102 Results Found

Remote-sensing images of high spatial resolution (HSR) are valuable sources of fine-grained spatial information for various applications, such as urban surveys and governance. There is continuing research on positional errors in remote-sensing images...

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML...

This paper tests an automated methodology for generating training data from OpenStreetMap (OSM) to classify Sentinel-2 imagery into Land Use/Land Cover (LULC) classes. Different sets of training data were generated and used as inputs for the image cl...

Heart rate (HR) monitoring is a practical method for assessing internal load (IL). However, it remains unclear for which age group HR would be an appropriate predictor of IL considering the relationship with external load (EL). Thus, this study aims...

Energy management strategy (EMS) is critical for improving the economy of hybrid powertrains and the durability of energy sources. In this paper, a novel EMS based on a twin delayed deep deterministic policy gradient algorithm (TD3) is proposed for a...

In mobile multi-agent systems (MASs), achieving effective leader–follower coordination under unknown dynamics poses significant challenges. This study proposes a two-stage cooperative strategy that integrates Gaussian Processes (GPs) for modeli...

It was shown that deep reinforcement learning (DRL) has the potential to solve portfolio management problems in recent years. The Twin Delayed Deep Deterministic policy gradient algorithm (TD3) is an actor-critic method, a typical DRL method in conti...

The present study evaluates the application of different artificial intelligence methods associated with remote sensing data processing for assessing water quality parameters, with a focus on fish cage farming in the reservoirs. Three AI methods were...

Estimating groundwater quality parameters through conventional methods is time-consuming through laboratory measurements for megacities. There is a need to develop models that can help decision-makers make policies for sustainable groundwater reserve...

Proteomics monitoring of an elite adventure athlete (age 33 years) was conducted over a 28-week period that culminated in the successful, solo, unassisted, and unsupported two month trek across the Antarctica (1500 km). Training distress was monitore...

The present study links monitoring and simulation models to predict water quality distribution in lakes using an optimized neural network and remote sensing data processing. Two data driven models were developed. First, a monitoring model was establi...

In this research, we address the problem of evaluating physical stability (PS) to close tailings dams (TD) from medium-sized Chilean mining using artificial intelligence (AI) algorithms. The PS can be analyzed through the study of critical variables...

The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short....

Little is known about how recreational triathletes prepare for an Olympic distance event. The aim of this study was to identify the training characteristics of recreational-level triathletes within the competition period and assess how their preparat...

The temporal dominance of sensations (TDS) and temporal liking (TL) methods offer complementary insights into the evolution of sensory and hedonic responses during food consumption. This study investigates the feasibility of predicting TL curves for...

This paper combines deep reinforcement learning (DRL) with meta-learning and proposes a novel approach, named meta twin delayed deep deterministic policy gradient (Meta-TD3), to realize the control of unmanned aerial vehicle (UAV), allowing a UAV to...

Water pollution is an increasing global issue that societies are facing and is threating human health, ecosystem functions and agriculture production. The distinguished features of artificial intelligence (AI) based modeling can deliver a deep insigh...

This study proposes a robust methodology for vibration suppression and trajectory tracking in rotary flexible-link systems by leveraging guided reinforcement learning (GRL). The approach integrates the twin delayed deep deterministic policy gradient...

Modern review websites, namely Yelp and Amazon, permit the users to post online reviews for numerous businesses, services and products. Currently, online reviewing is an imperative task in the manipulation of shopping decisions produced by customers....

Our research project specifically focuses on evaluating groundwater quality in six West Texas counties. We aim to determine whether environmental changes have any impact on the levels of Total Dissolved Solids (TDS) in the water supplied to the publi...

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification; however, the EVCS has various vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, commun...

The temporal dominance of sensations (TDS) method captures assessors’ real-time sensory experiences during food tasting, while the temporal liking (TL) method evaluates dynamic changes in food preferences or perceived deliciousness. These senso...

