The measurement of aircraft noise is very important for the industry of air transportation, for residents and for the municipality.In the United States of America, the Federal Aviation Authority (FAA) has a managing tool for aircraft noise map called Aeronautical Environmental Design Tool (AEDT) (Boeker Fleming, 2008); in French Bruitparif is the tool which satisfies the European directive for noise map elaboration strategy. The noise map enables city authorities, residents, and the airport manager to control the noise produced by aeronautical activities in order to master, for instance, the building of infrastructures surrounding the airport zone - a dataset built with a relational data base management system obtained from secondary radar detection. These data, after having gone through a data cleaning process, will be brought and tested with functions such as Naive Bayes, decision tree and random forest. Based on confusion matrix as a metric, we assessed the provided result by the decision tree with metrics as “ 98% for precision, 97% for recall and 97% for f1-score”. This article emphasizes that on the one hand, the data used for machine learning have the same quality in terms of integrity and precision of the radar itself and on the other hand the performance resides on the confusion matrix.

This paper presents a deep learning model for road accident captioning in the Malagasy language. As we have observed, the road accident is a deserved study case due to the relentless increase in Madagascar. In addition, image description in the Malagasy language is a subject of research that is not yet realized. Thus, we have created our dataset in the Malagasy language to do this. We have also adopted deep learning and word embedding as a method. As a result, we obtained a deep learning model which can describe a road accident image in the Malagasy language at 95.9898 percent of accuracy. The results obtained in this research are beneficial to protecting the life of Malagasy people in a road accidents. Moreover, our research is distinguished from all the related works in this domain by collaborating with an expert in the Malagasy language during the creation of the dataset, by the innovation of this domain in Madagascar, and by the relevance of our result.

As road traffic is becoming increasingly dense, new needs in terms of intelligent human-machine interaction are emerging for their control by human operators. One avenue of research consists in assisting them in their control task by an assistant agent. This paper presents a feasibility study in this field, involving interactions between humans and an assistant agent. For this purpose, a game theory-based model is proposed in order to be able to model a context-sensitive system for the cooperative realization of complex tasks. In this case, the participants of the game are human operators and an assisting agent interacting within the framework of the realization of a control task. Thus, each participants can choose an action between two possible ones (to cooperate or not). Then, the proposed utility functions allow to build the context-sensitive payoff matrix at each observation cycle of the human-machine interaction. To validate our model, we have implemented a simulated control situation; it concerns the regulation of traffic through intersections; this involves two human operators and an assistant agent. Thus, the assistant agent uses the game payoff matrix for its decision-making in using Nash equilibrium. This paper describes a feasibility study, focusing on an analysis of the results obtained during the execution of the simulation. Different research perspectives arise from this study in order to improve and generalize the proposed model.

This work contributes to the field of human-system interaction modeling through an artificial intelligence approach. It focuses on the cooperative realization of a complex task. For this purpose, we propose a human-agent interaction model based on game theory to describe the decision making between the human operator and the assistant agent. The proposed model is based on searching Nash equilibria for a repeated two-player game in which each player has a choice between two actions. In particular, the assistant agent knows how to calculate the equilibrium that depends on information coming from the context (human operator and work environment). This approach allows us to consider a context aware human-machine system. Then, the assistant agent knows how to optimize its intervention with regard to the human operator assisted by this agent during a complex task. For the validation of our model, we highlight the efficiency of the assistant agent using this principle by considering a road traffic simulator using Netlogo. An analysis of the simulation results is provided to illustrate the effectiveness of our approach.

This article discusses interactive system evaluation from the perspective of inspection methods, specifically the Cognitive Walkthrough (CW) method. The basic principles of CW are reviewed as proposed in the original version and the first two revisions. Then 11 significant extensions of CW are examined: Heuristic Walkthrough, The Norman Cognitive Walkthrough Method, Streamlined Cognitive Walkthrough, Cognitive Walkthrough for the Web, Groupware Walkthrough, Activity Walkthrough, Interaction Walkthrough, Cognitive Walkthrough with Users, Extended Cognitive Walkthrough, Distributed Cognitive Walkthrough, and Enhanced Cognitive Walkthrough. Four summaries are proposed: The first one concerns the conceptual, methododological, and technological aspects; the next two summaries deal with existing studies, first comparative and then noncomparative; and the last summary provides help for choosing a version or variant.