Relevant bibliographies by topics / Predictive model accuracy

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Predictive model accuracy'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Predictive model accuracy"

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Zhang, Binzhe, Min Duan, Yufan Sun, Yatong Lyu, Yali Hou, and Tao Tan. "Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model." Atmosphere 14, no. 10 (2023): 1478. http://dx.doi.org/10.3390/atmos14101478.

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Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehens
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Pali, Prof Pankaj, and Prof Saurabh Verma. "High-Accuracy Machine Learning Model for Predicting Diabetes Mellitus Progression." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 06 (2024): 9101–9. http://dx.doi.org/10.15680/ijircce.2024.1206106.

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Diabetes mellitus, a chronic metabolic disorder marked by persistent hyperglycemia, presents a major global health challenge, affecting over 463 million adults worldwide. Timely and accurate prediction of disease progression is crucial for mitigating complications and improving patient outcomes. This research paper details the development and validation of an advanced machine learning model designed to predict diabetes progression. The proposed model integrates various machine learning algorithms, such as regression analysis, decision trees, and neural networks, to enhance predictive accuracy.
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Brunello, Gabriel Hideki Vatanabe, and Eduardo Yoshio Nakano. "A Bayesian Measure of Model Accuracy." Entropy 26, no. 6 (2024): 510. http://dx.doi.org/10.3390/e26060510.

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Ensuring that the proposed probabilistic model accurately represents the problem is a critical step in statistical modeling, as choosing a poorly fitting model can have significant repercussions on the decision-making process. The primary objective of statistical modeling often revolves around predicting new observations, highlighting the importance of assessing the model’s accuracy. However, current methods for evaluating predictive ability typically involve model comparison, which may not guarantee a good model selection. This work presents an accuracy measure designed for evaluating a model
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Allemar Jhone P. Delima. "An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization." International Journal of Engineering and Technology Innovation 10, no. 4 (2020): 280–92. http://dx.doi.org/10.46604/ijeti.2020.4646.

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The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability.
 The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improve
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Jiang, Min Lan, Xiao Dong Wang, and Xiu Hui He. "Dynamic Accuracy Loss Prediction Model Based on BPNN." Advanced Materials Research 108-111 (May 2010): 795–98. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.795.

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In this paper, study the dynamic accuracy loss prediction model of measurement system, using measurement standard deviation as the systemic accuracy, the change of measurement standard deviation in different time as the systemic accuracy loss. Using BPNN build the dynamic accuracy loss prediction model about the practical measurement, model precision reach , realized real-time prediction of systemic accuracy loss , the establishment of predictive models to the system Real-time error correction and compensation to provide a theoretical basis, and that can cost-effectively improve the system of
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Han, Runxing, Ruixuan Meng, and Qianwei Zhu. "Predictive Analytics in Heart Disease: Leveraging LightGBM for Improved Diagnostic Accuracy." Applied and Computational Engineering 112, no. 1 (2024): 210–17. https://doi.org/10.54254/2755-2721/2025.18135.

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The prevalence of heart disease is significant, making it a prominent health concern, and early prediction and diagnosis are critical. The application of artificial intelligence algorithms in heart disease prediction is highly promising. This study utilizes the LightGBM algorithm for the predictive modelling of a heart disease dataset containing 1025 records and 14 health indicators. Predictive models were effectively built through data preprocessing, feature selection, and model training. The results show that the LightGBM model got 98.54% accuracy in the test set is better than the model of
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Yang, Ke. "Predicting Student Performance Using Artificial Neural Networks." Journal of Arts, Society, and Education Studies 6, no. 1 (2024): 45–77. http://dx.doi.org/10.69610/j.ases.20240515.

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<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of s
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Wei, Li-Li, Yue-Shuai Pan, Yan Zhang, Kai Chen, Hao-Yu Wang, and Jing-Yuan Wang. "Application of machine learning algorithm for predicting gestational diabetes mellitus in early pregnancy†." Frontiers of Nursing 8, no. 3 (2021): 209–21. http://dx.doi.org/10.2478/fon-2021-0022.

