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Journal articles on the topic 'Predictive estimate'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Tansitpong, Praowpan. "Probabilistic Model of Patient Classification Using Bayesian Model." International Journal of Reliable and Quality E-Healthcare 13, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijrqeh.348579.

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The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on sta

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Fung, Juan F., Siamak Sattar, David T. Butry, and Steven L. McCabe. "A predictive modeling approach to estimating seismic retrofit costs." Earthquake Spectra 36, no. 2 (2020): 579–98. http://dx.doi.org/10.1177/8755293019891716.

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This article presents a methodology for estimating seismic retrofit costs from historical data. In particular, historical retrofit-cost data from Federal Emergency Management Agency (FEMA) 156 is used to build a generalized linear model (GLM) to predict retrofit costs as a function of building characteristics. While not as accurate as an engineering professional’s estimate, this methodology is easy to apply to generate quick estimates and is especially useful for decision makers with large building portfolios. Moreover, the predictive modeling approach provides a measure of uncertainty in term

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Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (2021): 932. http://dx.doi.org/10.3390/s21030932.

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipme

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Amry, Zul. "Bayesian Estimate of Parameters for ARMA Model Forecasting." Tatra Mountains Mathematical Publications 75, no. 1 (2020): 23–32. http://dx.doi.org/10.2478/tmmp-2020-0002.

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AbstractThis paper presents a Bayesian approach to finding the Bayes estimator of parameters for ARMA model forecasting under normal-gamma prior assumption with a quadratic loss function in mathematical expression. Obtaining the conditional posterior predictive density is based on the normal-gamma prior and the conditional predictive density, whereas its marginal conditional posterior predictive density is obtained using the conditional posterior predictive density. Furthermore, the Bayes estimator of parameters is derived from the marginal conditional posterior predictive density.

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Carpenter, Chris. "Decision Tree Regressions Estimate Liquid Holdup in Two-Phase Gas/Liquid Flows." Journal of Petroleum Technology 73, no. 11 (2021): 75–76. http://dx.doi.org/10.2118/1121-0075-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203448, “Decision-Tree Regressions for Estimating Liquid Holdup in Two-Phase Gas/Liquid Flows,” by Meshal Almashan, SPE, Yoshiaki Narusue, and Hiroyuki Morikawa, University of Tokyo, prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually 9–12 November. The paper has not been peer reviewed. In the authors’ study, a machine-learning predictive model—boosted decision tree regression (BDTR)—is trained, tested, and evaluated in predicting liquid hold

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GX-Chen, Anthony, Veronica Chelu, Blake A. Richards, and Joelle Pineau. "A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6829–37. http://dx.doi.org/10.1609/aaai.v36i6.20639.

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Estimating value functions is a core component of reinforcement learning algorithms. Temporal difference (TD) learning algorithms use bootstrapping, i.e. they update the value function toward a learning target using value estimates at subsequent time-steps. Alternatively, the value function can be updated toward a learning target constructed by separately predicting successor features (SF)—a policy-dependent model—and linearly combining them with instantaneous rewards. We focus on bootstrapping targets used when estimating value functions, and propose a new backup target, the ?-return mixture,

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Li, Yun, Xiangfeng Yin, and Xueting Wen. "SETAR Model Research Based on Support Vector Regression Parameter Estimation Method and Empirical Analysis." Journal of Physics: Conference Series 2216, no. 1 (2022): 012093. http://dx.doi.org/10.1088/1742-6596/2216/1/012093.

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Abstract In this paper, we use support vector regression (SVR) to estimate the parameters of the SETAR model for predicting the log-return of stock price data. SETAR model is usually estimated using maximum likelihood (ML) and minimize conditional sum-of-squares (CSS), assuming that the data are normal distribution. SVR is a powerful tool for regression estimation. Therefore, this paper proposes to use SVR instead of maximum likelihood and conditional least squares to estimate the parameters of the SETAR model. Empirical analysis on the two stock price data of China Unicom and ICBC, the result

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Rust, Nicole C., and Stephanie E. Palmer. "Remembering the Past to See the Future." Annual Review of Vision Science 7, no. 1 (2021): 349–65. http://dx.doi.org/10.1146/annurev-vision-093019-112249.

