Browsing by Author "Marcelino, Vitor Fernandes"
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- Data analysis and processing from remote sensors for detection of forest fires riskPublication . Marcelino, Vitor Fernandes; Pereira, Ana I.; Lima, José; Fagundes, Luis PauloThis work focuses on developing fire risk detection and prevention algorithms using data collected by sensors in the forest. The study involved a State of the Art review and a Theoretical Foundation, followed by Data Characterization and Data Analysis, which were divided into several sub-sections. The study developed regression models for different types of data and found that the random forest regression model was the best performing for transition times. The study compared different regression models, finding that the Support Vector Regression (SVR) model performed worse than the Gradient Boosting Regression (GBR) and Random Forest Regression (RFR) models. The study concluded that using algorithms to identify periods of the day was a useful strategy for avoiding false alerts and that training the models for each individual module was the best strategy. Furthermore, the RFR and GBR regression models were found to be the most effective for the data available in this study. However, improvements are necessary to reduce false positives and facilitate abnormality detection. Overall, this work provides insight into the most effective methods for analyzing and processing data collected by sensors in the forest for fire risk detection and prevention, with the potential to create alerts for those involved in fighting forest fires.
- Effect of Weather Conditions and Transactions Records on Work Accidents in the Retail Sector – A Case StudyPublication . Borges, Lucas D.; Sena, Inês; Marcelino, Vitor Fernandes; Silva, Felipe G.; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara B.; Lima, José; Pereira, Ana I.Weather change plays an important role in work-related accidents, it impairs people’s cognitive abilities, increasing the risk of injuries and accidents. Furthermore, weather conditions can cause an increase or decrease in daily sales in the retail sector by influencing individual behaviors. The increase in transactions, in turn, leads employees to fatigue and overload, which can also increase the risk of injuries and accidents. This work aims to conduct a case study in a company in the retail sector to verify whether the transactions records in stores and the weather conditions of each district in mainland Portugal impact the occurrence of work accidents, as well as to perform predictive analysis of the occurrence or non-occurrence of work accidents in each district using these data and comparing different machine learning techniques. The correlation analysis of the occurrence or non-occurrence of work accidents with weather conditions and some transactions pointed out the nonexistence of correlation between the data. Evaluating the precision and the confusion matrix of the predictive models, the study indicates a predisposition of the models to predict the non-occurrence of work accidents to the detriment of the ability to predict the occurrence of work accidents.