This study proposes a blockchain-based framework to enhance data security in Internet of Things (IoT) systems. It integrates several node types—Transmission, Inspection, and Forwarding Nodes, plus a Blockchain Security Service—to protect sensor data end-to-end. Using both conceptual modeling and expert evaluation from 32 specialists, the results show strong security and reliability for blockchain components, though Inspection Nodes need improvement. Overall, the model strengthens trust, integrity, and performance in decentralized IoT environments.

This study introduces an innovative deep learning–based approach to detect and classify Self-Admitted Technical Debt (SATD) along with related software defects. The model was trained using data from open-source projects such as Apache, Mozilla Firefox, and Eclipse, applying several architectures including LSTM, GRU, BERT, and GPT-3. Results show that the GPT-3 model achieved the highest accuracy (0.984), outperforming other models. The research contributes to improving software quality by enhancing the detection and understanding of technical debt and software defects, thereby supporting sustainable software maintenance and development.

Graph neural networks (GNN) have seen significant growth recently for modeling temporal evolution in dynamic networks. Representation of complex networks in the form of graph data structures has enabled researchers to study how entities within these networks interact with each other. These interactions evolve over time. Developing a generic methodology for modeling this temporal evolution in complex networks for tracking evolving relationships has been a significant challenge. Most of the existing methods fail to extract contextual representations of historical neighborhood interactions for future link prediction. To address these challenges, this paper presents a novel method for modeling temporal evolution in complex networks using GNNs. A Context-Aware Graph Temporal Neural Network (CATGNN) method that uses dynamic neighborhood selection based on common neighbors for a given node is presented. The method uses dynamic neighborhood selection using contextual embeddings extracted from the historical interactions of the down-sampled set of neighbors of a central node based on a common neighborhood. Fixed-sized contextual memory modules are constructed for each node that store the historical interactions of its neighbors and are updated based on the recency and significance of interactions. The proposed method has been evaluated using six real-world datasets and has comparable performance against state-of-the-art methods, both in terms of accuracy and efficiency. It shows an improvement of 7.52 to 0.05% over the baselines in terms of average precision. The results demonstrate that the proposed CATGNN model can capture complex patterns of change that are difficult to identify using traditional techniques by propagating information over the graph structure. The model can be applied in various fields involving complex systems.