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In addition to the role that our visual system plays in determining what we are seeing right now, visual computations contribute in important ways to predicting what we will see next. While the role of memory in creating future predictions is often overlooked, efficient predictive computation requires the use of information about the past to estimate future events. In this article, we introduce a framework for understanding the relationship between memory and visual prediction and review the two classes of mechanisms that the visual system relies on to create future predictions. We also discus

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Kopec, Jacek A. "Estimating Disease Prevalence in Administrative Data." Clinical and Investigative Medicine 45, no. 2 (2022): E21–27. http://dx.doi.org/10.25011/cim.v45i2.38100.

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Purpose: Disease prevalence estimates from population-based administrative databases are often biased due to measurement (misclassification) errors. The purpose of this article is to review the methodology for estimating disease prevalence in administrative data, with a focus on bias correction. Source: Several approaches to bias correction in administrative data were reviewed and application of these methods was demonstrated using an example from the literature: physician claims and hospitalization data were employed to estimate diabetes prevalence in Ontario, Canada. Findings: Misclassificat

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Vehtari, Aki, and Jouko Lampinen. "Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities." Neural Computation 14, no. 10 (2002): 2439–68. http://dx.doi.org/10.1162/08997660260293292.

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In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important to obtain the distribution of the expected utility estimate because it describes the uncertainty in the estimate. The distributions of the expected utility estimates can also be used to compare models, for example, by computing the probability of one model having a better expected

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Albistur, Cristian F., Pablo A. Aravena, Luis A. Moran, and Jose R. Espinoza. "A Simple Predictive Method to Estimate Flicker." IEEE Transactions on Industry Applications 50, no. 3 (2014): 2150–55. http://dx.doi.org/10.1109/tia.2013.2289951.

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Chun Chet Gan. "The Prediction of Time Trending Techniques In Manufacturing." International Journal of Industrial Management 14, no. 1 (2022): 580–87. http://dx.doi.org/10.15282/ijim.14.1.2022.7547.

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The paper writes about utilizing time trend predictive techniques to calculate an estimated prediction in machine setup application. The prediction is based on arithmetic equations to estimate a future time. With these techniques in place, time trend predictors are able to know an estimate time in setting up machine, which is a variable time factor, with known prediction limits. Based on actual data collected over a period of time, a time trend is perceivable. It is concluded that time trend techniques provide an estimate predictor, sufficient to know a rough value within a categorical limit.

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Khandelwal, Utkal, Gudapalli Karuna, Sadda Bharath Reddy, et al. "Energy-Efficient Urban Transportation Planning using Traffic Flow Optimization." E3S Web of Conferences 581 (2024): 01039. http://dx.doi.org/10.1051/e3sconf/202458101039.

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This study examines how predictive analytics and the IoT might improve sustainable urban transportation systems. Using IoT device data, this study will explore how predictive analytics and IoT integration alter urban transportation. The data covers vehicle speed, traffic density, AQI, and weather. The research estimates traffic congestion, AQI, and volume using predictive modeling. This assesses prediction accuracy and data match. Unfavorable weather increases congestion, whereas traffic density decreases vehicle speed. Predictive methods accurately estimate congestion and air quality, but tra

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Abootty, Sheza, Swathi Rao, Vijaya D. Shenoy, and Amiya Ameer. "Serum anion gap versus lactate clearance as mortality predictors in critically ill children." Sri Lanka Journal of Child Health 53, no. 1 (2024): 26–29. http://dx.doi.org/10.4038/sljch.v53i1.10657.

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Introduction: Mortality prediction is important for optimal resource allocation in the paediatric intensive care unit (PICU). Objectives: To estimate the predictive value of serum corrected anion gap (cAG) and lactate clearance for predicting mortality in PICU. Method: We conducted a prospective study of children admitted to the PICU of a tertiary hospital. PRISM III and IV score, cAG and lactate clearance were done in all patients, and the predictive value was calculated for mortality. Results: The mortality in the study group was 12%. The cAG was significantly lower in survivors than in non-

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Makaya, Eugine. "Predictive Leakage Estimation using the Cumulative Minimum Night Flow Approach." American Journal of Water Resources 5, no. 1 (2017): 1–4. https://doi.org/10.12691/ajwr-5-1-1.

