Статті в журналах: "RMSHE"

1

S, Khamar Sabha, Usha H.N, Mithun B.N, and Prathibha G.C. "Contrast enhancement using SHE, BHE, RMSHE and NOSHE with Entropy and EME." IJIREEICE 3, no. 10 (October 15, 2015): 29–32. http://dx.doi.org/10.17148/ijireeice.2015.31007.

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2

Hashmi, Adeel, Abhinav Juneja, Naresh Kumar, Deepali Gupta, Hamza Turabieh, Grima Dhingra, Ravi Shankar Jha, and Zelalem Kiros Bitsue. "Contrast Enhancement in Mammograms Using Convolution Neural Networks for Edge Computing Systems." Scientific Programming 2022 (April 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/1882464.

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A good contrast is significant for analysis of medical images, and if the images have poor contrast, then some methods of contrast enhancement can be of much benefit. In this paper, a convolution neural network-based transfer learning approach is utilized for contrast enhancement of mammographic images. The experiments are conducted on ISP and MIAS datasets, where ISP dataset is used for training and MIAS dataset is used for testing (contrast enhancement). Experimental comparison of the proposed technique is done with the most popular direct and indirect contrast enhancement techniques such as CLAHE, BBHE, RMSHE, and contrast stretching. A qualitative comparison is done using mean square error (MSE), signal to noise ratio (SNR), and peak signal to noise ratio (PSNR). It is observed that the proposed technique outperforms the other techniques HE, RMSHE, CLAHE, BBHE, and contrast stretching.

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3

KUMI-BOATENG, Bernard, and Yao Yevenyo ZIGGAH. "Empirical study on the integration of total least squares and radial basis function neural network for coordinate transformation." Ghana Journal of Science, Technology and Development 7, no. 1 (August 8, 2020): 38–57. http://dx.doi.org/10.47881/220.967x.

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Total Least Squares (TLS) is noted to be a solution approach to solving several geodetic problems. The method has the ability to estimate unknown quantities that are useful for many geodetic applications. Hence, the main objective of this study was to improve the estimation performance of TLS via Radial Basis Function Neural Network (RBFNN) in coordinate transformation. This hybrid approach called TLS-RBFNN was applied to Ghana geodetic reference network, which has a coverage area of 79857 km2 representing 33.5% of the total land mass (238540 km2). A comparative performance analysis of TLS, RBFNN and TLS-RBFNN was carried out using Root Mean Square Horizontal Error (RMSHE) and Standard Deviation (SD). Based on the testing results, it was found that the TLS-RBFNN improved the transformation accuracy of RBFNN and TLS by 20.2% and 37.3% based on the RMSHE. In addition, it was observed that the TLS-RBFNN improved the transformation precision based on SD by 0.37% and 8.52%, respectively. Furthermore, the Bayesian Information Criterion (BIC) applied confirmed the superiority of the hybrid approach than using TLS and RBFNN as independent transformation methods. Consequently, the hybrid approach is recommended for enhanced coordinate transformation results in Ghana geodetic reference network.

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4

Kumi-Boateng, B., and Y. Y. Ziggah. "A 3D Procrustean Approach to Transform WGS84 Coordinates to Ghana War Office 1926 Reference Datum." Ghana Mining Journal 20, no. 1 (July 7, 2020): 1–10. http://dx.doi.org/10.4314/gm.v20i1.1.

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Abstract Satellite positioning technique such as Global Positioning System (GPS) is available to all countries for geospatial positioning. The availability of such positioning technique has revolutionised surveying in Ghana. The GPS operates on a global reference frame to fix control points for surveying and mapping purposes. There is therefore the need to transform coordinates from the satellite-based datum to the Ghana War Office 1926 datum. Several iterative methods have been proposed over the years for coordinate transformation and have been found to exhibit good transformation accuracy. However, these iterative methods always demand the linearisation of the transformation model equations and initial approximation values of the yet to be determined transformation parameters. These computational processes further enhance the computational complexity of the iterative methods and longer convergence time. As alternative solution, the Procrustes method has been proposed and applied to solve coordinate transformation problems in different geodetic reference networks. Review of previous studies indicates that the Procrustes method is direct, simple to use and produce satisfactory transformation accuracy. This method, however, is yet to be applied to ascertain its efficiency in the Ghana geodetic reference network. Therefore, this study utilised the 3D Procrustean approach to transform coordinates from World Geodetic System 1984 (WGS84) to Ghana War Office 1926 reference datum. The technique produced Root Mean Square Horizontal Error (RMSHE), Arithmetic Mean of the Horizontal Error (AMHE) and Standard Deviation (SD) values of 1.003 m, 0.901 m and 0.452 m, respectively. This study is serving as an extension to the ongoing research works to determine optimal transformation model for Ghana geodetic reference network. Keywords: Procrustean Approach, Coordinate Transformation, Conformal Model, Satellite Positioning