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Abstract Objective To study the application of a machine learning algorithm for predicting gestational diabetes mellitus (GDM) in early pregnancy. Methods This study identified indicators related to GDM through a literature review and expert discussion. Pregnant women who had attended medical institutions for an antenatal examination from November 2017 to August 2018 were selected for analysis, and the collected indicators were retrospectively analyzed. Based on Python, the indicators were classified and modeled using a random forest regression algorithm, and the performance of the prediction
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Ji, Jung-Hwan, Sung-Gwe Ahn, Youngbum Yoo, et al. "Prediction of a Multi-Gene Assay (Oncotype DX and Mammaprint) Recurrence Risk Group Using Machine Learning in Estrogen Receptor-Positive, HER2-Negative Breast Cancer—The BRAIN Study." Cancers 16, no. 4 (2024): 774. http://dx.doi.org/10.3390/cancers16040774.

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This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2− breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting.
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Maju, Sonam V., and Gnana Prakasi Oliver Sirya Pushpam. "A novel two-tier feature selection model for Alzheimer's disease prediction." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 227–35. https://doi.org/10.11591/ijeecs.v33.i1.pp227-235.

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The interdisciplinary research studies of artificial intelligence in health sector is bringing drastic life saving changes in the healthcare domain. One such aspect is the early disease prediction using machine learning and regression algorithms. The purpose of this research is to improve the prediction accuracy of Alzheimer ’s disease by analysing the correlation of unexplored Alzheimer causing diseases. The work proposes Chi square-lasso ridge linear (Chi-LRL) model, a new two-tier feature ranking model which recognizes the significance of including diabetes, blood pressure and body ma
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Dissertations / Theses on the topic "Predictive model accuracy"

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Mitchinson, Pelham James. "Crowding indices : experimental methodology and predictive accuracy." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302320.

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Modin, Larsson Jim. "Predictive Accuracy of Linear Models with Ordinal Regressors." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-273958.

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This paper considers four approaches to ordinal predictors in linear regression to evaluate how these contrast with respect to predictive accuracy. The two most typical treatments, namely, dummy coding and classic linear regression on assigned level scores are compared with two improved methods; penalized smoothed coefficients and a generalized additive model with cubic splines. A simulation study is conducted to assess all on the basis of predictive performance. Our results show that the dummy based methods surpass the numeric at low sample sizes. Although, as sample size increases, differenc
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Horii, M. Michael. "A Predictive Model for Multi-Band Optical Tracking System (MBOTS) Performance." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579658.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV<br>In the wake of sequestration, Test and Evaluation (T&E) groups across the U.S. are quickly learning to make do with less. For Department of Defense ranges and test facility bases in particular, the timing of sequestration could not be worse. Aging optical tracking systems are in dire need of replacement. What's more, the increasingly challenging missions of today require advanced technology, flexi
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Sowan, Bilal I. "Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stag
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Sowan, Bilal Ibrahim. "Enhancing fuzzy associative rule mining approaches for improving prediction accuracy : integration of fuzzy clustering, apriori and multiple support approaches to develop an associative classification rule base." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5387.

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Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stage
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Bye, Robin Trulssen Electrical Engineering &amp Telecommunications Faculty of Engineering UNSW. "The BUMP model of response planning: a neuroengineering account of speed-accuracy tradeoffs, velocity profiles, and physiological tremor in movement." Publisher:University of New South Wales. Electrical Engineering & Telecommunications, 2009. http://handle.unsw.edu.au/1959.4/43542.

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Speed-accuracy tradeoffs, velocity profiles, and physiological tremor are fundamental characteristics of human movement. The principles underlying these phenomena have long attracted major interest and controversy. Each is well established experimentally but as yet they have no common theoretical basis. It is proposed that these three phenomena occur as the direct consequence of a movement response planning system that acts as an intermittent optimal controller operating at discrete intervals of ~100 ms. The BUMP model of response planning describes such a system. It forms the kernel of adapti
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Macdonald, Kristian I. "Development and Validation of an Administrative Data Algorithm to Identify Adults who have Endoscopic Sinus Surgery for Chronic Rhinosinusitis." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35148.