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Several methods have been used in estimating leakages. Although the minimum night flow analysis method has been widely used in leakage estimation, the cumulative minimum night flow method is one method that can yield comparatively good leakage estimates. This paper applies the cumulative minimum night flow method to estimate water leakage in a water distribution system. The cumulative minimum night flow method develops a model from empirical night flows which is used to estimate mean minimum night flows and hence estimate leakages. The result was compared with the South Africa minimum night fl

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Wamatu, Jane, Ashraf Alkhtib, and Barbara Rischkowsky. "Simple and robust model to estimate liveweight of Ethiopian Menz sheep." Animal Production Science 59, no. 12 (2019): 2265. http://dx.doi.org/10.1071/an18006.

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Heart girth (HG) bands have been predominantly used in Ethiopia by smallholder farmers, traders and extension workers to estimate liveweight (LW) of livestock. They are produced using recommended and published predictive models from Ethiopia. More recently, some farmers and traders have abandoned the bands due to perceived inaccuracy of LW estimation and reverted to eye-ball estimations. The present study generated a novel algorithm using multiple criteria to develop a robust predictive model for LW estimation of Ethiopian Menz sheep by using HG. Subsequently, recommended models currently in u

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Jensen, A. L. "Functional Regression and Correlation Analysis." Canadian Journal of Fisheries and Aquatic Sciences 43, no. 9 (1986): 1742–45. http://dx.doi.org/10.1139/f86-218.

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In fisheries, many applications of regression analysis are based on functional relations, but application of predictive regression results in two regression equations. Ricker proposed application of a method developed by Teissier to estimate the geometric mean functional relation when the parameters of a functional relation are of biological significance. Functional regression results in a single equation relating variables as opposed to the two equations that result when predictive regression is applied. The geometric mean functional relation also is given by bivariate normal correlation anal

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Ferster, Colin J., J. A. (Tony) Trofymow, Nicholas C. Coops, Baozhang Chen, T. Andrew Black, and François A. Gougeon. "Determination of ecosystem carbon-stock distributions in the flux footprint of an eddy-covariance tower in a coastal forest in British Columbia." Canadian Journal of Forest Research 41, no. 7 (2011): 1380–93. http://dx.doi.org/10.1139/x11-055.

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An important consideration when interpreting eddy-covariance (EC) flux-tower measurements is the spatial distribution of forest land surface cover and soil type within the EC flux-tower footprint. At many EC flux-tower sites, there is a range of geospatial data available with the ability to estimate the spatial distribution of forest land cover and soils. Developing methods that utilize multiple geospatial data sets will result in more thorough estimates of ecosystem C stock distributions. The objective of this study was to develop, apply, and validate methods to obtain comprehensive estimates

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Maury-Sintjago, Eduard, Carmen Muñoz-Mendoza, Alejandra Rodríguez-Fernández, and Marcela Ruíz-De la Fuente. "Predictive Equation to Estimate Resting Metabolic Rate in Older Chilean Women." Nutrients 14, no. 15 (2022): 3199. http://dx.doi.org/10.3390/nu14153199.

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Resting metabolic rate (RMR) depends on body fat-free mass (FFM) and fat mass (FM), whereas abdominal fat distribution is an aspect that has yet to be adequately studied. The objective of the present study was to analyze the influence of waist circumference (WC) in predicting RMR and propose a specific estimation equation for older Chilean women. This is an analytical cross-sectional study with a sample of 45 women between the ages of 60 and 85 years. Weight, height, body mass index (BMI), and WC were evaluated. RMR was measured by indirect calorimetry (IC) and %FM using the Siri equation. Ade

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Lin, Fen, Yuke Chen, Youqun Zhao, and Shaobo Wang. "Path tracking of autonomous vehicle based on adaptive model predictive control." International Journal of Advanced Robotic Systems 16, no. 5 (2019): 172988141988008. http://dx.doi.org/10.1177/1729881419880089.

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In most cases, a vehicle works in a complex environment, with working conditions changing frequently. For most model predictive tracking controllers, however, the impacts of some important working conditions, such as speed and road conditions, are not concerned. In this regard, an adaptive model predictive controller is proposed, which improves tracking accuracy and stability compared with general model predictive controllers. First, the proposed controller utilizes the recursive least square algorithm to estimate tire cornering stiffness and road friction coefficient online. Then, the estimat

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Palwinder, Kaur Mangat, and Singh Saini Kamaljit. "Predictive Analytics for Students" Performance Prediction." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 300–305. https://doi.org/10.35940/ijrte.C4417.099320.