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5

Adekoya, Adebayo Felix, Isaac Kofi Nti, and Benjamin Asubam Weyori. "Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi." FinTech 1, no. 1 (December 9, 2021): 25–43. http://dx.doi.org/10.3390/fintech1010002.

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An accurate prediction of the Exchange Rate (ER) serves as the basis for effective financial management, monetary policies, and long-term strategic decision making worldwide. A stable and competitive ER enables economic diversification. Economists, researchers, and investors have conducted several studies to predict trends and facts that influence the ER’s rise or fall. This paper used the Long Short-Term Memory Networks (LSTM) framework to predict the weekly exchange rate of one Ghanaian Cedis (GH₵) to three different currencies (United States Dollar, British Pound, and Euro), using Google Trends and historical macroeconomic data. We fused past exchange rates, fundamental macroeconomic variables, commodity prices (cocoa, gold, and crude oil) and public search queries (Google Trends) as input parameters. An empirical analysis using publicly available ER data from the Bank of Ghana (BoG) from January 2004 to October 2019 showed satisfactory results. We observed that the proposed LSTM model outperformed the Support Vector Regressor (SVR) and Back-propagation Neural Network (BPNN) models in accuracy and closeness metrics. That is, our LSTM model obtained (MAE = 0.033, MSE = 0.0035, RMSE = 0.0551, R2 = 0.9983, RMSLE = 0.0129 and MAPE = 0.0121) compared with SVR (MAE = 0.05, MAE = 0.005, RMSE = 0.0683, R2 = 0.9973, RMSLE = 0.0191 and MAPE = 0.0241) and BPNN (MAE = 0.04, MAE = 0.0056, RMSE = 0.0688, R2 = 0.9974, RMSLE = 0.0172 and MAPE = 0.0168). Moreover, we observed a strong positive correction (0.98–0.99) between Google Trends on the currency of focus and its exchange rate to the Ghanaian cedis. The study results show the importance of incorporating public search queries from search engines to predict the ER accurately.

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6

Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature." Geoscientific Model Development 7, no. 3 (June 30, 2014): 1247–50. http://dx.doi.org/10.5194/gmd-7-1247-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.

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7

Mentaschi, L., G. Besio, F. Cassola, and A. Mazzino. "Problems in RMSE-based wave model validations." Ocean Modelling 72 (December 2013): 53–58. http://dx.doi.org/10.1016/j.ocemod.2013.08.003.

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8

Hoe, Michael S., Christopher J. Dunn, and Hailemariam Temesgen. "Multitemporal LiDAR improves estimates of fire severity in forested landscapes." International Journal of Wildland Fire 27, no. 9 (2018): 581. http://dx.doi.org/10.1071/wf17141.

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Landsat-based fire severity maps have limited ecological resolution, which can hinder assessments of change to specific resources. Therefore, we evaluated the use of pre- and post-fire LiDAR, and combined LiDAR with Landsat-based relative differenced Normalized Burn Ratio (RdNBR) estimates, to increase the accuracy and resolution of basal area mortality estimation. We vertically segmented point clouds and performed model selection on spectral and spatial pre- and post-fire LiDAR metrics and their absolute differences. Our best multitemporal LiDAR model included change in mean intensity values 2–10 m above ground, the sum of proportion of canopy reflection above 10 m, and differences in maximum height. This model significantly reduced root-mean-squared error (RMSE), root-mean-squared prediction error (RMSPE), and bias when compared with models using only RdNBR. Our top combined model integrated RdNBR with LiDAR return proportions <2 m above ground, pre-fire 95% heights and pre-fire return proportions <2 m above ground. This model also significantly reduced RMSE, RMSPE, and bias relative to RdNBR. Our results confirm that three-dimensional spectral and spatial information from multitemporal LiDAR can isolate disturbance effects on specific ecological resources with higher accuracy and ecological resolution than Landsat-based estimates, offering a new frontier in landscape-scale estimates of fire effects.

