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.

The lack of interpretability in AI-based intrusion detection systems poses a critical barrier to their adoption in forensic cybersecurity, which demands high levels of reliability and verifiable evidence. To address this challenge, the integration of explainable artificial intelligence (XAI) into forensic cybersecurity offers a powerful approach to enhancing transparency, trust, and legal defensibility in network intrusion detection. This study presents a comparative analysis of SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) applied to Extreme Gradient Boosting (XGBoost) and Attentive Interpretable Tabular Learning (TabNet), using the UNSW-NB15 dataset. XGBoost achieved 97.8% validation accuracy and outperformed TabNet in explanation stability and global coherence. In addition to classification performance, we evaluate the fidelity, consistency, and forensic relevance of the explanations. The results confirm the complementary strengths of SHAP and LIME, supporting their combined use in building transparent, auditable, and trustworthy AI systems in digital forensic applications.

Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately predict wind generation. The Wind Power Prediction Tool (WPPT) has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs) model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed, but using LSTM blocks as units in the hidden layer. The training process of this network has two key stages: (i) the hidden layer is trained with a descending gradient method online using one epoch; (ii) the output layer is adjusted with a regularized regression. In particular, the case is proposed where Step (i) is used as a target for the input signal, in order to extract characteristics automatically as the autoencoder approach; and in the second stage (ii), a quantile regression is used in order to obtain a robust estimate of the expected target. The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used.

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.

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.

Malicious Software, commonly known as Malware, represents a persistent threat to cybersecurity, targeting the confidentiality, integrity, and availability of information systems. The digital era, marked by the proliferation of connected devices, cloud services, and the advancement of machine learning, has brought numerous benefits; however, it has also exacerbated exposure to cyber threats, affecting both individuals and corporations. This systematic review, which follows the PRISMA 2020 framework, aims to analyze current trends and new methods for malware detection and classification. The review was conducted using data from Web of Science and Scopus, covering publications from 2020 and 2024, with over 47 key studies selected for in-depth analysis based on relevance, empirical results and citation metrics. These studies cover a variety of detection techniques, including machine learning, deep learning and hybrid models, with a focus on feature extraction, malware behavior analysis and the application of advanced algorithms to improve detection accuracy. The results highlight important advances, such as the improved performance of ensemble learning and deep learning models in detecting sophisticated threats. Finally, this study identifies the main challenges and outlines opportunities of future research to improve malware detection and classification frameworks.