Lalla Amina Charaf

As part of a successful IoV implementation, testing of IoV-based systems is essential for detecting errors related to device availability, communication reliability, quality of service, and system security. However, this remains a challenge due to the dynamic nature of IoV susceptibility to failures, heterogeneity, and large data streams associated with remotely distributed IoV system components. This paper proposes an intelligent agent-based architecture with a special focus on semantic interoperability testing in order to ensure better interaction and coherence between the various components of the IoV-based system. As a proof of concept, the proposed architecture is implemented in the NetLogo (v6.4.0) multi-agent environment and applied to a case study that detects semantic interoperability issues at both the data and query levels.

Background/Objectives: Cardiovascular diseases (CVDs) remain the primary cause of mortality worldwide, underscoring the critical importance of developing accurate early prediction models. In this study, we propose an advanced stacking ensemble learning framework to improve the predictive performance for CVD diagnosis. Methods: The methodology encompasses comprehensive data preprocessing, feature selection, cross-validation, and the construction of a stacking architecture integrating Random Forest (RF), Support Vector Machine (SVM), and CatBoost as base learners. Two meta-learning configurations were examined: Logistic Regression (LR) and a Multilayer Perceptron (MLP). Results: Experimental results indicate that the MLP-based stacking model achieves superior performance, with an accuracy of 97.06%, outperforming existing approaches reported in the literature. Furthermore, the model demonstrates high recall (96.08%) and precision (98%), confirming its robustness and generalization capacity. Conclusions: Compared to individual classifiers and traditional ensemble methods, the proposed approach yields significantly enhanced predictive outcomes, highlighting the potential of deep learning-based stacking strategies in cardiovascular risk assessment.

Nowadays, information represents an important resource for any organization and to ensure its management, it is important to set up an information system. The latter being essential in various fields, it can bring improvements in the care of patients and in the quality of health services. However, the use of information and communication technologies on the quality of health services was exclusive in developed countries. It is with this in mind that we have focused our study on the proposal of architecture to make hospital intelligent based on the Internet of Things (IoT) and the Blockchain. To do this, we used three databases namely Scopus, Web of Science and PubMed. First, we collected references to open-source Hospital Information Systems (OS HIS) and we were able to extract thirteen popular projects of which seven are active and 6 are not. Then, we continued our study with the seven active projects. Second, we collected references to IoT architectures for healthcare and after extensive reading we opted for a three-layer architecture (perception layer, network layer and application layer). Third, we collected references to lightweight blockchains and after in-depth reading of the documents found, we selected the Hyperledger foundation which was created to ensure the promotion of blockchain through an open-source ecosystem. As a result, we were able to come up with a three-layer architecture which is on the concepts of IoT and Blockchain. The proposed work is promising and can be used as a reference for the deployment of a hospital information system in an environment with limited resources.