Objective: Mild cognitive impairment (MCI) is a condition characterized by impairment in a single cognitive domain or mild deficit in several cognitive domains. MCI patients are at increased risk of progression to dementia with almost 50% of MCI patients developing dementia within five years. Early detection can play an important role in early intervention, prevention, and appropriate treatments. In this study, we examined heart rate variability (HRV) as a novel physiological biomarker for identifying individuals at higher risk of MCI. We investigated if measuring HRV using non-invasive sensors might offer reliable, non-invasive techniques to distinguish MCI patients from healthy controls. Methods: Twenty-one MCI patients were recruited to examine this possibility. HRV was assessed using CorSense wearable device. HRV indices were analyzed and compared in rest between MCI and healthy controls. The significance of difference of numerical data between two groups was assessed using parametric unpaired t-test or non-parametric Wilcoxon rank sum test based on the fulfilment of unpaired t-test assumptions. Multiple linear regression models were performed to assess the association between individual HRV parameter with the cognitive status adjusting for gender and age. Results: Time-domain parameters i.e., the standard deviation of NN intervals (SDNN), and the root mean square of successive differences between normal heartbeats (RMSSD) were significantly lower in MCI patients compared with healthy controls. Prediction accuracy for the logistic regression using 10-fold cross-validation was 76.5%, Specificity was 0.8571, while sensitivity was 0.8095. Conclusion: our study demonstrated that healthy participants have higher HRV indices compared to older adults with MCI using non-invasive biosensors technologies. Our results are of clinical importance in terms of showing the possibility that MCI of older people can be predicted using only HRV PPG-based data.

Teacher performance evaluation is essential to a teacher’s primary responsibilities. This study investigates the process of evaluating teachers’ performance to provide a basis for planning an ongoing professional development program. AI-based systems present an opportunity for objective and reliable evaluations, facilitating the identification of areas requiring improvement and contributing to the overall enhancement of educational quality. Consequently, incorporating AI into evaluating teachers has become a research hotspot. This research proposes an analytical comprehensive AI-based system for teacher evaluation. Moreover, we introduce a novel analytical index system derived from the current adopted Key Performance Indicators (KPI) in the Ministry of Education in Saudi Arabia and an overall algorithm. This study is part of an integrated project to evaluate teachers’ performance using AI. To build our model, we are accurately determining the evaluative weights through 13 workshops held with 283 field experts. The main hypothesis is that the weights were evenly distributed. After studying them, we found they were relatively different by 0.1 to 0.3%. By analyzing T-KPI, we believe that our proposed index system could overcome the limitations of human-based assessments as it assigned appropriate AI techniques for each, thus ensuring accurate, reliable, and objective evaluation. This innovation would also economically impact rationalizing spending and reducing costs.

With the global population surpassing 8 billion, waste production has skyrocketed, leading to increased pollution that adversely affects both terrestrial and marine ecosystems. Public littering, a significant contributor to this pollution, poses severe threats to marine life due to plastic debris, which can inflict substantial ecological harm. Additionally, this pollution jeopardizes human health through contaminated food and water sources. Given the annual global plastic consumption of approximately 475 million tons and the pervasive issue of public littering, addressing this challenge has become critically urgent. The Surveillance and Waste Notification (SAWN) system presents an innovative solution to combat public littering. Leveraging surveillance cameras and advanced computer vision technology, SAWN aims to identify and reduce instances of littering. Our study explores the use of the MoViNet video classification model to detect littering activities by vehicles and pedestrians, alongside the YOLOv8 object detection model to identify individuals responsible through facial recognition and license plate detection. Collecting appropriate data for littering detection presented significant challenges due to its unavailability. Consequently, project members simulated real-life littering scenarios to gather the required data. This dataset was then used to train different models, including LRCN, CNN-RNN, and MoViNets. After extensive testing, MoViNets demonstrated the most promising results. Through a series of experiments, we progressively improved the model’s performance, achieving accuracy rates of 93.42% in the first experiment, 95.53% in the second, and ultimately reaching 99.5% in the third experiment. To detect violators’ identities, we employed YOLOv8, trained on the KSA vehicle plate dataset, achieving 99.5% accuracy. For face detection, we utilized the Haar Cascade from the OpenCV library, known for its real-time performance. Our findings will be used to further enhance littering behavior detection in future developments.

Information dissemination and preservation are crucial for societal progress, especially in the technological age. While technology fosters knowledge sharing, it also risks spreading misinformation. Audio deepfakes—convincingly fabricated audio created using artificial intelligence (AI)—exacerbate this issue. We present Sonic Sleuth, a novel AI model designed specifically for detecting audio deepfakes. Our approach utilizes advanced deep learning (DL) techniques, including a custom CNN model, to enhance detection accuracy in audio misinformation, with practical applications in journalism and social media. Through meticulous data preprocessing and rigorous experimentation, we achieved a remarkable 98.27% accuracy and a 0.016 equal error rate (EER) on a substantial dataset of real and synthetic audio. Additionally, Sonic Sleuth demonstrated 84.92% accuracy and a 0.085 EER on an external dataset. The novelty of this research lies in its integration of datasets that closely simulate real-world conditions, including noise and linguistic diversity, enabling the model to generalize across a wide array of audio inputs. These results underscore Sonic Sleuth’s potential as a powerful tool for combating misinformation and enhancing integrity in digital communications.

Phishing detection is a critical challenge in virtual realities, where malicious activities can compromise user security. This paper presents a novel approach integrating AI and Semantic Web Technologies for robust phishing detection. The proposed model preprocesses text data and leverages a reduced six-layer BERT encoder to extract contextual embeddings. Outputs from BERT, including classifier, attention, and encoder layers, are combined with features derived from Semantic Web Technologies and a custom deep learning layer to form a unified representation. The concatenated features are passed to a linear layer for classification. Experiments demonstrate superior performance, achieving 95\% accuracy, 96\% F1-score, and a 0.99 ROC-AUC, outperforming standard machine learning models. This framework provides a reliable phishing detection solution for virtual environments.