Traditional oilfields face challenges such as high energy consumption, imprecise control, and lax management in mid-to-late development stages, leading to increased costs and reduced efficiency. To address these issues, this work aims to develop an i...
The degradation of the ecosystem and the loss of natural capital have seriously threatened the sustainable development of human society and economy. Currently, most research on Gross Ecosystem Product (GEP) is based on statistical modeling methods, w...
Background: Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utili...
Gradient-boosted decision trees (GBDTs) are among the leading methods for tabular data; however, their comparative robustness to corrupted labels remains unclear. This study resolves that uncertainty with a controlled benchmark protocol in which libr...
In this study, we evaluated the use of five different ML algorithms (CatBoost, XGBoost, random forest, gradient boosting, and support vector regression [SVR]) to estimate daily ET0 based only on four independent variables: 2 m air temperature, vapor...
The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, co...
This study investigates how firms in high-emission sectors progress along the low-carbon transition by analysing the joint dynamics of Management Quality (MQ) and Carbon Performance (CP) using probabilistic modelling and explainable machine-learning...
Lightning-induced tripping poses a major threat to the safe operation of high-voltage transmission lines in subtropical hilly regions. To reveal the multi-factor driving mechanisms of tripping events and realize quantitative risk prediction, this stu...
Churn in online retail is gradual and non-contractual, and the behavioural records used to predict it tend to combine strong monotone effects with a few saturating ones. Linear models capture the first kind of effect and tree ensembles capture the se...
In chemical reaction processes, yield prediction frequently faces challenges, such as multi-variable coupling, significant nonlinearity, and the limited accuracy of traditional mechanistic models. This study develops a datadriven prediction model tha...
Atmospheric particulate matter (PM) has major threats to global health, especially in urban regions around the world. Dhaka, Narayanganj and Gazipur of Bangladesh are positioned as top ranking polluted metropolitan cities in the world. This study ass...
Accurate estimation of lithium-ion battery (LIB) state of health (SOH) underpins safe operation, predictive maintenance, and lifetime-aware energy management. Despite recent advances in machine learning (ML), systematic benchmarking across heterogene...
In the present study, the sputtered aluminum nitride (AlN) films were processed in a reactive pulsed DC magnetron system. We applied a total of 15 different design of experiments (DOEs) on DC pulsed parameters (reverse voltage, pulse frequency, and d...
Fiber-reinforced polymer (FRP) composites offer high specific strength, high stiffness, and good corrosion resistance, which makes them suitable for aerospace, automotive, marine, and wind-energy structures. Their interlaminar fracture behavior is ha...
The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation...
Hydrometallurgical leaching processes contain complex and nonlinear parameter interactions that are difficult to capture with conventional empirical models. In this study, a multiple hybrid machine learning approach was developed to predict manganese...
The massive integration of variable renewable energy sources (RESs) poses the gradual necessity for new power system architectures with wide implementation of distributed battery energy storage systems (BESSs), which support power system stability, e...
An assessment and prediction of PM2.5 for a port city of eastern peninsular India is presented. Fifteen machine learning (ML) regression models were trained, tested and implemented to predict the PM2.5 concentration. The predicting ability of regress...
There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits....
Reliable tool-wear monitoring is essential for maintaining machining quality and preventing unscheduled downtime in manufacturing. This investigation presents a sound-based classification framework for identifying wear states in the turning of AISI 3...
The energy consumption of electric vehicles is closely related to the problems of charging station planning and vehicle route optimization. However, due to various factors, such as vehicle performance, driving habits and environmental conditions, it...
Background/Objectives: This study develops machine learning (ML) models to predict hypoxemia severity during emergency triage, particularly in Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) scenarios, using physiological data from...
The introduction of digital technologies into the activities of companies is based on software and hardware systems, which must function reliably and without interruption. The forecasting of the completion of storage area networks (SAN) is an essenti...
Dental care is a well-established marker of both oral and systemic health, driven by behavioral, socioeconomic, and geographic factors. This study aimed to develop and evaluate machine learning models to classify the presence and severity of permanen...
Reinforced concrete deep beams (RCDBs) provide significant strength and serviceability for building structures. However, a simple, general, and universally accepted procedure for predicting their shear strength (SS) has yet to be established. This st...
