Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction

Insulators installed outdoors are vulnerable to the accumulation of contaminants on their surface, which raise their conductivity and increase leakage current until a flashover occurs. To improve the reliability of the electrical power system, it is possible to evaluate the development of the fault in relation to the increase in leakage current and thus predict a shutdown might occur. This paper proposes the use of empirical wavelet transform (EWT) to reduce the influence of non-representative variations and combines the attention mechanism with long short-term memory (LSTM) recurrent network for prediction. The Optuna framework has been applied for hyperparameter optimization, resulting in a method called Optimized EWT-Seq2Seq-LSTM with Attention. The proposed model had a 10.17% lower mean square error (MSE) than the standard LSTM and a 5.36% lower MSE than the model without optimization, showing that the attention mechanism and hyperparameter optimization is a promising strategy

Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico

The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Optuna using tree-structured Parzen estimator, optimizes the structure of the ensembles through a voter by combining several ensemble frameworks; thus an optimized hybrid ensemble learning method is proposed. The results show that the proposed method has a higher performance than the state-of-the-art ensemble learning methods, with a mean squared error of 3.37E−9 in the testing phase

Complex graph neural networks for medication interaction verification

This paper presents the development and application of graph neural networks to verify drug interactions, consisting of drug-protein networks. For this, the DrugBank databases were used, creating four complex networks of interactions: target proteins, transport proteins, carrier proteins, and enzymes. The Louvain and Girvan-Newman community detection algorithms were used to establish communities and validate the interactions between them. Positive results were obtained when checking the interactions of two sets of drugs for disease treatments: diabetes and anxiety; diabetes and antibiotics. There were found 371 interactions by the Girvan-Newman algorithm and 58 interactions via Louvain

Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19

info:eu-repo/semantics/acceptedVersio

The mobility of professors in performing distance education activities

The article investigates the strategies used by Distance Education (DE) tutors to mobilize students in carrying out the activities available in the Virtual Learning Environment. To reflect on the mobility of tutors, learning theories outlined for Distance Education were revisited. The methodology consisted of a study applied at a university in southern Brazil. As a data collection instrument, a questionnaire was developed and applied to a group of tutor professors who work in DE. The testimonies obtained were analyzed to show the strategies used to mobilize students in the perspective of meaningful learning. The analysis showed that the terms mobilization and motivation are used interchangeably; the dimensions of meaningful learning (active, authentic, cooperation, constructive and intentional) are used to mobilize students, but not all dimensions have been captured in digital reports. This can be indicative of prioritizing one dimension over another. It was concluded that further investigations should be carried out to demystify the tutor\u27s strategies regarding learning theories