With advances in digital transformation (DX) in education and digital technologies becoming more deeply integrated into educational settings, global demand for video-based learning materials continues to rise, resulting in substantial effort being required from teachers to create e-learning videos. Furthermore, while many existing services offer visual content, they primarily rely on templates, making it challenging to design custom content that addresses specific needs. In this study, we develop a web service that facilitates e-learning video creation through integrated artificial intelligence (AI) and digital human technology. This service enhances educational content by integrating digital human characters and voice synthesis technologies, aiming to create comprehensive e-learning videos by incorporating visual motion and synchronized audio into educational content. In addition, this service also aims to enable the creation of engaging content through advanced visuals and animations, effectively maintaining learner interest.

In current mobile communication networks, handover substantially influences the performance of heterogeneous networks (HetNet). The decision process and trigger timing of the handover decision algorithm (HDA) are integral aspects of handover and are indispensable for mobile user equipment (UE). However, the major A3RSRP (reference signal received power) HDA with the parameter of handover margin (HOM) used in co-frequency environments has poor performance with low-speed pedestrian scenarios in HetNet, including high handover failure (HoF) ratio, frequent handover (FHO), and high number of HoFs. In this paper, we focus on reducing the HoF ratio and propose a novel CQI and hysteretic (CH)-based HDA using downlink channel quality indicator (CQI) and hysteretic control (HC). This novel HDA enables the cell to check the downlink CQI of the UE during HDA, thereby correctly determining the trigger timing of the handover. Moreover, the cell can use the HC to dynamically adjust the HOM based on the CQI of the UE to adapt the handover to the HetNet environment. We evaluate and compare the performance of the CH and A3RSRP HDAs in three scenarios with different speeds, each with multiple HOM settings. The ns-3 simulation results indicate that the CH HDA shows a maximum improvement of 100% in HoF ratio and 5% and 100% in the number of handovers and HoFs, respectively, in the small-scale environment with a speed of 0.5 m/s and an initial HOM of 2.0 dB, while the CH HDA shows a minimum improvement of −4% in HoF ratio and 58% and 44% improvement in the number of handover and HoFs, respectively, in the large-scale environment with a speed of 0.5 m/s and an initial HOM of 3.0 dB, compared with the A3RSRP HDA.

Federated learning (FL) enables collaborative model training without centralizing raw data, but its application to large-scale vision models remains constrained by high communication cost, data heterogeneity, and privacy risks. Furthermore, in real-world applications such as autonomous driving and healthcare, model updates can inadvertently expose sensitive information even without direct data sharing. This highlights the need for frameworks that balance privacy, efficiency, and accuracy. The current approach to addressing information exposure involves encrypting data by incorporating additional encoding. However, such approaches to encrypting data significantly increase communication costs. In this paper, we propose Federated Share-A Low-Rank Adaptation with Differential Privacy (FedSA-LoRA-DP), a parameter-efficient and privacy-preserving federated learning framework. The framework combines selective aggregation of low-rank parameters with Differential Privacy (DP), ensuring that only lightweight components are shared while formally bounding individual data influence. Since DP simply perturbs the numeric values of existing parameters without altering their dimensionality or structure, it does not increase communication cost. This design allows FedSA-LoRA-DP to provide strong privacy guarantees while maintaining communication efficiency and model accuracy. Experiments on CIFAR-100, MNIST, and SVHN datasets demonstrate that the proposed framework achieves accuracy comparable to non-private counterparts, even under heterogeneous non-independent and identically distributed data and partial client participation. These results demonstrate that integrating differential privacy into low-rank adaptation enables privacy-preserving and communication-efficient federated learning without sacrificing model performance across heterogeneous environments.

Establishing an energy-balanced data routing and clustering approach is among the most fundamental steps to extend the longevity of wireless sensor networks (WSNs). This study presents an intelligent and energy-aware framework for data routing and clustering in WSN employing an adjusted Rand index (ARI)-guided discrete particle swarm optimization algorithm: ARI-DPSO. This method uses Dijkstra’s algorithm to establish energy-efficient data paths and uses the network lifetime as the ARI-DPSO’s fitness function. The discrete particle swarm optimization searches for the globally optimal cluster configuration that extends the network’s operational lifetime. The novelty of the ARI-DPSO lies in its capability to avoid premature convergence by using the ARI metric to quantify the similarity between the swarm’s global best solution and the current particles. Based on this level of similarity, ARI-DPSO employs an ARI-derived mechanism to trigger a dynamic perturbation element to force the swarm of particles to explore new search areas. The simulation results show that the ARI-DPSO improves the dynamics and diversity of the swarm, thereby maximizing the stable operational lifetime of wireless sensor networks (first node death).

Handover is crucial for ensuring seamless connectivity in heterogeneous networks (HetNet) by enabling user equipment (UE) to switch its connection link between cells based on signal conditions. However, conventional analytical approaches ignored the distinctions between macro-cell to small-cell (M2S) and small-cell to macro-cell (S2M) scenarios during a handover decision-making process, which resulted in handover failures (HoF) or ping-pong handovers. Therefore, this paper proposes a novel framework, Do-SPN-PPP, that combines stochastic Petri net (SPN) and the Poisson point process (PPP) to quantitatively analyze M2S and S2M handover performance differences. The proposed framework also reveals and predicts how handover parameters affect UE residence time in a cell within the HetNet, and it exhibits a higher predictive accuracy compared with the traditional conventional analytical approach. In addition, the Monte Carlo simulation verified the Do-SPN-PPP framework, and the proposed framework exhibits a 96% reduction in computation time while maintaining a 95% confidence interval and 0.5% error tolerance compared with the simulation.