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Showing 1 to 20 of 108 for “"Mean Absolute Error"”.

  1. Ranging of Aircraft Using Wide-baseline Stereopsis

    Rigby, Kevin Todd

    … Humans in the pilot study ranged aircraft with a mean absolute error of 50.34%. The wide-baseline stereo system ranged aircraft within 2 kilometers with a mean absolute error of 17.62%. A t-test was performed and there was a significant difference between the mean absolute error of the humans in …

    embry-riddle Repository record

  2. AI-ML Powered Pig Behavior Classification and Body Weight Prediction

    Bharadwaj, Sanjana ManjunathVirginia Tech2024

    … pipeline, we train DL models to extract meaningful information from depth images for weight prediction. Our findings show that XceptionNet gives promising results, with a mean absolute error of 2.82 kg and a mean absolute percentage error of 7.42%. In comparison, the best performing …

    vt Repository record

  3. Assessing the effects of field plot size and stand structure on forest inventory estimates derived from laser altimetry data

    Bramel, Jody Paul

    … effects of varying plot size on the prediction errors are not well understood. We investigated the effects of plot size on prediction errors using lidar data for a western Montana forest using five sizes derived from stem-mapped field data and multiple regression modeling techniques. Models were …

    montana-tech Repository record

  4. Assessing the effects of field plot size and stand structure on forest inventory estimates derived from laser altimetry data

    Bramel, Jody Paul

    … effects of varying plot size on the prediction errors are not well understood. We investigated the effects of plot size on prediction errors using lidar data for a western Montana forest using five sizes derived from stem-mapped field data and multiple regression modeling techniques. Models were …

    montana Repository record

  5. Cartographic visualization of Saskatchewan’s population using dasymetric mapping.

    Krahnen, AnneFaculty of Graduate Studies and Research, University of Regina2012

    … data available for this project) using the Mean Absolute Error. In both analyses, the binary method using raster data was determined to be the method producing the most accurate maps for the test area. Based on the results, three dasymetric maps were produced for the Province of …

    regina Repository record

  6. Predicting hybrid vehicle fuel economy and emissions with neural network models trained with real world data

    Galang, Abril, author, Jathar, Shantanu, advisor, Bradley, Thomas, advisor, Anderson, Chuck, committee memberColorado State University. Libraries2017

    … FE. The NN model predicted fuel economy within a mean absolute error of 0-5% for on-road measurements over a 40 minute, real world, city and highway drive cycle. NN models trained with varying lengths of datasets did not improve with training data longer than 35 minutes. When trained with this …

    colostate Repository record

  7. Predicting price volatility crytocurrency ethereum

    Rambevha, Vhukhudo Ronny2025

    … network. The main evaluationmetric used is the mean absolute error. In this study, according to MAE, RNN without tweets forecasts outperformthe SVR model without tweets forecasts, with the best model being the RNN without tweets producing an MAE of 0.0309.

    venda Repository record

  8. Hydrochemical Assessment and Modelling of Groundwater Quality of an Urban Aquifer Near A Sanitary Landfill

    Pan, ConglianFaculty of Graduate Studies and Research, University of Regina2018

    … the concentration of total hardness with small errors (R2 = 0.995, slope = 0.995). In the second study, total dissolved solids (TDS) is selected and modeled using both conventional statistical approaches (Multiple Linear Regression, Hybrid PCR) and machine learning methods (Artificial Neural …

    regina Repository record

  9. Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates

    Nemavhola, Andisani2022

    … being surpassed by the ARIMA model when the Mean Absolute Error (MAE) was assessed.

    venda Repository record

  10. Utilizing Google Trends data for effective modeling of COVID-19 outcomes: a vector auto regression (VAR) approach

    Osei, Gertrude2023

    … VAR model input search terms. RMSE (root mean square error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and MASE (Mean Absolute Scaled Error) were used to compare forecast accuracies. Also analyzed are Long-Covid search trends.

