Relevant bibliographies by topics / Mean Squared Error (M.S.E)

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Academic literature on the topic 'Mean Squared Error (M.S.E)'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Mean Squared Error (M.S.E)"

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Erenturk, Koksal, Bircan Kose, and Saliha Erenturk. "Fractional order calculus approach for drying modeling of eggplants." Food Science and Technology International 26, no. 5 (2019): 379–87. http://dx.doi.org/10.1177/1082013219895852.

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In order to determine the drying modeling of eggplants, fractional order calculus modeling was applied and compared to regression analysis in this study. The fractional order calculus based on the Caputo derivative approach was considered and applied for modeling of eggplant drying. The drying experiments were performed on three levels of drying air temperatures (60, 70, and 80 ℃), with two different air flow velocity levels (0.5 and 1 m/s), and three levels of thickness (3.5, 6.5, and 9.5 mm) to measure the effects of different drying conditions for eggplants in a convective dryer. Four diffe
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Nurbaroqah, Ana, Budi Pratikno, and Supriyanto Supriyanto. "PENDEKATAN REGRESI ROBUST DENGAN FUNGSI PEMBOBOT BISQUARE TUKEY PADA ESTIMASI-M DAN ESTIMASI-S." Jurnal Ilmiah Matematika dan Pendidikan Matematika 14, no. 1 (2022): 19. http://dx.doi.org/10.20884/1.jmp.2022.14.1.5669.

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Least Square Method is one of methods for estimating of parameters of regression model. Model of least square methods is not valid if there are some disobeydiance in classical assumptions, for example, there are outliers. To resolve the problem, robust regression method is usually used. The method is used because it can detect the outliers and give stable results. In this research, data used is data for human development index of districts in Central Java from 2019 to 2020. Estimation for robust regression method chosen is estimation-M and estimation-s with Tukey Bisquare as a weight function.
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Soliman, Faten Mohamed Ali, Amany Moussa Mohamed, and Mohamed R. Abonazel. "New Robust Estimators for the Nonparametric Regression Model: Application and Simulation Study." International Journal of Analysis and Applications 23 (July 16, 2025): 163. https://doi.org/10.28924/2291-8639-23-2025-163.

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This paper introduces new two robust kernel-based estimators (S Kernel and MM Kernel) for the nonparametric regression mode in the presence of outliers. Through comprehensive simulations, we evaluate their performance using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Relative Efficiency (RE) under varying sample sizes and outlier contamination levels. Results demonstrate that robust estimators consistently outperform traditional kernel estimator, delivering the lowest estimation errors and highest efficiency, particularly in high-contamination scenarios. In contrast, the tradition
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Liu, Hongsu, Shuanggen Jin, and Qingyun Yan. "Evaluation of the Ocean Surface Wind Speed Change following the Super Typhoon from Space-Borne GNSS-Reflectometry." Remote Sensing 12, no. 12 (2020): 2034. http://dx.doi.org/10.3390/rs12122034.

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Ocean surface wind speed is an essential parameter for typhoon monitoring and forecasting. However, traditional satellite and buoy observations are difficult to monitor the typhoon due to high cost and low temporal-spatial resolution. With the development of spaceborne GNSS-R technology, the cyclone global navigation satellite system (CYGNSS) with eight satellites in low-earth orbit provides an opportunity to measure the ocean surface wind speed of typhoons. Though observations are made at the extremely efficient spatial and temporal resolution, its accuracy and reliability are unclear in an a
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R.M., Kapila Tharanga Rathnayaka D.M Kumudu Nadeeshani Seneviratna. "G M (1, 1) Analysis and Forecasting for Efficient Energy Production and Consumption." International Journal of Business, Economics and Management Works 1, no. 1 (2014): 6–11. https://doi.org/10.5281/zenodo.15762.

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Electricity Generation and Forecasting is prerequisite to enhance industrialization, farming and residential requirement of one’s nation. It has great impact on both nation’s economy and standard of living that can be achieved through new forecasting techniques, enhanced electricity generation methodologies and better electricity conservation techniques. Most of the countries are allocating significant amount for power generation and forecasting from nation’s annual budgets. Much research work has been carried out to analyze and propose innovative methodologies for efficient
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Yang, Junxin, Xinzhe Yuan, Bing Han, et al. "Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement." Remote Sensing 13, no. 2 (2021): 269. http://dx.doi.org/10.3390/rs13020269.

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There are two useful methods of current measurement based on synthetic aperture radar (SAR): one is along-track interferometry (ATI), and the other is Doppler centroid analysis (DCA). For the ATI method, the interferometric phase must be accurate enough for ocean current measurements. Therefore, the space-varying of phase imbalances along the range, caused by antenna phase center position error, attitude error, antenna electronic miss pointing, antenna pattern mismatch, and other reasons, cannot be ignored. Firstly, this paper mainly analyzes the above possible factors by using real GF-3 ATI d
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Alves, Décio, Fábio Mendonça, Sheikh Shanawaz Mostafa, and Fernando Morgado-Dias. "The Potential of Machine Learning for Wind Speed and Direction Short-Term Forecasting: A Systematic Review." Computers 12, no. 10 (2023): 206. http://dx.doi.org/10.3390/computers12100206.

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Wind forecasting, which is essential for numerous services and safety, has significantly improved in accuracy due to machine learning advancements. This study reviews 23 articles from 1983 to 2023 on machine learning for wind speed and direction nowcasting. The wind prediction ranged from 1 min to 1 week, with more articles at lower temporal resolutions. Most works employed neural networks, focusing recently on deep learning models. Among the reported performance metrics, the most prevalent were mean absolute error, mean squared error, and mean absolute percentage error. Considering these metr
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Qiu, Hui, and Shuanggen Jin. "Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry." Remote Sensing 12, no. 3 (2020): 356. http://dx.doi.org/10.3390/rs12030356.

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Mean sea surface height (MSSH) is an important parameter, which plays an important role in the analysis of the geoid gap and the prediction of ocean dynamics. Traditional measurement methods, such as the buoy and ship survey, have a small cover area, sparse data, and high cost. Recently, the Global Navigation Satellite System-Reflectometry (GNSS-R) and the spaceborne Cyclone Global Navigation Satellite System (CYGNSS) mission, which were launched on 15 December 2016, have provided a new opportunity to estimate MSSH with all-weather, global coverage, high spatial-temporal resolution, rich signa
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Abdennasser, Dahmani, Yamina Ammi, and Salah Hanini. "Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study." International Journal of Energetica 7, no. 2 (2022): 8. https://doi.org/10.47238/ijeca.v7i2.198.

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The global solar radiation prediction is the most necessary part of the project and performance of solar energy applications. The objective of the present work is to predict global solar radiation (GSR) received on the horizontal surface using an artificial neural network (ANN). For the city (Relizane) in the west region of Algeria. The inputs used in the neural network are: time (h), day, month, year, temperature (k), relative humidity (%), pressure (mbar), wind speed (m/s), wind direction (°), and rainfall (kg/m2). The neural network-optimal model was trained and tested using 80 %, and 20 %
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Mukaramshah, Sayed Saad Ali Shah, and Juntakan Taweekun. "Missing Wind Speed Data Prediction using Artificial Neural Network." Journal of Advanced Research Design 135, no. 1 (2025): 154–68. https://doi.org/10.37934/ard.135.1.154168.

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In southern Thailand, where monsoon-driven storms disrupt meteorological data collection, missing wind speed records hinder accurate weather forecasting and renewable energy planning. This study leverages an Artificial Neural Network (ANN) to impute missing wind speed data from the ERA5 reanalysis dataset (2012–2024) at latitude 6.0, longitude 102.0, addressing gaps that reduce forecasting precision by up to 12%. Using 114,792 hourly observations of wind speed, temperature, pressure, and precipitation, the ANN model, with 2–3 hidden layers and ReLU activation, was trained on 70% of the data, v
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Book chapters on the topic "Mean Squared Error (M.S.E)"

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Ortiz, Juan P., Juan D. Valladolid, and Soledad Gutiérrez. "Performance Analysis of the LiDAR-Based 3D LIO-SAM Algorithm in Urban Areas: Case Study Cuenca City in the Ecuadorian Andes." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87065-1_20.

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Abstract Autonomous Vehicles (AVs) technology has increased dramatically over the last years. Accurate 3D point cloud maps are needed to perform this autonomous driving task. Mapping allows AVs to adapt to the surrounding environment and maneuver in complex situations. In this paper, a methodology for performance analysis of the LiDAR Inertia Odometry via Smoothing and Mapping (LIO-SAM) framework for the generation of 3D point cloud maps is proposed. The test scenario is intended for outdoor autonomous vehicles making use of an inertial measurement unit (IMU) sensor, a LiDAR sensor and a Globa
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Huang, Xianliang. "Eddy Around Zhoushan Island: Observations with Synthetic Aperture Radar at C-band." In Advances in Transdisciplinary Engineering. IOS Press, 2021. http://dx.doi.org/10.3233/atde210225.

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We studied mesoscale (∼100 km length) eddy around the Zhoushan Island (one Sentinel-1 (S-1) image at coastal East China Sea). The simultaneous sea surface temperature (SST) data from the Advanced Very High-Resolution Radiometer (AVHRR) confirms the existence of upwelling in the Western Pacific Ocean, although, the AVHRR data around the Zhoushan Islands were not available. The difference in the root mean square error (RMSE) between the simulations with the Region Ocean Modelling System (ROMS) and that of the AVHRR data was around 1 °C. Also, the RMSE of the model-simulated current speed compare
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Aitouhanni, Imane, Amine Berqia, Amol D. Vibhute, Yassine Mouniane, and Balakrishnama Manohar. "Modeling Log S." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8532-6.ch018.

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Assessing the aqueous solubility of chemical compounds is one of the main tasks of molecular chemistry, with important implications for drug discovery and other research. In this respect, this study proposes a predictive model for Log S and offers a comprehensive comparative analysis of several machine learning algorithms on six different datasets, evaluating performance in terms of R-squared value, mean square error and execution time on various datasets that differ in composition and source and are used in natural and synthetic contexts. In addition, a performance comparison of the algorithm
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Rapoo, Mogari Ishmael, Martin M. Chanza, and Gomolemo Motlhwe. "Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network." In Research Anthology on Macroeconomics and the Achievement of Global Stability. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7460-0.ch030.

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This study examines the performance of seasonal autoregressive integrated moving average (SARIMA), multilayer perceptron neural networks (MLPNN), and hybrid SARIMA-MLPNN model(s) in modelling and forecasting inflation rate using the monthly consumer price index (CPI) data from 2010 to 2019 obtained from the South African Reserve Bank (SARB). The forecast errors in inflation rate forecasting are analyzed and compared. The study employed root mean squared error (RMSE) and mean absolute error (MAE) as performance measures. The results indicate that significant improvements in forecasting accuracy
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Rapoo, Mogari Ishmael, Martin M. Chanza, and Gomolemo Motlhwe. "Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network." In Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8455-2.ch012.

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This study examines the performance of seasonal autoregressive integrated moving average (SARIMA), multilayer perceptron neural networks (MLPNN), and hybrid SARIMA-MLPNN model(s) in modelling and forecasting inflation rate using the monthly consumer price index (CPI) data from 2010 to 2019 obtained from the South African Reserve Bank (SARB). The forecast errors in inflation rate forecasting are analyzed and compared. The study employed root mean squared error (RMSE) and mean absolute error (MAE) as performance measures. The results indicate that significant improvements in forecasting accuracy
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Conference papers on the topic "Mean Squared Error (M.S.E)"

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GÓRNICKI, Krzysztof, Agnieszka KALETA, and Aneta CHOIŃSKA. "EXTENSION OF WEIBULL MODEL FOR DESCRIBING OF DRIED APPLE REHYDRATION." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.026.

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Sample of Ligol variety apples (slices of 3 and 10 mm thickness and cubes of 10 mm thickness) were dried using following methods: natural convection (drying air velocity amounted to 0.01 m/s), forced convection (0.5 and 2 m/s), fluidized bed drying (6 m/s). The drying air temperature was kept at 50, 60, and 70C. The dried apples samples were rehydrated in distilled water at 20, 45, 70, and 95C. The Weibull model given for describing time dependence of the moisture content change was fitted to experimental data and model parameters were determined by multiple regression analysis. The variatio
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Wei, Wang, Yang Kaiming, Zhu Yu, Qian Yuyang, Wan Chenhui, and Li Min. "Walking Speed Estimation From a Wearable Insole Pressure System Using a Bayesian Regularized Back Propagation Neural Network." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23363.

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Abstract In this study, we introduced a machine learning method for estimating human walking speed using plantar pressure and acceleration data. A pressure-derivative based with pretest feature selection method was proposed to extracted speed-related features from plantar pressure sensors. The maximum, minimum and standard deviation of acceleration data were also selected as neural network inputs. To improve the generalization ability of the neural network, a Bayesian regularization method was adopted. To validate the performance of the proposed method, experiments were conducted under seven d
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Salvação, N., M. Bernardino, and C. Guedes Soares. "Validation of a Regional Atmospheric Model for Assessing the Offshore Wind Resources Along the Portuguese Coast." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-11631.

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This study aims to determine the offshore wind energy potential for Portugal using the mesoscale atmospheric model WRF that provides a reliable and accurate wind data set for this area, and allows identifying the wind resources in potential areas for developing offshore wind parks. Before a model can be used to provide a wind climatology a validation study has to be carried out, for which accurate wind measurements are required. Therefore, two years of high resolution (9 km) weather modeling data are used to validate the model against measurements from meteorological stations along two Portugu
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Eldesouky, Dina M. Mustafa, Taha Helmy Abdelaziz, and Amr E. Mohamed. "Path Tracking of Autonomous Vehicles with Model Predictive Control (MPC) Optimized with Particle Swarm Optimization (PSO)." In WCX SAE World Congress Experience. SAE International, 2025. https://doi.org/10.4271/2025-01-8036.

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<div class="section abstract"><div class="htmlview paragraph">This paper presents a comparative study between many control techniques to investigate the efficiency of the path tracking in various driving scenarios. In this study the Model predictive control (MPC), the adaptive model predictive control (AMPC) and the Stanley controller are employed to ensure that the vehicle follows reference paths accurately and robustly under varying environmental and vehicular conditions. Two driving scenarios are utilized S-road and Curved-road with MATLAB/Simulink under three different vehicle
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Lin, Bochuan, Shen Zhu, Heng Ban, et al. "Thermal Property Measurement of Semiconductor Melt Using Modified Laser Flash Method." In ASME 2003 Heat Transfer Summer Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/ht2003-47448.

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This study further develops the standard laser flash method for the measurement of multiple thermal properties of semiconductor melts. The standard laser flash method is widely used to measure thermal diffusivity of solids. Our modified procedure allows thermal diffusivity, thermal conductivity, and specific heat capacity of molten semiconductor material to be determined simultaneously. The transient heat transfer process in the melt and its quartz container was computationally studied in detail. A fitting procedure based on the numerical result and the least root-mean-square error fitting to
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Lessard, Étienne, and Jun Yang. "Void Fraction Measurements for Air-Water Pipe Flows Using Quick-Closing Valves and Wire-Mesh Sensors." In ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fedsm2018-83405.