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9

Lara-Cerecedo, Luis O., Jesús F. Hinojosa, Nun Pitalúa-Díaz, Yasuhiro Matsumoto, and Alvaro González-Angeles. "Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO." Energies 16, no. 16 (August 18, 2023): 6050. http://dx.doi.org/10.3390/en16166050.

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The development and constant improvement of accurate predictive models of electricity generation from photovoltaic systems provide valuable planning tools for designers, producers, and self-consumers. In this research, an adaptive neuro-fuzzy inference model (ANFIS) was developed, which is an intelligent hybrid model that integrates the ability to learn by itself provided by neural networks and the function of language expression, how fuzzy logic infers, and an ANFIS model optimized by the particle swarm algorithm, both with a predictive capacity of about eight months. The models were developed using the Matlab® software and trained with four input variables (solar radiation, module temperature, ambient temperature, and wind speed) and the electrical power generated from a photovoltaic (PV) system as the output variable. The models’ predictions were compared with the experimental data of the system and evaluated with rigorous statistical metrics, obtaining results of RMSE = 1.79 kW, RMSPE = 3.075, MAE = 0.864 kW, and MAPE = 1.47% for ANFIS, and RMSE = 0.754 kW, RMSPE = 1.29, MAE = 0.325 kW, and MAPE = 0.556% for ANFIS-PSO, respectively. The evaluations indicate that both models have good predictive capacity. However, the PSO integration into the hybrid model allows for improving the predictive capability of the behavior of the photovoltaic system, which provides a better planning tool.

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10

Liu, Julin, and Ken Chen. "Enterprise Financial Risk Prevention and Control and Data Analysis Method Based on Blockchain Technology." Mobile Information Systems 2022 (June 23, 2022): 1–7. http://dx.doi.org/10.1155/2022/9452342.

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With the development of engineering technology and computer networks, artificial neural networks, which mimic human brain neural networks, are being used in financial market forecasting to improve the accuracy of stock predictions and are making significant progress. Therefore, there is a great need to actively investigate the method of financial data analysis based on blockchain technology. The purpose of this paper is to investigate the neural network method of financial data analysis based on blockchain technology. Shanghai Index and Shenzhen 200 Index are chosen as experimental data, which are divided into two subsets: training and test samples. The BP model is constructed based on blockchain technology to analyze MARE, RMSRE, MSPEE, RMSPE, and MARE errors. The results show that the mean absolute error rate (MARE), RMSPE, and MSPEE of training samples of blockchain-based BP model are 0.0056, 0.0787, and 0.0085, respectively. Blockchain-based BP model plays an important role in solving financial data analysis problems.

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11

Lee, Seung Shin, and Ki-Choong Mah. "Analysis of the Optical Characteristics Using RMSPE in Progressive Additional Lens." Korean Journal of Vision Science 17, no. 4 (December 31, 2015): 461–70. http://dx.doi.org/10.17337/jmbi.2015.17.4.461.

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12

Hindmarsh, Diane, and David Steel. "Estimating the RMSE of Small Area Estimates without the Tears." Stats 4, no. 4 (November 17, 2021): 931–42. http://dx.doi.org/10.3390/stats4040054.

