Relevant bibliographies by topics / Prediction Accuracy

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  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: 11 January 2025

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Journal articles on the topic "Prediction Accuracy"

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Rasero, Javier, Amy Isabella Sentis, Fang-Cheng Yeh, and Timothy Verstynen. "Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability." PLOS Computational Biology 17, no. 3 (March 5, 2021): e1008347. http://dx.doi.org/10.1371/journal.pcbi.1008347.

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Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.
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Yang, Ke. "Predicting Student Performance Using Artificial Neural Networks." Journal of Arts, Society, and Education Studies 6, no. 1 (May 15, 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 student performance predictions. Future research directions may include further optimization of the model's algorithms and expansion of the data sample size to improve prediction accuracy and applicability. The method demonstrated exceptional performance in predicting student outcomes, offering high accuracy and efficacy. By mining and analyzing extensive educational data, a predictive model was established and validated through experiments. We introduce a novel predictive model to the field of education, providing robust support for student learning and educational decision-making. Future enhancements can optimize the model, increase prediction precision, and expand application fields to better serve the development of educational endeavors.</span></p>
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Diqi, Mohammad, and Hamzah Hamzah. "Improving Stock Price Prediction Accuracy with StacBi LSTM." JISKA (Jurnal Informatika Sunan Kalijaga) 9, no. 1 (January 25, 2024): 10–26. http://dx.doi.org/10.14421/jiska.2024.9.1.10-26.

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This research aimed to enhance stock price prediction accuracy using the Stacked Bidirectional Long Short-Term Memory (StacBi LSTM) model. The study addressed the challenge of capturing long-term dependencies and temporal patterns inherent in stock price data. The research objectives were to evaluate the model's performance across different input sequence lengths and identify the optimal length for prediction. Leveraging a dataset from the Indonesian Stock Exchange, the model's predictions were evaluated using key metrics such as RMSE, MAE, MAPE, and R2. Results indicated that the StacBi LSTM model excelled in capturing stock price trends and demonstrated strengths over traditional methods. The optimal input sequence length was identified, balancing computational efficiency and prediction accuracy. This research contributes valuable insights into improving stock price prediction techniques and offers practical implications for traders and investors. Future research directions encompass hybrid models and integrating external factors to enhance predictive capabilities further.
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Dai, Yaoda, Mingzhang Liao, and Zewei Li. "Navigating Complexity: GPT-4's Performance in Predicting Earnings and Stock Returns in China's A-Share Market." Highlights in Business, Economics and Management 42 (November 19, 2024): 189–203. http://dx.doi.org/10.54097/4rwdat95.

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This study investigates the application of GPT-4, a large language model, in predicting earnings changes and stock returns within China's A-share market from 2000 to 2023. We evaluate the model's performance using various metrics, including prediction accuracy, F1 score, stock returns, Sharpe ratio, and alpha. Our findings reveal significant fluctuations in the model's predictive accuracy, ranging from 10.62% to 48.67%, with an average F1 score of 0.30. Despite inconsistent accuracy, the model maintained high prediction confidence levels between 75% and 90%. Stock returns associated with the model's predictions varied widely, from -4.86% to 13.59%, showing no consistent correlation with prediction accuracy. The study highlights the challenges of applying AI models to financial analysis in emerging markets, particularly given the unique characteristics of China's A-share market, such as frequent policy interventions and a high proportion of retail investors. We discuss the implications of these findings for the future of AI-driven financial analysis, emphasizing the need for improved model calibration, ethical considerations, and regulatory frameworks.
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Mendoza, Nadia B., Chii-Dean Lin, Susan M. Kiene, Nicolas A. Menzies, Rhoda K. Wanyenze, Katherine A. Schmarje, Rose Naigino, Michael Ediau, Seth C. Kalichman, and Barbara A. Bailey. "Evaluating Imputation Methods to Improve Prediction Accuracy for an HIV Study in Uganda." Stats 7, no. 4 (November 24, 2024): 1405–20. http://dx.doi.org/10.3390/stats7040082.