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Personalized learning is being popular due to digitizations that enable a large number of technologies to support it. To predict students’ learning abilities, it is necessary to estimate their behavior to know about their weaknesses and strengths. If it is possible for teachers to predict in advance atrisk and dropout students, they can plan more effectively to handle them. We are describing in this paper various intelligent tutoring systems with Educational Data Mining, Predictive Learning Analytics, prediction of at-risk students at an earlier basis, how this prediction task is done. W

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Smith, Stephen J. "Use of Statistical Models for the Estimation of Abundance from Groundfish Trawl Survey Data." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 5 (1990): 894–903. http://dx.doi.org/10.1139/f90-103.

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Estimates of fish abundance from stratified random trawl surveys are highly variable and a number of estimators from various statistical models have been suggested to provide more precise estimates. However, model-based estimates of the survey finite population mean which are not based on the sample mean, can be biased and nonrobust to deviations from the model. This is demonstrated in particular for estimates based on the Δ-distribution. A criterion known as asymptotic design consistency (ADC) is presented for selecting those models that can provide estimates of the finite population mean whi

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Ocagli, Honoria, Corrado Lanera, Danila Azzolina, et al. "Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population." Nutrients 13, no. 2 (2021): 458. http://dx.doi.org/10.3390/nu13020458.

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Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to eva

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Wei, Cheng, Minzhe Li, Shaofeng Lin, and Jun Xiao. "Characterization of Tumor Mutation Burden-Based Gene Signature and Molecular Subtypes to Assist Precision Treatment in Gastric Cancer." BioMed Research International 2022 (May 13, 2022): 1–27. http://dx.doi.org/10.1155/2022/4006507.

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Objective. Tumor mutation burden (TMB) represents a useful biomarker for predicting survival outcomes and immunotherapy response. Here, we aimed to conduct TMB-based gene signature and molecular subtypes in gastric cancer. Methods. Based on differentially expressed genes (DEGs) between high- and low-TMB groups in TCGA, a LASSO model was developed for predicting overall survival (OS) and disease-free survival (DFS). The predictive performance was externally verified in the GSE84437 dataset. Molecular subtypes were conducted via consensus clustering approach based on TMB-related DEGs. The immune

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Silber, Jeffrey H., Paul R. Rosenbaum, Xuemei Zhang, and Orit Even-Shoshan. "Estimating Anesthesia and Surgical Procedure Times from Medicare Anesthesia Claims." Anesthesiology 106, no. 2 (2007): 346–55. http://dx.doi.org/10.1097/00000542-200702000-00024.

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Background Procedure times are important variables that often are included in studies of quality and efficiency. However, due to the need for costly chart review, most studies are limited to single-institution analyses. In this article, the authors describe how well the anesthesia claim from Medicare can estimate chart times. Methods The authors abstracted information on time of induction and entrance to the recovery room ("anesthesia chart time") from the charts of 1,931 patients who underwent general and orthopedic surgical procedures in Pennsylvania. The authors then merged the associated b

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Marçal, Maria Fernanda Magioni, Zigomar Menezes de Souza, Rose Luiza Moraes Tavares, Camila Viana Vieira Farhate, Stanley Robson Medeiros Oliveira, and Fernando Shintate Galindo. "Predictive Models to Estimate Carbon Stocks in Agroforestry Systems." Forests 12, no. 9 (2021): 1240. http://dx.doi.org/10.3390/f12091240.

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This study aims to assess the carbon stock in a pasture area and fragment of forest in natural regeneration, given the importance of agroforestry systems in mitigating gas emissions which contribute to the greenhouse effect, as well as promoting the maintenance of agricultural productivity. Our other goal was to predict the carbon stock, according to different land use systems, from physical and chemical soil variables using the Random Forest algorithm. We carried out our study at an Entisols Quartzipsamments area with a completely randomized experimental design: four treatments and six replit

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Clayton, Douglass B., and Donald A. Urban. "Point: TheValue of Predicting Life Expectancy in Men with Clinically Localized Prostate Cancer." Journal of the National Comprehensive Cancer Network 5, no. 7 (2007): 703–8. http://dx.doi.org/10.6004/jnccn.2007.0062.

