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- External climate data extraction using the forward feature selection method in the context of occupational safetyPublication . Silva, Felipe G.; Sena, Inês; Lima, Laíres; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara B.; Lima, José; Pereira, Ana I.Global climate changes and the increase in average temperatures are some of the major contemporary problems that have not been considered in the context of external factors to increase accident risk. Studies that include climate information as a safety parameter in machine learning models designed to predict the occurrence of accidents are not usual. This study aims to create a dataset with the most relevant climatic elements, to get better predictions. The results will be applied in future studies to correlate with the accident history in a retail sector company to understand its impact on accident risk. The information was collected from the National Oceanic and Atmospheric Administration (NOAA) climate database and computed by a wrapper method to ensure the selection of the most features. The main goal is to retain all the features in the dataset without causing significant negative impacts on the prediction score.
- Analyzing the mathE platform through clustering algorithmsPublication . Azevedo, Beatriz Flamia; Amoura, Yahia; Rocha, Ana Maria A.C.; Fernandes, Florbela P.; Pacheco, Maria F.; Pereira, Ana I.University lecturers have been encouraged to adopt innovative methodologies and teaching tools in order to implement an interactive and appealing educational environment. The MathE platform was created with the main goal of providing students and teachers with a new perspective on mathematical teaching and learning in a dynamic and appealing way, relying on digital interactive technologies that enable customized study. The MathE platform has been online since 2019, having since been used by many students and professors around the world. However, the necessity for some improvements on the platform has been identified, in order to make it more interactive and able to meet the needs of students in a customized way. Based on previous studies, it is known that one of the urgent needs is the reorganization of the available resources into more than two levels (basic and advanced), as it currently is. Thus, this paper investigates, through the application of two clustering methodologies, the optimal number of levels of difficulty to reorganize the resources in the MathE platform. Hierarchical Clustering and three Bio-inspired Automatic Clustering Algorithms were applied to the database, which is composed of questions answered by the students on the platform. The results of both methodologies point out six as the optimal number of levels of difficulty to group the resources offered by the platform.
- Occupational behaviour study in the retail sectorPublication . Sena, Inês; Fernandes, Florbela P.; Pacheco, Maria F.; Pires, Abel A.C.; Maia, Jaime P.; Pereira, Ana I.The health, safety, and well-being of employees, service providers, and customers are important priorities for retail companies. Based on this principle, an intelligent system that contributes to the reduction of accidents at work will be developed, monitoring risk control, preventing work-related illnesses, promoting a culture of zero accidents, and seeking to ensure the health of employees, customers, and stakeholders. In order to achieve such goals, it is necessary to determine the local and global variables (internal and external) that feed the system. This study comprises the first strategy applied to collect the local variables involved in the problem. To obtain this, a data analysis study in a retail store was performed. Data analysis procedures were performed namely clustering analysis with algorithm k-means, correlation procedures, like Pearson coefficient and matrix of correlation, and relationship analysis with parallel coordinate graphs. From the preliminary results, it is possible to indicate a set of local variables that have influence in the occupational behavior and accidents at work.