Relevant bibliographies by topics / Rooted Mean Square Error (RMSE)

Contents

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

Academic literature on the topic 'Rooted Mean Square Error (RMSE)'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Rooted Mean Square Error (RMSE)"

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Purva, Sharma, Saini Deepak, and Saxena Akash. "Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN." Bulletin of Electrical Engineering and Informatics 5, no. 3 (2016): 284–95. https://doi.org/10.11591/eei.v5i3.537.

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Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observe
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Chicco, Davide, Matthijs J. Warrens, and Giuseppe Jurman. "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation." PeerJ Computer Science 7 (July 5, 2021): e623. http://dx.doi.org/10.7717/peerj-cs.623.

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Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of t
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Saminu, Umar, and M. Oyeyemi Gafar. "Evaluating the Forecast Accuracy of MGARCH Models and LSTM Networks for Multivariate Financial Time Series." International Journal of Novel Research in Physics Chemistry & Mathematics 12, no. 2 (2025): 1–8. https://doi.org/10.5281/zenodo.15449496.

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<strong>Abstract:</strong> Forecasting financial time series is a fundamental challenge in finance and econometrics, largely due to the complexity of volatility dynamics and interdependencies among assets. This study evaluates and compares the forecasting performance of MGARCH models, BEKK GARCH and DCC GARCH, with two deep learning networks, Single LSTM and BiLSTM, across short, medium and long-term forecast horizons. Two datasets, comprising simulated data and bank stock data were used. Forecast accuracy was assessed using Root Mean Squared Error (RMSE) on both simulated data and real-world
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Ferdiansyah, Hajar Othman Siti, Zahilah Md Radzi Raja, Stiawan Deris, and Sutikno Tole. "Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy." International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (2023): 251–61. https://doi.org/10.11591/ijai.v12.i1.pp251-261.

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Cryptocurrency is a digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency and decentralization. Because cryptocurrency prices fluctuate so much, tools for monitoring and forecasting them are required. Long short-term memory (LSTM) is a deep learning model that is capable of strongly predicting data time series. LSTM has been used in previous studies to predict the common currency. In this study, we used the gate recurrent unit (GRU) and bidirectional&ndash;LSTM (Bi-LSTM) hybrid model to predict cryptocurrency prices to i
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Hadi, Suyono, Santoso Hari, Nur Hasanah Rini, Wibawa Unggul, and Musirin Ismail. "Prediction of Solar Radiation Intensity using Extreme Learning Machine." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 2 (2018): 691–98. https://doi.org/10.11591/ijeecs.v12.i2.pp691-698.

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The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This
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Sugondo, Hadiyoso, Nugroho Heru, Latifah Erawati Rajab Tati, and Surendro Kridanto. "Data prediction for cases of incorrect data in multi-node electrocardiogram monitoring." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1540–47. https://doi.org/10.11591/ijece.v12i2.pp1540-1547.

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The development of a mesh topology in multi-node electrocardiogram (ECG) monitoring based on the ZigBee protocol still has limitations. When more than one active ECG node sends a data stream, there will be incorrect data or damage due to a failure of synchronization. The incorrect data will affect signal interpretation. Therefore, a mechanism is needed to correct or predict the damaged data. In this study, the method of expectationmaximization (EM) and regression imputation (RI) was proposed to overcome these problems. Real data from previous studies are the main modalities used in this study.
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Liu, Wanyue, Jiaguo Li, Ying Zhang, Limin Zhao, and Qiuming Cheng. "Preflight Radiometric Calibration of TIS Sensor Onboard SDG-1 Satellite and Estimation of Its LST Retrieval Ability." Remote Sensing 13, no. 16 (2021): 3242. http://dx.doi.org/10.3390/rs13163242.

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The thermal Infrared Spectrometer (TIS) is the thermal infrared (TIR) sensor on-board the first Sustainable Development Goals (SDG-1) satellite. The TIS data can potentially be used to support improved monitoring of ground conditions with high-spatial resolutions, so accurate radiometric calibration is required. A meticulous radiometric calibration was conducted on the prototype of TIS to test its ability to convert a raw digital number (DN) to at-aperture radiance. The initial maximum radiometric error was 2.19 K at 300 K for Band 1(B1) and the minimum radiometric error was 0.25 K at 300 K ro
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Sanaa, Hammad Dhahi, Hammad Dhahi Estqlal, Jawad Khadhim Ban, and Taha Ahmed Shaymaa. "Using support vector machine regression to reduce cloud security risks in developing countries." Using support vector machine regression to reduce cloud security risks in developing countries 30, no. 2 (2023): 1159–66. https://doi.org/10.11591/ijeecs.v30.i2.pp1159-1166.

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The use of the cloud by governments throughout the world is being aggressively investigated to increase efficiency and reduce costs. The majority of cloud computing risk management programs prioritize addressing cloud security issues that government organizations may face when they choose to adopt cloud computing systems, but these programs lack evidence of security risks, and problems with using cloud computing in developing nations are uncommon, so they called for more research in this area. The objective of this paper is to use quantitative models namely Spearman&#39;s Rank correlation coef
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Egop, S.E. "Approximation of Rainfall Intensity-Duration Frequency for Bayelsa State, Nigeria." Journal of Water Resource Research and Development 8, no. 1 (2025): 20–28. https://doi.org/10.5281/zenodo.14626734.

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<em>One of the most often utilized methods in water resources engineering is the approximation of the Rainfall Intensity-Duration-Frequency (IDF) relationship. Establishing the rainfall intensity-duration-frequency model and curves for Bayelsa State, Nigeria, is the goal of this study. The Nigeria Meteorological Agency (NIMET) provided 32 years of rainfall data, which was sorted for frequency analysis. The IDF model was developed using storm durations of 5, 10, 30, 60, 120, 240, 360, and 720 minutes, as well as the corresponding frequency of recurrence. The IDF curves for this investigation we
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Egop, S.E. "Approximation of Rainfall Intensity-Duration Frequency for Delta State, Nigeria." Journal of Advances in Civil Engineering and Management 8, no. 1 (2025): 17–25. https://doi.org/10.5281/zenodo.14636159.

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<em>A crucial tool in water resources engineering is the approximation of the Rainfall Intensity-Duration-Frequency (IDF) relationship. Establishing the rainfall intensity-duration-frequency model and curves for Delta State, Nigeria, is the goal of this study. The Nigeria Meteorological Agency (NIMET) provided 32 years of rainfall data, which was sorted for frequency analysis. The IDF model was developed using storm durations of 5, 10, 30, 60, 120, 240, 360, and 720 minutes, as well as the corresponding frequency of recurrence. The IDF curves for this investigation were created using the gener
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Dissertations / Theses on the topic "Rooted Mean Square Error (RMSE)"

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Thomas, Robin Rajan. "Optimisation of adaptive localisation techniques for cognitive radio." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/27076.

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Spectrum, environment and location awareness are key characteristics of cognitive radio (CR). Knowledge of a user’s location as well as the surrounding environment type may enhance various CR tasks, such as spectrum sensing, dynamic channel allocation and interference management. This dissertation deals with the optimisation of adaptive localisation techniques for CR. The first part entails the development and evaluation of an efficient bandwidth determination (BD) model, which is a key component of the cognitive positioning system. This bandwidth efficiency is achieved using the Cramer-Rao lo
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Šimoník, Petr. "Měřič odstupu signálu od šumu obrazových signálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217681.

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The diplomma thesis is dealing with possibilities of Signal to noise ratio measurement by method, which is based on direct measurement. It is chosen the most suitable method – signal and noise separation to two different parallel signal branches, where is measured signal strength in one branch and root mean square value in the other. The thesis is consisted of a concept of detail block scheme of Signal to noise ratio meter, which was designed in terms of theoretical knowledge. Particular functional blocks were circuit-designed, the active and passive parts were chosen and their function were d
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, i
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Molapo, Mojalefa Aubrey. "Employing Bayesian Vector Auto-Regression (BVAR) method as an altenative technique for forecsating tax revenue in South Africa." Diss., 2017. http://hdl.handle.net/10500/25083.

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Hashim, Che Gon. "Identifying predictors of postoperative persistent pain in women with breast cancer: assessments of investigative tools." Master's thesis, 2018. http://hdl.handle.net/1885/162744.

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Persistent pain after surgery in breast cancer has a significant impact on the patient’s survival. The value of escalating research on breast cancer in Malaysia cannot be underestimated. However, it is not known how many of these women experience persistent pain after surgery. This study surveyed previously unknown figures on prevalence, and explored the predictive factors of persistent pain women with breast cancer in Malaysia. There were three objectives. First, to assess the reliability of the already established investigative tools, namely, the Brief
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Book chapters on the topic "Rooted Mean Square Error (RMSE)"

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Li, Jingyi, and Hong Chen. "Optimization and Prediction of Design Variables Driven by Building Energy Performance—A Case Study of Office Building in Wuhan." In Proceedings of the 2020 DigitalFUTURES. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_22.

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AbstractThis research focuses on the energy performance of office building in Wuhan. The research explored and predicted the optimal solution of design variables by Multi-Island Genetic Algorithm (MIGA) and RBF Artificial neural networks (RBF-ANNs). Research analyzed the cluster centers of design variable by K-means cluster method. In the study, the RBF-ANNs model was established by 1,000 simulation cases. The RMSE (root mean square error) of the RBF-ANNs model in different energy aspects does not exceed 15%. Comparing to the reference case (the largest energy consumption case in the optimizat
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Faiem, Nabid, Tunc Asuroglu, Koray Acici, Antti Kallonen, and Mark van Gils. "Assessment of Parkinson’s Disease Severity Using Gait Data: A Deep Learning-Based Multimodal Approach." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_3.

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AbstractThe ability to regularly assess Parkinson’s disease (PD) symptoms outside of complex laboratories supports remote monitoring and better treatment management. Multimodal sensors are beneficial for sensing different motor and non-motor symptoms, but simultaneous analysis is difficult due to complex dependencies between different modalities and their different format and data properties. Multimodal machine learning models can analyze such diverse modalities together, thereby enhancing holistic understanding of the data and overall patient state. The Unified Parkinson’s Disease Rating Scal
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Yizhuo, Wang, Li Zhonlian, Li Long, Li Runhua, Cui Xinglei, and Fang Zhi. "Prediction and Evaluation Method of Modification Effect of Large-Scale DBD Insulation Materials Based on Distributed Current Measurement and Neural Network Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_8.

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Abstract Wide dielectric barrier discharge (DBD) has broad application prospects in the modification of insulating materials, but the aging of the electrode directly affects the modification effect in the application process. As the size of the DBD device increases, the real-time evaluation of its modification effect becomes more complicated. Therefore, this paper proposes a real-time prediction and evaluation method for the modification effect of wide DBD insulation materials based on distributed current measurement and neural network model. The operating condition parameters such as DBD exci
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Akanmu, Abiola, Adedeji Afolabi, and Akinwale Okunola. "Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.69.

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Exoskeletons are gaining attention as a potential solution for addressing low back injury in the construction industry. However, use of active back-support exoskeletons in construction can trigger unintended consequences which could increase mental workload of users while working with exoskeletons. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Prediction of mental workload during exoskeleton-use could inform strategies to mitigate the triggers. This study investigates a machine-learning framework for predicting mental workload of workers while using ac
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Akanmu, Abiola, Adedeji Afolabi, and Akinwale Okunola. "Predicting Mental Workload of Using Exoskeletons for Construction Work: A Deep Learning Approach." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.69.

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Exoskeletons are gaining attention as a potential solution for addressing low back injury in the construction industry. However, use of active back-support exoskeletons in construction can trigger unintended consequences which could increase mental workload of users while working with exoskeletons. Prolonged increase in mental workload could impact workers’ wellbeing and productivity. Prediction of mental workload during exoskeleton-use could inform strategies to mitigate the triggers. This study investigates a machine-learning framework for predicting mental workload of workers while using ac
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Chen, Guanhua, and Xinqi Gong. "The Application of Time Series Analysis in the Fiscal Budget Variance of China." In Financial Mathematics and Fintech. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2366-3_12.

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AbstractDuring the process of budget planning and execution, irregular behaviors will be reflected in the level of the difference between budgeted and actual figures (named budget variance). Considering that these two processes are both led by Government Of China (hereinafter called GOC), the budget variance is widely used to evaluate the fiscal system. This chapter collects State General Public Budget data from 2000 to 2018 and analyzes their influence on budget variance. Then the forecast for budget variance is completed by modeling the budget execution and budget variance rate separately. T
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Ariyarathna, Imaya, and Katsuo Sasahara. "Procedure of Data Processing for the Improvement of Failure Time Prediction of a Landslide Based on the Velocity and Acceleration of the Displacement." In Progress in Landslide Research and Technology. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44296-4_14.

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AbstractTime prediction methods based on monitoring surface displacement (SD) are effective for early warning against shallow landslides. However, failure time prediction by Fukuzono’s original inverse-velocity (INV) method is less accurate due to variation in the inverse-velocity (1/v) caused by noise in the measured SD, which amplifies the fluctuation in the resultant 1/v. Therefore, the present study incorporates pre-analysis to acquire better prediction by reducing the effect of noise on the measured SD. The data extraction (DE) and moving average (MA) methods are used to filter the measur
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Akeh, Ugbah Paul, Steve Woolnough, and Olumide A. Olaniyan. "ECMWF Subseasonal to Seasonal Precipitation Forecast for Use as a Climate Adaptation Tool Over Nigeria." In African Handbook of Climate Change Adaptation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_97.

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AbstractFarmers in most parts of Africa and Asia still practice subsistence farming which relies minly on seasonal rainfall for Agricultural production. A timely and accurate prediction of the rainfall onset, cessation, expected rainfall amount, and its intra-seasonal variability is very likely to reduce losses and risk of extreme weather as well as maximize agricultural output to ensure food security.Based on this, a study was carried out to evaluate the performance of the European Centre for Medium-range Weather Forecast (ECMWF) numerical Weather Prediction Model and its Subseasonal to Seaso
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Del Dottore, Emanuela, Alessio Mondini, Davide Bray, and Barbara Mazzolai. "Miniature Soil Moisture Sensors for a Root-Inspired Burrowing Growing Robot." In Biomimetic and Biohybrid Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38857-6_15.

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AbstractThis paper shows the implementation of miniature sensors for soil moisture measurement and their integration in a root-inspired burrowing growing robot. Three kinds of sensors are combined to estimate the water content in soil: a resistivity sensor composed of two brass electrodes, a commercial air humidity sensor interfaced with the soil by a filter membrane of PTFE with polyester scrim, and an RGB sensor used for visible reflectance spectroscopy. We show their integration and embeddability in a burrowing growing robot based on additive manufacturing with a 4 cm probe diameter. The mu
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Yusuf, Anthony, Abiola Akanmu, Adedeji Afolabi, and Homero Murzi. "Prediction of Cognitive Load during Industry-Academia Collaboration via a Web Platform." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.06.

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Web platforms are increasingly being used to connect communities, including construction industry and academia. Design features of such platforms could impose excessive cognitive workload thereby impacting the use of the platform. This is a crucial consideration especially for new web platforms to secure users’ interest in continuous usage. Understanding users’ cognitive workloads while using web platforms could help make necessary modifications and adapt the features to users’ preferences. Users’ usage patterns can be leveraged to predict the needs of users. Hence, the pattern of cognitive de
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Conference papers on the topic "Rooted Mean Square Error (RMSE)"

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Jadoon, Usman Khan, Ismael D�az, and Manuel Rodr�guez. "Comparative Assessment of Aspen Plus Modeling Strategies for Biomass Steam Co-gasification." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.124830.

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The urgent need for sustainable energy drives the exploration of biomass and plastic waste co-gasification, a promising route for producing clean fuels and chemicals, reducing greenhouse gas emissions, and minimizing fossil fuel dependence. Modeling and simulation are vital for optimizing this process, particularly syngas yield, yet comparative studies on Aspen Plus modeling techniques for steam co-gasification are limited. This research addresses this gap by comparing three Aspen Plus strategies: thermodynamic equilibrium modeling (TEM), restricted thermodynamic modeling (RTM), and kinetic mo
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Sinha, Tanaya, Mahmoud Hayajnh, and J. V. R. Prasad. "Development of Rotor Control Equivalent Gust Input (RCEGI) Models." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-292.

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This study investigates the application of neural network architectures to predict control inputs required to replicate rotorcraft responses under vertical gust disturbances. Two modeling approaches are developed: the Control Equivalent Gust Input (CEGI) model, using body-axis inputs and the Rotor Control Equivalent Gust Input (RCEGI) model using rotor-specific inputs. Initial models employed single-input single-output (SISO) LSTM networks, which demonstrated limitations in capturing transient behavior and exhibited delay in predicted control inputs. By incorporating multiple vehicle response
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Zhang, Zhuoran, and Guanlan Liu. "A Prediction of Corrosion-related Leakage on Distribution Pipelines via Machine Learning Method." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-18972.

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Abstract Distribution pipelines are system of main and service lines that transports the product to each individual home and business place. Typically, it operates at a lower pressure than transmission pipes, and it is not linear referenced in the database. In the meantime, distribution pipelines have more leak records available, which encourages the ability to do machine learning on them. This study applied machine learning methods, including the benchmark performance multiple linear regression (MLR) and decision tree-based extreme gradient boosting regression (XGB), to predict the corrosion-
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Oliveira Junior, Adair da Silva, Marcio Carneiro Brito Pache, Fábio Prestes Cesar Rezende, et al. "An Investigation of Parameter Optimization in Fingerling Counting Problems." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18881.

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The objective of this paper is to investigate which combination of parameters for the fingerling counting software results in the smallest Mean Absolute Error (MAE) and smallest Root Mean Squared Error (RMSE). For this, an image dataset called FISHCV155V was created and separated into training and test sets, where different combinations of parameters for the software were tested. From the obtained results were extracted individual performance metrics for each combination of parameters, such as MAE, Mean Square Error (MSE) and RMSE. Video frames were analysed comparing the parameter combination
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Narayan, Subrahmanya Keremane, Viren S. Ram, and Rajshekhar Gannavarpu. "Conditional generative modelling based fringe pattern normalization." In 3D Image Acquisition and Display: Technology, Perception and Applications. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/3d.2023.jw2a.25.

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In this article, we propose a generative adversarial network based fringe pattern normalization method. We investigate the method's effectiveness under various noise levels by evaluating root mean square error (RMSE) and structural similarity index measure (SSIM).
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Gavrilenko, A. D. "DEVELOPMENT OF A MODEL FOR PREDICTING PROTEIN DENATURATION TEMPERATURE BY MACHINE LEARNING METHODS." In OpenBio-2023. ИПЦ НГУ, 2023. http://dx.doi.org/10.25205/978-5-4437-1526-1-12.

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he protein language neural network model ESM-2 and the neural network regression model TabNet. ESM-2 is used to calculate vector representations of amino acid sequences, based on which the melting temperature is predicted using TabNet. Comparison of the developed method with one of the best existing methods, ProTstab2, showed that PMTPred had a lower prediction error. The root mean square error (RMSE) between the observed and predicted Tm values using PMTPred was 5,76 °C, while for ProTstab2 the RMSE value was 9,09 °C.
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Sabancioglu, Mert, Mustafa Demirci, and Yunus Ziya Kaya. "Estimation of Beginning Points of Cross-Shore Sandbars Using Artificial Neural Network." In Air and Water – Components of the Environment 2025 Conference Proceedings. Casa Cărţii de Ştiinţă, 2025. https://doi.org/10.24193/awc2025_06.

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Sediment transport is critical for the design of coastal structures. In this paper, beginning points of cross-shore sandbars predicted using artificial neural network (ANN), multi-linear regression (MLR), and Quadratic-Multivariable Regression (Q-MR). The dataset was obtained as a result of a physical model . In experiments, 3 different bed slopes and 5 different grain sizes were used. Bed slope, grain size, wave period, and wave steepness were used as independent variables. The dependent variable was the beginning point of cross-shore sandbars (Xb). Mean Average Error (MAE), Mean Square Error
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Effiong, Augustine James, Joseph Okon Etim, and Anietie Ndarake Okon. "Artificial Intelligence Model for Predicting Formation Damage in Oil and Gas Wells." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207129-ms.

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Abstract An artificial neural network (ANN) was developed to predict skin, a formation damage parameter in oil and gas drilling, well completion and production operations. Four performance metrics: goodness of fit (R2), mean square error (MSE), root mean square error (RMSE), average absolute percentage relative error (AAPRE), was used to check the performance of the developed model. The results obtained indicate that the model had an overall MSE of 355.343, RMSE of 18.850, AAPRE of 4.090 and an R2 of 0.9978. All the predictions agreed with the measured result. The generalization capacity of th
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Šošić, Darko, Mileta Žarković, and Goran Dobrić. "THE FORECAST OF MEDIUM-VOLTAGE FEEDER LOAD USING NEURAL NETWORK AND CLUSTERING." In 14. Savetovanje o elektrodistributivnim mrežama Srbije, sa regionalnim učešćem. CIRED Liaison Committee of Serbia, 2024. http://dx.doi.org/10.46793/cired24.r-5.01ds.

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This paper presents a model for forecasting the load of medium-voltage distribution network feeders. The model conducts load forecasting with a 15-minute resolution, employing a neural network. Meteorological data relevant to the analyzed location and data on previous electrical energy demand at the observed feeders were utilized for training the model. To enhance forecasting precision, characteristic load diagrams for the observed feeders were established. The first group comprises load diagrams corresponding to typical working and non-working days for each month. The second group of diagrams
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Satimehin, A. A., M. O. Oluwamukomi, V. N. Enujiugha, and M. Bello. "Drying characteristics and mathematical modelling of the drying kinetics of oyster mushroom (Pleurotus ostreatus)." In 21st International Drying Symposium. Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/ids2018.2018.7847.

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This study was conducted to determine the drying characteristics of oyster mushroom (Pleurotus ostreatus) at 50, 60 and 70 °C. Pleurotus ostreatus were cleaned and dried in a laboratory cabinet dryer. The drying data were fitted to six model equations namely Newton, Pabis and Henderson, Logarithmic, Two-term diffusion, Wang and Singh, as well as Modified Henderson and Pabis equations. The goodness of fit of the models were evaluated by means of the coefficient of determination (R2), root mean square error (RMSE) and reduced chi-square (χ2). The Logarithmic model best describes the drying data
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Reports on the topic "Rooted Mean Square Error (RMSE)"

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Fisher, Andmorgan, Taylor Hodgdon, and Michael Lewis. Time-series forecasting methods : a review. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49450.

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Time-series forecasting techniques are of fundamental importance for predicting future values by analyzing past trends. The techniques assume that future trends will be similar to historical trends. Forecasting involves using models fit on historical data to predict future values. Time-series models have wide-ranging applications, from weather forecasting to sales forecasting, and are among the most effective methods of forecasting, especially when making decisions that involve uncertainty about the future. To evaluate forecast accuracy and to compare among models fitted to a time series, thre
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Pradhan, Nawa Raj. Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42128.

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A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reyno
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Brodie, Katherine, Brittany Bruder, Richard Slocum, and Nicholas Spore. Simultaneous mapping of coastal topography and bathymetry from a lightweight multicamera UAS. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41440.