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The prediction of life expectancy in prostate cancer screening and treatment is a controversial topic that evokes various opinions regarding its validity. The authors believe incorporating life expectancy prediction into the treatment algorithms for prostate cancer is important. Using a combination of clinical judgment and specific predictive tools, physicians can estimate the life expectancy of patients with prostate cancer. These estimates can then be used to help guide treatment discussion. Estimating life expectancy benefits older men in whom decisions regarding the best form of treatment

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SEHIDA, Kateryna. "ESTIMATE AND PREDICTIVE MODEL OF REGIONAL GEODEMOGRAPHIC SYSTEM." Ekonomichna ta Sotsialna Geografiya, no. 77 (2017): 85–91. http://dx.doi.org/10.17721/2413-7154/2017.77.85-91.

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He, Guofeng, Shicheng Zheng, Yanfei Dong, Guojiao Li, and Wenjie Zhang. "Model Predictive Voltage Control of Uninterruptible Power Supply Based on Extended-State Observer." Energies 15, no. 15 (2022): 5489. http://dx.doi.org/10.3390/en15155489.

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Finite-set model predictive controls have been widely used in inverter control because of the flexible target control and no need of a modulation unit. However, the mismatching of prediction model parameters produces prediction errors, resulting in a significant decline in the performance of finite-set model predictive controls. Aiming at the problem of model parameter mismatch, an extended-state observer was proposed to accurately estimate the disturbance of the system in this paper, and the obtained disturbance value was added to a finite-set model predictive control controller to compensate

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Westra, Seth, and Ashish Sharma. "An Upper Limit to Seasonal Rainfall Predictability?" Journal of Climate 23, no. 12 (2010): 3332–51. http://dx.doi.org/10.1175/2010jcli3212.1.

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Abstract The asymptotic predictability of global land surface precipitation is estimated empirically at the seasonal time scale with lead times from 0 to 12 months. Predictability is defined as the unbiased estimate of predictive skill using a given model structure assuming that all relevant predictors are included, thus representing an upper bound to the predictive skill for seasonal forecasting applications. To estimate predictability, a simple linear regression model is formulated based on the assumption that land surface precipitation variability can be divided into a component forced by l

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Quantin, C., E. Benzenine, M. Fassa, et al. "Evaluation of the interest of using discharge abstract databases to estimate breast cancer incidence in two French departments." Statistical Journal of the IAOS: Journal of the International Association for Official Statistics 28, no. 1-2 (2012): 73–85. https://doi.org/10.3233/sji-2012-0742.

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Context and objective: In France, national estimates of the incidence of cancers are based on mortality data by extrapolating the ratio between observed incidence and mortality in areas covered by cancer registries. The objective of this study is to determine the interest of using the discharge abstracts database gathered in hospitals to estimate breast cancer incidence in two French departments: Côte d'Or and Doubs. Methods: This study concerns the incident breast cancer cases identified in 2004 and 2005 through Côte d'Or and Doubs cancer registries and potential incident breast cancer cases

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Moldovan, Max, Jyoti Khadka, Renuka Visvanathan, Steve Wesselingh, and Maria C. Inacio. "Using elastic nets to estimate frailty burden from routinely collected national aged care data." Journal of the American Medical Informatics Association 27, no. 3 (2020): 419–28. http://dx.doi.org/10.1093/jamia/ocz210.

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Abstract Objectives To (1) use an elastic net (EN) algorithm to derive a frailty measure from a national aged care eligibility assessment program; (2) compare the ability of EN-based and a traditional cumulative deficit (CD) based frailty measures to predict mortality and entry into permanent residential care; (3) assess if the predictive ability can be improved by using weighted frailty measures. Materials and Methods A Cox proportional hazard model based EN algorithm was applied to the 2003–2013 cohort of 903 996 participants for selecting items to enter an EN based frailty measure. The out-

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Frederiksen, Rasmus Dovnborg, Grzegorz Bocewicz, Grzegorz Radzki, Zbigniew Banaszak, and Peter Nielsen. "Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study." Energies 17, no. 13 (2024): 3147. http://dx.doi.org/10.3390/en17133147.

