Journal of Applied Artificial Intelligence <p>Journal of Applied Artificial Intelligence (JAAI) is an international and interdisciplinary scholarly peer reviewed journal on artificial intelligence published by Saba Publishing.<br />JAAI devoted entirely to Artificial Intelligence and welcomes papers in the overall field including, but not limited to, machine learning and cognition, deep learning, supervised learning, unsupervised learning, classification, regression, clustering, big and streaming data, optimization algorithms, feature selection and extraction, pattern recognition, bio-informatics, uncertain information processes, recommender systems, E-service personalization, distributed and parallel processing, computer vision, neural networks, natural language processing, heuristic search, multi-objective optimization, multi-agent systems, advances in social network systems, reasoning under uncertainty, forecasting and predication models as well as other hot topics.</p> <p><strong>Editor in Chief: <a href="" target="_blank" rel="noopener">Dr Nibras Abdullah</a></strong><br /><strong>ISSN (online)</strong>: <a href="" target="_blank" rel="noopener">2709-5908</a><br /><strong>Frequency:</strong> Semiannual</p> Saba Publishing en-US Journal of Applied Artificial Intelligence 2709-5908 Understanding the impact of requirements evolution and reaction on evolution of software: a survey and comparison <p class="abstract">In software systems, the continuous changing of requirements, known as requirements evolution, is considered one of the significant issues. Requirements' evolution denotes the postـdeployment changes in the requirements. This article reviews the most related requirements evolution approaches. Different approaches have been presented in modelling requirements evolution, managing requirements evolution, and relevant analysis techniques, like inconsistency detection and change impact analysis. The relevant approaches of requirements evolution can be generally classified into the impact of evolution and reaction on evolution. The article also has given a comparison among those approaches. The approaches that have been surveyed in this article exhibited many limitations. These limitations need to be addressed and coped with for the approaches to be more effective in managing the evolution of software requirements. One of the solutions to these limitations is to develop an approach that addresses the reasoning behind software requirements evolution.</p> Ahmed Mahdi Salih Mazni Omar Osamah Mohammed Alyasiri Pantea Keikhorakani Sharifah Mashita Syed Mohamad Copyright (c) 2023 Journal of Applied Artificial Intelligence 2023-12-28 2023-12-28 4 2 1 11 10.48185/jaai.v4i2.867 A Comprehensive Analysis of Cybersecurity Threats based IoTs <p>The rapid growth of the Internet of Things (IoT) in our daily activities, has led to serious concerns regarding to potential cybersecurity threats. Therefore, there is a real need to have active and proactive solutions. This research undertakes an extensive analysis review of literature for the existing cybersecurity challenges and threats within various IoT devices. Also, it presents the suggested solutions as well as the structural frameworks. Moreover, it helps to detect and identify possible threats using different methods. Furthermore, it makes a contribution by drawing attention to research gaps within industrial and economic fields based IoTs. According to our findings, the main concern issues in IoT systems are cybercrimes and privacy cases. Artificial Intelligence, on the other hand, presents promising opportunities to improve cybersecurity. Nonetheless, certain attacks including authentication and confidentiality remain unaddressed when applying current solutions. This is, in fact, calling for more investigation and practical testing of suggested defences.</p> Fatina Shukur Sinan T. Shukur Copyright (c) 2023 Journal of Applied Artificial Intelligence 2023-12-28 2023-12-28 4 2 12 21 10.48185/jaai.v4i2.920 Security analysis of the data of social networks using AI techniques <p>This proposed research explores the potential of AI techniques, particularly user engagement prediction, for analyzing social network data and identifying potential security threats. Utilizing a Random Forest classifier, we developed a highly accurate model achieving 100% accuracy and a 1.0 AUC-ROC score. This exceptional performance demonstrates the ability of engagement prediction to accurately flag suspicious accounts with unusually low engagement, often associated with bots or fake profiles. Based on these findings, we implemented mitigation strategies such as flagging low-engagement accounts for further investigation and analyzing engagement trends to inform proactive security measures. Furthermore, our work opens doors for future research in combining engagement prediction with other AI techniques like sentiment analysis for even more sophisticated threat detection, ultimately contributing to the development of robust solutions for enhanced social network security and user privacy protection.</p> Sadoon Hussain Abida Tahsin Ahmed Sami Copyright (c) 2023 Journal of Applied Artificial Intelligence 2023-12-28 2023-12-28 4 2 22 30 10.48185/jaai.v4i2.928 Image Analysis through the lens of ChatGPT-4 <p>Numerous studies have delved into the applications of ChatGPT across various domains such as medicine, sports, education, and business analysis. ChatGPT emerges as a potential replacement for key contributors in these diverse fields, sparking an ongoing quest to validate this assertion. One focal point of this paper is the examination of GPT-4's, the fourth generation of Chat GPT, capacity to handle a spectrum of visual elements like images, pictures, flowcharts, plots, and diagrams. The inquiry extends to assessing how the gleaned information from these visuals compares with human intuition, both inductive and deductive. To investigate, GPT-4 was presented with samples of human faces, flowcharts, plots, and diagrams, leading to remarkably accurate and error-free results within the specified timeframe, surpassing human capabilities. The outcomes underscore GPT-4's impressive prowess in image analysis, covering identification, recognition, and contextual understanding of visual content. Furthermore, GPT-4's proficiency in identifying objects within individual images opens the door to be utilized comprehensively in the field of object detection. However, GPT-4 exhibits limitations in recognizing individual images due to privacy considerations.</p> Olanrewaju Victor Johnson Osamah Mohammed Alyasiri Dua’a Akhtom Olabisi Esher Johnson Copyright (c) 2023 Journal of Applied Artificial Intelligence 2023-12-28 2023-12-28 4 2 31 46 10.48185/jaai.v4i2.870 Simulation modeling with memory-type control charts for monitoring the process variability <p>Memory-type control charts, renowned for their effectiveness in identifying small deviations in the process variance, are commonly used to monitor the process variability. In this article, we introduce a new tool, the Quadruple Exponentially Weighted Moving Average (QEWMA) chart, which is designed for the specific purpose of monitoring changes in the process variability. We refer to this chart as the -QEWMA chart. The performance of the -QEWMA chart is assessed through an extensive series of Monte-Carlo simulations, carefully considering the run-length distribution. Comparing it with other well-known memory-type charts, it becomes evident that the -QEWMA chart excels in its ability to effectively detect small shifts in the process dispersion. To illustrate the practical application of this chart, we provide an example.</p> Kashinath Chatterjee Mustapha Hached Christos Koukouvinos Angeliki Lappa Copyright (c) 2023 Journal of Applied Artificial Intelligence 2023-12-28 2023-12-28 4 2 47 64 10.48185/jaai.v4i2.875 Artificial intelligence, sports, and athlete’s performance <p>The artificial intelligence (AI) has big changes in the sports sector in extenuating the problem of health in sports. According to studied approaches, AI learning methods and techniques can decrease accidents during practicing sports by predicting the health situation of players before the happen of any incident in the match. Thus, players can participate and complete their matches with all security owing to AI.</p> <p>In this paper, we introduce our approach that is predicting sport type adequate for athletes joining sportive organizations from their blood characteristics using PCA method. We do analyze and standardization of the dataset of players and then utilizing three types of classifiers in the generalization of dataset and do a comparison between them to conclude test training scores of the data.</p> imen Louizi Rekik Malek Fairouz Azaiez Copyright (c) 2023 Journal of Applied Artificial Intelligence 2023-12-28 2023-12-28 4 2 65 85