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Standard statistical analyses often exclude incomplete observations, which can be particularly problematic when predicting rare outcomes, such as HIV positivity. In the linkage to the HIV care dataset, there were initially 553 complete HIV positive cases, with an additional 554 cases added through imputation. Imputation methods amelia, hmisc, mice and missForest were evaluated. Simulations were conducted across various scenarios using the complete data to guide imputation for the full dataset. A random forest model was used to predict HIV status, assessing imputation precision, overall prediction accuracy, and sensitivity. While missForest produced imputed values closer to the observed ones, this did not translate into better predictive models. Hmisc and mice imputations led to higher prediction accuracy and sensitivity, with median accuracy increasing from 64% to 76% and median sensitivity rising from 0.4 to 0.75. Hmisc and amelia were the fastest imputation methods. Additionally, oversampling the minority class combined with undersampling the majority class did not improve predictions of new HIV positive cases using only the complete observations. However, increasing the minority class information through imputation enhanced sensitivity for predicting cases in this class.
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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 (January 14, 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, prediction using one input and one output obtained the best precision model accuracy of 72% and for poor accuracy using three inputs and one output with a prediction model accuracy of 33% in the process Covid-19 predictions in Bali.
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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 (March 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 using six different training algorithms, and the prediction accuracies of six algorithms were evaluated using seven different accuracy metrics. Test results showed that the training algorithm of “TRAINGLM” exhibited the best performance for predicting damage locations in deep piles. Subsequently, the artificial neural networks were trained using three different datasets collected from three acoustic emission sensor groups, and the prediction accuracies of three models were evaluated with the seven different accuracy metrics. The test results showed that the dataset collected from the pile body-installed sensors group exhibited the highest accuracy for predicting damage locations in deep piles. Subsequently, the correlations between the seven accuracy metrics and the sensitivity of each accuracy metrics were discussed based on the analysis results. Eventually, a novel selection method for an appropriate accuracy metric to evaluate the accuracy of specific predictions was proposed. This novel method is useful to select an appropriate accuracy metric for wide predictions, especially in the engineering field.
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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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Xiao, Yang. "Individual Stock Price Prediction Using Stacking Method." Advances in Economics, Management and Political Sciences 99, no. 1 (September 10, 2024): 17–22. http://dx.doi.org/10.54254/2754-1169/99/2024ox0212.

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This paper explores the application of stacking in machine learning to predict the price of a single stock. The complexity of financial markets and the high noise in data make stock price prediction a challenging task. To improve prediction accuracy, this paper combines multiple machine learning models, including linear regression, decision trees, and random forests, using stacking to integrate the predictions of these base models. Experimental results indicate that the stacking model performs exceptionally well in predicting the stock price of Apple Inc. (AAPL), significantly outperforming individual models. This paper evaluates the model using mean squared error (MSE) and root mean squared error (RMSE) and demonstrated the models prediction accuracy and robustness. The findings demonstrate that the stacking model not only reduces prediction errors but also enhances robustness against the volatile nature of stock prices. This study underscores the high potential of stacking in financial time series prediction, providing valuable insights and references for investment decisions. By integrating multiple predictive models, stacking offers a powerful tool for navigating the complexities of financial markets and making informed investment choices.
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Grover, Arman, Debajyoti Roy Burman, Priyansh Kapaida, and Neelamani Samal. "Stock Market Price Prediction." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (December 31, 2023): 591–95. http://dx.doi.org/10.22214/ijraset.2023.57184.