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Objective: 1) Systematic review on the accuracy of Chronic Rhinosinusitis (CRS) identification in administrative databases; 2) Develop an administrative data algorithm to identify CRS patients who have endoscopic sinus surgery (ESS). Methods: A chart review was performed for all ESS surgical encounters at The Ottawa Hospital from 2011-12. Cases were defined as encounters in which ESS for performed for Otolaryngologist-diagnosed CRS. An algorithm to identify patients who underwent ESS for CRS was developed using diagnostic and procedural codes within health administrative data. This algorit
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Nan, Fany. "Forecasting next-day electricity prices: from different models to combination." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426510.

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As a result of deregulation of most power markets around the world electricity price modeling and forecasting have obtained increasing importance in recent years. Large number of models has been studied on a wide range of power markets, from linear time series and multivariate regression models to more complex non linear models with jumps, but results are mixing and there is no single model that provides convincing superior performance in forecasting spot prices. This study considers whether combination forecasts of spot electricity prices are statistically superior to a wide range of single m
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Dang, Huong Dieu. "Rating History, Time and The Dynamic Estimation of Rating Migration Hazard." Thesis, The University of Sydney, 2010. http://hdl.handle.net/2123/6397.

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This thesis employs survival analysis framework (Allison, 1984) and the Cox’s hazard model (Cox, 1972) to estimate the probability that a credit rating survives in its current grade at a certain forecast horizon. The Cox’s hazard model resolves some significant drawbacks of the conventional estimation approaches. It allows a rigorous testing of non-Markovian behaviours and time heterogeneity in rating dynamics. It accounts for the changes in risk factors over time, and features the time structure of probability survival estimates. The thesis estimates three stratified Cox’s hazard models, inc
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Dang, Huong Dieu. "Rating History, Time and The Dynamic Estimation of Rating Migration Hazard." University of Sydney, 2010. http://hdl.handle.net/2123/6397.

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Doctor of Philosophy(PhD)<br>This thesis employs survival analysis framework (Allison, 1984) and the Cox’s hazard model (Cox, 1972) to estimate the probability that a credit rating survives in its current grade at a certain forecast horizon. The Cox’s hazard model resolves some significant drawbacks of the conventional estimation approaches. It allows a rigorous testing of non-Markovian behaviours and time heterogeneity in rating dynamics. It accounts for the changes in risk factors over time, and features the time structure of probability survival estimates. The thesis estimates three stratif
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Books on the topic "Predictive model accuracy"

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Moses, O. Douglas. Learning curve and rate adjustment models: Comparative prediction accuracy under varying conditions. Naval Postgraduate School, 1990.

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Clark, Todd E. Approximately normal tests for equal predictive accuracy in nested models. Research Division, Federal Reserve Bank of Kansas City, 2005.

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Rauscher, Harold M. The microcomputer scientific software series 4: Testing prediction accuracy. U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station, 1986.

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Trevits, M. A. An accurate, user-friendly subsidence prediction model for personal computers. s.n, 1987.

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Model Predictive Control for AC Motors: Robustness and Accuracy Improvement Techniques. Springer, 2023.

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Gong, Chao, Yaofei Han, and Jinqiu Gao. Model Predictive Control for AC Motors: Robustness and Accuracy Improvement Techniques. Springer Singapore Pte. Limited, 2022.

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Anderson, Mark, Francois Hemez, and Scott Doebling. Model Verification and Validation in Engineering Mechanics: Theory and Applications of Uncertainty Quantification and Predictive Accuracy. John Wiley & Sons, 2005.

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Model Verification and Validation in Engineering Mechanics: Theory and Applications of Uncertainty Quantification and Predictive Accuracy. Wiley & Sons, Limited, John, 2004.

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Thompson, Summer L., and Stephanie C. Dulawa. Pharmacological and Behavioral Rodent Models of OCD. Edited by Christopher Pittenger. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228163.003.0035.

