higher level of education. Smoking is associated with adverse perinatal outcomes. Appropriate preventive strategies should address modifiable risk factors for smoking during pregnancy.
An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form Zaher Salah, Hamza Abu Owida, Esraa Abu Elsoud, Esraa Alhenawi, Suhaila Abuowaida, et al. Future Internet, 2024 Phishing email assaults have been a prevalent cybercriminal tactic for many decades. Various detectors have been suggested over time that rely on textual information. However, to address the growing prevalence of phishing emails, more sophisticated techniques are required to use all aspects of emails to improve the detection capabilities of machine learning classifiers. This paper presents a novel approach to detecting phishing emails. The proposed methodology combines ensemble learning techniques with various variables, such as word frequency, the presence of specific keywords or phrases, and email length, to improve detection accuracy. We provide two approaches for the planned task; The first technique employs ensemble learning soft voting, while the second employs weighted ensemble learning. Both strategies use distinct machine learning algorithms to concurrently process the characteristics, reducing their complexity and enhancing the model’s performance. An extensive assessment and analysis are conducted, considering unique criteria designed to minimize biased and inaccurate findings. Our empirical experiments demonstrates that using ensemble learning to merge attributes in the evolution of phishing emails showcases the competitive performance of ensemble learning over other machine learning algorithms. This superiority is underscored by achieving an F1-score of 0.90 in the weighted ensemble method and 0.85 in the soft voting method, showcasing the effectiveness of this approach.
Optimizing intrusion detection in 5G networks using dimensionality reduction techniques Zaher Salah, Esraa Elsoud, Waleed Al-Sit, Esraa Alhenawi, Fuad Alshraiedeh, et al. International Journal of Electrical and Computer Engineering, 2024 The proliferation of internet of things (IoT) technologies has expanded the user base of the internet, but it has also exposed users to increased cyber threats. Intrusion detection systems (IDSs) play a vital role in safeguarding against cybercrimes by enabling early threat response. This research uniquely centers on the critical dimensionality aspects of wireless datasets. This study focuses on the intricate interplay between feature dimensionality and intrusion detection systems. We rely on the renowned IEEE 802.11 security-oriented AWID3 dataset to implement our experiments since AWID was the first dataset created from wireless network traffic and has been developed into AWID3 by capturing and studying traces of a wide variety of attacks sent into the IEEE 802.1X extensible authentication protocol (EAP) environment. This research unfolds in three distinct phases, each strategically designed to enhance the efficacy of our framework, using multi-nominal class, multi-numeric class, and binary class. The best accuracy achieved was 99% in the three phases, while the lowest accuracy was 89.1%, 60%, and 86.7% for the three phases consecutively. These results offer a comprehensive understanding of the intricate relationship between wireless dataset dimensionality and intrusion detection effectiveness.
Hybrid Deep Learning Approach for Accurate Prediction of Flowability in Ultra-High-Performance Concrete Ayat Mahmoud Al-Hinawi, Radwan A. Alelaimat, Esraa Alhenawi, Mohammad I. AlBiajawi, and Engineered Science, 2024 Ayat Mahmoud Al-Hinawi 1*, Radwan A. Alelaimat , Esraa Alhenawi 2, Mohammad I. AlBiajawi 3, 1Department of Allied Engineering Sciences, Hashemite University, Zarqa 2 Faculty of Information Technology, Zarqa University, Zarqa, Jordan 3 Faculty of Civil Engineering Technology, Universiti Malaysia Pahang AL-Sultan Abdullah, Persiaran Tun Khalil Yaakob, 26300 Gambang, Pahang, Malaysia
Choosing a Suitable Requirement Prioritization Method: A Survey Pedro A. Castillo-Valdivieso, Esraa Alhenawi, Shatha Awawdeh, Ruba Abu Khurma, Maribel García-Arenas, et al. Journal of Computer Science and Technology Argentina, 2024 Software requirements prioritization plays a crucial role in software development. It can be viewed as the process of ordering requirements by determining which requirements must be done first and which can be done later. Powerful requirements prioritization techniques are of paramount importance to finish the implementation on time and budget. Many factors affect requirement prioritization such as stakeholder expectations, complexity, dependency, scalability, risk and cost. Therefore, finding the proper order of requirements is a challenging process. Hence, different types of requirements prioritization techniques have been developed to support this task. In this survey we propose a novel classification that can classify the prioritization techniques under two major classes: relative and exact prioritization techniques class where each class is divided into two subclasses. We also provide an overview about fifteen different requirements prioritization techniques that are classified under our proposed classification. Moreover, we make a comparison between methods that are related to the same subclass to analyze their strengths and weakness. Based on the comparison results, the properties for each proposed subclass of techniques are identified. Depending on these properties, we present some recommendations to help project managers in the process of selection the most suitable technique to prioritize requirements based on their project characteristics.
A Novel Cellular Automaton Model for Optimized Multi-Exit Crowd Evacuation in High-Density Environments Omar Khair Alla Alidmat, Suhaila Abuowaida, Esraa Alhenawi, Hebatullah Khattab Awwad, Adai Al-Momani, et al. 2024 25th International Arab Conference on Information Technology Acit 2024, 2024 Evacuation systems play a critical role in mitigating casualties and property damage during emergency situations. Comprehending evacuee behavior in overcrowded scenarios is vital for developing effective evacuation strategies. Nonetheless, evacuating large crowds from buildings with multiple exits presents a significant challenge, particularly when exits are asymmetrical and crowds are dense. This paper proposes an innovative two-dimensional cellular automaton model for multi-exit evacuation, designed to streamline evacuee decision-making in an asymmetrical exit layout within dense crowds. The proposed model addresses limitations in traditional evacuation models by incorporating dynamic counting areas and enhanced parameters, including congestion, distance, and empty parameters. These improvements enable the model to more accurately represent evacuee behavior during emergency situations, particularly in environments with multiple exits. The model effectively captures complex crowd dynamics, such as arching and shockwaves, and adjusts exit selection based on real-time congestion levels. Simulations demonstrate that the proposed model significantly reduces evacuation time, unit evacuation time, and travel distance compared to existing models. Key findings highlight the model's ability to mitigate bottlenecks at congested exits, optimize exit utilization, and enhance overall evacuation efficiency. Future research aims to refine the model with real-world data for broader applications in diverse settings like stadiums and airports, thereby improving safety protocols during emergencies.