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Small area estimation (SAE) methods can provide information that conventional direct survey estimation methods cannot. The use of small area estimates based on linear and generalized linear mixed models is still very limited, possibly because of the perceived complexity of estimating the root mean square errors (RMSEs) of the estimates. This paper outlines a study used to determine the conditions under which the estimated RMSEs, produced as part of statistical output (‘plug-in’ estimates of RMSEs) could be considered appropriate for a practical application of SAE methods where one of the main requirements was to use SAS software. We first show that the estimated RMSEs created using an EBLUP model in SAS and those obtained using a parametric bootstrap are similar to the published estimated RMSEs for the corn data in the seminal paper by Battese, Harter and Fuller. We then compare plug-in estimates of RMSEs from SAS procedures used to create EBLUP and EBP estimators against estimates of RMSEs obtained from a parametric bootstrap. For this comparison we created estimates of current smoking in males for 153 local government areas (LGAs) using data from the NSW Population Health Survey in Australia. Demographic variables from the survey data were included as covariates, with LGA-level population proportions, obtained mainly from the Australian Census used for prediction. For the EBLUP, the estimated plug-in estimates of RMSEs can be used, provided the sample size for the small area is more than seven. For the EBP, the plug-in estimates of RMSEs are suitable for all in-sample areas; out-of-sample areas need to use estimated RMSEs that use the parametric bootstrap.

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13

Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (February 28, 2014): 1525–34. http://dx.doi.org/10.5194/gmdd-7-1525-2014.

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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.

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14

Babu,, S. K. Khadar. "Mathematical Modelling of RMSE Approach on Agricultural Financial Data Sets." International Journal of Pure & Applied Bioscience 5, no. 6 (December 30, 2017): 942–47. http://dx.doi.org/10.18782/2320-7051.5802.

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15

Kovács, Dávid Péter, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, and Gábor Csányi. "Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE." Journal of Chemical Theory and Computation 17, no. 12 (November 4, 2021): 7696–711. http://dx.doi.org/10.1021/acs.jctc.1c00647.

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16

Lv, Xian Qiang, Song Yang, Xin Zhang, Ying Wang, Yun Feng Shi, and Liu Wei. "Fast Fractal Image Coding Method Based on RMSE and DCT Classification." Applied Mechanics and Materials 241-244 (December 2012): 3034–39. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.3034.

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To solve the problem of long time consuming in the fractal encoding process, a fast fractal encoding algorithm based on RMSE (Root mean square error) and DCT (Discrete Cosine Transform) classification is proposed. During the encoding process, firstly, the image is divided into range blocks and domain blocks by quadtree partition according to RMSE, then, according to DCT coefficients of image block, three classes of image blocks are defined, which are smooth class, horizontal/vertical edge class, diagonal/sub-diagonal class. At last, every range block is limited to search the best matched block in the corresponding domain block class, and the fractal coding are recorded until the process is completed. When searching the best matched block, the nearest neighbor block will be found in the sense of RMSE in the ordered codebook, and the best matched block will be further found in the vicinity of the nearest neighbor block. The experimental results show that the proposed algorithm can efficiently reduce the search space and shorten the encoding time, while achieving the same reconstructed image quality as that of the full search method.

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17

Fortin, V., M. Abaza, F. Anctil, and R. Turcotte. "Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?" Journal of Hydrometeorology 15, no. 4 (July 30, 2014): 1708–13. http://dx.doi.org/10.1175/jhm-d-14-0008.1.

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Abstract When evaluating the reliability of an ensemble prediction system, it is common to compare the root-mean-square error of the ensemble mean to the average ensemble spread. While this is indeed good practice, two different and inconsistent methodologies have been used over the last few years in the meteorology and hydrology literature to compute the average ensemble spread. In some cases, the square root of average ensemble variance is used, and in other cases, the average of ensemble standard deviation is computed instead. The second option is incorrect. To avoid the perpetuation of practices that are not supported by probability theory, the correct equation for computing the average ensemble spread is obtained and the impact of using the wrong equation is illustrated.

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18

Sugandi, Budi, and Yuniatmi Syamsudin. "Deteksi Tepi Canny dan RMSE untuk Identifikasi Kerusakan pada Kemasan Minuman." JURNAL INTEGRASI 14, no. 2 (October 31, 2022): 110–13. http://dx.doi.org/10.30871/ji.v14i2.4420.

