RESUMO
Predicting case outcomes is useful for legal professionals to understand case law, file a lawsuit, raise a defense, or lodge appeals, for instance. However, it is very hard to predict legal decisions since this requires extracting valuable information from myriads of cases and other documents. Moreover, legal system complexity along with a huge volume of litigation make this problem even harder. This paper introduces an approach to predicting Brazilian court decisions, including whether they will be unanimous. Our methodology uses various machine learning algorithms, including classifiers and state-of-the-art Deep Learning models. We developed a working prototype whose F1-score performance is ~80.2% by using 4,043 cases from a Brazilian court. To our knowledge, this is the first study to present methods for predicting Brazilian court decision outcomes.
RESUMO
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.
RESUMO
Computational Intelligence Re-meets Medical Image Processing A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration BACKGROUND: Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods. OBJECTIVES: Exploring the potential of dimensionality reduction combined with ML methods for diagnosis of DLDs; improving the classification accuracy over state-of-the-art methods. METHODS: A data set composed of 3252 regions of interest (ROIs) was used, from which 28 features were extracted per ROI. We used Principal Component Analysis, Linear Discriminant Analysis, and Stepwise Selection - Forward, Backward, and Forward-Backward to reduce feature dimensionality. The feature subsets obtained were used as input to the following ML methods: Support Vector Machine, Gaussian Mixture Model, k-Nearest Neighbor, and Deep Feedforward Neural Network. We also applied a Deep Convolutional Neural Network directly to the ROIs. RESULTS: We achieved the maximum reduction from 28 to 5 dimensions using LDA. The best classification results were obtained by DFNN, with 99.60% of overall accuracy. CONCLUSIONS: This work contributes to the analysis and selection of features that can efficiently characterize the DLDs studied.