    utc Repository record

  11. MODELING MEDIAN HOUSEHOLD INCOME DISTRIBUTION

    FOSTER, KEVIN MATTHEW2008

    … These distributions will be tested using Mean Squared Error, Mean Absolute Error, Chi-square Goodness-Of-Fit, Akaike's Information Criterion and Bayesian Information Criterion. We also use the graphical technique of QQ Plots. We discover that the Singh-Maddala most often provides the best …

    maryland Repository record

  12. Automated Essay Evaluation Using Natural Language Processing and Machine Learning

    Ghanta, Harshanthi2020

    … with human scorers as it achieved the lowest mean absolute error for the test dataset.</p>

    columbus-state Repository record

  13. Vocal modulation features in the prediction of major depressive disorder severity

    Horwitz-Martin, Rachelle (Rachelle Laura)Massachusetts Institute of Technology2014

    … subjects' Beck MDD severity score by the root mean square error (RMSE), mean absolute error (MAE), and Spearman correlation between the actual Beck score and predicted score. Our lowest MAE and RMSE values are 8.46 and 10.32, respectively (Spearman correlation=0.487, p<0.001), relative to the …

    mit Repository record

  14. Physics-Based Artificial Intelligence Models for Vehicle Emissions Prediction

    Panneer Selvam, Harish2021

    … patterns from the OBD data where prediction errors are high. The proposed framework is validated for generalizability with a separate vehicle OBD dataset, a sensitivity analysis is performed on the prediction model, and its predicted values are compared with that from a black-box deep neural …

    umn Repository record

  15. Illustris-TNG Simulated Central Black Mass(MBH) and Galaxy Properties Correlations with a Machine Learning Approach

    Dindy, Imani LThe Graduate School and University Center of The City University of New York2024

    … simple formula that predicts their mass within a mean absolute error of 1.14%, 0.95% and 0.68% for TNG 50, 100 and 300 respectfully. We are also able to construct intuitive equations for both 100 and 300 boxes that estimates MBH well when used in the box it was trained on.</p>

    cuny-grad Repository record

  16. Design and characterization of a smart bit for in-situ force measurement, using capacitive load cells and acoustic spectra analysis to determine rock type and tool wear in rock excavation process

    Oltmanns, Austin F.Colorado School of Mines. Arthur Lakes Library2024

    … it is able to measure rock cutting forces with a mean absolute error less than 4 kilonewtons and an R 2 score greater than 0.8 under tested conditions. This is slightly better than the linear regression performance on the same data, with a mean absolute error less than 6 kilonewtons and an R 2 …

    colo-mines Repository record

  17. Mean Hellinger Distance as an Error Criterion in Univariate and Multivariate Kernel Density Estimation

    Anver, Haneef Mohamed

    Ever since the pioneering work of Parzen the mean square error( MSE) and its integrated form (MISE) have been used as the error criteria in choosing the bandwidth matrix for multivariate kernel density estimation. More recently other criteria have been advocated as competitors to the MISE, such as …

    siu-theses Repository record

  18. A natural language processing approach to improve demand forecasting in long supply chains

    Teo, William W. J.Massachusetts Institute of Technology2020

    … All three models returned large forecast errors. However, NEMO tracked the volatility of actual data better than the ARIMA model. NEMO also had better success in predicting demand than the XGBoost model, returning approximately 20% better Root Mean Square Error (RMSE) and Mean Absolute …

    mit Repository record

  19. Machine Learning for Structure-Agnostic Chemical Analysis from Chromatographic Data

    Lahouar, AdamVirginia Tech2026

    … how machine learning (ML) can extract chemically meaningful information directly from chromatographic data to overcome these limitations. First, ML models are developed to establish a bidirectional relationship between chromatographic retention behavior on orthogonal GC phases and key …

    vt Repository record

  20. Prediction of HPLC Retention Index Using Artificial Neural Networks and IGroup E-State Indices

    Albaugh, Daniel Ray2001

    … statistics with training correlation r2 = 0.65, mean absolute error (MAE) = 83 RI units. External validation of 104 compounds not used for model development yielded validation v2 = 0.49 and MAE = 73 RI units. The distribution of residuals for the fit and validate datasets suggest a non-linear …

    uconn-diss Repository record

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