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Salah satu minuman kemasan yang banyak dipakai adalah kemasan kaleng. Kekurangan kemasan minuman kaleng adalah sifatnya yang mudah rusak akibat benturan dengan benda lain maupun terjatuh. Kemasan yang rusak mengakibatkan produk menjadi tidak sempurna. Sehingga proses identifikasi kerusakan kemasan kaleng menjadi sangat penting sebagai proses penjamin kualitas produk. Penelitian ini ditujukan sebagai salah satu solusi untuk mengidentifikasi kerusakan pada kemasan minuman kaleng. Metode deteksi yang diusulkan berdasarkan pada deteksi tepi Canny dan RMSE (Root Mean Square Error). Proses awal deteksi dimulai dengan pengkapturan citra kaleng oleh kamera. Citra asli RGB ini akan dikonversi ke citra biner untuk kemudian dilakukan deteksi tepi Canny. Pada penelitian ini, digunakan nilai high threshold 20 dan low threshold 10 pada proses deteksi tepi Canny. Citra hasil deteksi tepi Canny akan dibandingkan dengan citra deteksi Canny yang menjadi referensi menggunakan nilai RMSE. Nilai RMSE yang digunakan untuk kategori OK dan NG dibatasi pada nilai 70. Hasil pengujian menunjukan nilai RMSE untuk kategori OK berada pada rentang 70.72 dan 85.24 sedangkan kategori NG berada pada rentang 47.99 dan 69.93. Pengujian dilakukan menggunakan citra kaleng bagian atas dan tengah.

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19

Musleh, Dhiaa A., and Maissa A. Al Metrik. "Machine Learning and Bagging to Predict Midterm Electricity Consumption in Saudi Arabia." Applied System Innovation 6, no. 4 (July 10, 2023): 65. http://dx.doi.org/10.3390/asi6040065.

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Electricity is widely regarded as the most adaptable form of energy and a major secondary energy source. However, electricity is not economically storable; therefore, the power system requires a continuous balance of electricity production and consumption to be stable. The accurate and reliable assessment of electrical energy consumption enables planning prospective power-producing systems to satisfy the expanding demand for electrical energy. Since Saudi Arabia is one of the top electricity consumers worldwide, this paper proposed an electricity consumption prediction model in Saudia Arabia. In this work, the authors obtained a never-before-seen dataset of Saudi Arabia’s electricity consumption for a span of ten years. The dataset was acquired solely by the authors from the Saudi Electrical Company (SEC), and it has further research potential that far exceeds this work. The research closely examined the performance of ensemble models and the K* model as novel models to predict the monthly electricity consumption for eighteen service offices from the Saudi Electrical Company dataset, providing experiments on a new electricity consumption dataset. The global blend parameters for the K* algorithm were tuned to achieve the best performance for predicting electricity consumption. The K* model achieved a high accuracy, and the results of the correlation coefficient (CC), mean absolute percentage error (MAPE), root mean squared percentage error (RMSPE), mean absolute error (MAE), and root mean squared error (RMSE) were 0.9373, 0.1569, 0.5636, 0.016, and 0.0488, respectively. The obtained results showed that the bagging ensemble model outperformed the standalone K* model. It used the original full dataset with K* as the base classifier, which produced a 0.9383 CC, 0.1511 MAPE, 0.5333 RMSPE, 0.0158 MAE, and 0.0484 RMSE. The outcomes of this work were compared with a previous study on the same dataset using an artificial neural network (ANN), and the comparison showed that the K* model used in this study performed better than the ANN model when compared with the standalone models and the bagging ensemble.

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20

Dennison, Philip E., and Dar A. Roberts. "Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE." Remote Sensing of Environment 87, no. 2-3 (October 2003): 123–35. http://dx.doi.org/10.1016/s0034-4257(03)00135-4.

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21

Bradai, Sonia, Slim Naifar, Carlo Trigona, Salvatore Baglio, and Olfa Kanoun. "An electromagnetic/magnetoelectric transducer based on nonlinear RMSHI circuit for energy harvesting and sensing." Measurement 177 (June 2021): 109307. http://dx.doi.org/10.1016/j.measurement.2021.109307.

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22

Liemohn, Michael W., Alexander D. Shane, Abigail R. Azari, Alicia K. Petersen, Brian M. Swiger, and Agnit Mukhopadhyay. "RMSE is not enough: Guidelines to robust data-model comparisons for magnetospheric physics." Journal of Atmospheric and Solar-Terrestrial Physics 218 (July 2021): 105624. http://dx.doi.org/10.1016/j.jastp.2021.105624.

