Machine Learning (ML)

Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive twits to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text "twits" via one of the machine learning algorithms. In this paper, we have reviewed algorithms for automatic cyberbullying detection in Arabic of machine learning, and after comparing the highest accuracy of these classifications we will propose the techniques Ridge Regression (RR) and Logistic Regression (LR), which achieved the highest accuracy between the various techniques applied in the automatic cyberbullying detection in English and between the techniques that was used in the sentiment analysis in Arabic text, The purpose of this work is applying these techniques for detecting cyberbullying in Arabic.

Background : Artificial Intelligence (AI) has recently emerged as a transformative innovation within the software industry, disrupting conventional approaches to application development by automating tasks, refining code, and enhancing resource efficiency. Prior research indicates the effectiveness of AI-powered tools across various domains. However, contemporary studies lack a detailed analysis of the diverse sectors utilizing AI tools for software development. Objective : This article aims to identify the potential benefits and impacts of AI in software development, specifically regarding time-to-market, productivity, code quality, bug-fixing rates, resource flexibility, and developer satisfaction. The goal is to present fact-based information about AI’s impact on multiple industries and scopes of work. Methods : A mixed-methods research design was employed to analyze quantitative data from 40 projects across healthcare, financial services, retail, technology, and e-commerce industries. Data were collected using various project management tools, automated testing environments, and online questionnaires addressed to developers. The study incorporated a comparative evaluation of AI-based projects and traditional projects, with statistical analysis. Results : AI-driven software development projects demonstrated a mean reduction in time-to-market by 34.6%, an improvement in code quality by 70%, and a mean reduction in bug-fixing time by 57.7%. Productivity per sprint increased by over 70%, resource flexibility was higher (90.2% in AI projects vs. 67.8% in traditional projects), and developers reported higher satisfaction levels. These findings reinforce the concept that AI significantly enhances workflow and the achievement of optimal results. Conclusion : AI substantially improves both the speed and quality of software development. Further research should expand to explore the experiences of different sectors, the application of AI-driven tools, their differentiation, and usage, as well as the ethical considerations to promote sustainable and innovative software engineering solutions.

Background: Contemporary multi-protocol networks necessitate scalability, reliability, energy efficiency, and security due to the increasing number of devices and the diversification of network traffic. Conventional network management methods are inadequate to meet these demands, necessitating sophisticated solutions. Artificial intelligence (AI) has emerged as a significant field, offering advanced methods including predictive maintenance, anomaly detection, and intelligent resource management. Objective: This article aims to critically evaluate the effectiveness, flexibility, and productivity of AI-based applications in addressing major challenges in network management, including performance, scalability, energy consumption, threat detection rates, and cost. Methods: The study employs simulations and modeled datasets to assess AI-oriented solutions across various network environments, such as industrial IoT, smart cities, and telecommunications. The evaluation encompasses factors including Mean Time Between Failure (MTBF), resource utilization, delay minimization, and operating cost reduction. Digital twins, intelligent routing algorithms, and self-attention-based anomaly detection models are utilized, and the overall performance of these integrated technologies is analyzed. Results: The analysis demonstrates that AI-powered systems achieve near-optimal performance across all evaluated indicators. Specifically, the Manufacturing and Automotive Knowledge (MAK) sector observed a 52% increase in MTBF, the Banking, Financial Services, and Insurance (BFSI) sector noted a 32.39% improvement in energy efficiency, and the Defense and Public Enterprise (DPE) sector experienced a 94% increase in advanced threat detection. Conclusion: The findings indicate that AI solutions can effectively address many of the challenges present in current networks, offering cost-efficient and secure methods for implementing new communication networks with vast potential. Nonetheless, further empirical research is necessary to generalize these results and validate their applicability in real-world scenarios.

