Automatedvehicle damage assessment remains challenging because damaged regions frequently exhibit irregular geometries, diffuse boundaries, substantial scale variability, and limited visual separability between categories such as cracks and scratches...
The aim of this research was to analyze the characteristics of each type of learner among university students in online classes. The P sample was composed of 28 students who had participated in online classes. We prepared a Q sample for advanced rese...
With the rapid increase in data scale, real-world datasets tend to exhibit long-tailed class distributions (i.e., a few classes account for most of the data, while most classes contain only a few data points). General solutions typically exploit clas...
This paper aims to solve the asymmetric problem of sample classification recognition in extreme class imbalance. Inspired by Krawczyk (2016)’s improvement direction of extreme sample imbalance classification, this paper adopts the AdaBoost mode...
In this paper, a defused decision boundary which renders misclassification issues due to the presence of cross-pairs is investigated. Cross-pairs retain cumulative attributes of both classes and misguide the classifier due to the defused data samples...
The rapid and sensitive quantification of antibiotic residues in aquatic animals is crucial for ensuring food safety and protecting public health. Electrochemical (bio)sensors show great potential in this field due to their quick response time, low c...
Background/Objectives: Estimating the post-mortem interval (PMI) of human skeletal remains remains challenging because bone undergoes structural, molecular and optical changes that evolve differently over time and are strongly influenced by taphonomi...
The inherent class imbalance and scarcity of labeled samples remain major challenges in hyperspectral image (HSI) classification. SMOTE-based methods alleviate class imbalance by generating synthetic minority-class samples. However, their interpolati...
Current synthetic aperture radar automatic target recognition (SAR ATR) tasks face challenges including limited training samples and poor generalization capability to novel classes. To address these issues, few-shot class-incremental learning (FSCIL)...
The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness...
Afforestation is one of the most effective processes for removing carbon dioxide from the atmosphere and combating global warming. Landsat data and machine learning approaches can be used to map afforestation (i) indirectly, by constructing two maps...
Class imbalance is a prevalent problem that not only reduces the performance of the machine learning techniques but also causes the lacking of the inherent complex characteristics of data. Though the researchers have proposed various ways to deal wit...
With the growth of data in the real world, datasets often encounter the problem of long-tailed distribution of class sample sizes. In long-tailed image recognition, existing solutions usually adopt a class rebalancing strategy, such as reweighting ba...
The angular distribution of surface-strands in oriented strand board (OSB) is closely associated with board mechanical properties and mat formation quality. By acquiring surface images through vision sensing and combining them with deep learning-base...
High renewable energy penetration makes power system operating scenarios increasingly complex and variable. Extreme operating scenarios have a low probability of occurrence and insufficient valid samples, which can cause class imbalance in the traini...
Few-shot fault diagnosis is a challenging task in rotating machinery health monitoring because only limited labeled fault samples are available in practical industrial scenarios. Under such conditions, deep learning models are prone to overfitting an...
Microplastics (MPs) are widely recognized as emerging contaminants in freshwater environments. Their identification often relies on extensive sample preparation and chemical treatments, which increase analysis time, reagent use, and overall resource...
The eruption of technology has revolutionized the sports sector, incorporating new elements and new forms, and has therefore targeted sports activities. The inclusion of virtual fitness classes is leading to an increase in the offers available to con...
A novel sparse representation classification method (SRC), namly SRC based on Method of Optimal Directions (SRC_MOD), is proposed for electronic nose system in this paper. By finding both a synthesis dictionary and a corresponding coefficient vector,...
The topic of nearly zero-energy buildings (n-ZEB), introduced by the Directive 2010/31/EU will direct the building market toward ever greater energy efficiency of new buildings. In some contexts, however, the building market for high-efficiency build...
Kneeosteoarthritis (KOA) is a common degenerative joint disease that causes pain, stiffness, and impaired mobility. Automated Kellgren–Lawrence (KL) grading from knee X-ray images facilitates efficient screening and follow-up assessment. Howeve...
Industrial heritage conservation has shifted toward adaptive reuse, making accurate differentiation of historical components, later interventions, and other architectural elements essential for quantitative spatial analysis and renovation assessment....
The increasing usage of interconnected devices within the Internet of Things (IoT) and Industrial IoT (IIoT) has significantly enhanced efficiency and utility in both personal and industrial settings but also heightened cybersecurity vulnerabilities,...
Machine learning-base fire detection methods play a vital role in the current forest fire monitoring systems. In the literature, fire detection is usually viewed as a two-class (or multi-class) classification task. However, this violates the most fou...
