1,478 Results Found

Voxel-based morphometry (VBM) is an established method for assessing grey matter volumes across the brain. The quality of magnetic resonance imaging (MRI) and the chosen data preprocessing steps can affect the outcome of VBM analyses. We recognized a...

Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended...

Fine-grained image classification is a hot topic that has been widely studied recently. Many fine-grained image classification methods ignore misclassification information, which is important to improve classification accuracy. To make use of misclas...

Misclassification of a binary response variable and nonrandom sample selection are data issues frequently encountered by empirical researchers. For cases in which both issues feature simultaneously in a data set, we formulate a sample selection model...

In randomized clinical trials and observational studies alike, it is difficult and challenging to collect gold-standard outcome measures for all participants. Although it would be ideal to use gold-standard measures, the costs and logistics of collec...

Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologie...

This study examined the longitudinal association between adolescent body weight misclassifications and body fat and waist circumference during adulthood. A sample was derived from a large Australian birth cohort study. The data analyses were restrict...

Accurate large-area mangrove classification is a challenging task due to the complexity of mangroves, such as abundant species within the mangrove category, and various appearances resulting from a large latitudinal span and varied habitats. Existing...

Land cover maps with high accuracy are essential for environmental protection and climate change research. The 30-meter-resolution maps, with their better resolution and longer historical records, are extensively utilized to assess changes in land co...

Exposure to PM_2.5 has been associated with increased mortality in urban areas. Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here, we quantified the misclassification that occurr...

California’s San Joaquin Valley is both drought-prone and water-scarce but relies on high-intensity agriculture as its primary economy. Climate change adaptation strategies for high-intensity agriculture require reliable and highly resolved lan...

Hospital discharge (HD) records contain important information that is used in public health and health care sectors. It is becoming increasingly common to rely mostly or exclusively on HD data to assess and monitor severe maternal morbidity (SMM) ove...

Despite the theoretical success of obviating the need for hypothesis-generating studies, they live on in epidemiological practice. Cole asserted that “… there is boundless number of hypotheses that could be generated, nearly all of them wrong” and ur...

The purpose of this analysis was to quantify and adjust for disease misclassification from loss to follow-up in a historical cohort mortality study of workers where exposure was categorized as a multi-level variable. Disease classification parameters...

Accurate recognition of basic facial emotions is well documented, yet the mechanisms of misclassification and their relation to gaze allocation remain under-reported. The present study utilized a within-subjects eye-tracking design to examine both ac...

Surface defects in hot-rolled steel strip alter the material’s properties and degrade its overall quality. Especially in real production environments, due to time sensitivity, lightweight Convolutional Neural Network models are suitable for ins...

Investigational diagnostic tests are validated by using a reference standard (RS). If the RS is imperfect (i.e., it has sensitivity [Se] and/or specificity [Sp] < 1), incorrect values for the investigational test’s Se and Sp may result becau...

Background: Body mass index (BMI) remains the standard tool for obesity screening; however, it does not account for body fat distribution or visceral adiposity, potentially leading to clinically relevant misclassification—particularly in young...

Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies have investigated the performance of different exposure models on population-weighted exposure e...

Background: Valid measurement of determinants of HIV infection among men who have sex with men (MSM) is critical for intervention planning and resource allocation. However, sexual minority research concerning HIV risk often relies on proxy exposures...

Potential outcomes play a fundamental and important role in many causal inference problems. If the potential-outcome means are identifiable, a series of causal effect measures, including the risk difference, the risk ratio, and the treatment benefit...

This paper proposes a bi-objective approach to address the data-driven Bayesian network structure learning problem. The objectives considered for optimization are minimum description length (MDL) and misclassification. An algorithm based on the well-...

Reducing the lag in the accuracy assessment of multi-temporal land-cover products has been a hot research topic. By identifying the changed strata, the annual accuracy in multi-temporal products can be quickly evaluated. However, there are still two...

Generally, following an omnibus (overall equality) test, multiple pairwise comparison (MPC) tests are typically conducted as the second step in a sequential testing procedure to identify which specific pairs (e.g., proportions) exhibit significant di...

The Mahalanobis–Taguchi System (MTS) is a pattern recognition tool employing Mahalanobis Distance (MD) and Taguchi Robust Engineering philosophy to explore and exploit data in multidimensional systems. The MD metric provides a measurement scale to cl...

Background/Objectives: Clavicle injuries are common and seem to be frequently subject to diagnostic misclassification. The accurate identification of clavicle fractures is essential, particularly for registry and Big Data analyses. This study aims to...