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Obsessive-compulsive disorder (OCD) is a severe psychiatric disorder characterized by obsessions and/or compulsions. Only half of patients respond to first-line pharmacological treatments, and symptom relief is typically partial, even in responders. Gaining a better understanding of OCD etiology could lead to better treatments, and potentially to prevention. Animal models are a useful tool for studying neurobiological mechanisms underlying psychiatric phenotypes. Effective use of animal models requires identification of reliable, quantifiable features of the disorder of interest that can be me
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Methods for evaluating the predictive accuracy of structural dynamic models: Final report. The Administration, 1991.

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Book chapters on the topic "Predictive model accuracy"

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Han, Yaofei, Chao Gong, and Jinqiu Gao. "MPC Accuracy Improvement for PMSMs—Part I." In Model Predictive Control for AC Motors. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8066-3_4.

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Han, Yaofei, Chao Gong, and Jinqiu Gao. "MPC Accuracy Improvement for PMSMs—Part II." In Model Predictive Control for AC Motors. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8066-3_5.

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Kim, Yang-Jin. "Predictive Accuracy of Prediction Model for Interval-Censored Data." In Emerging Topics in Modeling Interval-Censored Survival Data. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12366-5_3.

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Stephan, Blossom Christa Maree. "Models for Predicting Risk of Dementia: Predictive Accuracy and Model Complexity." In International Perspectives on Aging. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06650-9_10.

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Hurtado, Remigio, and Eduardo Ayora. "Intelligent System for Predicting Bank Policy Acceptance by Ensemble Machine Learning and Model Explanation." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_41.

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Abstract Efficient management of financial resources is crucial for the sustainability and competitiveness of banks, particularly in optimizing term deposit subscriptions to maintain liquidity. This paper introduces an advanced intelligent system for predicting term deposit acceptance using ensemble machine learning techniques. Our approach combines Random Forest and K-Nearest Neighbors (KNN) models to enhance prediction accuracy while providing clear explanations. The system follows the CRISP-DM methodology, which includes detailed phases of data preparation, modeling, fine-tuning, and model
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Qian, Shenghua. "Vehicle Collision Prediction Model on the Internet of Vehicles." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_53.

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AbstractAn active collision prediction model on the Internet of Vehicles is proposed. Through big data calculation on the cloud computing platform, the model predicts whether the vehicles may collide and the time of the collision, so the server actively sends warning signals to the vehicles that may collide. Firstly, the vehicle collision prediction model preprocesses the data set, and then constructs a new feature set through feature engineering. For the imbalance of the data set, which affects predictive results, SMOTE algorithm is proposed to generate new samples. Then, the LightGBM algorit
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Khodabandeh, Peyman, Fazel Azarhomayun, Mohammad Shekarchi, and Mahdi Kioumarsi. "Predicting Dry Shrinkage Using Machine Learning Methods." In Lecture Notes in Civil Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69626-8_61.

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AbstractModeling drying shrinkage presents significant challenges due to the complexity and multitude of contributing parameters. This study provides detailed insights into the input requirements and predictive capabilities of established models by leveraging various datasets from the NU-ITI database. Initially, the performance of a shrinkage model was evaluated. The data for a machine learning random forest model included eight variables, interpreted through SHapley Additive exPlanations (SHAP), which elucidates the most influential inputs. However, the partial dependency graphs yielded minim
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Mostofi, Fatemeh, Onur Behzat Tokdemir, and Vedat Toğan. "Leveraging Variational Autoencoder for Improved Construction Progress Prediction Performance." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4355-1_51.

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AbstractThe imbalanced construction dataset reduces the accuracy of the machine learning model. This issue that addressed by recent construction management research through different sampling approaches. Despite their advantages, the utilized sampling approaches are reducing the reliability of the prediction model, while posing the risk of artificial bias. The objective of this study is to address the challenge of imbalanced datasets in construction progress prediction models using a novel variational autoencoder (VAE) that generates synthetic data for underrepresented classes. The VAE's encod
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Plevris, Vagelis, Alejandro Jiménez Rios, and Usama A. Ebead. "Exploring the Predictive Performance of Simple Regression Models and ANN in 2D Truss Analysis." In Lecture Notes in Civil Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69626-8_123.

