Public EEG-based brain–computer interface (BCI) datasets are expanding rapidly, yet differences in sensors, experimental protocols, task/event semantics, preprocessing, participant context, and evaluation limit reproducibility and cross-dataset...
The conditions in Mira variable atmospheres make them wonderful laboratories to study a variety of stellar physics such as molecule–grain formation, dust production, shock chemistry, stellar winds, mass loss, opacity-driven pulsation, and shock...
This publication presents a dataset consisting of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Additionally, a CSV file i...
Performance modeling using robotic tasks can be used to identify motor control impairments. Motor development in children and youth occurs non-linearly, with higher accuracy and decreased variability in performance as they reach adulthood. However, p...
Background/Objectives: Interpretable ECG rhythm classification remains challenging because of variability in recording conditions, patient characteristics, and annotation practices across datasets. Although deep learning models can achieve high class...
29 December 2021
Poor air quality has been a major urban environmental issue in large high-density cities all over the world, and particularly in Asia, where the multiscale complex of pollution dispersal creates a high-level spatial variability of exposure level. Inv...
The fidelity of gridded rainfall datasets is important for the characterization of rainfall features across the globe. This study investigates the climatology, interannual variability, and spatial-temporal variations of seasonal rainfall over Thailan...
The development of foundation models for brain MRI depends critically on the scale, diversity, and consistency of available data, yet systematic assessments of these factors remain scarce. In this study, we analyze 54 publicly accessible brain MRI da...
Discriminating marsh vegetation is critical for the rapid assessment and management of wetlands. The study area, Honghe National Nature Reserve (HNNR), a typical freshwater wetland, is located in Northeast China. This study optimized the parameters (...
The determination of evapotranspiration (ET) using ground-based meteorological data does not adequately capture the spatial patterns of mass and energy fluxes in mountainous areas. In this work, we evaluate the daily spatial distribution of ET over a...
Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, co...
Accurate precipitation data are essential for understanding hydrological processes and supporting environmental and water resource management in the Amazon, where observational networks remain sparse and spatially uneven. This study evaluates the per...
Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a...
Accurate measurement of forest growing stem volume (GSV) is important for forest resource management and ecosystem dynamics monitoring. Optical remote sensing imagery has great application prospects in forest GSV estimation on regional and global sca...
Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machin...
Convolutional neural networks (CNNs) have established themselves over time as a fundamental tool in the field of copy-move forgery detection due to their ability to effectively identify and analyze manipulated images. Unfortunately, they still repres...
Glucose forecasting serves as a backbone for several healthcare applications, including real-time insulin dosing in people with diabetes and physical activity optimization. This paper presents a study on the use of machine learning (ML) and deep lear...
K-means clustering is a fundamental tool in data mining, yet its scalability and efficacy decline when faced with massive datasets. In this work, we introduce BiModalClust, a novel clustering algorithm that leverages a bimodal optimization paradigm t...
Rapid quantitative characterization of protein-rich agricultural materials remains challenging due to the molecular and elemental complexity of plant-derived matrices. In this study, external-cavity quantum cascade laser-based mid-infrared (QCL-MIR)...
The ability to predict the rate of permeation of new compounds across biological membranes is of high importance for their success as drugs, as it determines their efficacy, pharmacokinetics, and safety profile. In vitro permeability assays using Cac...
This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather...
Demand for pure electric vehicles has been found to be increasing over the years. This has necessitated the development of a model that would serve as a predicting machine for manufacturing different types of pure electric vehicles. Direct Artificial...
While numerous studies have investigated the NDVI-LST relationship at local or regional scales, existing global analyses are outdated and fail to incorporate recent environmental changes driven by climate change and human activity. This study aims to...
Mass spectrometry-based metabolomics approaches generate high-dimensional datasets that enable comprehensive profiling of metabolites and other biomolecules. However, these datasets are influenced by multiple sources of variability, including both th...
The climate in Svalbard has been warming dramatically compared with the global average for the last few decades. Seasonal snow cover, which is sensitive to temperature and precipitation changes, is therefore expected to undergo both spatial and tempo...
Dynamic hedging has been adopted by many insurance companies to mitigate the financial risks associated with variable annuity guarantees. To simulate the performance of dynamic hedging for variable annuity products, insurance companies rely on nested...