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23

Kamble, V. B., and S. N. Deshmukh. "Comparision Between Accuracy and MSE,RMSE by Using Proposed Method with Imputation Technique." Oriental journal of computer science and technology 10, no. 04 (December 28, 2017): 773–79. http://dx.doi.org/10.13005/ojcst/10.04.11.

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Presence of missing values in the dataset leads to difficult for data analysis in data mining task. In this research work, student dataset is taken contains marks of four different subjects in engineering college. Mean, Mode, Median Imputation were used to deal with challenges of incomplete data. By using MSE and RMSE on dataset using with proposed Method and imputation methods like Mean, Mode, and Median Imputation on the dataset and found out to be values of Mean Squared Error and Root Mean Squared Error for the dataset. Accuracy also found out to be using Proposed Method with Imputation Technique. Experimental observation it was found that, MSE and RMSE gradually decreases when size of the databases is gradually increases by using proposed Method. Also MSE and RMSE gradually increase when size of the databases is gradually increases by using simple imputation technique. Accuracy is also increases with increases size of the databases.

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24

Zhang, Wu, Aijun Wen, Qing Wang, and Yuanyuan Li. "Simple and Flexible Photonic Microwave Waveform Generation With Low RMSE of Square Waveform." IEEE Photonics Technology Letters 31, no. 11 (June 1, 2019): 829–32. http://dx.doi.org/10.1109/lpt.2019.2909692.

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25

Hong, Taehoon, Chan-Joong Kim, Jaemin Jeong, Jimin Kim, Choongwan Koo, Kwangbok Jeong, and Minhyun Lee. "Framework for Approaching the Minimum CV(RMSE) using Energy Simulation and Optimization Tool." Energy Procedia 88 (June 2016): 265–70. http://dx.doi.org/10.1016/j.egypro.2016.06.157.

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26

Adekola, Sulaiman, Ayorinde Ayotunde, Hisham Abubakar Muhammed, Francis Okewole, and Ike Mowete. "A Quasi-moment-method Modelling of Energy Demand Forecasting." ELEKTRIKA- Journal of Electrical Engineering 22, no. 1 (April 28, 2023): 15–28. http://dx.doi.org/10.11113/elektrika.v22n1.427.

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This paper presents a novel approach to the modelling of electrical energy demand forecasting, based on the Quasi-Moment-Method (QMM). The technique, using historical energy consumption/demand data, essentially calibrates nominated ‘base’ models (in this case, nominal Harvey and Autoregressive models) to provide significantly better performing models. In addition to the novelty of the use of QMM, the paper identifies hitherto unreported singularities of the generic Harvey / logistic model, through which a simple, but remarkably pivotal modification is proposed, prior to the model’s use as base model in QMM calibration schemes. The treatment of the ‘Harvey singularities’ informed a similar and equally significant modification of the Autoregressive model utilized in the paper. For the purposes of validation and performance evaluation, computational results due to the QMM models are compared with corresponding results reported in three different journal publications, which utilized the Harvey and Autoregressive models in conventional regression schemes. And in terms of the usual model performance metrics (including Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE)), the results very clearly demonstrate the superiority of the QMM models for both energy demand prediction and forecasting. As representative examples, a QMM-calibrated Harvey model recorded an RMSE value of 495.45dB for total energy consumption prediction, as against 618.60dB obtained for the corresponding nominal Harvey model: and for the Autoregressive case, RMSE was obtained as 131.35dB for QMM model’s prediction of peak load demand, compared with the 173.40dB due to the nominal model.

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Cataltas, Ozcan, and Kemal Tutuncu. "Detection of protein, starch, oil, and moisture content of corn kernels using one-dimensional convolutional autoencoder and near-infrared spectroscopy." PeerJ Computer Science 9 (March 9, 2023): e1266. http://dx.doi.org/10.7717/peerj-cs.1266.

