The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine lea...
Tax fraud is a common problem for many tax administrations, costing billions of dollars. Different tax administrations have considered several options to optimize revenue; among them, there is the so-called electronic billing machine (EBM), which aim...
The Performance Efficiency Factor (PEF) is a key composite metric in broiler production that integrates livability, average body weight, feed conversion ratio (FCR) and age. While tree-based machine learning models have shown promising results for li...
Implied volatility is known to have a string structure (smile curve) for a given time to maturity and can be captured by the B-spline. The parameters characterizing the curves can change over time, which complicates the modeling of the implied volati...
This study employed tree-based machine learning (ML) algorithms to predict low-level wind shear (LLWS) at Jeju International Airport (ICAO: RKPC). Hourly meteorological data from 47 observation stations across Jeju Island, collected between 2019 and...
Pension plans play a vital role in the economy by impacting savings, consumption, and investment allocation. Despite declining mortality rates and increasing life expectancy, pension enrollment remains low, affecting the long-term financial stability...
12 November 2026
The increasing complexity of Internet of Things (IoT) environments necessitates efficient and reliable anomaly detection. This study experimentally evaluates established tree-based machine-learning models instead of proposing a new detection algorith...
Sales forecasting plays a critical role in business decision-making, including financial planning, resource allocation, and inventory management. Despite the growing adoption of machine learning in forecasting applications, controlled evaluations of...
Tree-based machine learning (ML) models offer a powerful classification framework for rapidly screening groundwater for heavy metal contamination and associated health risks. This study applies several tree-based algorithms to classify groundwater sa...
Background: Dengue fever remains a major global health problem, and early diagnosis is challenging where confirmatory testing is limited. Machine-learning studies using routine hematological data have focused mainly on discrimination, whereas calibra...
Predicting the bulk-average velocity (UB) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or caus...
Reliable Predictive Maintenance (PdM) in Industrial Internet of Things (IIoT) environments is challenged by severe class imbalance, heterogeneous sensor variables, inconsistent experimental protocols, and deployment constraints. This study proposes a...
The transmission characteristics of the printed circuit board (PCB) ensure signal integrity and support the entire circuit system, with impedance matching being critical in the design of high-speed PCB circuits. Because the factors affecting impedanc...
Generalized age–period–cohort (GAPC) models are mortality models that incorporate stochasticity, which can be represented in a generalized linear or non-linear context. By fitting the data to either mortality model, one can make forecasts...
Attacks using Uniform Resource Locators (URLs) and their JavaScript (JS) code content to perpetrate malicious activities on the Internet are rampant and continuously evolving. Methods such as blocklisting, client honeypots, domain reputation inspecti...
1 December 2022
Tree-based machine learning methods have gained traction in the statistical and data science fields. They have been shown to provide better solutions to various research questions than traditional analysis approaches. To encourage the uptake of tree-...
This research work highlights significant achievements in the domain of intrusion detection systems (IDSs) for autonomous vehicles, which are crucial in enhancing their safety, reliability, and cybersecurity. This study introduces an approach that le...
Conventional methods of estimating pressure coefficients of buildings retain time and cost constraints. Recently, machine learning (ML) has been successfully established to predict wind pressure coefficients. However, regardless of the accuracy, ML m...
The localization of gambling establishments is a relevant topic in gambling research. In this paper, we analyze the spatial distribution of two types of gambling establishments—private and public—over the last 10 years in the municipality...
Accurate state of charge (SoC) estimation is critical for the safety, performance, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. This study investigates the application of Electrochemical Impedance Spectrosco...
Accurate prediction of shallow soil temperature is essential for agriculture, geotechnical design, and ground-coupled energy systems. This study proposes a novel hybrid machine-learning framework in which four tree-based regressors (Decision Tree, Ra...
Machine learning (ML) promises advances in automation and threat detection for the future generations of communication networks. However, new threats are introduced, as adversaries target ML systems with malicious data. Adversarial attacks on tree-ba...
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...
This paper presents the application of imitation learning—specifically, Behavioral Cloning—to collision avoidance and path planning for Unmanned Surface Vehicles. The proposed approach enables machine learning models to learn navigation p...
The advent of next-generation sequencing has greatly accelerated the field of human microbiome studies. Currently, investigators are seeking, struggling and competing to find new ways to diagnose, treat and prevent human diseases through the human mi...
In today’s dynamic business world, organizations are increasingly relying on innovative technologies to improve the efficiency and effectiveness of their human resource (HR) management. Our study uses historical time and attendance data collect...
With the development of big data and cloud computing technologies, the importance of pseudonym information has grown. However, the tools for verifying whether the de-identification methodology is correctly applied to ensure data confidentiality and u...
Background: This study investigates the application of machine learning models to classify electrocardiogram signals, addressing challenges such as class imbalances and inter-class overlap. In this study, “normal” and “abnormal&rdqu...
High power density and better efficiency make the multiterminal high-voltage direct current (MT-HVDC) system the best candidate for long-distance bulk power transfer in the cases of onshore and offshore power systems. Many machine learning-based algo...
Automated machine learning (AutoML) has been recognized as a powerful tool to build a system that automates the design and optimizes the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimization too...
Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduc...
This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These varia...
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 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...
Network Intrusion Detection System (NIDS) is one of the key technologies to prevent network attacks and data leakage. In combination with machine learning, intrusion detection has achieved great progress in recent years. However, due to the diversity...
Personalized drug selection is crucial for treating complex diseases such as Acute Myeloid Leukemia, where maximizing therapeutic efficacy is essential. Although precision medicine aims to tailor treatments to individual molecular profiles, existing...
Aerosols play a crucial role in the climate system due to direct and indirect effects, such as scattering and absorbing radiant energy. They also have adverse effects on visibility and human health. Humans are exposed to fine PM2.5, which has adverse...
Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a s...
In this research, the effect of change in stacking sequences on the impact performance of bio-hybrid fiber-reinforced polymer (bio-HFRP) composite materials was analyzed and evaluated. The methodology was developed, based on the mechanical testing an...
Accurate retrieval of cloud liquid water content (LWC) and ice water content (IWC) vertical profiles remains limited by strong vertical variability and nonlinear dependencies among observed variables. Ground-based cloud radar reflectivity and microwa...
The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and s...
This study aims to demonstrate the use of the tree-based machine learning algorithms to predict the 3- and 5-year disease-specific survival of oral and pharyngeal cancers (OPCs) and compare their performance with the traditional Cox regression. A tot...
Exhaust Gas Temperature (EGT) is a critical parameter in Gas Turbines (GTs) in terms of performance monitoring, fault detection, and operational optimization. In this study, a comprehensive and data-driven modeling approach was developed to predict E...
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...
Background: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques. Method...
The PM10 prediction has received considerable attention due to its harmful effects on human health. Machine learning approaches have the potential to predict and classify future PM10 concentrations accurately. Therefore, in this study, three machine...
Machine learning algorithms have been applied to various animal husbandry and veterinary-related problems; however, its use in Johne’s disease diagnosis and control is still in its infancy. The following proof-of-concept study explores the appl...
Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak detection operations in water distribution systems (WDSs). However, studies on leakage detection based on on-site leak signals are limited...
As a crucial pillar industry in the country, the automotive industry continues to evolve with the increasing number of vehicles in operation, leading to a continual rise in the need for aftermarket parts and repair services. Fluctuations in automotiv...
Accurate estimation of individual tree stem volume is essential for forest resource assessment and the implementation of sustainable forest management. In South Korea, traditional regression models based on non-destructive and easily measurable field...
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