The objective of this work-in-progress paper is to present a theoretical framework for a multi-device, multi-modal Continuous Authentication (CA) system that combines behavior biometric data from smartphones, tablets, and laptops. Six different attack scenarios are identified as test benchmarks. We describe the architectural components of the proposed system, including data capture, preprocessing, machine learning-based analysis, and decision fusion. While the paper introduces and describes unimodal, multimodal, and multi-device CA approaches, the primary focus is on outlining a multi-device CA methodology and its potential for real-time threat detection and dynamic security response.

Medication iatrogeny is a significant patient safety challenge in the healthcare field. This issue pertains to the undesirable effects resulting from the use of drugs, including errors in prescribing, dosing, or administration. In this context, the use of Machine Learning (ML) techniques to predict clinical outcomes is becoming increasingly common. The objective of this work is to develop a decision-support system designed to provide recommendations and assist pharmacists in analyzing prescriptions to reduce the risks associated with iatrogenic medication use for patients. ML algorithms are applied to classify prescriptions as valid or invalid using a MIMIC database containing patient medical data. We followed strict guidelines to process the data to improve model performance and then evaluated the model's performance using cross-validation, referring to standard metrics. The system integrates with existing hospital software, allowing pharmacists to receive recommendations and alerts for potential medication errors. We obtained an average accuracy of 96% for predicting the validity of medical prescriptions. Our study demonstrates that the use of ML algorithms for predicting the validity of medical prescriptions is an effective method. The results also suggest that diversifying the data could improve the model's performance. The findings of this study have valuable implications for clinical practice by providing a useful tool for the early detection of medication errors and could contribute to the enhancement of decision support systems in medicine.

In this paper, we propose an innovative meta-heuristic characterized by a multi-dimensional chromosome where each dimension is driven by a rational agent. These agents have to communicate in order to implement evolving and adaptive genetic operators to accelerate the convergence towards the optimal solution. This cooperative approach is applied to solve the patient scheduling problem in emergency department (ED). This problem is NP-difficult due to the permanent interference between three types of arrival: already programmed patients, non-programmed patients and urgent non-programmed patients. Our scheduling problem has to integrate several dimensions such as medical dimensional, patient dimensional, temporal dimensional. The multi-dimensional aspect of the chromosome is crucial to model the different dimensions of the ED. The main goal of the simulation results is to assess the performance of the proposed agent driven multidimensional chromosome. The simulation results confirm that the intra and inter chromosomal interactions allow to avoid the blind aspect of the genetic operators and impacts the quality of solutions. The agents’ cooperation and its ability to improve efficiently the quality of the solutions by exploring intelligently the research space are confirmed by the drop in average total patient waiting time by 15.09%

This paper focuses on scheduling patients in emergency department (ED) according to the priority of patients’ treatments, determined by the triage process. This multi-skills patient scheduling problem is modeled through four dimensional (hypercube) solutions search space whose axes are: Medical staff, Patients, ED structure and Time and it can be formulated as a flexible job shop scheduling problem. We have then to solve a NP-hard combinatorial optimization problem (COP) in the emergency department (ED). The objective is to minimize a score integrating the total waiting time of patients in the (ED) with emphasis on patients with severe conditions. The Friends and Enemies collaboration protocol between agents is developed for solving the problem where each agent integrate a complete metaheuristic scheme in its behavior. Each agent act autonomously in the solution environment and interacts cooperatively with it and with the other agents. The interaction between agents allows the metaheuristic hybridization including the tuning of its parameters. The simulation results show that the scenarios with 2 or more agents were significantly higher in performance than the scenarios with 1 single agent. Thus, it is confirmed that the collaboration protocol between agents influences the quality of the solutions and the scalability of our approach, with the addition of new agents, there is an improvement in the results. Our approach is tested on a set of real (ED) data and the simulation results show that the proposed friends end enemies collaboration protocol can significantly improve the efficiency of the (ED) by reducing the score and especially the total waiting time of multi-skills patient scheduling problem.

The aim of this paper is to describe our proposed ABOS framework (Agent-Based Optimization Systems) by demonstrating the interest in using the multi-agent approach while operating hybrid metaheuristics to solve Combinatorial Optimization Problems (COP). Two main contributions are highlighted in this work: 1) to show that the alliance of the multi-agent systems (MAS) and the metaheuristics, based on the interaction and the parallelisms concepts, facilitates the hybrid metaheuristics development and allows the simultaneous exploration of different regions of the search space and 2) to demonstrate that the use the multi-agent approach, in the context of optimization, is a crucial option in the process of hybridization allowing the development of generic structures. These later promote the interaction between metaheuristics independent of the problem to be addressed. Our challenge in this ABOS framework is to endow the participant agents, with a set of rational behaviours allowing them to change in real time their strategies, according to the optimization process evolution. The simulation results show that the collaborative optimization can be effective in some cases, hence the need to set effectively the parameters of the optimization algorithms behaviours and the collaborative protocols. We also demonstrate that the use of ABOS framework with MAS allows a more robust and generic structure, capable with minimal changes handling different COP.

People with motor disabilities require assistance for navigating form one location to another. In order to improve the integration of wheelchair users into their daily life and work, we propose a real time adaptive planning algorithm for routing the user through an obstacle free optimal path. Our application is based on an augmented reality system for the assistance of wheelchair people (ARSAWP) and uses augmented reality (AR) smart glasses. The main goal is to support the development of indoor and outdoor navigation systems devoted to wheelchair users. In this paper we detail the design, the implementation and the evaluation of the proposed application, which was implemented in java for the Android operational system. Two types of database are used (local database and remote database). The information about navigation is displayed on AR glasses which give the user the possibility to interact with the system according to the external environment. The prototype is designed for use within the University of Lille campus.