Industrial-Internet security faces a core challenge: improving detection accuracy for critical minority-class network attacks. The existing intrusion detection methods based on Conditional Generative Adversarial Nets (CGANs) aim to achieve data balan...
Model validation is essential for evaluating how reliably a machine learning model performs on unseen data, especially in imbalanced classification where performance estimates can be affected by an underrepresentation of the minority class. Cross-val...
Malicious Tor traffic data contains deep domain-specific knowledge, which makes labeling challenging, and the lack of labeled data degrades the accuracy of learning-based detectors. Real-world deployments also exhibit severe class imbalance, where ma...
Semantic communication offers a method for task-oriented information delivery in artificial intelligence (AI)-enabled space networks, but compact semantic representations remain vulnerable to fading, noise, and structured corruption during transmissi...
Soybean seed variety identification is essential for seed quality control, germplasm management, and variety authentication. However, few-shot classification remains challenging because different varieties often exhibit highly similar one-dimensional...
In this article, a novel algorithm for tonal mode classification utilizing the signature of fifths is presented. The proposed method is an extension of previously developed algorithms used for determining the key and key signature within the Western...
Spectroscopic methods combined with statistics have recently gathered substantial interest in pedological studies. Near-infrared (NIR) spectroscopy has been utilized, for example, for reconstructions of the history and transformations of Chernozems,...
Cyanobacterial harmful algal blooms (cyanoHABs) typically involve the release of a wide spectrum of toxic metabolites produced by different cyanobacteria, which can cause adverse health effects in animals and humans. At the same time, many cyanobacte...
This study investigates the intricate interplay between social class, sex, and self-reported health (SRH) using data from the European Health Survey of Spain 2020 (EESE2020). Employing a cross-sectional design and a representative sample of 22,072 in...
To improve/enhance the intelligence and accuracy of fault diagnosis in high-voltage direct current (HVDC) systems, this paper proposes a fault diagnosis model for HVDC systems based on the multimodal sensor feature-light gradient boosting machine (MS...
Background: Luminex-based single antigen bead assays are widely used for HLA antibody assessment in transplantation, but their semi-quantitative fluorescence outputs may be affected by technical, signal-dependent, and bead-specific factors. This sing...
This work presents a microstructure-informed pathway for assigning a material symmetry class to distinguish between tensor components and scalar effective thermal conductivity (ETC) values derived from directional measurements. The framework combines...
Population growth rates in Sub-Saharan East Africa are among the highest in the world, creating increasing pressure for land cover conversion. To date, however, there has been no comprehensive assessment of regional land cover change, and most long-t...
Research suggests the existence of an association between chronotype and intellectual performance, but the nature of this link remains unclear. Studies conducted in a laboratory setting point to the synchrony effect (better performance at a person&rs...
Credit score models are essential tools for evaluating creditworthiness and mitigating financial risks. However, the imbalanced nature of multi-class credit score datasets poses significant challenges for traditional classification algorithms, leadin...
Traditional image classification often misclassifies unknown samples as known classes during testing, degrading recognition accuracy. Open-set image recognition can simultaneously detect known classes (KCs) and unknown classes (UCs) but still struggl...
Futures price-movement-direction forecasting has always been a significant and challenging subject in the financial market. In this paper, we propose a combination approach that integrates the XGBoost (eXtreme Gradient Boosting), SMOTE (Synthetic Min...
Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath samp...
This paper concentrates on the study of logic-based switching adaptive control. Two different cases will be considered. In the first case, the finite time stabilization problem for a class of nonlinear system is studied. Based on the recently develop...
Food additives recur in combinations across packaged foods, but their association with nutritional quality is uncertain. We identified latent profiles of Codex-mapped functional classes from label-declared additives in a sampled Turkish retail invent...
Measuring changes in energy consumption and carbon dioxide emissions of various large economies is fundamental for analyzing the impact and effectiveness of various policies in this direction. This study analyzes intertemporal changes in energy and C...
The purpose of this study was to identify the problems of online education perceived by learners in a time when non-face-to-face online lectures are conducted due to the spread of COVID-19, and suggest measures to improve educational outcomes. Theref...
Existing deep learning algorithms for synthetic aperture radar (SAR) image recognition are performed with offline data. These methods must use all data to retrain the entire model when new data are added. However, facing the real application environm...
Polymeric fibers are encountered in numerous forensic circumstances. This study focused on polymeric carpet fibers most encountered at a crime scene, which are nylons, polyesters and olefins. Analysis of the multiple polymer types was done using Dire...
In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive a...
Decentralized applications (DApps) have developed rapidly owing to their censorship resistance, user autonomy, and distributed architecture. However, privacy and regulatory restrictions can limit access to representative traffic samples, while class...
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