Asim Suleman Abdullah Alwabel

The recognition of user behavioural patterns provides an innovative method for access control systems, as it relies on automatic detection of human behaviour and does not require user involvement or the recall of credentials. These actions can be captured surreptitiously by passive sensors placed within the user’s environment. Existing solutions fail to integrate real-time physiological and behavioural patterns for robust user verification. This work presents an advanced multimodal user behavioural authentication framework that incorporates data from electroencephalogram (EEG) signals, Internet of Things (IoT) signals, radar point clouds, and cognitive workload metrics National Aeronautics and Space Administration Task Load Index (NASA-TLX) to capture a user’s unique interaction signature. A specialised feature-extraction pipeline called unified multi-model behavioural embedding (U-MoBE) converts raw sensor data into statistically and semantically rich representations using spectral, entropy-based, temporal, and spatial descriptors. These features are processed by a custom deep neural network for user behaviour analysis (UserBehaviorNet, UB-NET), which supports both closed-set (unknown) and open-set (registered) user identification via a confidence-based rejection method. The model achieves 100% accuracy on both the training and test sets, with minimal cross-entropy losses of 0.0203 and 0.0167, respectively. A confusion matrix assures perfect classification, while the SHapley Additive exPlanations (SHAP) analysis explains the impact of NASA-TLX components on model decisions. Additionally, inference validation on unknown users exhibits strong generalisation and secure rejection, establishing the framework’s efficacy in real-time authentication scenarios.

This study explores the multifaceted impact of Artificial Intelligence (AI) on education, particularly its transformative role in reshaping teacher‐student interactions, enhancing personalised learning and contributing to sustainable educational development. As AI technology rapidly advances, its integration in education is driving significant changes in teaching methodologies, student autonomy and the overall educational ecosystem. While AI demonstrates substantial potential to improve learning outcomes through personalised learning paths, intelligent assessments and real‐time feedback, it also brings forth several challenges. These include concerns regarding teacher‐student dynamics, ethical implications and educational equity. The study reveals that the acceptance and adaptability of both teachers and students play a crucial role in determining AI's effectiveness in the classroom. Teachers' evolving roles, as well as students' increasing autonomy, underscore the need for critical thinking and ethical decision‐making skills, which are essential for maximising the benefits of AI. However, the research also identifies the risks associated with AI exacerbating existing inequalities, particularly in regions with limited technological access. The findings highlight the necessity of ensuring equitable access to AI resources, especially in disadvantaged areas, and emphasise the importance of comprehensive teacher training to support effective AI integration. By examining the global application of AI in education, the study provides policy recommendations aimed at fostering the fair use of AI, ensuring that its integration does not deepen educational disparities but rather contributes to the broader goals of sustainable development in education. This research offers insights for policymakers and educators seeking to navigate the complexities of AI in educational practice, ensuring that AI acts as a tool for empowerment rather than displacement.

Real‐time detection of gas leaks and food spoilage poses significant challenges, particularly in settings where human olfactory perception is unreliable. This paper introduces Artificial Sensing, a novel AI‐powered electronic nose (E‐nose) system developed to accurately classify odors and evaluate food freshness in indoor environments. The system combines MQ‐series sensors and the Grove Multichannel Gas Sensor v2, integrated with a Seeed Studio Wio Terminal for edge‐level processing. To effectively handle the variability and complexity of time‐series sensor data, we propose a Time‐Series Transformer model enhanced with Multi‐Scale Adaptive Convolution (MAC) and multi‐head self‐attention, enabling robust feature extraction across temporal scales. Additionally, a multimodal fusion strategy incorporating ambient temperature and humidity improves model resilience. A comprehensive dataset comprising 4040 gas leak samples and 4080 food spoilage samples (milk and chicken) was collected under varied conditions. The proposed model achieved F1‐scores of 99.3% for gas leak detection and 94.8% for spoilage classification, surpassing traditional classifiers (SVM, Random Forest, ANN) and baseline ViT models by 7%–12%. Deployed on the Edge Impulse platform, the system demonstrated real‐time responsiveness (120 ms latency) and low power consumption. These findings highlight the promise of Transformer‐based architectures for edge‐level environmental monitoring and food safety applications.

In this study, we develop a strategy for the fractional analysis of time-fractional diffusion problems in the Caputo sense. Initially, we apply fractional complex transform (FCT) to convert the fractional ordered problems into its differential parts. Then, we use homotopy perturbation scheme (HPS) to compute the iterative series solutions in the shape of He’s polynomials. This approach has significant results without any assumptions or restrictive variables. The derived components are leading to the convergent results of the problem when [ ]. The validity of proposed technique is investigated by addressing two numerical applications. We provide the variant graphical structures of the derived results and compared it with the precise solution. These results yield the validity and dependability of the suggested approach for the fractional ordered problems.