Students dropping out of high school is a nationwide problem in the United States, plaguing communities and often greatly reducing the prospects of a quality life for those students who do not complete their high school education. The state of Louisi...
This study analyses the spatiotemporal variability of meteorological drought over Africa and its nine climate subregions from an ensemble of 19 multisource datasets (gauge-based, satellite-based and reanalysis) over the period 1983–2014. The st...
Human gait datasets play a central role in the development and evaluation of computer vision models. However, the current dataset landscape remains highly heterogeneous, with inconsistent reporting of acquisition conditions, user variability, and sen...
Multi-class classification in imbalanced datasets is a challenging problem. In these cases, common validation metrics (such as accuracy or recall) are often not suitable. In many of these problems, often real-world problems related to health, some cl...
Due to technological developments, wearable sensors for monitoring the behavior of farm animals have become cheaper, have a longer lifespan and are more accessible for small farms and researchers. In addition, advancements in deep machine learning me...
This data descriptor presents the HF radar and drifter datasets, along with the methods used to process and apply them in a previously published study on the validation of surface current measurements in a region characterized by highly variable coas...
The Bio-Radar system, useful for monitoring patients with infectious diseases and detecting driver drowsiness, has gained popularity in the literature. However, its efficiency across diverse populations considering physiological and body stature vari...
Various cloud cover products have been developed over the past few decades, but their uncertainties have not been sufficiently assessed, especially at a regional scale, which is vital for the application of satellite products to climate studies. In t...
This data descriptor presents δ-MedBioclim, a newly developed dataset for the Euro-Mediterranean region. This dataset applies the delta-change method by comparing the values of 25 General Circulation Models (GCMs) for the reference period (1981...
Despite several years passing since the COVID-19 pandemic was declared, challenges remain in understanding the factors that can predict the severity of COVID-19 disease and complications of SARS-CoV-2 infection. While many large-scale multi-omic data...
Bearings are the backbone of industrial machines that can shut down or damage the whole process when a fault occurs in them. Therefore, health diagnosis and fault identification in the bearings are essential to avoid a sudden shutdown. Vibration sign...
The date palm mite (DPM), Oligonychus afrasiaticus (McGregor), is a key pest of unripe date fruits. The detection of this mite depends largely on the visual observations of the webs it produces on the green fruits. One of the most important problems...
Detection of fatigue is extremely important in the development of different kinds of preventive systems (such as driver monitoring or operator monitoring for accident prevention). The presence of fatigue for this task should be determined with physio...
Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and condition the weak electrical signals produced by the heart. To evaluate sensor performance under realistic conditions, diverse ECG signals with embedde...
Accurate detection of apples in orchards under variable weather and illumination remains a key challenge for precision horticulture. This study presents a flexible framework for automated ensemble selection and optimization of convolutional neural ne...
Due to growing demands for quality, sustainability, and digitalization, data science and artificial intelligence are gaining importance across industries. The extensive product range in many sectors often poses considerable challenges. For example, m...
High Mountain Asia (25–40° N, 70–100° E) plays a critical role in sustaining water resources for nearly two billion people; however, the accurate estimation of precipitation remains challenging. Numerous gridded products have been...
Forecasting hydropower plant power production is a great challenge in the context of maintaining power system stability, reliability and efficiency, especially in an age with variable renewable energy sources when demand for electricity is steadily r...
12 August 2021
Construction and demolition waste (DW) generation information has been recognized as a tool for providing useful information for waste management. Recently, numerous researchers have actively utilized artificial intelligence technology to establish a...
Terrestrial biophysical variables play an essential role in quantifying the amount of energy budget, water cycle, and carbon sink over the Three-River Headwaters Region of China (TRHR). However, direct field observations are missing in this region, a...
Active remote sensing technology represented by multi-beam and lidar provides an important approach for the effective acquisition of underwater coral reef geomorphological information. A spatially continuous surface model of coral reef geomorphology...
More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Sa...
Focusing on West Africa, a region riddled with in situ data scarcity, we evaluate the summer monsoon monthly rainfall characteristics of five global reanalysis datasets: ERA5, ERA-Interim, JRA-55, MERRA2, and NCEP-R2. Their performance in reproducing...
This paper introduces RSoANU, a dataset of real multichannel room impulse responses (RIRs) obtained in a recording studio. Compared to the current publicly available datasets, RSoANU distinguishes itself by featuring RIRs captured using both a 32-cha...
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