Missing values in time series data present a significant challenge, often degrading the performance of downstream tasks such as classification and forecasting. Traditional approaches address this issue by first imputing the missing values and then in...
Remaining useful life (RUL) prediction is recently a hot spot in industrial big data analysis research. It aims at obtaining the health status of the equipment in advance and making intelligent maintenance decisions. However, values missing is a comm...
This article presents a new prediction model, the ordinary differential equations–memory kernel function (ODE–MKF), constructed from multiple backtracking initial values (MBIV). The model is similar to a simplified numerical model after s...
This study aimed to determine the apparent metabolizable energy (AME), nitrogen-corrected AME (AMEn), and net energy (NE) values of corn for Arbor Acres (AA) broilers aged 12–14 and 26–28 days and to develop prediction equations. Ten corn...
This study investigates the efficacy of various genomic prediction models—Genomic Best Linear Unbiased Prediction (GBLUP), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), and Multilayer Perceptron (MLP)—...
We examine in this paper the implementation of Bayesian point predictors of order statistics from a future sample based on the
Although genomic selection can accelerate livestock breeding, its application in many countries is hindered due to the limited size of reference populations. To address this issue, researchers have explored methods of combining multiple breeds to cre...
The accuracy of diagnostic tests with binary end-points is most frequently measured by sensitivity and specificity. However, from the clinical perspective, the main purpose of a diagnostic agent is to assess the probability of a patient actually bein...
Diagrammatic formats are useful for summarizing the processes of evaluation and comparison of forecasts in plant pathology and other disciplines where decisions about interventions for the purpose of disease management are often based on a proxy risk...
A binary diagnostic test is a medical test that is applied to an individual in order to determine the presence or the absence of a certain disease and whose result can be positive or negative. A positive result indicates the presence of the disease,...
Based on branch prediction, value prediction has emerged as a solution for problems caused by true data dependencies in pipelined processors. While branch predictors have binary outcomes (taken/not taken), value predictors face a challenging task as...
We present Value Prediction for Security (VPsec), a novel hardware-only framework to counter fault attacks in modern microprocessors, while preserving the performance benefits of Value Prediction (VP.) VP is an elegant and hitherto mature microarchit...
The predictive receiver operating characteristic (PROC) curve is a diagrammatic format with application in the statistical evaluation of probabilistic disease forecasts. The PROC curve differs from the more well-known receiver operating characteristi...
The natural gas quality fluctuates in complex natural gas pipeline networks, because of the influence of the pipeline transmission process, changes in the gas source, and fluctuations in customer demand in the mixing process. Based on the dynamic cha...
The resource utilization of recycled aggregates is of paramount importance, and the crushing value serves as a core indicator for evaluating their mechanical properties and classification applications. This paper systematically reviews the research p...
Accurate wind speed prediction is crucial for enhancing wind power generation efficiency and ensuring grid stability. While previous research has predominantly focused on point-valued or interval-valued predictions using common-frequency data, these...
Most machine learning (ML) models produce black box predictions that are difficult, if not impossible, to understand. In pharmaceutical research, black box predictions work against the acceptance of ML models for guiding experimental work. Hence, the...
Pixel value ordering and prediction error expansion (PVO+PEE) is a very successful reversible data hiding (RDH) scheme. A series of studies were proposed to improve the performance of the PVO-based scheme. However, the embedding capacity of those sch...
The conversion from functional requirements (FRs) to design parameters is the foundation of product customization. However, original customer needs usually result in incomplete FRs, limited by customers’ incomprehension on the design requirements of...
The value of ecosystem services (ESV) serves as a crucial metric for assessing the cost-effectiveness of ecosystems and evaluating their economic worth. Predicting the evolution of ESV across various land uses under different scenarios is essential f...
Use of a case-control design to compare the accuracy of two binary diagnostic tests is frequent in clinical practice. This design consists of applying the two diagnostic tests to all of the individuals in a sample of those who have the disease and in...
This study introduces an empirical model designed to predict the maximum values of time-dependent data across four turbulence-related fields: hydrogen combustion in renewable energy systems, urban microclimate effects on cultural heritage, shipping e...
The polished stone value (PSV) is a key parameter for assessing the resistance of aggregates to polishing in the laboratory. It is included in technical specifications and serves as both a regulatory and contractual criterion for selecting aggregates...
Background/Objectives: This work introduces accuracy- and precision-ROC curves in addition to SS– and PV–ROC curves and shows a novel means of profiling biomarker characteristics for validation of optimal cutoffs in clinical diagnostics a...
Accurate measurement of coal calorific value is critical for efficient power generation. To address the limitations of conventional models on large-scale, heterogeneous datasets, this study proposes a novel deep learning framework, the Multi-View Tra...