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The successful implementation of predictive maintenance for offshore wind farms suffers from a poor understanding of the consequential short-term impacts and a lack of research on how to evaluate the cost-efficiency of such efforts. This paper aims to develop a methodology to explore the short-term marginal impacts of predictive maintenance applied to an already existing preventive maintenance strategy. This method will be based on an analysis of the performance of the underlying predictive model and the costs considered under specific maintenance services. To support this analysis, we develop

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Sequí-Domínguez, Irene, Iván Cavero-Redondo, Celia Álvarez-Bueno, Diana P. Pozuelo-Carrascosa, Sergio Nuñez de Arenas-Arroyo, and Vicente Martínez-Vizcaíno. "Accuracy of Pulse Wave Velocity Predicting Cardiovascular and All-Cause Mortality. A Systematic Review and Meta-Analysis." Journal of Clinical Medicine 9, no. 7 (2020): 2080. http://dx.doi.org/10.3390/jcm9072080.

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Increased arterial stiffness has been associated with an increased risk of developing cardiovascular diseases and all-cause mortality. Pulse wave velocity (PWV) is an innovative and affordable measurement of arterial stiffness which may be an accessible tool to estimate mortality risk; however, no meta-analysis has estimated its predictive performance for cardiovascular and all-cause mortality. Moreover, reference values for PWV have only been established by consensus for healthy populations. The aim of this review was to estimate PWV and especially carotid femoral PWV performance predicting c

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Jeong, Yonghwan. "Stochastic Model-Predictive Control with Uncertainty Estimation for Autonomous Driving at Uncontrolled Intersections." Applied Sciences 11, no. 20 (2021): 9397. http://dx.doi.org/10.3390/app11209397.

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This paper presents an uncontrolled intersection-passing algorithm with an integrated approach of stochastic model-predictive control and prediction uncertainty estimation for autonomous vehicles. The proposed algorithm is designed to utilize information from sensors mounted on the autonomous vehicle and high-definition intersection maps. The proposed algorithm is composed of two modules, namely target state prediction and a motion planner. The target state prediction module has predicted the future behavior of intersection-approaching vehicles based on human driving data. The recursive covari

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Yan, Gui Yun, and Zheng Zhang. "Predictive Control of a Cable-Stayed Bridge under Multiple-Support Excitations." Applied Mechanics and Materials 66-68 (July 2011): 268–72. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.268.

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This paper presents a predictive control strategy for seismic protection of a benchmark cable-stayed bridge with consideration of multiple-support excitations. In this active control strategy, a multi-step predictive model is built to estimate the seismic dynamics of cable-stayed bridge and the effects of some complicated factors such as time-varying, model mismatching, disturbances and uncertainty of controlled system, are taken into account by the prediction error feedback in the multi-step predictive model. The prediction error is that the actual system output is compared to the model predi

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Zhong, Xin, Yanxia Lu, Qi Gao, et al. "Estimating Biological Age in the Singapore Longitudinal Aging Study." Journals of Gerontology: Series A 75, no. 10 (2019): 1913–20. http://dx.doi.org/10.1093/gerona/glz146.

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Abstract Background Biological age (BA) is a more accurate measure of the rate of human aging than chronological age (CA). However, there is limited consensus regarding measures of BA in life span and healthspan. Methods This study investigated measurement sets of 68 physiological biomarkers using data from 2,844 Chinese Singaporeans in two age subgroups (55–70 and 71–94 years) in the Singapore Longitudinal Aging Study (SLAS-2) with 8-year follow-up frailty and mortality data. We computed BA estimate using three commonly used algorithms: Principal Component Analysis (PCA), Multiple Linear Regr

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Stenhaug, Benjamin A., and Benjamin W. Domingue. "Predictive Fit Metrics for Item Response Models." Applied Psychological Measurement 46, no. 2 (2022): 136–55. http://dx.doi.org/10.1177/01466216211066603.

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The fit of an item response model is typically conceptualized as whether a given model could have generated the data. In this study, for an alternative view of fit, “predictive fit,” based on the model’s ability to predict new data is advocated. The authors define two prediction tasks: “missing responses prediction”—where the goal is to predict an in-sample person’s response to an in-sample item—and “missing persons prediction”—where the goal is to predict an out-of-sample person’s string of responses. Based on these prediction tasks, two predictive fit metrics are derived for item response mo

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Xu, Lei, Nengcheng Chen, Chao Yang, Hongchu Yu, and Zeqiang Chen. "Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning." Hydrology and Earth System Sciences 26, no. 11 (2022): 2923–38. http://dx.doi.org/10.5194/hess-26-2923-2022.

