Drowsiness-related car accidents continue to have a significant effect on road safety. Many of these accidents can be eliminated by alerting the drivers once they start feeling drowsy. This work presents a non-invasive system for real-time driver drowsiness detection using visual features. These features are extracted from videos obtained from a camera installed on the dashboard. The proposed system uses facial landmarks and face mesh detectors to locate the regions of interest where mouth aspect ratio, eye aspect ratio, and head pose features are extracted and fed to three different classifiers: random forest, sequential neural network, and linear support vector machine classifiers. Evaluations of the proposed system over the National Tsing Hua University driver drowsiness detection dataset showed that it can successfully detect and alarm drowsy drivers with an accuracy up to 99%.

The past few years have witnessed a substantial increase in cyberattacks on Internet of Things (IoT) devices and their networks. Such attacks pose a significant threat to organizational security and user privacy. Utilizing Machine Learning (ML) in Intrusion Detection Systems (NIDS) has proven advantageous in countering novel zero-day attacks. However, the performance of such systems relies on several factors, one of which is prediction time. Processing speed in anomaly-based NIDS depends on a few elements, including the number of features fed to the ML model. NetFlow, a networking industry-standard protocol, offers many features that can be used to predict malicious attacks accurately. This paper examines NetFlow features and assesses their suitability in classifying network traffic. Our paper presents a model that detects attacks with (98–100%) accuracy using as few as 13 features. This study was conducted using a large dataset of over 16 million records released in 2021.

Driver drowsiness is one of the leading causes of traffic accidents and has a substantial impact on road safety. Many traffic accidents can be avoided if sleepy drivers were given early warnings. Drowsiness detection systems monitor the driver condition and generate an alarm if drowsiness signs are detected. In this paper, a real-time visual-based driver drowsiness detection system is presented aiming to detect drowsiness by extracting an eye feature called the eye aspect ratio. In the proposed system, which is applied on videos obtained from a public drowsiness detection dataset, the face region is first localized in each frame. Then, the eye region is detected and extracted as the region of interest using facial landmarks detector. Following that, the eye aspect ratio value of each frame is calculated, analyzed, and recorded. Finally, three different classifiers, namely, linear support vector machine, random forest, and sequential neural network, are employed to improve the detection accuracy. Subsequently, the extracted data are classified to determine if the driver's eyes are closed or open. An alarm will then be triggered to alert the drowsy driver if an eye closure is recognized for a specified duration of time.

The spread of deepfake technology has become a growing concern, especially with the rapid advancement of generative models. This progress has made it increasingly difficult to distinguish between real and fake facial videos. This poses serious threats to security, privacy, and the spread of misinformation. Despite the existence of deepfake detection models, many suffer from high computational costs. This makes them impractical for deployment in resource-constrained environments. To address this challenge, this paper proposes a solution that accurately identifies deepfakes while reducing computational complexity. To achieve this, a deepfake detection system using an improved version of a Self-Supervised BEiT, called BEiT-HPR (Hierarchical PatchReducer), is proposed. The enhancement adds a Hierarchical PatchReducer layer to reduce the number of patches in successive encoder blocks. This reduces computational complexity while maintaining high detection accuracy. Additionally, training speed increases by over 50%, while the number of parameters is reduced by 63.4%. The BEiT-HPR model was tested using three publicly available benchmark datasets: FaceForensics++ (FF++), Celeb-DF, and the Deepfake Detection Dataset (DFD). The evaluation results revealed that reducing the model complexity by 62.2% allowed the proposed model to achieve an accuracy of 83.92% on FF++, 97.59% on Celeb-DF, and 98.25% on DFD. These findings emphasize the importance of computationally efficient deepfake detection methods that maintain high detection accuracy while reducing the burden of heavy computation. Therefore, it offers a scalable solution for identifying deepfakes across diverse datasets.

The COVID-19 pandemic has imposed rapid and significant changes upon education systems. Most educational institutions transferred abruptly from traditional face-to-face delivery to distance learning formats through virtual classrooms. As a result of this shift, many faculty members have faced challenges in keeping their students engaged during lectures. Gamification is often proposed as a promising solution to enhance student motivation, engagement, and learning outcomes attainment. This study investigates the effectiveness of the gamification mechanism on undergraduate students in a computer programming course using Kahoot! and Nearpod during distance learning. The findings of the study show that there is some correlation between student engagement, learning motivation, academic performance, and the use of gamification.