DSpace Comunidad:
http://hdl.handle.net/10045/97822
2024-03-28T14:25:56ZImproving Landslides Prediction: Meteorological Data Preprocessing Based on Supervised and Unsupervised Learning
http://hdl.handle.net/10045/141435
Título: Improving Landslides Prediction: Meteorological Data Preprocessing Based on Supervised and Unsupervised Learning
Autor/es: Guerrero-Rodriguez, Byron; Salvador-Meneses, Jaime; Garcia-Rodriguez, Jose; Mejia-Escobar, Christian
Resumen: The hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant factors in landslides is intensive rainfall. The present work proposes the use of data generated by weather stations to predict landslides. We give special treatment to precipitation information as the most influential factor and whose data are accumulated in time windows (3, 5, 7, 10, 15, 20, and 30 days) looking for the persistence of meteorological conditions. To optimize the dataset composed of geological, geomorphological, and climatological data, a feature selection process is applied to the meteorological variables. We use filter-based feature ranking and Self-Organizing Map (SOM) with Clustering as supervised and unsupervised machine learning techniques, respectively. This contribution was successfully verified by experimenting with different classification models, improving the test accuracy of the prediction, and obtaining 99.29% for Multilayer Perceptron, 96.80% for Random Forest, and 88.79% for Support Vector Machine. To validate the proposal, a geographical area sensitive to this phenomenon was selected, which is monitored by several meteorological stations. Practical use is a valuable tool for risk management decision making, can help save lives and reduce economic losses.2023-11-07T00:00:00ZCuentosIE: can a chatbot about “tales with a message” help to teach emotional intelligence?
http://hdl.handle.net/10045/141209
Título: CuentosIE: can a chatbot about “tales with a message” help to teach emotional intelligence?
Autor/es: Ferrández, Antonio; Lavigne Cerván, Rocio; Peral, Jesús; Navarro Soria, Ignasi; Lloret, Ángel; Gil, David; Rocamora-Rodríguez, María C.
Resumen: In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE. The use of “tales with a message” is justified by their simplicity and easy understanding, thanks to their moral or associated metaphors. The main contributions of CuentosIE are the selection, collection, and classification of a set of highly specialized tales, as well as the provision of tools (searching, reading comprehension, chatting, recommending, and classifying) that are useful for both educating users about emotions and monitoring their emotional development. The preliminary evaluation of the tool has obtained encouraging results, which provides an affirmative answer to the question posed in the title of the article.2024-02-29T00:00:00ZDesign and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios
http://hdl.handle.net/10045/141049
Título: Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios
Autor/es: Gama García, Ángel Manuel; Alcaraz Calero, José M.; Mora, Higinio; Wang, Qi
Resumen: In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.2024-02-15T00:00:00ZSimilarity-based data transmission reduction solution for edge-cloud collaborative AI
http://hdl.handle.net/10045/140966
Título: Similarity-based data transmission reduction solution for edge-cloud collaborative AI
Autor/es: Elouali, Aya; Mora, Higinio; Mora Gimeno, Francisco José
Resumen: Edge-cloud collaborative processing for IoT data is a relatively new approach that tries to solve processing and network issues in IoT systems. It consists of splitting the processing done by a Neural Network model into edge part and cloud part in order to solve network, privacy and load issues. However, it also has it shortcomings such as the big size of the edge part's output that has to be transmitted to the cloud. In this paper, we are proposing a data transmission reduction method for edge-cloud collaborative solutions that is based on data similarities in stationary objects. The performed experiments proved that we were able to reduce 62% of the data sent.2022-12-01T00:00:00Z