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Abstract. Precipitation forecasting is an important mission in weather science. In recent years, data-driven precipitation forecasting techniques could complement numerical prediction, such as precipitation nowcasting, monthly precipitation projection and extreme precipitation event identification. In data-driven precipitation forecasting, the predictive uncertainty arises mainly from data and model uncertainties. Current deep learning forecasting methods could model the parametric uncertainty by random sampling from the parameters. However, the data uncertainty is usually ignored in the forec

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Norris, D. Ryan, and Caz M. Taylor. "Predicting the consequences of carry-over effects for migratory populations." Biology Letters 2, no. 1 (2005): 148–51. http://dx.doi.org/10.1098/rsbl.2005.0397.

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Migratory animals present a unique challenge for predicting population size because they are influenced by events in multiple stages of the annual cycle that are separated by large geographic distances. Here, we develop a model that incorporates non-fatal carry-over effects to predict changes in population size and show how this can be integrated with predictive models of habitat loss and deterioration. Examples from Barn swallows, Greater snow geese and American redstarts show how carry-over effects can be estimated and integrated into the model. Incorporation of carry-over effects should inc

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Luo, Laiwu, Feng Yu, Lei Ren, and Cheng Lu. "Parameter-Free Model Predictive Current Control for PMSM Based on Current Variation Estimation without Position Sensor." Energies 16, no. 19 (2023): 6792. http://dx.doi.org/10.3390/en16196792.

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To remove parameter dependence in existing sensorless control strategies, a parameter-free model predictive current control is proposed for permanent magnet synchronous motor without any position sensor. First, the current variation during one sampling period is analyzed and divided into two elements: natural attenuation and forced response. Second, recursive least squares algorithm is utilized to estimate the future current variation so that the model predictive current control can be successfully executed paying no attention to motor parameters. Meanwhile, the position information is obtaine

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Setiyono, Setiyono, Apif Miptahul Hajji, Aisyah Larasati, and Imam Alfianto. "Simulasi Progres Proyek Konstruksi Time Performance Menggunakan Earned Value Management dengan Integrasi Artificial Neural Network." Bentang : Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil 12, no. 1 (2024): 37–48. http://dx.doi.org/10.33558/bentang.v12i1.7296.

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Digitization of construction using technologies such as AI, Big Data, Machine Learning, and Internet of Things (IoT) can help improve the productivity and efficiency of the construction industry. Machine learning methods such as Artificial Neural Network (ANN) are used to solve complex problems, including in construction projects. In addition, Earned Value Management (EVM) is used as a method to analyze and control project performance and estimate project completion time. Although EVM has the disadvantage of predicting estimated project completion times that are linear in nature, the use of no

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PUCHKOV, Evgeniy Vladimirovich, Natal'ya Alekseyevna OSADCHAYA, and Anton Dmitrievich MURZIN. "Engineering Simulation of Market Value of Construction Materials." Journal of Advanced Research in Law and Economics 9, no. 6 (2019): 2096. http://dx.doi.org/10.14505//jarle.v9.6(36).25.

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Material resources are one of the main elements of the construction cost estimate. The nomenclature of material resources includes a large number of items. It is impossible to objectively estimate the construction cost, capital construction, and maintenance works without carrying out monitoring of the prices of constructional resources and accounting for these data in estimates. The purpose of the present study consists in developing an engineering approach to the simulation of pricing in the construction materials’ market. To achieve this purpose, we use mathematical statistics methods such a

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Bello A. and Usman J. D. "The Utility of Mid-Pelvic Dimensions Via Plain Radiography in Predicting Cesarean Delivery." Asian Journal of Medicine and Health 21, no. 11 (2023): 102–14. http://dx.doi.org/10.9734/ajmah/2023/v21i11924.