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AbstractThis research investigates the performance of various regression models in predicting critical structural parameters within a plane truss model. The study encompasses linear, second- and third-degree polynomial, and artificial neural network (ANN) regression models, which are evaluated for their accuracy in estimating the maximum displacement, maximum (tensile) stress, and minimum (compressive) stress of the truss under specific loading conditions. The findings unequivocally establish the superiority of the ANN model, showcasing its ability to capture complex nonlinear relationships wi
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Siami, Mohammad, Mohammad Reza Gholamian, Javad Basiri, and Mohammad Fathian. "An Application of Locally Linear Model Tree Algorithm for Predictive Accuracy of Credit Scoring." In Model and Data Engineering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24443-8_15.

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Conference papers on the topic "Predictive model accuracy"

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Dahake, Parihar Suresh, Rahul Mohare, Shravan Chandak, and Pritam Bhadade. "Predictive Analytics in Assessing Employee Retention Rates: An Analysis of Effectiveness and Accuracy of Different Predictive Model." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912426.

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Kamiya, Toshio, and Yusuke Ozawa. "Pointing Accuracy using Model Predictive Control-Based Guidance Control for DESTINY+ Flyby Mission." In IAF Astrodynamics Symposium, Held at the 75th International Astronautical Congress (IAC 2024). International Astronautical Federation (IAF), 2024. https://doi.org/10.52202/078368-0015.

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Gala, Deepali Mayur, Bhakti Pawar, Gayathri Band, Pallavi Dua, Biswa Ranjan Mohanty, and Bhaskar Vijayrao Patil. "Enhancing Predictive Accuracy for Customer Churn in Digital Banking: A Multi-Model Analysis." In 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON). IEEE, 2024. https://doi.org/10.1109/delcon64804.2024.10866346.

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Dehon, Victor, Paulina Quintanilla, and Antonio Del Rio Chanona. "Probabilistic Model Predictive Control for Mineral Flotation using Gaussian Processes." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.122018.

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Recent advancements in machine learning and time series analysis have opened new avenues for improving predictive control in complex systems such as mineral flotation. Techniques leveraging multivariate predictive control in mineral flotation have seen significant progress in recent years. However, challenges in developing an accurate dynamic model that encapsulates both the pulp and froth phases have hindered further advancements. Now, with a readily available model containing equations that describe the physics of flotation froths, an opportunity for novel control strategies presents itself.
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Farlessyost, William, and Shweta Singh. "Improving Mechanistic Model Accuracy with Machine Learning Informed Physics." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.121371.

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Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data.
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Jangama, Vamshi R., and Sridhar Srinivasan. "A Computer Model for Prediction of Corrosion of Carbon Steels." In CORROSION 1997. NACE International, 1997. https://doi.org/10.5006/c1997-97318.

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Abstract Developments in the area of corrosion research have led to predictive methodologies that have very specific applicability and relevance. This has led to a need for development of predictive methods and tools that can be used under a wide variety of conditions relevant to oil and gas production. A hierarchical approach has been used to combine available knowledge and data into a compact and accurate computer model that can be used as a prediction tool under different conditions. The tool is a stand-alone PC application, developed using principles of object-oriented programming and impl
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Herrera-Ruiz, Juan Federico, Javier Fontalvo, and Oscar Andr�s Prado-Rubio. "Hybrid model development for Succinic Acid fermentation: relevance of ensemble learning for enhancing model prediction." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.153338.

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Sustainable development goals have spurred advancements in bioprocess design, driven by improved process monitoring, data storage, and computational power. High-fidelity models are essential for advanced process system engineering, yet accurate parametric models for bioprocessing remain challenging due to overparameterization, often resulting in poor predictive accuracy. Hybrid modeling, combining parametric and non-parametric methods, offers a promising solution by enhancing accuracy while maintaining interpretability. This study explores hybrid models for succinic acid fermentation by Escher
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Jiet, Moses Makuei, Prateek Verma, Aahash Kamble, and Chetan Puri. "A Review on Bayesian Methods for Uncertainty Quantification in Machine Learning Models Enhancing Predictive Accuracy and Model Interpretability." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696308.

