Relevant bibliographies by topics / Prediction Accuracy

Contents

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

Academic literature on the topic 'Prediction Accuracy'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Prediction Accuracy"

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Wadi, Faska Aris Y. K., Putu Sugiartawan, Ni Nengah Dita Adriani, and Ni Nengah Dita Adriani. "Analisa Prediksi Time Series Jumlah Kasus Covid-19 Dengan Metode BPNN Di Bali." Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) 4, no. 1 (2022): 24–33. http://dx.doi.org/10.33173/jsikti.124.

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The COVID-19 pandemic has not yet subsided. This epidemic has spread to almost all countries in the world. In Indonesia, especially in the province of Bali, which experienced a large number of additional positive cases, recoveries and deaths from COVID-19, an analysis was carried out. The purpose of this analysis is to be able to obtain accuracy in predicting the addition of COVID-19 cases, recoveries and deaths in the province of Bali, predictions are made using the covid-19 time series data used in making predictions. what was done obtained the best and not good prediction accuracy, predicti
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Jierula, Alipujiang, Shuhong Wang, Tae-Min OH, and Pengyu Wang. "Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data." Applied Sciences 11, no. 5 (2021): 2314. http://dx.doi.org/10.3390/app11052314.

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Accuracy metrics have been widely used for the evaluation of predictions in machine learning. However, the selection of an appropriate accuracy metric for the evaluation of a specific prediction has not yet been specified. In this study, seven of the most used accuracy metrics in machine learning were summarized, and both their advantages and disadvantages were studied. To achieve this, the acoustic emission data of damage locations were collected from a pile hit test. A backpropagation artificial neural network prediction model for damage locations was trained with acoustic emission data usin
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Cheng, Dehe, Jinlong Li, Shuwei Guo, et al. "Genomic Prediction for Germplasm Improvement Through Inter-Heterotic-Group Line Crossing in Maize." International Journal of Molecular Sciences 26, no. 6 (2025): 2662. https://doi.org/10.3390/ijms26062662.

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Germplasm improvement is essential for maize breeding. Currently, intra-heterotic-group crossing is the major method for germplasm improvement, while inter-heterotic-group crossing is also used in breeding but not in a systematic way. In this study, five inbred lines from four heterotic groups were used to develop a connected segregating population through inter-heterotic-group line crossing (CSPIC), which comprised 5 subpopulations with 535 doubled haploid (DH) lines and 15 related test-cross populations including 1568 hybrids. Significant genetic variation was observed in most subpopulations
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Malkin, Zinovy. "On Estimate of Real Accuracy of EOP Prediction." International Astronomical Union Colloquium 178 (2000): 505–10. http://dx.doi.org/10.1017/s0252921100061674.

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AbstractTo estimate the real accuracy of EOP predictions, real-time predictions made by the IERS Subbureau for Rapid Service and Prediction (USNO) and at the IAA EOP Service are analyzed. Methods of estimating prediction accuracy are discussed.
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Zhang, Chenglong, and Hyunchul Ahn. "E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors." Education Sciences 13, no. 11 (2023): 1130. http://dx.doi.org/10.3390/educsci13111130.

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This study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s time series was considered, and data from previous semesters were used to predict the current semester’s results. Each course was predicted separately so that the research reflected reality as closely as possible. Three different methods for selecting training data were listed. Since the at-risk pre
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Harahap, Rahma Sari, Iskandar Muda, and Rina Br Bukit. "Analisis penggunaan metode Altman Z-Score dan Springate untuk mengetahui potensi terjadinya Financial Distress pada perusahaan manufaktur sektor industri dasar dan kimia Sub Sektor semen yang terdaftar di Bursa Efek Indonesia 2000-2020." Owner 6, no. 4 (2022): 4315–25. http://dx.doi.org/10.33395/owner.v6i4.1576.