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Introduction: Studies on the role of pelvimetry in predicting the mode of delivery have yielded mixed results. The aim of this study is to determine utility of the mid-pelvic measurement in predicting the mode of delivery in a cohort of women attempting vaginal delivery. Study Design and Methods: With this prospective cohort study, we evaluated the use of x-ray in predicting the delivery outcome of women attempting to deliver vaginally. Women were recruited before labor at >36 weeks gestational age and X-ray pelvimetry was performed after delivery. The exposures of interest were mid p

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Lee, Minhyeok, and Junhee Seok. "Estimation with Uncertainty via Conditional Generative Adversarial Networks." Sensors 21, no. 18 (2021): 6194. http://dx.doi.org/10.3390/s21186194.

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Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management in which not only discovering the prediction but also the uncertainty of the prediction is essentially required. In order to address such a problem, we propose a predictive probabilistic neural network model, which corresponds to a different manner of using the generator in the conditional Generative Adversari

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Chuemor, Panuwat, Kittipan Rerkasem, Apichat Tantraworasin, Jiraporn Khorana, Theerachai Thammathiwat, and Watchara Pichitsiri. "Twelve-Month Estimated Glomerular Filtration Rate Trajectory at the Time of Kidney Replacement Therapy Counseling Predicts Dialysis Initiation Within One Year." Journal of Clinical Medicine 14, no. 6 (2025): 1981. https://doi.org/10.3390/jcm14061981.

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Background/Objectives: Planned kidney replacement therapy (KRT) proactively selects and prepares appropriate dialysis modalities and ensures timely vascular access—be it arteriovenous or peritoneal—before dialysis is needed. This approach leads to better patient outcomes and fewer complications. We aimed to develop a predictive model using past estimated glomerular filtration rate (eGFR) measurements prior to KRT counseling to estimate individual patients’ likelihood of initiating dialysis. Methods: In this prognostic prediction study, we retrospectively analyzed data from chronic kidney disea

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Chmielewska, Anna, Krzysztof Kujawa, and Bożena Regulska-Ilow. "Accuracy of Resting Metabolic Rate Prediction Equations in Sport Climbers." International Journal of Environmental Research and Public Health 20, no. 5 (2023): 4216. http://dx.doi.org/10.3390/ijerph20054216.

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Resting metabolic rate (RMR) represents the energy required to maintain vital body functions. In dietary practice, RMR is determined by predictive equations on the basis of using body weight or fat-free mass. Our study aimed to assess whether predictive equations used to estimate RMR are reliable tools for estimating the energy requirements of sport climbers. The study included 114 sport climbers whose RMR was measured with a Fitmate WM. Anthropometric measurements were performed with X-CONTACT 356. The resting metabolic rate was measured by indirect calorimetry and was compared with the RMR e

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Molinaro, Annette M. "Diagnostic tests: how to estimate the positive predictive value." Neuro-Oncology Practice 2, no. 4 (2015): 162–66. http://dx.doi.org/10.1093/nop/npv030.

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Abstract When a patient receives a positive test result from a diagnostic test they assume they have the disease. However, the positive predictive value (PPV), ie the probability that they have the disease given a positive test result, is rarely equal to one. To assist their patients, doctors must explain the chance that they do in fact have the disease. However, physicians frequently miscalculate the PPV as the sensitivity and/or misinterpret the PPV, which results in increased anxiety in patients and generates unnecessary tests and consultations. The reasons for this miscalculation as well a

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Naik, Akash Kumar, and Krishna Prapoorna Biligiri. "Predictive Models to Estimate Phase Angle of Asphalt Mixtures." Journal of Materials in Civil Engineering 27, no. 8 (2015): 04014235. http://dx.doi.org/10.1061/(asce)mt.1943-5533.0001197.

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Vradi, Eleni, Thomas Jaki, Richardus Vonk, and Werner Brannath. "A Bayesian model to estimate the cutoff and the clinical utility of a biomarker assay." Statistical Methods in Medical Research 28, no. 8 (2018): 2538–56. http://dx.doi.org/10.1177/0962280218784778.

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To enable targeted therapies and enhance medical decision-making, biomarkers are increasingly used as screening and diagnostic tests. When using quantitative biomarkers for classification purposes, this often implies that an appropriate cutoff for the biomarker has to be determined and its clinical utility must be assessed. In the context of drug development, it is of interest how the probability of response changes with increasing values of the biomarker. Unlike sensitivity and specificity, predictive values are functions of the accuracy of the test, depend on the prevalence of the disease an

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