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Abstract: Investing in the stock market can be a convoluted and refined method of conducting business. Stock prediction is an extremely difficult and complex endeavor since stock values can fluctuate abruptly owing to a variety of reasons, making the stock market incredibly unpredictable.This paper explores predictive models for the stock market, aiming to forecast stock prices using machine learning algorithms. By analyzing historical market data and employing various predictive techniques, thestudy aims to enhance accuracy in predicting future stock movements. this paper contributes understanding into the potential of LSTM models for enhancing stock market prediction accuracy and reliability.
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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 predictor. Traditional works mainly focus on the algorithm for the predictor. In this dissertation, besides novel prediction algorithms, we investigate other components and develop untraditional ways to improve the prediction accuracy. First, we propose an adaptive information processing method to dynamically extract the most effective inputs to maximize the correlation to be exploited by the predictor. Second, we propose a new prediction algorithm, which improves the Prediction by Partial Matching (PPM) algorithm by selectively combining multiple partial matches. The PPM algorithm was previously considered optimal and has been used to derive the upper limit of branch prediction accuracy. Our proposed algorithm achieves higher prediction accuracy than PPM and can be implemented in realistic hardware budget. Third, we discover a new locality existing between the address of producer loads and the outcomes of their consumer branches. We study this address-branch correlation in detail and propose a branch predictor to explore this correlation for long-latency and hard-to-predict branches, which existing branch predictors fail to predict accurately.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD
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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 Quality Assurance (SQA) team. Out of the three organizations, the first two organizations used for the case study are working on reliability prediction and the third company is a growing company developing a product with less focus on quality. Data collection was by the means of interviewing an employee of the organization who leads a team and is in the managing position for at least last 2 years.
svra06@student.bth.se
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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 used for these evaluations exceeds the numbers of both individuals and marker genotypes of previously published reports, with the studied population consisting of up to 23,500 individuals, genotyped for up to 600K SNPs. The evaluation of GBLUP is preceded by evaluation of the variance components using traditional restricted maximum likelihood (REML) approach sourcing information from phenotypic records and pedigree, which provide an up to date reference for the estimates of variance components. Chapter 2 tested several models exploring potential sources of genetic variation and revealed the presence of significant maternal genetic and environmental effects affecting several commercial traits. In Chapter 3, a vast dataset containing 1.3M birds spread over 24 generations was used to evaluate changes in genetic variance of juvenile body weight and hen housed production over time. The results showed a slow but steady decline of the variance. Chapter 4 provided initial estimates of the accuracy and bias of genomic predictions for several sex-limited and fitness traits, obtained for a moderately sized population of over 5K birds, genotyped with 600K Affymetrix Axiom panel from which several chips of varying marker densities were extracted. The accuracy of those predictions showed a great potential for most traits, with GBLUP performance exceeding that of traditional BLUP. Chapter 5 investigated the effect of marker choice, with two chips used: one created from GWAS hits and second from evenly spaced markers, both with constant density of 27K SNPs. The two chips were used to calculate genomic relationship matrices using Linkage Analysis and Linkage Disequilibrium approaches. Markers selected through GWAS performed better in Linkage Analysis than in Linkage Disequilibrium approach. The optimum results however were found for relationship matrices which regressed the genomic relationships back to expected pedigree-based relationships, with the best regression coefficient dependent on the chip used. Chapter 6 formed a comprehensive evaluation of the utility of GBLUP in a large broiler population, exceeding 23,500 birds genotyped using 600K Affymetrix Axiom panel. By splitting the data into variable scenarios of training and testing populations, with several lower density chips extracted from the full range of genotypes available, the effect of population size and marker density was evaluated. While the latter proved to have little effect once 20K SNPs threshold was exceeded, the effect of the population size was found to be the major limiting factor for the accuracy of EBV predictions. The discrepancy between empirical results found and theoretical expectations of accuracy based on the similar genomic and population parameters showed an underestimation of the previously proposed requirements.
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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 establishing a more efficient A-CDM turn-round process. The research method chosen comprises a number of steps. Firstly, within the framework of a Cognitive Work Analysis, the sub-processes as well as the information requirements during turn-round were analysed. Secondly, a survey approach aimed at finding and describing situations during turn-round that are critical for TOBT adherence was pursued. The problems identified here were then investigated in field observations at different airlines’ operation control rooms. Based on the findings from these previous steps, small-scale human-in-the-loop experiments were designed aimed at testing hypotheses about data/information availability that influence TOBT predictability. A turn-round monitoring tool was developed for the experiments. As a result of this project, the critical chain of turn-round events and the decisions necessary during all stages of the turn-round were identified. It was concluded that information required but not shared among participants can result in TOBT inaccuracy swings. In addition, TOBT predictability was shown to depend on the location of the TOBT turn-round controller who assigns the TOBT: More reliable TOBT predictions were observed when the turn-round controller was physically present at the aircraft. During the experiments, TOBT prediction could be improved by eight minutes, if available information was cooperatively shared ten minutes prior turn-round start between air crews and turn-round controller; TOBT prediction could be improved by 15 minutes, if additional information was provided by ramp agents five minutes after turnround start.
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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 been computed for a benchmark is only measured with respect to the core columns of the reference alignment. When computing an alignment in practice, however, a reference alignment is not known, so the coreness of its columns can only be predicted. Results: We develop for the first time a predictor of column coreness for protein multiple sequence alignments. This allows us to predict which columns of a computed alignment are core, and hence better estimate the alignment's accuracy. Our approach to predicting coreness is similar to nearest-neighbor classification from machine learning, except we transform nearest-neighbor distances into a coreness prediction via a regression function, and we learn an appropriate distance function through a new optimization formulation that solves a large-scale linear programming problem. We apply our coreness predictor to parameter advising, the task of choosing parameter values for an aligner's scoring function to obtain a more accurate alignment of a specific set of sequences. We show that for this task, our predictor strongly outperforms other column-confidence estimators from the literature, and affords a substantial boost in alignment accuracy.
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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 accuracy of the algorithms. The tests yielded results which indicated that the FunkSVD algorithm may be more accurate than the Item-based collaborative filtering algorithm, but further research is required to come to a concrete conclusion.
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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. Austin, Tex: 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. Monterey, Calif: Naval Postgraduate School, 1990.

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

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Rauscher, Harold M. The microcomputer scientific software series 4: Testing prediction accuracy. St. Paul, Minn: 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. East Lansing, Mich: 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. [Ottawa]: 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. Haifa, Israel: 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. Sudbury, Ont: 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. Saarbrücken: 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. [Washington, DC: 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, 1–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11677437_1.