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The objective of the research is to find out the result of predicting bankruptcy, using Altman Z-Score and Springate methods in the manufacturing companies of basic industrial and chemistry sectors, cement sub-sector listed on BEI (Indonesia Stock Exchange) in the period of 2000-2020 and to determine the most accurate predicting method of bankruptcy to be applied in the manufacturing companies in basic industrial and chemistry sectors, cement sub-sector. The research employs descriptive quantitative method. The samples are taken by using purposive sampling method with three manufacture compani
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Abanades, Brennan, Guy Georges, Alexander Bujotzek, and Charlotte M. Deane. "ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation." Bioinformatics 38, no. 7 (2022): 1877–80. http://dx.doi.org/10.1093/bioinformatics/btac016.

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Abstract Motivation Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen-binding function. The key area for antigen binding and the main area of structural variation in antibodies are concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a st
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Fang, Yiheng. "Prediction of the Ammonia Nitrogen Content with Improved Grey Model by Markov Chain." Highlights in Science, Engineering and Technology 88 (March 29, 2024): 156–61. http://dx.doi.org/10.54097/zee1cd17.

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Water pollution prediction plays a crucial role in environmental protection and sustainable development. This study proposes an innovative approach to enhance the accuracy of water pollution prediction by combining the grey prediction model (GM) with Markov chain analysis. This research focuses on predicting the concentration of ammonia nitrogen (NH3-N) in Dongting Lake, a significant water body. Grey prediction models (GM) are utilized to forecast NH3-N content, addressing the challenge posed by incomplete or insufficient data. However, due to the dynamic nature of water quality indicators, G
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Wu, Menglong, Yicheng Ye, Nanyan Hu, Qihu Wang, Huimin Jiang, and Wen Li. "EMD-GM-ARMA Model for Mining Safety Production Situation Prediction." Complexity 2020 (June 8, 2020): 1–14. http://dx.doi.org/10.1155/2020/1341047.

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In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used
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Francisco, Micanaldo Ernesto, Thaddeus M. Carvajal, and Kozo Watanabe. "Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction." PLOS Neglected Tropical Diseases 18, no. 10 (2024): e0012599. http://dx.doi.org/10.1371/journal.pntd.0012599.

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Background Spatiotemporal dengue forecasting using machine learning (ML) can contribute to the development of prevention and control strategies for impending dengue outbreaks. However, training data for dengue incidence may be inflated with frequent zero values because of the rarity of cases, which lowers the prediction accuracy. This study aimed to understand the influence of spatiotemporal resolutions of data on the accuracy of dengue incidence prediction using ML models, to understand how the influence of spatiotemporal resolution differs between quantitative and qualitative predictions of
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Dissertations / Theses on the topic "Prediction Accuracy"

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GAO, HONGLIANG. "IMPROVING BRANCH PREDICTION ACCURACY VIA EFFECTIVE SOURCE INFORMATION AND PREDICTION ALGORITHMS." Doctoral diss., University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3286.

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Modern superscalar processors rely on branch predictors to sustain a high instruction fetch throughput. Given the trend of deep pipelines and large instruction windows, a branch misprediction will incur a large performance penalty and result in a significant amount of energy wasted by the instructions along wrong paths. With their critical role in high performance processors, there has been extensive research on branch predictors to improve the prediction accuracy. Conceptually a dynamic branch prediction scheme includes three major components: a source, an information processor, and a predict
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Vasudev, R. Sashin, and Ashok Reddy Vanga. "Accuracy of Software Reliability Prediction from Different Approaches." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1298.

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Many models have been proposed for software reliability prediction, but none of these models could capture a necessary amount of software characteristic. We have proposed a mixed approach using both analytical and data driven models for finding the accuracy in reliability prediction involving case study. This report includes qualitative research strategy. Data is collected from the case study conducted on three different companies. Based on the case study an analysis will be made on the approaches used by the companies and also by using some other data related to the organizations Software Qua
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Govender, Evandarin. "An intelligent deflection prediction system for machining of flexible components." Thesis, Nottingham Trent University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367158.

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Ilska, Joanna Jadwiga. "Understanding genomic prediction in chickens." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/15876.

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Genomic prediction (GP) is a novel tool used for prediction of EBVs by using molecular markers. Within the last decade, GP has been widely introduced into routine evaluations of cattle, pig and sheep populations, however, its application in poultry has been somewhat delayed, and studies published to date have been limited in terms of population size and marker densities. This study shows a thorough evaluation of the benefits that GP could bring into routine evaluations of broiler chickens, with particular attention given to the accuracy and bias of Genomic BLUP (GBLUP) predictions. The data us
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Groppe, Matthias. "Influences on aircraft target off-block time prediction accuracy." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/7277.

