Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract The Social Internet of Things (SIoT) enhances device interoperability through social relationships but introduces significant trust management challenges in dynamic, resource-constrained environments. Existing machine learning-based trust models often rely on static training data or centralized architectures, limiting their adaptability and scalability in real-world SIoT deployments. This paper proposes GossipTrust, a novel decentralized trust model that integrates fog computing with adaptive control mechanisms for robust trust management. Our approach deploys machine learning models on fog nodes to enable distributed trust computation while minimizing resource constraints on individual devices. The core innovation combines a MAPE-K (Monitor, Analyze, Plan, Execute, and Knowledge) control loop for real-time attack detection with a gossip learning protocol that allows fog nodes to collaboratively refine trust models through peer-to-peer updates without central coordination. Experimental results demonstrate that GossipTrust significantly outperforms baseline models, achieving 13% higher accuracy, 14% higher success rate, 10% higher F-measure, and 12% lower loss across various trust attack scenarios. The proposed solution effectively addresses key SIoT challenges, including scalability, adaptability, and resource efficiency.

Abstract This study presents an applied, multi-stage machine-learning pipeline for maternal health risk classification using the Maternal Health Risk dataset (1,014 samples, six clinical features, and three risk classes). Six classifiers were evaluated across four preprocessing stages: training-fold standardization alone, Edited Nearest Neighbors (ENN)-based cleaning, Synthetic Minority Oversampling Technique (SMOTE)-based class balancing, and sequential ENN-SMOTE preprocessing. Within each fold of 10-fold cross-validation, the scaler, ENN, SMOTE, model fitting, and ensemble construction were performed using only the training fold; the corresponding test fold remained untouched until evaluation. A fixed majority-voting ensemble comprising Random Forest, Extra Trees, and XGBoost was evaluated using class-wise, macro-averaged, and weighted-averaged accuracy, precision, and F1-score. In Stage 4, the hybrid ensemble achieved a weighted accuracy of 91.5% ± 3%, precision of 88% ± 3%, and F1-score of 87.2% ± 3%. The ensemble's precision for the high-risk class exceeded 90% across all four stages. These findings indicate competitive predictive performance on this dataset; however, direct comparison with previous studies is limited by differences in preprocessing methods, validation protocols, and model configurations. External and prospective validation is required before clinical use.