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Sharaouva, Aizada B., and Dinara N. Delikesheva. "PREVENTING PIPE STICKING IN OIL AND GAS WELLS USING AN IMPROVED TORQUE AND DRAG MODEL, ALONG WITH ANALYTICAL AND MACHINE LEARNING METHODS." In 24th SGEM International Multidisciplinary Scientific GeoConference 24. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/1.1/s06.81.

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Effectively preventing pipe sticking in oil and gas wells is a key aspect of safety and productivity in the oil and gas industry. This paper presents a new torque and drag model developed using machine learning techniques to improve accuracy and predictive capabilities. The model is based on a comprehensive analysis of many factors, including geological characteristics of the well, drilling parameters, fluid parameters, hydraulic conditions, and production equipment parameters. This research explores the application of a chained regression model combining Multilayer Perceptron (MLP) and XGBoos
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Ye, Xin, Karl Handwerker, and Sören Hohmann. "Adaptive Model Predictive Control for Differential-Algebraic Systems towards a Higher Path Accuracy for Physically Coupled Robots." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802620.

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Reports on the topic "Predictive model accuracy"

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Clark, Todd E., Gergely Ganics, and Elmar Mertens. What is the predictive value of SPF point and density forecasts? Federal Reserve Bank of Cleveland, 2022. http://dx.doi.org/10.26509/frbc-wp-202237.

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This paper presents a new approach to combining the information in point and density forecasts from the Survey of Professional Forecasters (SPF) and assesses the incremental value of the density forecasts. Our starting point is a model, developed in companion work, that constructs quarterly term structures of expectations and uncertainty from SPF point forecasts for quarterly fixed horizons and annual fixed events. We then employ entropic tilting to bring the density forecast information contained in the SPF’s probability bins to bear on the model estimates. In a novel application of entropic
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Khoshnevisan, Sara, Mehdi Norouzi, and Laith Sadik. Use of Machine Learning Methods to Obtain a Reliable Predictive Model for Resilient Modulus of Subgrade Soil. Purdue University, 2024. https://doi.org/10.5703/1288284317768.

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This project explores the development and optimization of predictive models for the resilient modulus (MR) of subgrade soil using advanced machine learning techniques. Comprehensive data from INDOT spanning several years was analyzed to enhance the accuracy of MR predictions. The study not only refined the modeling approach through statistical methods and validation but also identified crucial soil properties that significantly impact MR values. Recommendations for future data collection were made to further improve the models. The developed models and these recommendations will be used to gui
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Fisker, Peter. High-Resolution Estimation of Rural Poverty: Insights from Ethiopia. Data and Evidence to End Extreme Poverty, 2024. https://doi.org/10.55158/deepwp29.

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High-resolution satellite imagery and machine learning techniques enable precise mapping of rural poverty, as demonstrated in this paper for the case of Ethiopia. Using household survey data with precise GPS coordinates, combined with multiple geospatial datasets, we develop a novel two-stage Random Forest model that first predicts roof materials from satellite imagery and then uses this prediction alongside other geospatial features to estimate household consumption levels. The model achieves high predictive accuracy across different spatial scales, with Spearman correlations between 0.81 and
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Debuque-Gonzales, Margarita, and John Paul Corpus. Let's Get Fiscal: Extending the Small Macroeconometric Model of the Philippine Economy. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.43.

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This study presents a small macroeconometric model with a fiscal sector, extending the model presented in Debuque-Gonzales and Corpus (2022). The model retains the original core blocks of domestic demand, international trade, employment, prices, and monetary sectors and adds a fiscal sector consisting of equations for government revenues, expenditures, and debt. Behavioral equations are estimated in error-correction form (using ARDL methodology) on quarterly data from 2002 to 2019. In-sample simulations demonstrate acceptable levels of predictive accuracy for most macroeconomic variables, even
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Reyes, Celia, Connie Bayudan-Dacuycuy, Michael Abrigo, et al. PIDS-BSP Annual Macroeconometric Model for the Philippines: Preliminary Estimates and Ways Forward. Philippine Institute for Development Studies, 2020. https://doi.org/10.62986/dp2020.16.