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With Airport Collaborative Decision Making (A-CDM) as a generic concept of working together of all airport partners, the main aim of this research project was to increase the understanding of the Influences on the Target Off-Block Time (TOBT) Prediction Accuracy during A-CDM. Predicting the TOBT accurately is important, because all airport partners use it as a reference time for the departure of the flights after the aircraft turn-round. Understanding such influencing factors is therefore not only required for finding measures to counteract inaccurate TOBT predictions, but also for establishin
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DeBlasio, Dan, and John Kececioglu. "Core column prediction for protein multiple sequence alignments." BIOMED CENTRAL LTD, 2017. http://hdl.handle.net/10150/623957.

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Background: In a computed protein multiple sequence alignment, the coreness of a column is the fraction of its substitutions that are in so-called core columns of the gold-standard reference alignment of its proteins. In benchmark suites of protein reference alignments, the core columns of the reference alignment are those that can be confidently labeled as correct, usually due to all residues in the column being sufficiently close in the spatial superposition of the known three-dimensional structures of the proteins. Typically the accuracy of a protein multiple sequence alignment that has bee
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Salam, Patrous Ziad, and Safir Najafi. "Evaluating Prediction Accuracy for Collaborative Filtering Algorithms in Recommender Systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186456.

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Recommender systems are a relatively new technology that is commonly used by e-commerce websites and streaming services among others, to predict user opinion about products. This report studies two specific recommender algorithms, namely FunkSVD, a matrix factorization algorithm and Item-based collaborative filtering, which utilizes item similarity. This study aims to compare the prediction accuracy of the algorithms when ran on a small and a large dataset. By performing cross-validation on the algorithms, this paper seeks to obtain data that supposedly may clarify ambiguities regarding the ac
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Schellekens, Fons Jozef. "Fundamentals, accuracy and input parameters of frost heave prediction models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ26887.pdf.

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Norberg, Sven. "Prediction of the fatigue limit : accuracy of post-processing methods." Licentiate thesis, Stockholm, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4061.

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Schellekens, Fons Jozef Carleton University Dissertation Earth Sciences. "Fundamentals, accuracy and input parameters of frost heave prediction models." Ottawa, 1997.

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Books on the topic "Prediction Accuracy"

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University of Texas at Austin. Construction Industry Institute. Improving Early Estimates Research Team., ed. Quantitative prediction of estimate accuracy. Construction Industry Institute, University of Texas at Austin, 1999.

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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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Saleh, Muftah A. Distillation clear liquid and froth height prediction accuracy. UMIST, 1995.

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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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G, Sigari, Costi T, Michigan State University. Division of Engineering Research., and United States. National Aeronautics and Space Administration., eds. Effect of accuracy of wind power prediction on power system operator: Final report. College of Engineering, Michigan State University, 1985.

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Hanson, R. Karl. The accuracy of recidivism risk assessments for sexual offenders: A meta-analysis. Public Safety and Emergency Preparedness Canada, 2007.

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Alfred, Buch. Improvement of fatigue life prediction accuracy for various realistic loading spectra by use of correction factors. Technion-Israel Institute of Technology, Dept. of Aeronautical Engineering, 1985.

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Charuk, Kerry A. Accuracy in prediction of personality as a function of sex length of description, task order, and actual score. Laurentian University, Department of Psychology, 1985.

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Terlecki-Zaniewicz, Georg. Community evaluation of crowd-sourced ideas: An explorative study on how to improve the prediction accuracy of crowdsourcing communities. AV Akademikerverlag, 2017.

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A, Lipa John, and United States. National Aeronautics and Space Administration., eds. High accuracy thermal conductivity measurements near the lambda transition of helium with very high temperature resolution: Final report for NASA-FIR grant #NAG 2-276. National Aeronautics and Space Administration, 1989.

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Book chapters on the topic "Prediction Accuracy"

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Webb, Geoffrey I., and Damien Brain. "Generality Is Predictive of Prediction Accuracy." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11677437_1.

