This paper examines the critical factors influencing the performance of Learning Management Systems (LMS), emphasizing user satisfaction and loyalty. Employing a quantitative approach, structural equation modeling (SEM) was used to analyze survey data from 266 university students actively utilizing LMS platforms. Findings reveal that system quality significantly impacts user satisfaction, emphasizing reliability and usability. Adaptive learning enhances satisfaction and engagement by tailoring educational content to individual needs. Trust, particularly regarding data security and system reliability, further contributes to satisfaction, underscoring the necessity of robust and transparent security measures. Although information quality positively affects user satisfaction, it is less impactful than system-related factors. Service quality, while exerting a smaller influence, highlights the importance of responsive technical support for overall functionality. Additionally, user satisfaction strongly predicts user loyalty, indicating that satisfied users are more likely to continue using and recommending LMS platforms. The study provides actionable insights for developers and educators, advocating for enhanced system performance, adaptive learning tools, and trust-building measures to foster satisfaction and loyalty.

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience.

With the rapid growth of technology in intelligent city networks, navigating inside complex buildings has become increasingly difficult. The indoor Global Positioning System (GPS), which operates within an ultra-high radio frequency range, suffers from signal degradation due to thick walls. The lack of GPS signals and the inability to maintain a line of sight with orbiting satellites make indoor navigation more challenging than outdoor environments. Although Radio Frequency (RF) signals, Assisted GPS, and sensor-based solutions are used to track users indoors, they each have limitations. This paper introduces the Enhanced Smart Indoor Navigation System Using Augmented Reality (ESINS_AR), which offers a solution to these challenges using available and accessible resources. ESINS_AR facilitates easy location of places like shops within shopping malls, specific hotel rooms, or university lecture rooms by providing users with reasonably accurate visual assistance through their smartphones. Inspired by Augmented Reality (AR) and 2-D visual markers, the system uses several markers throughout the building. When scanned using the application, these markers display arrows on the screen, guiding users to their destination. The application allows users to select and change their desired destination as needed.

The early and accurate prediction of diabetes mellitus remains a significant challenge in clinical decision-making due to the high dimensionality, noise, and heterogeneity of medical data. This study proposes a novel hybrid classification framework that integrates the dwarf mongoose optimization (DMO) algorithm for feature selection with a convolutional neural network–long short-term memory (CNN-LSTM) deep learning architecture for predictive modeling. The DMO algorithm is employed to intelligently select the most informative subset of features from a large-scale diabetes dataset collected from 130 U.S. hospitals over a 10-year period. These optimized features are then processed by the CNN-LSTM model, which combines spatial pattern recognition and temporal sequence learning to enhance predictive accuracy. Extensive experiments were conducted and compared against traditional machine learning models (logistic regression, random forest, XGBoost), baseline deep learning models (MLP, standalone CNN, standalone LSTM), and state-of-the-art hybrid classifiers. The proposed DMO-CNN-LSTM model achieved the highest classification performance with an accuracy of 96.1%, F1-score of 94.6%, and ROC-AUC of 0.96, significantly outperforming other models. Additional analyses, including confusion matrix, ROC curves, training convergence plots, and statistical evaluations confirm the robustness and generalizability of the approach. These findings suggest that the DMO-CNN-LSTM framework offers a powerful and interpretable tool for intelligent diabetes prediction, with strong potential for integration into real-world clinical decision-support systems.