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.

With the rapid advances in Information Technology (IT), the focus on engaging computing students to gain practical experience in the IT industry before graduation is becoming increasingly complex without incorporating pedagogical strategies of success in curricula. The goal is to enable computing major students to gain in-depth knowledge and practical understanding of the IT working environment before graduating through essential industry-driven practical skills based on international standards and best practices. Unfortunately, tracking and analyzing students’ practical skills performance during their IT field training programs, which are conducted primarily off-campus at various public and private organizations, before, during, and after the training period, is a daunting task for both the college instructors and the industry trainers. To overcome these challenges, this paper introduces a Sustainable University Field Training (SUNFIT) framework, which is a pedagogical approach towards mining the educational data using machine learning to integrate and measure the field training programs against the internationally recognized accreditation standards such as Accreditation Board for Engineering and Technology (ABET). The study employs machine learning models aimed at continuously measuring and monitoring international ABET accreditation requirements on computing major courses’ academic data, elucidating student performance across various semesters, integrating best practices, and producing an evidence-based rationale approach for evaluating weak learning outcomes (LOs) with minimal manual intervention, as well as preventing faculty-specific portfolio errors. The proposed approach could be easily developed by academics, researchers, or even students, and for a variety of purposes, including enhancing poor student outcomes (SOs). In addition, various data mining and machine learning approaches have been investigated over field training assessment data for successful prediction in subsequent cycles. The results are promising, with Naïve Bayes obtaining the highest accuracy of 90.54% followed by J48 and PART algorithms at 87.83%.

In medical treatments and care, medical health is a critical area that requires immediate attention of and can benefit greatly from current day technology. Advancements in IOT development is transforming all facets of our everyday lives and it’s our belief that it can play an important role in mental healthcare too. This paper proposes a human centered model which incorporates the use of the latest IOT wearable technologies along with a fully encrypted cloud computing algorithm that can potentially help in improving the identification and treatment of mental healthcare patients by integrating suggested measures and means of data acquisition, collection, transmission, processing and protection. If done right, with further detailed research that focuses on mental health, we can expect to see significant improvements in treatment and diagnosis methods for patient suffering from mental conditions in the very near future. Our emphasis is on creating a human centered model that can be converted into an interactive app with responsive features that can suggest different treatment measures based on the information collected from the Big Data gathered through the IOT sources utilized and depending on the patient’s condition with utmost security protection of the confidential data.