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Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.

Abstract. The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers's ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user's performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

Currently, one of the main challenges for information systems in healthcare is focused on support for health professionals regarding disease classifications. This work presents an innovative method for a recommendation system for the diagnosis of breast cancer using patient medical histories. In this proposal, techniques of natural language processing (NLP) were implemented on real datasets: one comprised 160, 560 medical histories of anonymous patients from a hospital in Chile for the following categories: breast cancer, cysts and nodules, other cancer, breast cancer surgeries and other diagnoses; and the other dataset was obtained from the MIMIC III dataset. With the application of word-embedding techniques, such as word2vec's skip-gram and BERT, and machine learning techniques, a recommendation system as a tool to support the physician's decision-making was implemented. The obtained results demonstrate that using word embeddings can define a good-quality recommendation system. The results of 20 experiments with 5-fold cross-validation for anamnesis written in Spanish yielded an F1 of 0.980 ± 0.0014 on the classification of 'cancer' versus 'not cancer' and 0.986 ± 0.0014 for 'breast cancer' versus 'other cancer'. Similar results were obtained with the MIMIC III dataset.

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