Analyse de flux par des techniques de séries temporelles

Le transport sanitaire d’urgence est enclenchée, en France, suite à l’appel à un des numéros d’urgence, et suite à cet appel une ambulance est envoyée. Les accidents étant liés à l’activité humaine, qui elle-même est conditionnée à l’heure dans le jour, à la saison, au temps qu’il fait, etc., la sollicitation pour du secours à personnes n’est donc pas aléatoire. Les flux des différents opérateurs sont prévisibles, dans une certaine mesure, notamment du fait de leur caractère saisonnier. Et parvenir à les prévoir rend possible la mise en place de stratégies de planifications, qui pourraient aider grandement à la gestion de ce secteur actuellement en crise. Par exemple, être en mesure de prévoir la sollicitation à l’horizon de quelques heures, chez les pompiers, leur permet d’anticiper le besoin en pompiers volontaires. Avoir une visibilité à court terme permet de planifier au mieux les congés des ambulanciers ou au niveau des urgences, quand une visibilité à long terme aide à la planification des besoins futurs, tant matériel qu’humain. Dans ce contexte, la collecte de données de différentes filières varie sur des périodes s’étalant de quelques à une vingtaine d’années. L’objectif consiste à exploiter au mieux ces flux, tant pour en analyser la dynamique que pour être en mesure d’effectuer des prévisions à plus ou moins long terme. Certains de ces flux ont d’ores et déjà été exploités dans une approche d’apprentissage supervisé, qui nécessite la collecte en continu d’un certain nombre de variables explicatives, ce qui s’avère complexe à mettre en oeuvre pour un dispositif opérationnel: des scripts doivent être mis en place pour récupérer à chaque heure ces variables, planifier périodiquement de nouveaux apprentissages automatiques, etc. En conséquence, différentes approches ont été appliquées sur différents jeux de données de pompiers fournis par le service départemental d’incendie et de secours, avec l’objectif principal d’établir une meilleure planification et une meilleuregestion future des pompiers à moindre complexité.Emergency medical transport in France is triggered by the dispatch of an ambulance, either by the SMUR, SAMU, by a private ambulance company or by the fire department after dialing one of the emergency numbers. Since accidents are related to human activities, which in turn depend on the time of day, season, weather, climate, special events, etc., the emergency response is not hazardous but predictable. The flows of many actors are predictable to some extent, especially because of their seasonality. Being able to predict such operations makes it possible to put in place strategies for planning that could be very helpful in managing the emergency sector, which is currently in crisis. Forecasting firefighter interventions for theshort term allows for better planning for paramedic leave at the emergency level, while forecasting for the long term facilitates planning of future human and material resources. In this context, the collection of data from different streams varies over periods ranging from a few years to twenty years, the aim being to use these different flows both to analyze their dynamics and to make more or less long-term forecasts. Some of these data streams have already been used in a supervised learningapproach that requires the continuous collection of a set of explanatory variables, which proves to be complex for an operational device: scripts need to be set up to retrieve these variables on an hourly basis, scheduled periodically for new machine learning, etc. As a result, different approaches have been studied and applied to different firefighter datasets provided by the fire and rescue department, with the main objective being to study such operations for better future planning and management of the emergency response at lower complexity

Anomalies and breakpoint detection for a dataset of firefighters' operations during the COVID-19 period in France

International audienceFirefighters are exposed to many hazards. The main objective of this study is to apply machine learning techniques to tailor the need for firemen operations to their demands. This strategy enables fire departments to organize their resources, which leads to a reduction of human, material and financial requirements. This work focuses on predicting the number of firefighters' interventions during the sensitive period of the global pandemic COVID-19. Experiments applied to a dataset from 2016 to 2021 provided by the Fire and Rescue Department, SDIS 25, in the region Doubs-France have shown an accurate prediction and revealed the existence of a turning point in August 2020 due to an increase in coronavirus cases in France

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature