Pavement roughness is a critical indicator for road maintenance decisions and driving safety assessment. Existing methods primarily rely on multi-source explicit features, which have limited capability in capturing implicit information such as spatia...
Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem. In this paper, a short-term direct probabilistic prediction model of wind power is p...
Accurate and reliable runoff prediction is critical for solving problems related to water resource planning and management. Deterministic runoff prediction methods cannot meet the needs of risk analysis and decision making. In this study, a runoff pr...
Natural fiber composites have gained significant attention in recent years due to their environmental benefits and unique mechanical properties. These materials combine natural fibers with polymer matrices to create sustainable alternatives to tradit...
The California bearing ratio (CBR) value of subgrade is the most used parameter for dimensioning flexible and rigid pavements. The test for determining the CBR value is typically conducted under soaked conditions and is costly, labour-intensive, and...
Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation i...
The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction s...
The increasing severity of the urban heat island (UHI) effect is a consequence of rapid urban expansion and global climate change. The urban center of Da Nang, Vietnam, is currently experiencing severe UHI effects combined with increasingly frequent...
Coastal areas serve as a vital interface between the land and sea or ocean and host about 40% of the world’s population, providing significant social, economic, and ecological functions. Meanwhile, the sea-level rise caused by climate change, a...
(1) Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an aggressive disease course that requires accurate prognosis for individualized treatment planning. This study aims to develop and evaluate a radiomi...
Protein-based studies contribute significantly to gathering functional information about biological systems; therefore, the protein–protein interaction detection task is one of the most researched topics in the biomedical literature. To this end, man...
This study examines the dynamic transmission of international rubber prices along the SHFE–FOB Bangkok–local farm-gate chain in Thailand using weekly data and an integrated econometric–machine-learning framework. Engle–Granger...
Predicting fatigue behavior in steel components is highly challenging due to the nonlinear and uncertain nature of material degradation under cyclic loading. In this study, four hybrid machine learning models were developed—Histogram Gradient B...
Machine learning algorithms are applied to predict intense wind shear from the Doppler LiDAR data located at the Hong Kong International Airport. Forecasting intense wind shear in the vicinity of airport runways is vital in order to make intelligent...
Tensile strength and elastic modulus are key mechanical properties for continuous basalt fibers, which are inherently sustainable materials derived from naturally occurring volcanic rock. This study employs five ensemble learning models, including Ex...
Accurate estimation of flow discharge, Q, through hydraulic structures such as spillways and gates is of great importance in water resources engineering. Each hydraulic structure, due to its unique characteristics, requires a specific and comprehensi...
2 March 2022
Road traffic accidents are one of the world’s most serious problems, as they result in numerous fatalities and injuries, as well as economic losses each year. Assessing the factors that contribute to the severity of road traffic injuries has pr...
The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups na...
Climate change is one of the most pressing global challenges that could potentially threaten ecosystems, human populations, and weather patterns over time. Impacts including rising sea levels and soil salinization are caused by climate change, primar...
Recycled aggregate concrete (RAC) contributes to mitigating the depletion of natural aggregates, alleviating the carbon footprint of concrete construction, and averting the landfilling of colossal amounts of construction and demolition waste. However...
Psycholinguistic properties such as concept familiarity and concreteness have been investigated in relation to technological innovations in teaching and learning. Due to ongoing advances in semantic representation and machine learning technologies, t...
Despite the agricultural sector’s consistently high injury rates, formal reporting is often limited, leading to sparse national datasets that hinder effective safety interventions. To address this, our study introduces a comprehensive framework...
Low back pain (LBP) is one of the leading causes of disability in the world's population, yet there are limitations in providing an objective clinical assessment due to its widespread nature. In this work, five machine learning models (LightGBM, XGBo...
Local pier scour remains one of the leading causes of bridge failure, calling for predictions that are both accurate and uncertainty-aware. This study develops an interpretable data-driven framework that couples CatBoost (Categorial Gradient Boosting...
The ability to rapidly produce accurate land use and land cover maps regularly and consistently has been a growing initiative as they have increasingly become an important tool in the efforts to evaluate, monitor, and conserve Earth’s natural r...
In recent years, the accelerated urbanization process in China has led to increased land resource constraints and unregulated expansion, imposing significant pressure on ecosystems and the environment. As a critical node along the Silk Road Economic...
This study proposes a novel integration of the Extreme Gradient Boosting Machine (XGBoost) and Differential Flower Pollination (DFP) for constructing an intelligent method to predict the compressive strength (CS) of high-performance concrete (HPC) mi...
This research focuses on the environmental-friendly production of Borassus fruit fibers (BNF), its characterization, and hygroscopic properties determination via Dynamic Vapor Sorption (DVS). The experimental results obtained from the hygroscopic beh...
Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this stu...
There is growing tension between high-performance machine-learning (ML) models and explainability within the scientific community. In arsenic modelling, understanding why ML models make certain predictions, for instance, “high arsenic” in...
This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables atta...
Natural hazards such as landslides are among the most harmful and recurring hazards to infrastructure, communities, and the environment around the world. In Pakistan, the Balakot Valley is prone to severe landslides, especially along the Balakot&ndas...
Hydroelectricity, one of the oldest and most potent forms of renewable energy, not only provides low-cost electricity for the grid but also preserves nature through flood control and irrigation support. Forecasting hydroelectricity generation is vita...
The onset of COVID-19 has re-emphasized the importance of FinTech especially in developing countries as the major powers of the world are already enjoying the advantages that come with the adoption of FinTech. Handling of physical cash has been estab...
Wildfires generate substantial smoke containing fine particulate matter (PM2.5) that adversely impacts health. This study develops machine learning models integrating pre-wildfire factors like weather and fuel conditions with post-wildfire health imp...
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...
This article introduces a novel nature-inspired algorithm called the Plum Tree Algorithm (PTA), which has the biology of the plum trees as its main source of inspiration. The PTA was tested and validated using 24 benchmark objective functions, and it...
This research investigates the impact of coarse aggregate (CA) type, shape, and specimen size on the compressive behavior of concrete, aiming to better understand how these factors affect its mechanical performance. Eight concrete mixtures were desig...
Numerous tests are used to determine the performance of concrete, but compressive strength (CS) is usually regarded as the most important. The recycled aggregate concrete (RAC) exhibits lower CS compared to natural aggregate concrete. Several variabl...
Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacemen...
Vegetation water content (VWC) is a crucial parameter for evaluating vegetation growth, climate change, natural disasters such as forest fires, and drought prediction. Spaceborne global navigation satellite system reflectometry (GNSS-R) has become a...
Sinkholes, naturally occurring formations in karst regions, represent a significant environmental hazard, threatening infrastructure, agricultural lands, and human safety. In recent years, machine learning (ML) techniques have been extensively employ...
Bridges are essential structures that connect riverbanks and facilitate transportation. However, bridge piers and abutments can disrupt the natural flow of rivers, causing a rise in water levels upstream of the bridge. The rise in water levels, known...
Background: Excessive fructose intake has been linked to adverse metabolic outcomes, yet few studies have comprehensively described the clinical, behavioral, and nutritional patterns associated with different intake levels using machine learning. Met...
Soil moisture is an important component of the hydrologic cycle and ecosystem functioning, and it has a significant impact on agricultural production, climate change and natural disasters. Despite the availability of machine-learning techniques for e...
Farmland trees are a vital part of the local economy as trees are used by farmers for fuelwood as well as food, fodder, medicines, fibre, and building materials. As a result, mapping tree species is important for ecological, socio-economic, and natur...
Land use and land cover change constitute a significant driver of land degradation worldwide, and machine-learning algorithms are providing new opportunities for effectively classifying land use and land cover changes over time. The aims of this stud...
Meandering rivers are complex geomorphic systems that play an important role in the environment. They provide habitat for a variety of plants and animals, help to filter water, and reduce flooding. However, meandering rivers are also susceptible to c...
This review is devoted to experimental studies and modeling in the field of mechanical and physical properties of polymer concretes and polymer-modified concretes. The review analyzes studies carried out over the past two years. The paper examines th...
Debris flows are highly unpredictable and destructive natural hazards that present significant risks to both human life and infrastructure. Despite advances in machine learning techniques, current predictive models often fall short due to the insuffi...
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