We construct a point and interval estimation using a Bayesian approach for the difference of two population proportion parameters based on two independent samples of binomial data subject to one type of misclassification. Specifically, we derive an e...

Soil compaction is a severe threat to agricultural productivity, as it can lead to yield losses ranging from 5% to 40%. Quantification of the state of compaction can help farmers and land managers to determine the optimal management to avoid these lo...

Fine particles misclassification in the underflow (UF) of grinding-classification hydrocyclones might result in ore over-grinding, leading to both reduced ball mill throughput and metal recovery. In the current research, a W-shaped hydrocyclone is pr...

Discrepancies in trade statistics can be normal or benign and attributed to a wide variety of unintentional factors, or in some instances within the timber products sector, such discrepancies can be associated with “systemic” factors that...

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding m...

Background: Borderline dengue immunoglobulin M (IgM) enzyme-linked immunosorbent assay (ELISA) results create diagnostic and epidemiological uncertainty. Collapsing the manufacturer’s three qualitative categories into a binary outcome changes t...

Classification using linear discriminant analysis (LDA) is challenging when the number of variables is large relative to the number of observations. Algorithms such as LDA require the computation of the feature vector’s precision matrices. In a...

Confusion matrices are numerical structures that deal with the distribution of errors between different classes or categories in a classification process. From a quality perspective, it is of interest to know if the confusion between the true class A...

Background: Managing shock, a life-threatening emergency, is challenging. The influence of the initial misclassification of undifferentiated hypotension (UH) in the emergency department (ED) on patients’ outcomes remains uninvestigated. The aim...

Background: Trigeminal neuralgia (TN), a facial pain disorder classically characterized by recurrent brief electric-shock-like paroxysms in one or more trigeminal divisions, frequently traverses dental pathways before specialist evaluation. Conversel...

Accurate tumor classification is essential for guiding treatment, yet histology alone may overlook key molecular differences or result in misclassification. We present a multimodal strategy that integrates gene expression (mRNA) and DNA methylation d...

Aeromonas veronii is a Gram-negative bacterial species that causes disease in fish and is nowadays increasingly recurrent in enteric infections of humans. This study was performed to characterize newly sequenced isolates by comparing them with comple...

Credit risk scorecard models are utilized by lending institutions to optimize decisions on credit approvals. In recent years, ensemble learning has often been deployed to reduce misclassification costs in credit risk scorecards. In this paper, we com...

Variation in the odds ratio (OR) resulting from selection of cutoffs for categorizing continuous variables is rarely discussed. We present results for the effect of varying cutoffs used to categorize a mismeasured exposure in a simulated population i...

Accurate ethnicity data is essential for understanding the size and needs of communities in Aotearoa New Zealand. Potential misclassification of Fijian and Fijian Indian ethnicity has been identified in census and administrative collections, affectin...

Algorithms combining CNN (Convolutional Neural Network) and super-pixel based smoothing have been proposed in recent years for Synthetic Aperture Radar (SAR) image classification. However, the smoothing may lead to the damage of details. To solve thi...

Ensuring high data quality in traffic crash datasets is critical for effective safety analysis and policymaking. This study presents an AI-assisted framework for auditing crash data integrity by detecting potentially mislabeled records related to con...

As different hepatitis C virus (HCV) genotypes respond differently to initiated therapy, correct HCV genotyping is essential. A potential risk for misclassification of the intergenotypic HCV circulating recombinant form (CRF) 2k/1b strains exists, de...

In risk-stratified cancer screening, multiple risk factors are incorporated into the risk assessment. An individual’s estimated absolute cancer risk is linked to risk categories with tailored screening recommendations for each risk category. Absolute...

Increased road traffic congestion is due to different factors, such as population and economic growth, in different cities globally. On the other hand, many households afford personal vehicles, contributing to the high volume of cars. The primary pur...

Shadows in very high-resolution multispectral remote sensing images hinder many applications, such as change detection, target recognition, and image classification. Though a wide variety of significant research has explored shadow detection, shadow...

Background. The existing literature highlights a lack of comparative studies between subtypes of cholangiocarcinoma (CC) and the impact of misclassification on the epidemiological parameters. Methods. A retrospective study was conducted to evaluate t...

Improving HIV testing efficiency saves financial and material resources for health. We conducted a secondary data analysis of routinely collected HIV risk-screening program data in Uganda, from October to November 2019, to determine the performance c...

Postpartum haemorrhage (PPH) is a significant cause of maternal morbidity and mortality worldwide, particularly in low-resource settings. This study aimed to develop a predictive model for PPH using early risk factors and rank their importance in ter...