Billy Peralta

Billy Peralta

PUBLICACIONES

Publisher: Environmental and Ecological Statistics  Link>

ABSTRACT

Chile is one of the most seismic countries in the world especially due to the subduction of the Nazca plate under the South America plate along the Chilean cost. Normally, the spatial distribution of seismic events tends to form spatial and temporal clusters around the main event including both precursor and aftershock events. However, it is very difficult to identify whether an event is a precursor, a main event or an aftershock. In the literature, only some large earthquakes are well described but it does not exist an automatic method to classify them. In this work, we propose a new density based clustering method, called ST-DBSCAN-EV (Space-time DBSCAN with Epsilon Variable), which allows the Epsilon parameter (the radius) to vary depending on the density of the points. The results of the ST-DBSCAN-EV are validated on three important earthquakes with magnitude greater than 8.0 Mw occurred in Chile in the last 20 years, by carrying out a series of experiments considering different combinations of parameters. A comparison with some traditional clustering techniques such as the DBSCAN, ST-DBSCAN, and the K-means has been implemented for assessing the performance of the proposed method. Almost in all cases ST-DBSCAN-EV outperformed traditional ones by providing an F1-Score metric higher than 0.8. Finally, the results of classification are compared with a declustering method.

Publisher: 2023 IEEE World Conference on Applied Intelligence and Computing (AIC) Link>

ABSTRACT

Marketing is essential for the success of any company today, both to project itself abroad and to achieve its business objectives. The personalized approach to marketing plays a crucial role, enabling more effective interaction with potential customers and reducing errors in promotional campaigns. One way to improve personalized marketing is by using artificial intelligence to predict the effectiveness of campaigns in converting users to customers. In this study, the use of Siamese neural networks for elevation modelling in a Chilean online retail company is proposed. The results obtained using this model are presented, as well as the classical elevation modelling techniques. These results show that the Siamese neural network model achieves an increase of more than 30% compared to the area under the Qini curve, while also being competitive in other metrics. As future work, it is planned to obtain more real data to carry out a better validation of this proposal as well as to compare the model with other proposed neural networks.

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