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Given new programs and policies in the Philippines, there is a need to formulate a macroeconometric model (MEM) to gain more insights on how the economy and its sectors are affected. This paper discusses the estimation of an annual MEM that will be used for policy analysis and forecasting with respect to the opportunities and challenges brought about by new developments. The formulation of an annual MEM is useful in assisting major macroeconomic stakeholder, such as the National Economic and Development Authority and the Bangko Sentral ng Pilipinas (BSP) in their conduct of policy simulations,
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Nasr, Elhami, Tariq Shehab, Nigel Blampied, and Vinit Kanani. Estimating Models for Engineering Costs on the State Highway Operation and Protection Program (SHOPP) Portfolio of Projects. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2365.

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The State Highway Operation and Protection Program (SHOPP) is crucial for maintaining California’s 15,000-mile state highway system, which includes projects like pavement rehabilitation, bridge repair, safety enhancements, and traffic management systems. Administered by Caltrans, SHOPP aims to preserve highway efficiency and safety, supporting economic growth and public safety. This research aimed to develop robust cost-estimating models to improve budgeting and financial planning, aiding Caltrans, the California Transportation Commission (CTC), and the Legislature. The research team collected
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Roldán-Ferrín, Felipe, and Julián A. Parra-Polania. ENHANCING INFLATION NOWCASTING WITH ONLINE SEARCH DATA: A RANDOM FOREST APPLICATION FOR COLOMBIA. Banco de la República, 2025. https://doi.org/10.32468/be.1318.

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This paper evaluates the predictive capacity of a machine learning model based on Random Forests (RF), combined with Google Trends (GT) data, for nowcasting monthly inflation in Colombia. The proposed RF-GT model is trained using historical inflation data, macroeconomic indicators, and internet search activity. After optimizing the model’s hyperparameters through time series cross-validation, we assess its out-of-sample performance over the period 2023–2024. The results are benchmarked against traditional approaches, including SARIMA, Ridge, and Lasso regressions, as well as professional forec
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Lalisse, Matthias. Measuring the Impact of Campaign Finance on Congressional Voting: A Machine Learning Approach. Institute for New Economic Thinking Working Paper Series, 2022. http://dx.doi.org/10.36687/inetwp178.

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How much does money drive legislative outcomes in the United States? In this article, we use aggregated campaign finance data as well as a Transformer based text embedding model to predict roll call votes for legislation in the US Congress with more than 90% accuracy. In a series of model comparisons in which the input feature sets are varied, we investigate the extent to which campaign finance is predictive of voting behavior in comparison with variables like partisan affiliation. We find that the financial interests backing a legislator’s campaigns are independently predictive in both chambe
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Ziegler, Nancy, Nicholas Webb, John Gillies, et al. Plant phenology drives seasonal changes in shear stress partitioning in a semi-arid rangeland. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47680.

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Accurate representation of surface roughness in predictive models of aeolian sediment transport and dust emission is required for model accuracy. While past studies have examined roughness effects on drag partitioning, the spatial and temporal variability of surface shear velocity and the shear stress ratio remain poorly described. Here, we use a four-month dataset of total shear velocity (u*) and soil surface shear velocity (us*) measurements to examine the spatiotemporal variability of the shear stress ratio (R) before, during, and after vegetation green-up at a honey mesquite (Prosopis glan
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Naderer, Thomas, Alexander Hammer, Wolfgang Roland, Maximilian Zacher, and Gerald Berger-Weber. Optimizing modeling the multilayer coextrusion flow of non-newtonian fluids through rectangular ducts: appropriate shear rate definition for a local power law formulation. Universidad de los Andes, 2024. https://doi.org/10.51573/andes.pps39.gs.ms.4.

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The accuracy of viscosity predictions is a crucial aspect of polymer melt flow modeling and essential for the design of coextrusion die systems. In the field of non-Newtonian fluid modeling for coextrusion flows through rectangular ducts, significant progress has been made in understanding multilayer flow dynamics. Our fundamental research, employing numerical techniques such as the shooting method, finite element method, and finite difference method for flow evaluation, has established a critical base for the field. Our current research advances fluid dynamics by refining our existing numeric
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