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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, 496–502. Cham: 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 corresponding driving intention. This paper also combines the result of the LLM and the output of the traditional prediction model, and the future trajectory is modified with the driving intention, which can improve the final prediction accuracy. Finally, in the decision-making part, both the driving intention from the LLM and the trajectory from the traditional prediction model are considered in the boundary-based drivable area, and a safe planning path is then generated. According to the validation in the public motion forecasting dataset, this method has greatly improved the accuracy of the prediction and the safety of route planning.
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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, 211–23. Cham: 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 consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.
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Sanz-Cruzado, Javier, and Pablo Castells. "Beyond Accuracy in Link Prediction." In Communications in Computer and Information Science, 79–94. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52485-2_9.

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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, 154–66. Cham: 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 prediction methods while maintaining reliable levels of prediction accuracy.
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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, 538–45. Singapore: 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 encoder-decoder architecture, along with its latent space components, is optimized for this task. A comparative analysis using decision tree-based ML models, including grid search optimization, substantiated the effectiveness of the VAE approach. The results indicate that the hybrid dataset benefited the ML models from the addition of the synthesized dataset, showing 2% improvements in performance metrics across most models. The synthetic data generated by VAEs contributes to the construction of more balanced datasets, which, in turn, can lead to more reliable and accurate predictive models. The enhanced accuracy of the VAE-ML model addresses the class imbalance problem and improves the reliability of construction productivity predictions and related resource allocation plans.
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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, 181–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42996-0_15.

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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, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19186-2_1.

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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, 25–48. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12366-5_3.

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Mounter, William, Huda Dawood, and Nashwan Dawood. "The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression." In Springer Proceedings in Energy, 69–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_9.

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AbstractAdvances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.
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Conference papers on the topic "Prediction Accuracy"

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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), 1–5. 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), 213–18. IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10748155.

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Adamopoulos, Panagiotis. "Beyond rating prediction accuracy." In RecSys '13: Seventh ACM Conference on Recommender Systems. New York, NY, USA: 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 preference model itself (e.g., logit model), can complement existing efforts towards high-accuracy preference prediction. Using actual passenger car market data, we achieve significant improvement in prediction accuracy on a binary preference task by properly transforming the original consumer variables and passenger car variables to a sparse and high-dimensional representation.
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Ravi, Sujith, Kevin Knight, and Radu Soricut. "Automatic prediction of parser accuracy." In the Conference. Morristown, NJ, USA: 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, D. G. Infield, A. R. Burgers, G. Friesen, D. Chianese, 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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Tahboub, Khalid, Amy R. Reibman, and Edward J. Delp. "Accuracy prediction for pedestrian detection." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8297072.

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Liu, Ran, Fengzhen Yu, and Mingqiang Guo. "Accuracy Prediction of Negative Surveys." In 2022 4th International Conference on Data Intelligence and Security (ICDIS). IEEE, 2022. http://dx.doi.org/10.1109/icdis55630.2022.00025.

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Mobasher, Bamshad. "Session details: Beyond prediction accuracy." In RecSys '10: Fourth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2010. http://dx.doi.org/10.1145/3257557.

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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), December 2015. http://dx.doi.org/10.2172/1233821.

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Serão, Nick V. L., Bob Kemp, Benny Mote, Philip Willson, John Harding, Steve Bishop, Graham Plastow, and Jack C. M. Dekkers. Accuracy of Genomic Prediction for PRRS Antibody Response. Ames (Iowa): Iowa State University, January 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. Ames (Iowa): Iowa State University, January 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. St. Paul, MN: 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. Ames (Iowa): Iowa State University, January 2005. http://dx.doi.org/10.31274/ans_air-180814-1155.

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Imas, Alex, Minah Jung, Silvia Saccardo, and Joachim Vosgerau. The Impact of Joint versus Separate Prediction Mode on Forecasting Accuracy. Cambridge, MA: National Bureau of Economic Research, October 2022. http://dx.doi.org/10.3386/w30611.

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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, December 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 probit model (OPM) were evaluated as statistical models, while random forest (RF) and XGBoost were evaluated as ML models. For DL, multi-layer perceptron (MLP) and TabNet were evaluated. The performance of these models varied across severity levels, with property damage only (PDO) predictions performing the best and severe injury predictions performing the worst. The TabNet model performed best in predicting severe injury and PDO crashes, while RF was the most effective in predicting moderate injury crashes. However, all models struggled with severe injury classification, indicating the potential need for model refinement and exploration of other techniques. Hence, the choice of model depends on the specific application and the relative costs of false negatives and false positives. This conclusion underscores the need for further research in this area to improve the prediction accuracy of severe and moderate injury incidents, ultimately improving available data that can be used to increase road safety.
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Piyasatian, Napapan, and Jack C. M. Dekkers. Accuracy of Genomic Prediction when Accounting for Population Structure and Polygenic Effects. Ames (Iowa): Iowa State University, January 2013. http://dx.doi.org/10.31274/ans_air-180814-1252.

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

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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.
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