Background: In the coming years, the lateral cephalogram will remain, in many cases, the preferred diagnostic tool for planning and reliably assessing the results of orthodontic treatment. The aim of this study was to compare the predictive value and...
Background: Since the outbreak of the COVID-19 pandemic, a growing number of evidence suggests that COVID-19 presents sex-dependent differences in clinical course and outcomes. Nevertheless, there is still an unmet need to stratify the risk for poor...
This study addresses the limitations of traditional coal calorific value prediction models, which primarily rely on linear regression and single-source proximate analysis data. Based on 465 Chinese coal samples and integrating proximate analysis, ult...
Due to the poor thermal properties of conventional thermal fluids such as water, oil and ethylene glycol, small solid particles are added to these fluids to enhance heat transfer. Since the viscosity change determines the rheological behavior of a li...
Completing a project on time and on budget are essential factors for the success of any project. One technique that allows predicting the final cost of a project is earned value management (EVM). In this technique, different mathematical methods for...
Predicting driver-state evolution in extra-long tunnel corridors remains challenging because of prolonged spatial confinement and repeated lighting transitions. This study uses a statistical human–vehicle composite, the comprehensive driving in...
Anticholinergic burden (AB) is related to cognitive impairment (CI) and older complex chronic patients (OCCP) are more susceptible. Our objective was to evaluate the predictive value of ten anticholinergic scales to predict a potential CI due to anti...
A plethora of software vulnerabilities are exposed daily, posing a severe threat to the Internet. It is almost impossible for security experts or software developers to deal with all vulnerabilities. Therefore, it is imperative to rapidly assess the...
As an important part of the urban ecosystem, urban green space provides a variety of ecosystem services, including climate regulation, soil conservation, carbon sink and oxygen release, and biodiversity protection. However, existing remote sensing ev...
Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition mus...
With the widespread adoption of multiple-input–multiple-output (MIMO) technology, channel state information (CSI) prediction has become a crucial technique for enhancing the performance of wireless communication systems. Traditional channel pre...
Point prediction of future record values based on sequences of previous lower or upper records is considered by means of the method of maximum product of spacings, where the underlying distribution is assumed to be a power function distribution and a...
Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), a major challenge for the swine industry, can be transmitted both vertically and horizontally. Common sample types, including processing fluid, serum, and family oral fluid, can neither det...
To investigate the value of 18F-FDG-PET/CT in predicting the occurrence of brain metastases in melanoma patients, in this retrospective study 201 consecutive patients with pathology-proven melanoma, between 2008 and 2021, were reviewed. Those who und...
Recently, the area of sea ice is rapidly decreasing due to global warming, and since the Arctic sea ice has a great impact on climate change, interest in this is increasing very much all over the world. In fact, the area of sea ice reached a record l...
This study evaluated the nutrition composition, the nutrient digestibility, and the energy value of six protein ingredients used in pet food by the difference method in six beagles within a 7 × 6 incomplete Latin square design. The results show...
The advantage of quantum mechanics to shift up the ability to econometrically understand extreme tail losses in financial data has become more desirable, especially in cases of Value at Risk (VaR) and Expected Shortfall (ES) predictions. Behind the n...
We study prediction-to-decision certification for inverse design with vector-valued response surfaces. An unknown response map is estimated from data, a target response is prescribed, and a decision is obtained by minimizing a target-loss function. T...
With the widespread application of artificial intelligence in real estate price prediction, model explainability has become a critical factor influencing its acceptability and trustworthiness. The Shapley value, as a classic cooperative game theory m...
The low-carbon transition has strengthened the link between corporate carbon performance and financial risk. This study examines whether carbon performance improves the out-of-sample prediction of financial distress beyond conventional financial indi...
Senility has been identified among the strongest risk predictors for unfavorable COVID-19-outcome. However, even in the elderly population, the clinical course of infection in individual patients remains unpredictable. Hence, there is an urgent need...
Although the prognosis of tonsillar cancer (human papillomavirus-positive oropharyngeal squamous cell carcinoma) is improving, disease control failure (distant metastasis) still occurs in some cases. We explored whether several 18F-fluorodeoxyglucose...
Background and Objectives: Extubation success in ICU patients is crucial for reducing ventilator-associated complications, morbidity, and mortality. The Rapid Shallow Breathing Index (RSBI) is a widely used predictor for weaning from mechanical venti...
Background/Objectives: A perforated peptic ulcer (PPU) is a life-threatening surgical emergency, with high levels of mortality and morbidity reported in the literature. Recognizing which patients are at most risk of developing complications or losing...
This paper addresses the cold-start problem and rare event prediction challenges in Olympic medal forecasting by proposing a predictive framework that integrates multi-granularity transfer learning with extreme value theory. The framework comprises t...
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