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Kjeldsberg, Fabian, Ziaul Haque Munim, Morten Bustgaard, Sahil Bhagat, Emilia Lindroos, and Per Haavardtun. "Sensitivity of Predictive Performance Assessment Accuracy in Varying k-fold Cross Validation." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84170-5_7.

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Abstract In machine learning (ML) applications, cross-validation (CV) allows greater generalizability of a trained algorithm over out-of-sample or new data. This study explores the accuracy of trained ML algorithms in predicting student performance in a maritime simulator exercise scenario in four different k-fold CVs. Three, five, eight, and ten-fold CVs were trained using a cloud-ML platform. Three top-performing ML algorithms were evaluated considering log loss, accuracy, and area under the curve (AUC). The results indicate higher predictive accuracy with increasing k in CV folds. Consideri
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Sanz-Cruzado, Javier, and Pablo Castells. "Beyond Accuracy in Link Prediction." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52485-2_9.

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Spenrath, Yorick, Marwan Hassani, and Boudewijn F. van Dongen. "Online Prediction of Aggregated Retailer Consumer Behaviour." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.

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AbstractPredicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumer
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Cheng, Qian, Xinyu Jiao, Mengmeng Yang, Mingliang Yang, Kun Jiang, and Diange Yang. "Advancing Autonomous Driving Safety Through LLM Enhanced Trajectory Prediction." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_71.

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AbstractIn recent years, there has been remarkable progress in autonomous driving technology. To improve the safety of autonomous driving comprehensively, accurate predictions for all traffic agents are crucial. Typically, the graph neural network is widely employed for the trajectory prediction. To enhance the prediction accuracy rate, this paper utilizes a finetuned vision-to-language large model to extract driving intentions. With the well-designed prompt and the supervision of the specific dataset, the LLM (large language model) can analyze the current traffic condition and give the corres
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Fu, Lina, Faming Li, Jing Zhou, Xuejin Wen, Jinhui Yao, and Michael Shepherd. "Event Prediction in Healthcare Analytics: Beyond Prediction Accuracy." In Lecture Notes in Computer Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42996-0_15.

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Fani Sani, Mohammadreza, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. "Event Log Sampling for Predictive Monitoring." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_12.

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AbstractPredictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity predictio
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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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Renner, Christian, Sebastian Ernst, Christoph Weyer, and Volker Turau. "Prediction Accuracy of Link-Quality Estimators." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19186-2_1.

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Xaba, Diteboho, Katleho Makatjane, and Amogelang Senosi. "Prediction Accuracy of SARIMA-STAR-CNE." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-73324-6_31.

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Conference papers on the topic "Prediction Accuracy"

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Antad, Sonali, Leevan Herald, Prachi Lasurkar, Keyur Pande, Spandan Papade, and Shreyas Nagarkar. "High accuracy Diabetes Prediction Using Logistic Regression." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007358.

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Senthil Pandi, S., S. Dhanasekaran, S. Murugan B, and P. Kumar. "Accuracy Enhancement and Predictive Analysis in Cardiovascular Diseases Prediction Using Neural Networks." In 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2025. https://doi.org/10.1109/ickecs65700.2025.11035814.

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Sharma, Kuldeep, Raman Kumar, Rajan Verma, Amandeep Kaur, Sanjeev Kumar Shah, and Mohemmed Hussien. "Enhancing PCOS Prediction Accuracy Through Machine Learning Optimization." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10690877.

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Prasetyo, Simeon Yuda, Santy, and Rezki Yunanda. "Diabetes Risk Prediction Exploration: Uncovering Patterns and Enhancing Predictive Accuracy through Ensemble Learning." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10748155.

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Jadoon, Usman Khan, Ismael D�az, and Manuel Rodr�guez. "A Comparative Study of Aspen Plus and Machine Learning Models for Syngas Prediction in Biomass-Plastic Waste Co-gasification." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.174749.

