This study presents an automated system for monitoring Personal Protective Equipment (PPE) compliance using advanced computer vision techniques in industrial settings. Despite strict safety regulations, manual monitoring of PPE compliance remains inefficient and prone to human error, particularly in harsh environmental conditions like in Saudi Arabia’s Eastern Province. The proposed solution leverages the state-of-the-art YOLOv11 deep learning model to detect multiple safety equipment classes, including safety vests, hard hats, safety shoes, gloves, and their absence (no_hardhat, no_safety_vest, no_safety_shoes, no_gloves) along with person detection. The system is designed to perform real-time detection of safety gear while maintaining accuracy despite challenging conditions such as extreme heat, dust, and variable lighting. In this regard, a state-of-the-art augmented and rich dataset obtained from real-life CCTV, warehouse, and smartphone footage has been investigated using YOLOv11, the latest in its family. Preliminary testing indicates the highest detection rate of 98.6% across various environmental conditions, significantly improving workplace safety compliance and reducing the resources required for manual checks. Additionally, a user-friendly administrative interface provides immediate notification upon detection of breaches so that corrective action can be taken immediately. This initiative contributes to Industry 4.0 practice development and reinforces Saudi Vision 2030’s emphasis on workplace safety and technology.

With the rapid growth of internet usage, a new situation has been created that enables practicing bullying. Cyberbullying has increased over the past decade, and it has the same adverse effects as face-to-face bullying, like anger, sadness, anxiety, and fear. With the anonymity people get on the internet, they tend to be more aggressive and express their emotions freely without considering the effects, which can be a reason for the increase in cyberbullying and it is the main motive behind the current study. This study presents a thorough background of cyberbullying and the techniques used to collect, preprocess, and analyze the datasets. Moreover, a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages, and it was deduced that there is significant room for improvement in the Arabic language. As a result, the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing (NLP) for the classification of Arabic datasets duly collected from Twitter (also known as X). In this regard, support vector machine (SVM), Naïve Bayes (NB), Random Forest (RF), Logistic regression (LR), Bootstrap aggregating (Bagging), Gradient Boosting (GBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) were shortlisted and investigated due to their effectiveness in the similar problems. Finally, the scheme was evaluated by well-known performance measures like accuracy, precision, Recall, and F1-score. Consequently, XGBoost exhibited the best performance with 89.95% accuracy, which is promising compared to the state-of-the-art.

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, many researchers proposed game theoretic models to avoid and remove obstacles for crowd evacuation. Game theoretical models aim to study and analyze the strategic behaviors of individuals within a crowd and their interactions during the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. These models consider a group of individuals as homogeneous objects with the same goals, involve complex mathematical formulation, and cannot model real-world scenarios such as panic, environmental information, crowds that move dynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to remove the obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, and width, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection. The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacle removal, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existing literature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially useful for public safety situations such as emergency evacuations during disasters and calamities.

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy (DR). Early detection and treatment are crucial to prevent complete blindness or partial vision loss. Traditional detection methods, which involve ophthalmologists examining retinal fundus images, are subjective, expensive, and time-consuming. Therefore, this study employs artificial intelligence (AI) technology to perform faster and more accurate binary classifications and determine the presence of DR. In this regard, we employed three promising machine learning models namely, support vector machine (SVM), k-nearest neighbors (KNN), and Histogram Gradient Boosting (HGB), after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society (APTOS) (a standard dataset), which includes 3662 images and originally categorized DR into five levels, now simplified to a binary format: No DR and DR (Classes 1–4). The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset, achieving an excellent accuracy of 96.9%, compared to 95.6% for both the KNN and HGB models. This approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.