Smart contracts suffer from critical vulnerabilities such as reentrancy attacks, delegate call misuse, and timestamp dependencies, posing significant risks when deployed in smart grids. This paper proposes a solution that integrates deep learning techniques based on the Graph Isomorphism Network (GIN) to analyze Solidity code and detect vulnerabilities before deployment. This model was trained on a dataset of vulnerable contracts, leveraging automated hyperparameter optimization via Optuna to fine-tune the model’s performance. Performance evaluations supported by visual analysis of the model’s pre- and post-training weights provide essential insights into the behavior of the proposed approach. The experimental results demonstrated the effectiveness of GIN in identifying vulnerabilities with an accuracy of 95.26%, outperforming previous studies by more than 2%. These new findings highlight the potential of deep learning to secure and develop resilient smart grid infrastructures.

Road transport systems (RTS) play an essential role in global supply chains, facilitating the efficient transport of goods and services over long distances and thus supporting economic activity on a worldwide scale. However, these systems face numerous challenges, particularly regarding safety, cost, and route optimization, requiring innovative and practical solutions to improve their overall performance. This paper proposes an in-depth analysis of RTS features forming a detailed dataset collected on the route between Dakhla (Morocco) and Paris (France). The study relies on applying advanced mathematical modeling techniques and analyzing several datasets to train various machine learning algorithms. The main objective is to identify optimized routes, combining high safety standards, reduced costs, and shorter transport times. The results show that the adopted approach results in safer and more efficient routes and complies with operational and regulatory constraints. Furthermore, this analysis highlights the importance of data quality and the integration of advanced technologies to deliver an intelligent route optimization system with significant reductions in cost and time. Finally, our results reveal that neural networks outperform other algorithms in this field, proving their superior effectiveness for this specific application.

In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems.