1,721,041 research outputs found
An ontology-driven perspective on the emotional human reactions to social events
No full textSocial media has become a fulcrum for sharing information on everyday-life events: people, companies, and organisations express opinions about new products, political and social situations, football matches, and concerts. The recognition of feelings and reactions to events from social networks requires dealing with great amounts of data streams, especially for tweets, to investigate the main sentiments and opinions that justify some reactions. This paper presents an emotion-based classification model to extract feelings from tweets related to an event or a trend, described by a hashtag, and build an emotional concept ontology to study human reactions to events in a context. From the tweet analysis, terms expressing a feeling are selected to build a topological space of emotion-based concepts. The extracted concepts serve to train a multi-class SVM classifier that is used to perform soft classification aimed at identifying the emotional reactions towards events. Then, an ontology allows arranging classification results, enriched with additional DBpedia concepts. SPARQL queries on the final knowledge base provide specific insights to explain people's reactions towards events. Practical case studies and test results demonstrate the applicability and potential of the approach
Multi-grained wildfire damage estimation from satellite vegetative scenario by fuzzy decision tree
No full textAmong climate effects, fires are reported to be the most catastrophic events both economically and environmentally, in fact, recent statistics report about 340,000 hectares (ha) burnt in countries of the European Community during 2020, corresponding to an area 30% larger than Luxembourg. In worst cases, fire-affected areas will be permanently damaged. Then, there is a need for solutions to help institutions and researchers to keep the environment under monitoring, assess the damage severity and help planning burned area recovery. To this purpose, this paper presents a smart monitoring framework that bridges spectral image clustering with soft computing techniques to describe the effective fire damages in the monitored area. The approach collects images from Sentinel-2 satellite, assesses Spectral Indices (SIs) from them, then by clustering, divides the area into different sub-regions according to their vegetative features. Then these sub-regions are parsed by a fuzzy decision tree, able to interpret the damage of fire severity in each sub-region. Experiences show that by dividing the area into many sub-regions, fire damage can be detected at different levels of granularity, providing a detailed map of the most damaged sub-areas to plan, for example, ad-hoc recovery interventions
Incremental Knowledge Extraction from IoT-Based System for Anomaly Detection in Vegetation Crops
No full textPrecision agriculture systems collect spectral images from satellites, from which vegetation indices (VIs) can be assessed to monitor vegetation and soil condition. It requires a near-daily data acquisition to perform robust crop monitoring and data analysis. Satellites provide a periodic data acquisition that need a further data integration using multiple satellite sources along with camera-equipped drones to achieve an accurate data collection on a selected area. Moreover, VIs are not enough for a proper vegetation evaluation of the monitored areas due to differences among cultivars, the phenological season in which the vegetation is evaluated, the latitude of the areas, etc. This article introduces a system model to detect anomalies regarding the vegetation and soil conditions according to the area phenology and the historical vegetation trends. The system collects spectral images of the regions of interest (ROIs) from satellites and drones, harmonized to calculate VIs and feeds a dataset of near-daily high-resolution integrated images. The harmonic analysis allows phenological data extraction about the ROIs, hence the territorial observation model (TOM) has been extended to represent phenological stages and build knowledge on the ROIs and their phenology that is stored on a triple store. The system selects the VI values, calculated during the learned growing seasons of the ROIs, and classifies them to detect vegetation anomalies affecting those ROIs. The collected knowledge can be used by end-users (e.g., agronomists, experts, etc.) to analyze the anomalies correlated to historical results and vegetation trends
Towards a layered agent-modeling of IoT devices to precision agriculture
No full textPrecision agriculture employs IoT devices to smartly monitoring plant vegetation and support food production. Precision agriculture is highly required to improve product quality and better suit the requirements of the market. Among the IoT devices, Unmanned Aerial Vehicles (UAVs), can be equipped with many sensors that allow precise assessments of plant stress by flying over the plots. Notwithstanding the great benefits introduced, IoT devices may suffer from some issues. Many devices provide data in different formats on the same task, therefore they need solutions to integrate data and support a more thorough crop monitoring. This paper introduces a multitier architecture to deal with IoT-based intelligent monitoring, as well as an implementation of the architecture through multiagent modeling of the IoT devices for precision agriculture. The introduced model allows data acquisition from various sources (i.e., IoT devices), an ontology-based integration of data provided by the devices and a knowledge integration process to deal with domain-specific applications
Volatile components of Centaurea eryngioides Lam. and Centaurea iberica Trev. var. hermonis Boiss. Lam., two Asteraceae growing wild in Lebanon.
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A fuzzy tree-based framework for vegetation state monitoring
No full textThe climate change emergency strongly affects vegetation growth in terrestrial ecosystems: large scale vegetationclimate interactions reveal an increased frequency of extreme weather and climate events, with significant impacts on ecosystems at different spatiotemporal scales. Vegetation monitoring is a critical element to assess the changes and treats to the environment also aimed at sustainable conservation of wildlife. A framework is proposed to aggregate vegetation indices described by fuzzy sets to assess vegetation health. Several fuzzy rules have been defined grouped by feature estimation (cover, vigor, water stress, etc.) and then triggered according to a decision tree schema to obtain a robust interpretation of vegetation status. The control and flow of the activation rules is driven and optimized by an agent-based modeling. Case studies highlight the applicability of the proposed framework
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