Effective waste management and recycling are essential for sustainable development and environmental conservation. It is a global issue around the globe and emerging in Saudi Arabia. The traditional approach to waste sorting relies on manual labor, which is both time-consuming, inefficient, and prone to errors. Nonetheless, the rapid advancement of computer vision techniques has paved the way for automating garbage classification, resulting in enhanced efficiency, feasibility, and management. In this regard, in this study, a comprehensive investigation of garbage classification using a state-of-the-art computer vision algorithm, such as Convolutional Neural Network (CNN), as well as pre-trained models such as DenseNet169, MobileNetV2, and ResNet50V2 has been presented. As an outcome of the study, the CNN model achieved an accuracy of 88.52%, while the pre-trained models DenseNet169, MobileNetV2, and ResNet50V2, achieved 94.40%, 97.60%, and 98.95% accuracies, respectively. That is considerable in contrast to the state-of-the-art studies in the literature. The proposed study is a potential contribution to automating garbage classification and to facilitating an effective waste management system as well as to a more sustainable and greener future. Consequently, it may alleviate the burden on manual labor, reduce human error, and encourage more effective recycling practices, ultimately promoting a greener and more sustainable future.

To constructively ameliorate and enhance traffic safety measures in Saudi Arabia, a prolific number of AI (Artificial Intelligence) traffic surveillance technologies have emerged, including Saher, throughout the past years. However, rapidly detecting a vehicle incident can play a cardinal role in ameliorating the response speed of incident management, which in turn minimizes road injuries that have been induced by the accident’s occurrence. To attain a permeating effect in increasing the entailed demand for road traffic security and safety, this paper presents a real-time traffic incident detection and alert system that is based on a computer vision approach. The proposed framework consists of three models, each of which is integrated within a prototype interface to fully visualize the system’s overall architecture. To begin, the vehicle detection and tracking model utilized the YOLOv5 object detector with the DeepSORT tracker to detect and track the vehicles’ movements by allocating a unique identification number (ID) to each vehicle. This model attained a mean average precision (mAP) of 99.2%. Second, a traffic accident and severity classification model attained a mAP of 83.3% while utilizing the YOLOv5 algorithm to accurately detect and classify an accident’s severity level, sending an immediate alert message to the nearest hospital if a severe accident has taken place. Finally, the ResNet152 algorithm was utilized to detect the ignition of a fire following the accident’s occurrence; this model achieved an accuracy rate of 98.9%, with an automated alert being sent to the fire station if this perilous event occurred. This study employed an innovative parallel computing technique for reducing the overall complexity and inference time of the AI-based system to run the proposed system in a concurrent and parallel manner.

Personal protective equipment (PPE) can increase the safety of the worker for sure by reducing the probability and severity of injury or fatal incidents at construction, chemical, and hazardous sites. PPE is widely required to offer a satisfiable safety level not only for protection against the accidents at the aforementioned sites but also for chemical hazards. However, for several reasons or negligence, workers may not commit to and comply with the regulations of wearing the equipment, occasionally. Since manual monitoring is laborious and erroneous, the situation demands the development of intelligent monitoring systems to offer the automated real-time and accurate detection of PPE compliance. As a solution, in this study, Deep Learning and Computer Vision are investigated to offer near real-time and accurate PPE detection. The four colored hardhats, vest, safety glass (CHVG) dataset was utilized to train and evaluate the performance of the proposed model. It is noteworthy that the solution can detect eight variate classes of the PPE, namely red, blue, white, yellow helmets, head, person, vest, and glass. A two-stage detector based on the Fast-Region-based Convolutional Neural Network (RCNN) was trained on 1699 annotated images. The proposed model accomplished an acceptable mean average precision (mAP) of 96% in contrast to the state-of-the-art studies in literature. The proposed study is a potential contribution towards the avoidance and prevention of fatal/non-fatal industrial incidents by means of PPE detection in real-time.

Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa. However, since said diseases are related to the lungs, there might be a likelihood of more than one disease being present in the same patient. In this study, a deep learning model that is able to detect the mentioned diseases from the chest X-ray images of patients is proposed. To evaluate the performance of the proposed model, multiple public datasets have been obtained from Kaggle. Consequently, the proposed model achieved 98.72% accuracy for all classes in general and obtained a recall score of 99.66% for Pneumonia, 99.35% for No-findings, 98.10% for Tuberculosis, and 96.27% for COVID-19, respectively. Furthermore, the model was tested using unseen data from the same augmented dataset and was proven to be better than state-of-the-art studies in the literature in terms of accuracy and other metrics.

Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.