The use of Satellite-Derived Bathymetry (SDB) constitutes an efficient, cost-effective, time-saving, and scalable approach for generating high-resolution shallow-water bathymetry. In this context, SDB can be proven to be a valuable method for support...
This study investigates multi-horizon forecasting of solar-based green hydrogen (H2) production in Southern Morocco (Dakhla: 23.7° N, 15.9° W; Laâyoune: 27.2° N, 13.2° W; and Guelmim: 29.0° N, 10.1° W) using daily meteor...
Over the past few decades, lithium-ion batteries (LIBs) have gained significant attention due to their inherent potential for environmental sustainability and unparalleled energy storage efficiency. Meanwhile, polymer electrolytes have gained popular...
The objective of this study was to analyze price variability and the factors influencing the formation of monthly prices of by-products of the wood industry in Poland between October 2017 and January 2025. The analysis considered the impact of econom...
Background: Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is an escalating global health challenge, affecting over 82 million individuals each year. The increasing resistance to commonly used antibiotics such as azithromycin, ciprofloxacin,...
Defibering equipment is employed in the production of scrimber for the purpose of wood veneer rolling, cutting, and directional fiber separation. However, the current defibering equipment exhibits a notable degree of automation deficiency, relying mo...
Alzheimer’s disease (AD) remains a critical global health challenge for which early diagnosis is essential for effective intervention. However, AD detection is a challenging and complex task due to the scarcity of labeled clinical data, class i...
Landslides are widespread and highly destructive geological hazards that pose serious threats to infrastructure and densely populated areas. Conducting scientific and accurate predictions of landslide spatial distribution is therefore of great practi...
Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise an...
The implementation of machine learning methods as one of the artificial intelligence technologies has allowed bringing the construction process to a new qualitative level. Significant interest in these methods is observed in predictive modeling of th...
Slope stability assessment is a critical component of engineering safety. Conventional analytical methods frequently struggle to integrate heterogeneous slope data and model intricate failure mechanisms, thereby constraining their efficacy in practic...
Accurate prediction of concrete dam deformation is essential for ensuring structural safety and operational efficiency. This study presents a novel approach for monitoring and predicting concrete dam deformation using observed air temperature data, i...
Chlorophyll-a (Chla) and total suspended solid (TSS) concentrations are important parameters for water quality assessment, and in recent years, machine learning has been shown to have great potential in this field. However, current water quality para...
Background/Objectives: Predicting 30-day mortality after in-hospital cardiac arrest (IHCA) remains challenging. We developed an interpretable CatBoost model that incorporates the m-NUTRIC score, age, and selected micronutrient biomarkers (i.e., magne...
Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral d...
As one of the most important post-transcriptional modifications, m6Am plays a fairly important role in conferring mRNA stability and in the progression of cancers. The accurate identification of the m6Am sites is critical for explaining its biologica...
The growth and yield of crops are highly dependent on irrigation. Implementing irrigation plans that are tailored to the specific water requirements of crops can enhance crop yield and improve the quality of tomatoes. The mastery and prediction of tr...
The study of secondary craters on the Moon is vital for understanding lunar impact dynamics and surface evolution. However, this task is complicated by sample imbalance, with primary crater samples outnumbering those of secondary craters, and by the...
This paper presents an efficient parameter optimization approach to the plastic injection molding process to achieve high productivity. In collaboration with a company specializing in plastic injection-mold-based production, real process data was col...
Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for t...
Hyperkalemia is a major complication of chronic kidney disease (CKD). However, owing to the absence of specific symptoms in its early stages, hyperkalemia frequently remains undiagnosed. This study aimed to develop a machine learning model for predic...
The peak height of the F2 layer (hmF2) is a key parameter describing the vertical structure of the ionosphere. It is important for high-frequency radio communication planning and space-weather background assessment, particularly at high latitudes. To...
Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpirati...
Rapid urbanization and increasing traffic density have increased the crash risk of vulnerable road users (VRUs), particularly pedestrians and cyclists. Meanwhile, the conventional statistical models have difficulties in capturing the nonlinear and co...
Harmful gas is a major hazard in underground engineering construction, and accurate prediction of its risk level is essential for tunnel safety. Existing prediction methods for harmful-gas risk in non-coal strata tunnels are limited by empirical scor...
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