The world of work is at the dawn of major transformations and is still poorly prepared for it. The current uncertainties on the consequences of the combined development of artificial intelligence (AI) and big data are gradually dissipating by impacting a few business processes in the hope of a future improvement in organizational productivity and social well-being [3]. Thanks to the analysis of the latest publications and in particular articles and reports published by academics, consulting firms and think tanks, this article aims to build an exploratory representation of the expected effects of the development of AI in the process of analysis for the allocation or not of bank credit compared to the statistical approaches currently used [27]. This exploratory review of the literature makes it possible to deduce the potential of the exploitation of big data in the detection of interactions between several pieces of information, making it possible to better assess the solvency of credit applicants. Thus, if the predictive improvement of this new credit analysis process allows precision in terms of decision-making, it also generates numerous legal, ethical and regulatory biases.

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.

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.

This paper carried out a hybrid clustering model for foreign exchange market volatility clustering. The proposed model is built using a Gaussian Mixture Model and the inference is done using an Expectation Maximization algorithm. A mono-dimensional kernel density estimator is used in order to build a probability density based on all historical observations. That allows us to evaluate the behavior’s probability of each symbol of interest. The computation result shows that the approach is able to pinpoint risky and safe hours to trade a given currency pair.