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A low-cost multicamera Unmanned Aircraft System (UAS) is used to simultaneously estimate open-coast topography and bathymetry from a single longitudinal coastal flight. The UAS combines nadir and oblique imagery to create a wide field of view (FOV), which enables collection of mobile, long dwell timeseries of the littoral zone suitable for structure-from motion (SfM), and wave speed inversion algorithms. Resultant digital surface models (DSMs) compare well with terrestrial topographic lidar and bathymetric survey data at Duck, NC, USA, with root-mean-square error (RMSE)/bias of 0.26/–0.05 and
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Anderson, Dylan, Annika O'Dea, Jessamin Straub, et al. Evaluation of the Version 1 Advanced Tactical Awareness Kit–Expeditionary Radar (ATAK-ER) for accuracy and reliability in surf-zone characterization in a range of environmental conditions. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48760.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) presents the evaluation of a rapidly deployable radar and associated software for characterizing surf-zone waves, currents, and bathymetries at the US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory (CHL), Field Research Facility (FRF), in Duck, North Carolina. This project was conducted at the request of the US Marine Corps (USMC) Warfighting Laboratory. The Version 1 Advanced Tactical Awareness Kit–Radar Expeditionary (ATAK-ER V1) system was deployed 15 times between July and August 2023 to
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สิริภัทราวรรณ, อุบลรัตน์, та สุวัสสา พงษ์อำไพ. การประเมินอายุการเก็บแบบรวดเร็วของผลิตภัณฑ์อาหารแปรรูปโดยใช้ NIR specytoscopy และ Chemometrics : รายงานการวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 2014. https://doi.org/10.58837/chula.res.2014.58.

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งานวิจัยนี้พัฒนาการประเมินคุณภาพและอายุการเก็บของผลิตภัณฑ์อาหารแปรรูปพร้อมบริโภค ด้วยวิธี แบบรวดเร็วโดยใช้ NIR spectroscopy ร่วมกับ chemometrics ทำโดยเตรียมผลิตภัณฑ์ไส้กรอกหมูบรรจุใน ถุงพลาสติกภายใต้ภาวะสุญญากาศและเก็บรักษาที่อุณหภูมิ 4 °C ติดตามการเปลี่ยนแปลงคุณภาพทางเคมี (ค่า pH) ทางกายภาพ (ค่าแรงตัดขาด และ ค่าสี) ทางจุลินทรีย์ (จำนวนแบคทีเรียทั้ง หมด และ แบคทีเรียแลกติก) และทาง ประสาทสัมผัส (odor, color, appearance และ overall acceptability) ของผลิตภัณฑ์ในระหว่างการเก็บรักษา รวมทั้งวิเคราะห์โดยใช้ near infrared spectroscopy จากผลการทดลองพบว่า ผลิตภัณฑ์มีค่า pH ค่าแรงตัดขาด ค่าสี (L* (ความเข
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Conery, Ian, Brittany Bruder, Connor Geis, Jessamin Straub, Nicholas Spore, and Katherine Brodie. Applicability of CoastSnap, a crowd-sourced coastal monitoring approach for US Army Corps of Engineers district use. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47568.

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This US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, technical report details the pilot deployment, accuracy evaluation, and best practices of the citizen-science, coastal-image monitoring program CoastSnap. Despite the need for regular observational data, many coastlines are monitored infrequently due to cost and personnel, and this cell phone-image-based approach represents a new potential data source to districts in addition to providing an outreach opportunity for the public. Requiring minimal hardware and signage, the system is simple to install but re
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Konsam, Manis Kumar, Amanda Thounajam, Prasad Vaidya, Gopikrishna A, Uthej Dalavai, and Yashima Jain. Machine Learning-Enhanced Control System for Optimized Ceiling Fan and Air Conditioner Operation for Thermal Comfort. Indian Institute for Human Settlements, 2024. http://dx.doi.org/10.24943/mlcsocfacotc6.2023.

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This paper proposes and tests the implementation of a sustainable cooling approach that uses a machine learning model to predict operative temperatures, and an automated control sequence that prioritises ceiling fans over air conditioners. The robustness of the machine learning model (MLM) is tested by comparing its prediction with that of a straight-line model (SLM) using the metrics of Mean Bias Error (MBE) and Root Mean Squared Error (RMSE). This comparison is done across several rooms to see how each prediction method performs when the conditions are different from those of the original ro
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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental
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