Recently given the rise of types of social media networks, the analysis of sentiment and opinions in textual data has gained significant importance. However, sentiment analysis in informal Arabic text presents challenges due to morphological complexities and dialectal variances. This research aims to develop an Emoji Sentiment Lexicon (Emo-SL) tailored to Arabic-language tweets and demonstrate performance improvements by combining emoji-based features with machine learning (ML) for sentiment classification. We constructed the Emo-SL using a corpus of 58K Arabic tweets containing emojis, calculating sentiment scores for 222 frequently occurring emojis based on their distribution across positive and negative categories. Emoji weighting is integrated with text-based feature extraction using lexicons to train classifiers on an Arabic tweet dataset. ML models, including Support Vector Machines (SVM), Naive Bayes, Random Forests, and K-Nearest Neighbors (KNN) are evaluated after optimal preprocessing and normalization. The results show that adding Emo-SL derived emoji features to ML classifiers can significantly improve accuracy by 26.7% over just textual features. The emoji-aware integrated approach achieves 89% F1 score, outperforming the rule-based VADER sentiment analyzer. Additionally, analysis of n-gram impacts further confirms the value of fusing emoji and text semantics for Arabic sentiment classification. The Emo-SL lexicon provides an effective framework for extracting nuanced emotional insights from noisy micro-text, which demonstrates the potential of contextualized emoji understanding to advance multilingual sentiment analysis performance.

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.

The fast growth of technology in online communication and social media platforms alleviated numerous difficulties during the COVID-19 epidemic. However, it was utilized to propagate falsehoods and misleading information about the disease and the vaccination. In this study, we investigate the ability of deep neural networks, namely, Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Network (CNN), and a hybrid of CNN and LSTM networks, to automatically classify and identify fake news content related to the COVID-19 pandemic posted on social media platforms. These deep neural networks have been trained and tested using the “COVID-19 Fake News” dataset, which contains 21,379 real and fake news instances for the COVID-19 pandemic and its vaccines. The real news data were collected from independent and internationally reliable institutions on the web, such as the World Health Organization (WHO), the International Committee of the Red Cross (ICRC), the United Nations (UN), the United Nations Children’s Fund (UNICEF), and their official accounts on Twitter. The fake news data were collected from different fact-checking websites (such as Snopes, PolitiFact, and FactCheck). The evaluation results showed that the CNN model outperforms the other deep neural networks with the best accuracy of 94.2%.

To adapt to the rapidly increasing vulnerabilities in software products and cyber threats that exploit them, security professionals are actively working with software developers to produce more secure systems. In software development, agile methods are increasingly adopted in critical software projects where security risks are prominent challenges. This adoption stems from the fact that agile methods are highly iterative and support delivering services and products in smaller batches which allows security professionals to seamlessly integrate software development security activities with agile methodologies. In addition, the iterative nature of agile software development encourages frequent inspections, tests, and patching of software systems to mitigate cybersecurity risks and vulnerabilities. Considering the massive growth of the Internet of Things (IoT) and Intelligent Transportation Systems (ITS) products, the challenge of software development while addressing the security and safety concerns of these devices will continue to increase. This paper presents a comprehensive and detailed review of agile software development in the context of IoT, ITS, and their cybersecurity and risk challenges. Furthermore, we provide a systematic comparison of the reviewed literature based on a set of defined criteria. Finally, we provide a broader outlook and an outline for designing future security-enhanced agile software development solutions for IoT and ITS systems.