Arrhythmia is an irregularity in the rhythm of the heartbeat, and it is the primary method for detecting cardiac abnormalities. The electrocardiogram (ECG) identifies arrhythmias and is one of the methods used to diagnose cardiac issues. Traditional arrhythmia detection methods are time-consuming, error-prone, and often subjective, making it difficult for doctors to discern between distinct patterns of arrhythmia. To understand ECG signals, this study presents a multi-class classifier and an autoencoder with long short-term memory (LSTM) network layers for extracting signal properties on a dataset from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital (MIT-BIH). The suggested model had an accuracy rate of 98.57% on the arrhythmia dataset and 97.59% on the supraventricular dataset. In contrast to other deep learning models, the proposed model eliminates the problem of the gradient disappearing in classification tasks.

Energy planning in systems heavily influenced by hydroelectric power is based on assessing the availability of water in the future. In Brazil, based on the soil moisture active passive, the National Electricity System Operator defines electricity dispatch concerning a stochastic optimization problem. Currently, machine learning models are an alternative for improving forecasts, and could be a promising solution for predicting reservoir levels at hydroelectric dams. In this paper, neural hierarchical interpolation for time series (NHITS) is applied to improve forecasts and thus help decision-making in the management of electric power systems. The NHITS model achieved a root mean square error of 4.64×10⁻⁴ for a 1-hour forecast horizon, and 1.03×10⁻³ for a 10-hour forecast horizon, being superior to multilayer perceptron (MLP) neural network, long short-term memory (LSTM), convolutional neural network with long shortterm memory (CNN-LSTM), recurrent neural network (RNN), Dilated RNN, temporal convolutional neural (TCN), neural basis expansion analysis for interpretable time series forecasting (N-BEATS), and deep nonparametric time series forecaster (DeepNPTS) deep learning approaches.

The use of technology in teaching has grown significantly in recent years. Digital media and educational software are commonly used to complement traditional teaching methods. Online learning platforms such as Moodle and Blackboard are also widely used to provide students with access to course materials, assignments and other resources. Additionally, virtual and augmented reality technologies are being used to create immersive learning experiences across subjects such as science, history and geography. Technology has become an essential tool in teaching, providing numerous advantages, from greater engagement, access to resources and personalized learning. However, there are also challenges and limitations that need to be addressed, such as infrastructure and resource limitations, teacher training and possible distractions. As such, striking a balance between the use of technology and traditional teaching methods is essential to provide students with a complete and effective learning experience. The use of digital devices such as laptops, tablets and smartphones has become increasingly popular in the classroom. These devices can be used to access educational resources such as e-books and online libraries, allowing students to learn at their own pace and style. Additionally, educational software and apps can be used to provide interactive learning experiences that cater to different learning styles. In addition, multimedia resources such as videos, images and audio recordings can also be used to engage students and enhance their learning experiences. From this perspective, it is necessary for school management to encourage teachers to update themselves in handling digital technologies in the classroom.

Engine fault diagnosis is a critical task in automotive aftermarket management. Developing appropriate fault-labeled datasets can be challenging due to nonlinearity variations and divergence in feature distribution among different engine kinds or operating scenarios. To solve this task, this study experimentally measures audio emission signals from compression ignition engines in different vehicles, simulating injector failures, intake hose failures, and absence of failures. Based on these faults, a hybrid approach is applied to classify different conditions that help the planning and decision-making of the automobile industry. The proposed hybrid approach combines the wavelet packet transform (WPT), Markov blanket feature selection, random convolutional kernel transform (ROCKET), tree-structured Parzen estimator (TPE) for hyperparameters tuning, and ten machine learning (ML) classifiers, such as ridge regression, quadratic discriminant analysis (QDA), naive Bayes, k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extra trees (ET), gradient boosting machine (GBM), and LightGBM. The audio data are broken down into sub-time series with various frequencies and resolutions using the WPT. These data are subsequently utilized as input for obtaining an informative feature subset using a Markov blanket-based selection method. This feature subset is then fed into the ROCKET method, which is paired with ML classifiers, and tuned using Optuna using the TPE approach. The generalization performance applying the proposed hybrid approach outperforms other standard ML classifiers.