This research explored the use of deep learning to classify impact events on wind turbine blades using spectrogram images. Audio data were recorded through indoor and outdoor simulations using smartphones. The data preparation process involved converting stereo recordings to mono, applying downcasting …

Introduction Recording sap flow in plants is essential to understanding water usage, especially for herbaceous species like tomatoes. While plant physiology research has progressed, there remains a gap in applying sap flow sensor data to these species. In this study, the predictive capabilities of Recurrent …

Agriculture and horticulture are essential for ensuring safe food to the growing global population, but they also contribute significantly to climate change and biodiversity loss due to the extensive use of chemicals. Integrated pest management is currently employed to monitor and control pest populations, …

Fluorescent Penetrant Inspection (FPI) is a widely used inspection technique in the aerospace industry. Because of the aging aerospace sector, and because of the safety-criticality of the inspection, aerospace companies aim to automate (parts of this) inspection process to support inspectors. This paper …

Purpose This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations …

In recent years, drones have increasingly supported First Responders (FRs) in monitoring incidents and providing additional information. However, analysing drone footage is time-intensive and cognitively demanding. In this research, we investigate the use of AI models for the detection of humans in drone …

This study proposes a novel loss function for neural network models that explores the topological structure of stock realized volatility (RV) data by adding Wasserstein Distance (WD). The study shows that the proposed loss statistically significantly improves the forecast accuracy of neural network models …

This paper investigates the application of neural basis expansion analysis with exogenous variables (NBEATSx) in the prediction of daily stock realized volatility for various time steps. It compares NBEATSx’s forecasting accuracy and robustness with several commonly used models, namely Long-Short Term …

Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning …

Continuous brain function monitoring in a pediatric intensive care unit is often done using electroencephalography (EEG) for the detection of epileptic seizures in comatose patients. Monitoring an EEG is a time-intensive task for medical professionals, especially when the seizures manifest in a subtle …