Stephane Cormier

Bio: Stephane Cormier is an academic researcher from University of Reims Champagne-Ardenne. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 3, co-authored 20 publications receiving 20 citations.

Towards the Integration of Agricultural Data from Heterogeneous Sources: Perspectives for the French Agricultural Context Using Semantic Technologies

Abstract: Sustainable agriculture is crucial to society since it aims at supporting the world’s current food needs without compromising future generations. Recent developments in Smart Agriculture and Internet of Things have made possible the collection of unprecedented amounts of agricultural data with the goal of making agricultural processes better and more efficient, and thus supporting sustainable agriculture. These data coming from different types of IoT devices can also be combined with relevant information published in online social networks and on the Web in the form of textual documents. Our objective is to integrate such heterogeneous data into knowledge bases that can support farmers in their activities, and to present global, real-time and comprehensive information to researchers. Semantic technologies and linked data provide a possibility for data integration and for automatic information extraction. This paper aims to give a brief review on the current semantic web technology applications for agricultural corpus, then to discuss the limits and potentials in construction and maintenance of existing ontologies in agricultural domain.

Anomaly Detection on Data Streams for Smart Agriculture

Abstract: Smart agriculture technologies are effective instruments for increasing farm sustainability and production. They generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues on farm productivity and efficiency. In this context, the detection of anomalies can help in the identification of observations that deviate from the norm. This paper proposes an adaptation of an ensemble anomaly detector called enhanced locally selective combination in parallel outlier ensembles (ELSCP). On this basis, we define an unsupervised data-driven methodology for smart-farming temporal data that is applied in two case studies. The first considers harvest data including combine-harvester Global Positioning System (GPS) traces. The second is dedicated to crop data where we study the link between crop state (damaged or not) and detected anomalies. Our experiments show that our methodology achieved interesting performance with Area Under the Curve of Precision-Recall (AUCPR) score of 0.972 in the combine-harvester dataset, which is 58.7% better than that of the second-best approach. In the crop dataset, our analysis showed that 30% of the detected anomalies could be directly linked to crop damage. Therefore, anomaly detection could be integrated in the decision process of farm operators to improve harvesting efficiency and crop health.

Towards a Generic Multi-agent Approach for Medical Image Segmentation

Abstract: Medical image segmentation is a difficult task, essentially due to the inherent complexity of human body structures and the acquisition methods of this kind of images. Manual segmentation of medical images requires advance radiological expertize and is also very time-consuming. Several methods have been developed to automatize medical image segmentation, including multi-agent approaches. In this paper, we propose a new multi-agent approach based on a set of autonomous and interactive agents that integrates an enhanced region growing algorithm. It does not require any prior knowledge. This approach was implemented and experiments were performed on brain MRI simulated images and the obtained results are promising.

Intelligent Transport System Based on Bluetooth

Abstract: A Cooperative Intelligent Transport System (C-ITS) is a system where mobile stations OBU (On-Board Units) exchange messages with other ITSS-V (Intelligent Transport System Station Vehicle) or RSU (Road Side Units) Messages are sent through a specific WIFI (IEEE 80211p) denoted also ETSI ITS-G5 The efficiency of this technology has been proven in terms of latency However, RSU are common everywhere and stations equipped with G5 interface are not widely deployed For this reason we look for another mean to guarantee this communication Bluetooth Low Energy (BLE) is deployed on smartphones We take advantage of this deployment to propose an architecture based on this protocol in order to build a Cooperative Intelligent Transport System Cellular networks are widely deployed and can support these communications We have adapted the ITS stack provided by the ETSI (designed for G5) to the BLE protocol

Automated segmentation of MS lesions from multi-channel MR images

Abstract: Quantitative analysis of MR images is becoming increasingly important as a surrogate marker in clinical trials in multiple sclerosis (MS). This paper describes a fully automated model-based method for segmentation of MS lesions from multi-channel MR images. The method simultaneously corrects for MR field inhomogeneities, estimates tissue class distribution parameters and classifies the image voxels. MS lesions are detected as voxels that are not well explained by the model. The results of the automated method are compared with the lesions delineated by human experts, showing a significant total lesion load correlation and an average overall spatial correspondence similar to that between the experts.

Concept of Li-Fi on Smart Communication between Vehicles and Traffic Signals

Abstract: Communication is an essential thing for any work to be done on desired condition. There are several ways are there to communicate between a person to a machine. Switches are made as primary communication medium to do a specific task by a machine. The advancement of technologies introduced a remote switch for operation. The advancement is still continuing with voice recognition, hand gesture movement and mind reading models. Recently machine to machine communication was introduced to communicate their status between another machine or a system for implementing smart work on its own intelligence. The paper introduces a smart communication between vehicles to traffic signals and vehicle to vehicle using Li-Fi (Light Fidelity) technology for sharing necessary information to the nearby vehicle. The paper also describes the limitations and challenges in the Li-Fi technology for further communication improvement.

An Ontology-Based Information Extraction System for Organic Farming

Abstract: In the existing farming system, information is obtained manually, and most times, farmers act based on their discretion. Sometimes, farmers rely on information from experts and extension officers for decision making. In recent times, a lot of information systems are available with relevant information on organic farming practices; however, such information is scattered in different context, form, and media all over the internet, making their retrieval difficult. The use of ontology with the aid of a conceptual scheme makes the comprehensive and detailed formalization of any subject domain possible. This study is aimed at acquiring, storing, and providing organic farming-based information available to current and intending software developer who may wish to develop applications for farmers. It employs information extraction (IE) and ontology development techniques to develop an ontology-based information extraction (OBIE) system called ontology-based information extraction system for organic farming (OBIESOF). The knowledge base was built using protégé editor; Java was used for the implementation of the ontology knowledge base with the aid of the high-level application programming language for working web ontology language application program interface (OWL API). In contrast, HermiT was used to checking the consistencies of the ontology and for submitting queries in order to verify their validity. The queries were expressed in description logic (DL) query language. The authors tested the capability of the ontology to respond to user queries by posing instances of the competency questions from DL query interface. The answers generated by the ontology were promising and serve as positive pointers to its usefulness as a knowledge repository.