Air pollution has detrimental impacts on our physical health and the quality of our living environment, particularly in smart cities. Monitoring and predicting air pollution is crucial to empower individuals to make informed decisions that protect their health. Predicting air quality accurately plays an important effective action plan to mitigate air pollution and create healthier and more sustainable environments. This can be achieved by relying on the Air Quality Index, one of the most reliable indicators for air pollutant concentration levels in certain cities. This study provides a comparative analysis study for air quality prediction using three regression techniques: Random Forest regression, Linear regression, and Decision Tree regression, employing the AQI values. This comparison aims to identify the most efficient model based on various evaluation criteria, such as Mean Absolute Error and R ² measures. Additionally, it considers both error rate minimization and processing time efficiency of each regression model evaluated within two distinct frameworks as other important measures to determine the best-fitted model. The findings of this study showcase the superiority of the Decision Tree regression technique over the other models, demonstrating its exceptional accuracy with a high R ² score and low error rate. Moreover, integrating cloud computing technology has yielded significant improvements in model execution time, substantially enhancing the overall efficiency of the prediction process. By leveraging distributed computing resources, real-time air quality forecasting becomes feasible, enabling timely decision-making and proactive measures to address air pollution episodes.

Code readability and software complexity are considered essential components of software quality. They significantly impact software metrics, such as reusability and maintenance. The maintainability process consumes a high percentage of the software lifecycle cost, which is considered a very costly phase and should be given more focus and attention. For this reason, the importance of code readability and software complexity is addressed by considering the most time-consuming component in all software maintenance activities. This paper empirically studies the relationship between code readability and software complexity using various readability and complexity metrics and machine learning algorithms. The results are derived from an analysis dataset containing roughly 12,180 Java files, 25 readability features, and several complexity metric variables. Our study empirically shows how these two attributes affect each other. The code readability affects software complexity with 90.15% effectiveness using a decision tree classifier. In addition, the impact of software complexity on the readability of code using the decision tree classifier has a 90.01% prediction accuracy.

CCovert timing channels (CTC) enable surreptitious information leakage through manipulated data transmission timing, posing a severe cybersecurity risk. This research introduces a framework, called LinguTimeX, which utilizes explainable artificial intelligence approach that synergizes linguistic analysis with detection of timing irregularities to pinpoint CTC activities with high accuracy. The LinguTimeX extracts multidimensional features integrating statistical properties of textual content and temporal characteristics. An ensemble of machine learning and deep learning models, tailored for cross-lingual covert channel identification , demonstrates outstanding detection performance, with over 98% precision and recall in Arabic and 81% F1-score in Chinese datasets. Quantitative analysis and visualizations elucidate how innate language complexities govern covert signal amplitudes, guiding targeted optimization of feature engineering. The