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In support of a header/feeder phenomena study, an adiabatic, near-atmospheric, air-water flow loop was commissioned simulating a single feeder of a Pressurized Heavy Water Reactor’s primary heat transport system under a postulated Loss of Coolant Accident scenario. An extensive database in representative two-phase flow conditions was collected, 750 tests in total, in order to create a two-phase flow map to be used in the more complex geometries such as header/feeder systems. The flow loop consists of two vertical test sections, for upwards and downwards flow, and one horizontal test section, e
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Gao, Xinyu, Fei Ju, Gang Chen, Yuhua Zong, and Liangmo Wang. "Optimization-Oriented Adaptive Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles." In WCX SAE World Congress Experience. SAE International, 2025. https://doi.org/10.4271/2025-01-8552.

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<div class="section abstract"><div class="htmlview paragraph">Fuel economy and the ability to maintain the state of charge (SOC) of the battery are two key metrics for the energy management of a full-power fuel cell hybrid vehicle fitted with a small-capacity battery pack. To achieve stable maintenance of SOC and near-optimal fuel consumption, this paper proposes an adaptive equivalent consumption minimization strategy (PA-ECMS) based on power prediction. The strategy realizes demand power prediction through a hybrid deep learning model, and periodically updates the optimal equival
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"Analisis Perbandingan Distribusi Kecepatan Angin Untuk Mengetahui Probabilitas Kecepatan Angin di Pulau Jawa." In Conference on Marine Engineering and its Application. Politeknik Perkapalan Negeri Surabaya, 2024. https://doi.org/10.33863/cmea.v6i1.2661.

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- Wind speed is the movement of air from areas of high pressure to areas of low pressure. As the use of electric energy increases due to the growing human population in a region, there is a need for renewable energy sources to convert renewable energy into electricity. Wind energy is highly potential if utilized, considering its relatively low utilization for electricity generation in Indonesia. In the initial stage of using wind energy for electricity generation, data on the wind potential of a region is required, thus an analysis of wind speed in several areas on Java Island is necessary. Th
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Ouadoudi Belabzioui, Hasnaa, Pierre Plantard, Charles Pontonnier, Georges Dumont, and Franck Multon. "Impact of introducing sparse inertial Measurement Units in Computer Vision-Based Motion Capture Systems for Ergonomic Postural Assessment." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006571.

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In ergonomics, considering a worker's movement is important for assessing the risks of musculoskeletal disorders, among many other factors. Several commercial motion capture systems are available, mostly based on monocular or multi RGB (THEIA system www.theiamarkerless.com) or RGB-D videos (Microsoft Kinect system). Hybrid systems combining computer vision and Inertial Measurement Units (IMUs) have been introduced, such as the KIMEA (1 RGB-D+4 IMUs) and the KIMEA Cloud (1 RGB+4 IMUs) solutions (www.Moovency.com). Although previous works analyzed the accuracy of some of these systems, the relev
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Chen, Hongbo, and Ergun Kuru. "A Generalized Model for Field Assessment of Particle Settling Velocity in Viscoelastic Fluids." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-78264.

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Abstract It has been long known that drilling fluid viscoelastic properties have a significant impact on various elements of drilling hydraulics design (e.g., assessment of frictional pressure loss, particle settling velocity, hole cleaning, etc.). However, efforts for considering the viscoelastic fluid properties in drilling hydraulics design are traditionally hindered by the fact that there was no practical methodology to measure these critical fluid properties in the field. Previous studies have highlighted the advantages of using the concept of “energy dissipation” for the quantitative eva
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Reports on the topic "Mean Squared Error (M.S.E)"

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Bouezmarni, Taoufik, Mohamed Doukali, and Abderrahim Taamouti. Copula-based estimation of health concentration curves with an application to COVID-19. CIRANO, 2022. http://dx.doi.org/10.54932/mtkj3339.

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COVID-19 has created an unprecedented global health crisis that caused millions of infections and deaths worldwide. Many, however, argue that pre-existing social inequalities have led to inequalities in infection and death rates across social classes, with the most-deprived classes are worst hit. In this paper, we derive semi/non-parametric estimators of Health Concentration Curve (HC) that can quantify inequalities in COVID-19 infections and deaths and help identify the social classes that are most at risk of infection and dying from the virus. We express HC in terms of copula function that w
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