The provision of mental health education provides a basis for improved self-awareness, emotional regulation, and positive behavioral development in learners. However, current evaluations of mental health education use static models that do not adequately represent the complex, dynamic interactions between learners and their educational environments. Therefore, this study develops a new approach for evaluating the effectiveness of mental health education based on Dynamic Sled Dog Optimizer (DSDO)-driven Enhanced Graph Neural Network (EGNN) methodology, which optimizes and classifies the Physiological, Behavioral, and Learning Interaction features derived from the IMHED-320 dataset. The proposed architecture helps in understanding the mental status of students and devising educational programs in institutions. The preprocessing starts with normalization of values using Min-Max Normalization. Next, adaptive outlier detection is used to identify remove all detectable anomalous values based on the distribution of each feature. Feature Extraction Dimensionality Reduction are done using Principal Component Analysis (PCA). The performance evaluation of the proposed DSDO-EGNN architecture is done on a Python platform. The obtained experimental results achieve 0.978 accuracy 0.982 AUC for predicting physiological features; 0.963 accuracy and 0.962 F1 Score for predicting behavioral features; and 0.982 accuracy with 0.989 AUC for predicting learning interactions. This indicates that the DSDO-EGNN architecture consistently performed better than traditional evaluation methods across all performance metrics. The research work provides a scalable, efficient, and robust architecture specifically designed to estimate the effectiveness of Mental Health Education via IoT Technologies used in Higher Education Institutions.
A photonic band gap (PBG) substrate-based 1 × 2 multiple-input and multiple-output (MIMO) antenna is presented for terahertz (THz) 6 G and Internet of Things (IoT) applications. The proposed radiating element comprises a graphene patch and ground plane on a polyimide substrate, and a hybrid slot-elliptical PBG unit cell with a slotted decoupling wall is introduced to reduce the mutual coupling. It demonstrates four resonant frequencies (2.35, 3.6, 4.51, and 6.42 THz) covering two operation bands (1.477–2.67 THz, bandwidth (BW) = 1.19 THz and 3.35–7.08 THz, BW = 3.72 THz) with an excellent peak gain of 10.58 dB, radiation efficiency of 85.82
Blind channel estimation in Narrowband Internet of Things links is challenged by weak coverage, multipath superposition, and locally non-uniform complex-domain observations. Manually tuned density clustering and global-bandwidth kernel density estimation may therefore produce unstable cluster structures and oversmooth weak propagation modes. To address these problems, a K-Nearest Adaptive Neighborhood and Density-Based Spatial Clustering of Applications with Noise blind channel estimation method is proposed. K-nearest adaptive neighborhood statistics and expected neighborhood counts are used to generate paired Eps and MinPts candidates, after which a cluster-count stability and noise-ratio rule determines the clustering parameters. Robust cluster-specific bandwidths are then assigned for local kernel density estimation, and the equivalent channel responses are recovered through posterior weighting and block-energy rescaling. At an SNR of 0 dB, the proposed method achieves an NMSE of −13.14 dB and a BER of 9.71
Abstract The MEC infrastructure supports latency-sensitive IoT applications by placing computation and storage resources close to end users. However, limited cache size, unpredictable service demands, mobile users, and cache replacement make adaptive service management difficult. Improper replacement may lead to increased cold-start latencies, migration costs, cloud failover, energy utilization, and SLA breaches. In this paper, we propose ACGSR: Adaptive Cooperative Graph-Attention Service Replacement, which represents a learning-enabled MEC architecture for adaptive service caching and offloading. ACGSR incorporates service demand forecasting using graph attention networks, cache replacement using reinforcement learning, service eviction based on utility maximization, and cooperative edge service identification. The service caching and offloading problem is defined as a normalized multi-objective optimization function, considering service latency, cache hit rate, energy consumption, migration cost, queue stability, and SLA compliance. The graph attention network learns spatial correlations between demands from nearby edge nodes. The reinforcement learning agent chooses suitable caching, replacement, and offloading operations under cache and computation resource constraints. Moreover, cooperative edge identification improves cloud fallback by assisting cache-missing services from adjacent edge nodes. Simulations using an EUA-derived edge-server topology and synthetic service workloads show that ACGSR outperforms CSC-noLM by 30–40%, CDOSC by 24–32%, DRL by 18–24%, and TOSC by 12–18% regarding service latencies. Furthermore, ACGSR boosts cache hit rates by 12–22% and lowers energy utilization by 20–35%.
With the advent of fast-growing Internet of Things (IoT) technologies, intelligent creative systems offer new opportunities for adaptive digital media output. Current computational art design frameworks, however, do not sufficiently incorporate IoT-driven human-machine interaction mechanisms with deep-learning-based aesthetic and style intelligence. Accordingly, this paper introduces IoT ArtNet, a novel IoT-based intelligent multimodal framework for adaptive digital media creation. To achieve three different deep learning tasks, IoT ArtNet uses the Art Style Representation Module trained on the WikiArt Art Movements and Styles dataset for artistic style classification, the Aesthetic Intelligence Module trained on the AVA (Aesthetic Visual Analysis) dataset for aesthetic quality prediction, and the Gesture-Based Human-Machine Interaction Module trained on the LeapGestRecog dataset for gesture command recognition using sensors. The framework achieves multimodal fusion of style embeddings, aesthetic quality scores, and gesture control via attention-weighted fusion, enabling adaptive media output decisions in a new Adaptive Media Fusion Module. The framework includes edge and cloud inference layers to support low-latency, IoT-oriented deployment. Offline prototype benchmark evaluations indicate that IoT ArtNet achieves 93.2