This paper proposes a three-dimensional (3D) deep reinforcement learning-based integrated guidance and control (DRLIGC) method, which is restricted by the narrow field-of-view (FOV) constraint of the strap-down seeker. By leveraging the data-driven n...

Text-dependent speaker verification (TD-SV), which verifies speaker identity using predefined phrases, has gained attention as a reliable contactless biometric authentication method for smart devices, internet of things (IoT), and real-time applicati...

Timely screening and surveillance of children for developmental delay and social–emotional learning difficulties are essential in Low- and Middle-Income Countries like India. Screening measures like the Parents’ Evaluation of Developmenta...

Total dissolved solids (TDS) and electrical conductivity (EC) are important parameters in determining water quality for drinking and agricultural water, since they are directly associated to the concentration of salt in water and, hence, high values...

Background: Understanding the balance between intensity and volume during training and competition is crucial for optimizing players’ performance and recovery in professional soccer. While worst-case scenarios (WCSs) are commonly used to assess...

Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and incon...

Predictions of total dissolved solids (TDS) in water bodies including rivers and lakes are challenging but essential for the effective management of water resources in agricultural and drinking water sectors. This study developed a hybrid model combi...

This study proposes a convolutional neural network (CNN) model using action potential (AP) shapes as input for proarrhythmic risk assessment, considering the hypothesis that machine-learning features automatically extracted from AP shapes contain mor...

Purpose: To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors. Methods: A GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius...

The temporal dominance of sensations (TDS) method has received particular attention in the food science industry due to its ability to capture the time–series evolution of multiple sensations during food tasting. Similarly, the temporal liking...

Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) chil...

Large-sided games (LSG) are commonly used in the training contexts for providing either technical/tactical or locomotor/physiological stimuli. Despite natural similarities with the official match, the locomotor profile seems to be different, which mu...

Deep learning networks might require re-training for different datasets, consuming significant manual labeling and training time. Transfer learning uses little new data and training time to enable pre-trained network segmentation in relevant scenario...

This study explores how age influences the relationship between physical performance (PP) and technical–tactical parameters (TPs) in youth soccer, analyzing 80 matches across four age groups: U15, U17, U19, and NB1 (adults). Team-level data wer...

The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison o...

Match running speed demands vary across competitive levels of football, influenced by player position, tactical considerations, and Head Coach changes. In England, the level directly below professional football, Non-League Football (NLF), comprises f...

Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to...

Perceived exertion (RPE, RPEdur) and wellness scores (Hooper) are common methods to assess the training load and readiness in football. However, in professional football, there is a lack of data concerning the application of these tools in microcycle...

Total dissolved solids (TDS) concentration determination in water bodies is sophisticated, time-consuming, and involves expensive field sampling and laboratory processes. TDS concentration has, however, been linked to electrical conductivity (EC) and...

The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and s...

Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual...

This paper proposes a deep reinforcement learning-based predictive control scheme to address cushion pressure prediction and stabilization in hovercraft systems subject to modeling complexity, dynamic instability, and system delay. Notably, this work...

To address the challenges of accurate indoor positioning in complex environments, this paper proposes a two-stage indoor positioning method, ResT-IMU, which integrates the ResNet and Transformer architectures. The method initially processes the IMU d...

Deep Reinforcement Learning (DRL) has been an active research area in view of its capability in solving large-scale control problems. Until presently, many algorithms have been developed, such as Deep Deterministic Policy Gradient (DDPG), Twin-Delaye...

Urban and peri-urban lakes are increasingly threatened by water quality degradation due to rising anthropogenic pressures and environmental variability. This study proposes an integrated framework that combines multi-source data and machine learning...

The chain-of-thought (CoT) approach in large language models (LLMs) has markedly enhanced their performance on complex tasks; however, effectively distilling this capability into LLMs with smaller parameter scales remains a challenge. Studies have fo...

The stratospheric airship is a vital platform in near-space applications, and achieving autonomous transfer has become a key research focus to meet the demands of diverse mission scenarios. The core challenge lies in planning feasible and efficient p...

Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors af...