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The co-gasification of biomass and plastic waste offers a promising pathway for sustainable syngas production, necessitating precise prediction of its composition to optimize efficiency. This study compares the performance of Aspen Plus models, including the thermodynamic equilibrium model (TEM) and restricted thermodynamic equilibrium model (RTM), with machine learning (ML) techniques, focusing on the support vector regression (SVR) for syngas prediction during steam and air co-gasification. Aspen Plus simulations provided valuable mechanistic insights, while the ML model demonstrated superio
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Adamopoulos, Panagiotis. "Beyond rating prediction accuracy." In RecSys '13: Seventh ACM Conference on Recommender Systems. ACM, 2013. http://dx.doi.org/10.1145/2507157.2508073.

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Burnap, Alex, Yi Ren, Honglak Lee, Richard Gonzalez, and Panos Y. Papalambros. "Improving Preference Prediction Accuracy With Feature Learning." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35440.

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Motivated by continued interest within the design community to model design preferences, this paper investigates the question of predicting preferences with particular application to consumer purchase behavior: How can we obtain high prediction accuracy in a consumer preference model using market purchase data? To this end, we employ sparse coding and sparse restricted Boltzmann machines, recent methods from machine learning, to transform the original market data into a sparse and high-dimensional representation. We show that these ‘feature learning’ techniques, which are independent from the
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Ravi, Sujith, Kevin Knight, and Radu Soricut. "Automatic prediction of parser accuracy." In the Conference. Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1613715.1613829.

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Williamson, Hugh S. "Accuracy Prediction for Directional MWD." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 1999. http://dx.doi.org/10.2118/56702-ms.

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Williams, S. R., T. R. Betts, R. Gottschalg, et al. "Accuracy of Energy Prediction Methodologies." In Conference Record of the 2006 IEEE 4th World Conference on Photovoltaic Energy Conversion. IEEE, 2006. http://dx.doi.org/10.1109/wcpec.2006.279946.

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Reports on the topic "Prediction Accuracy"

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King, Bruce Hardison, Clifford Hansen, and Joshua Stein. Final Technical Report: Increasing Prediction Accuracy. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1233821.

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Serão, Nick V. L., Bob Kemp, Benny Mote, et al. Accuracy of Genomic Prediction for PRRS Antibody Response. Iowa State University, 2015. http://dx.doi.org/10.31274/ans_air-180814-1361.

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Hayr, Melanie K., Mahdi Saatchi, Dave Johnson, and Dorian J. Garrick. Improving Accuracy of Genomic Prediction in Holstein Friesians. Iowa State University, 2013. http://dx.doi.org/10.31274/ans_air-180814-717.

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Rauscher, H. Michael. The microcomputer scientific software series 4: testing prediction accuracy. U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station, 1986. http://dx.doi.org/10.2737/nc-gtr-107.

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Hayr, Melanie K., Mahdi Saatchi, Dave Johnson, and Dorian J. Garrick. Improving the Accuracy of Genomic Prediction of Milk Fat. Iowa State University, 2005. http://dx.doi.org/10.31274/ans_air-180814-1155.

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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Imas, Alex, Minah Jung, Silvia Saccardo, and Joachim Vosgerau. The Impact of Joint versus Separate Prediction Mode on Forecasting Accuracy. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30611.

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Mosalam, Khalid, Issac Pang, and Selim Gunay. Towards Deep Learning-Based Structural Response Prediction and Ground Motion Reconstruction. Pacific Earthquake Engineering Research Center, 2025. https://doi.org/10.55461/ipos1888.

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This research presents a novel methodology that uses Temporal Convolutional Networks (TCNs), a state-of-the-art deep learning architecture, for predicting the time history of structural responses to seismic events. By leveraging accelerometer data from instrumented buildings, the proposed approach complements traditional structural analysis models, offering a computationally efficient alternative to nonlinear time history analysis. The methodology is validated across a broad spectrum of structural scenarios, including buildings with pronounced higher-mode effects and those exhibiting both line
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Piyasatian, Napapan, and Jack C. M. Dekkers. Accuracy of Genomic Prediction when Accounting for Population Structure and Polygenic Effects. Iowa State University, 2013. http://dx.doi.org/10.31274/ans_air-180814-1252.

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10

Moses, O. D. Learning Curve and Rate Adjustment Models: Comparative Prediction Accuracy Under Varying Conditions. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230075.

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