Editorial: Jordanian Journal of Informatics and Computing
By Shahed Almobydeen
PDFAbstract
Dear Readers, It is with great pleasure that we introduce to you our upcoming journal, "Jordanian Journal of Informatics and Computing." This journal is dedicated to exploring the advancements in the field of Informatics and Computing and providing a platform for researchers and scholars to exchange ideas, fostering progress in the recent areas of Informatics and Computing. On behalf of the editorial team, I extend our heartfelt gratitude and a warm welcome to the scholars, experts, researchers, and readers who support and follow our journal.
Vehicular Ad-hoc Networks (VANETs): A Key Enabler for Smart Transportation Systems and Challenges
By Haitham Albinhamad, Abdullah Alotibi, Ali Alagnam, MohammedAlmaiah, SaidSalloum
PDFAbstract
Vehicular Ad-hoc Networks (VANETs) are a specialized subset of Mobile Ad-hoc Networks (MANETs) designed to facilitate data exchange between vehicles and infrastructure. These networks operate in two primary modes: Vehicle-to-Vehicle (V2V) communication, which is decentralized and mobile, and Vehicle-to-Infrastructure (V2I) communication, which is centralized and relies on Road Side Units (RSUs). VANETs play a crucial role in Smart City applications, particularly in Intelligent Transportation Systems (ITS), which enhance road safety, reduce traffic congestion, and minimize environmental impact by leveraging real-time data collected from vehicle sensors. The unique characteristics of VANETs, such as high mobility and dynamic topology, enable critical functionalities like collision detection, traffic management, and emergency response coordination. Public service entities, including traffic police, ambulances, and firefighters, benefit significantly from VANETs by receiving real-time alerts and optimizing response times. However, several challenges hinder the widespread deployment of VANETs, including high vehicle speeds, data transmission delays, and cybersecurity concerns. Addressing these challenges is essential to fully realizing the potential of VANETs as a transformative technology in modern transportation systems.
The Role of Artificial Intelligence in Bodybuilding: A Systematic Review of Applications, Challenges, and Future Prospects
By Mahmood A. Al-Shareeda, Ahmed Abdulazeez Obaid, Amjad Abdul Hamid Almaji
PDFAbstract
Artificial Intelligence (AI) is making more and more impact on bodybuilding and helps providing data driven insights for improved training, nutrition, performance analytics, injury prevention and supplementation. This article systematically reviews the impact of AI on five key aspects of bodybuilding. For example, the adaptive workout plans and real-time training feedback in AI-Based Training Optimization improve progressive overload and movement accuracy. Second, it is AI-Driven Nutrition & Diet Planning that will refine macronutrient tracking, each meal customization, and genetics-based diet optimization. Thirdly, AI in Performance Monitoring & Biomechanics uses wearables and computer vision to correct exercise form and analyze body composition. The fourth challenge AI solves is in Injury Prevention & Recovery - using world-class predictive models for muscle strain detection, personalized rehabilitation, and optimized rest protocols. Finally, it forms a part of the AI in Supplementation & Pharmacology encompassing supplement initiatives, hormonal regulation, performance-enhancing drugs, and its detection. However, existing AI systems encounter difficulties, including limited adaptability to individual physiology, inherent dataset biases, privacy issues, and the absence of regulatory frameworks for AI-assisted supplementation and doping detection. Looking ahead, we will have smart gym gear, hybrid AI and human coaches, nutrigenomics supported by AI, and regenerative medicine methodologies. This review highlights the excesses of bodybuilding and the potentiality of AI in moderating bodybuilding under the normative edges of ethics, regulations, and human expertise.
Unsupervised text feature selection approach based on improved Prairie dog algorithm for the text clustering
By Mohammad Alshinwan, Abdul Ghafoor Memon, Mohamed Chahine Ghanem, MohammedAlmaayah
PDFAbstract
Text clustering is suitable for dividing many text documents into distinct groups. The size of the documents has an impact on the performance of text clustering, reducing its effectiveness. Text documents often include sparse and uninformative characteristics, which can negatively impact the efficiency of the text clustering technique and increase the computational time required. Feature selection is a crucial strategy in unsupervised learning that involves choosing a subset of informative text features to enhance the efficiency of text clustering and decrease computing time. This work presents a novel approach based on an improved Prairie dog algorithm to solve the feature selection problem. K-means clustering is employed to assess the efficacy of the acquired subgroups of features. The proposed algorithm is being compared to other algorithms published in the literature. The feature selection strategy ultimately promotes the clustering algorithm to get precise clusters.
Risk Assessment for Identifying Threats, vulnerabilities and countermeasures in Cloud Computing
By Santosh Reddy Addula, Sajedeh Norozpour, Mohammed Amin
PDFAbstract
The main objective of this study is to conduct a comprehensive analysis of cyber risks in cloud computing, including classifying threats, vulnerabilities, impacts, and countermeasures. This classification helps to identify suitable security controls to mitigate cyber risks for each type of threat. Additionally, this study aims to explore the main vulnerabilities in terms of infrastructure, service and platform in cloud computing. This study uses the content analysis technique to collect, analyze, and classify data in terms of types of threats, vulnerabilities, and countermeasures. The methodology comprises four primary stages: (1) identifying key components, (2) threat identification, (3) vulnerability identification, and (4) countermeasure identification. The results indicate that DoS attacks and account hijacking attacks were the most prevalent infrastructure vulnerabilities in cloud computing, each accounting for 14% and 10% of incidents. The results found that unpatched software and weak access controls were classified as the most critical threats in the service level in cloud computing, comprising 17% and 12% of incidents, respectively. The results also indicated that encryption methods, access controls mechanisms and firewall malware protection are the most significant and effective countermeasures for protecting the infrastructure, service and platform in cloud computing environment. The findings of this study provides valuable recommendations for academic research in classifying the different types of cyber threats and understanding the significant security controls against cyber-attacks in cloud computing.