Background : Security has been a major issue of discussion due to increase in the number and sophistication of Cyber threats in the modern era. Conventional approaches to threat identification might face difficulties in a number of things, namely the relevancy and the ability to process new and constantly evolving threats. Machine learning (ML) and deep learning (DL) based Approaches present AI as a potential solution to the problem of efficient threat detection.   Objective : The article aims to compare the RF, SVM, CNNs, and RNNs models’ performance, computational time, and resilience in identifying potential cyber threats, such as malware, phishing, and DoS attacks.   Methods : The proposed models were trained as well as evaluated on the NSL-KDD and CICIDS 2017 datasets. This was done based on common scheme indicators including accuracy, precision, recollection, F1 measure, detection rate of efficiency, AUC-ROC, False Alarm Rate (FAR), and the stability to adversaries. Rating of computational efficiency was defined by training time and memory consumption.   Results : The findings indicate that the CNNs gave the best accuracy (96%) and resisted perturbation better, and the RF showed good performance with little computational load. RNNs have been proved effective in sequential data analysis and SVM also performed fairly well on binary data classification although there is a problem of scalability.   Conclusion : CNNs used in AI models are the best solutions to protection from the threats in the cybersecurity space. Nevertheless, some of them still require computational optimization in order to make those beneficial in scenarios with a limited usage of computational resources. It is suggested that these findings can be used in the context of subsequent research and practical applications.

Background: The telecommunications industry is at the crossroad of change seemingly precipitated by the use of Artificial Intelligence (AI) and Machine Learning (ML). These technologies have yielded new features like network automation, prescriptive analytics, and contextual-consumer engagement, solving traditional dilemmas in service delivery and operationalization. Objective: The current article seeks to understand how AI and ML has positively affected customer experience and service provision in the telecommunication industry. The research objectives focus on how to increase KPIs to service latencies, network reliability, and customer retention while at the same time establishing the problems associated with big data large-scale implementation. Methods: Samples were gathered using systematic reviews of the current literature, meta-analysis of case studies, and assessment of industry datasets. This concerned artificial intelligence enabled operations such as dynamic resource management, real-time customer emotions analysis and real-time fault detection. Regression analysis and time series models were used in order for measuring performance indices. Results: AI and ML integration led to multifaceted advancements: a decrease of average service latency by 55%, reduction of network downtime by 70%, and an increase of maintenance predictions accuracy by 35%. The customer retention rate which had improved to 25% was also credited to better personalization of the services as well as having proper service management. AI-equipped resource allocation also raised efficiency in bandwidth utilization by 60%. Conclusion: AI and ML are positively disrupting telecommunications as they deliver remarkable enhancements in the caliber of services and client satisfaction. With all the challenges in data governance and interoperability, it is clear that their adoption promises a great chance in enhancing the current standards within the telecommunications field and creating the basis for the development of a more sophisticated environment.

Background: With the continued advance in cyber threats, traditional network security systems offer little returns to organizations. AI has turned out to be a useful technology in improving network security because it proactively identifies and responds to threats in a short time. Objective: This article seeks to discuss the role played by AI self-defending mechanisms in autonomous network security given their effectiveness in threat detection, response time, and the overall harm that can be caused to networks by cyber criminals. Methods: Three separate studies were made, including conventional security systems, and analytically compared them with the AI-driven system across 100 different network environments. Machine learning (ML), deep learning (DL), and other forms of AI were applied to identify and counteract distinct threats like viruses, phishing, and even DDoS attacks. Detecting accuracy, response time and ability to mitigate attacks where among some of the other factors that were examined. Results: Automated threat intelligence systems have a 92% accuracy while legacy systems only have 78%. Mean response time was also decreasing by 65% from 45 seconds to 15 seconds. A significant increase to attack mitigation rates was noted with fifty percent effectiveness of the AI programs averting 85 percent of the threats in the first 30 seconds of identification. Conclusion: Autonomous threat response systems substantiate AI, which function as a radically superior replacement to conventional network security structures, minimizing threat response time and boosting the overall threat neutralization outcome. Incorporation of these types of secure mechanisms into contemporary security landscapes is important as a means of counteraction against new forms of cyber threats.