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Background Analysis of the nutritional values and chemical composition of grain products plays an essential role in determining the quality of the products. Near-infrared spectroscopy has attracted the attention of researchers in recent years due to its advantages in the analysis process. However, preprocessing and regression models in near-infrared spectroscopy are usually determined by trial and error. Combining newly popular deep learning algorithms with near-infrared spectroscopy has brought a new perspective to this area. Methods This article presents a new method that combines a one-dimensional convolutional autoencoder with near-infrared spectroscopy to analyze the protein, moisture, oil, and starch content of corn kernels. First, a one-dimensional convolutional autoencoder model was created for three different spectra in the corn dataset. Thirty-two latent variables were obtained for each spectrum, which is a low-dimensional spectrum representation. Multiple linear regression models were built for each target using the latent variables of obtained autoencoder models. Results R2, RMSE, and RMSPE were used to show the performance of the proposed model. The created one-dimensional convolutional autoencoder model achieved a high reconstruction rate with a mean RMSPE value of 1.90% and 2.27% for calibration and prediction sets, respectively. This way, a spectrum with 700 features was converted to only 32 features. The created MLR models which use these features as input were compared to partial least squares regression and principal component regression combined with various preprocessing methods. Experimental results indicate that the proposed method has superior performance, especially in MP5 and MP6 datasets.

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Mulyana, Dadang Iskandar, and Marjuki. "OPTIMASI PREDIKSI HARGA UDANG VANAME DENGAN METODE RMSE DAN MAE DALAM ALGORITMA REGRESI LINIER." Jurnal Ilmiah Betrik 13, no. 1 (April 19, 2022): 50–58. http://dx.doi.org/10.36050/betrik.v13i1.439.

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Salah satu komoditas unggulan di Indonesia adalah budi daya udang dan beberapa pengusaha menjadikan udang ini sebagai komoditi ekspor seperti halnya kawasan Asia, Unit Eropa bahkan sampai Amerika. Berbagai jenis yang ada di Indonesia, udang vaname adalah salah satunya yang mempunyai nilai ekonomis yang cukup tinggi, selain itu ada juga udang putih dan udang windu yang cukup banyak peminat pasarnya. Nama lain udang vaname juga sering dikenal dengan nama udang putih. Beberapa keunggulan udang vaname adalah adaptasi yang tinggi terhadap cuaca, wilayah, maupun jenis air yang biasanya dapat mempengaruhi tumbuh kembang udang. Keunggulan terhadap adaptasi yang tinggi bisa menjadi nilai lebih bagi para petani tambak sebagai pilihan budidaya, dikarenakan dewasa ini perubahan yang sering dan cepat terhadap cuaca sedikit banyaknya mempengaruhi ekosistem yang ada. Namun minat yang tinggi terhadap budidaya udang vaname tidak diimbangin dengan pemerataan jalur distribusi pakan udang maupun pemasaran. Hal ini yang terkadang menjadi faktor utama tidak terkendalinya harga udang bahkan sulit untuk diprediksi. Terkadang harga udang dari pengepul terlalu rendah maupun terlalu tinggi yang berpotensi terjadinya permainan harga. Oleh Sebab itu diperlukannya sebuah penelitian terkait prediksi harga udang vaname sehingga dapat digunakan sebagai penentu ideal atau tidak ideal harga udang vaname. Penelitian ini menggunakan Algoritma Linear Regression yang mana adalah sebuah data statistik yang dapat memprediksi sesuatu kedepannya menggunakan data pada saat ini dan juga lampau, dengan metode pengukur keakuratan RMSE dan MAE dengan hasil penelitian ini masing-masing nilai RMSE 1932587 dan hasil MAE -0.01.

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Han, Bo, Bo He, Mengmeng Ma, Tingting Sun, Tianhong Yan, and Amaury Lendasse. "RMSE-ELM: Recursive Model Based Selective Ensemble of Extreme Learning Machines for Robustness Improvement." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/395686.

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For blended data, the robustness of extreme learning machine (ELM) is so weak because the coefficients (weights and biases) of hidden nodes are set randomly and the noisy data exert a negative effect. To solve this problem, a new framework called “RMSE-ELM” is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different ensemble groups concurrently and then employs selective ensemble approach to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble approach is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of our method is increased to some degree, but the result has shown that RMSE-ELM significantly improves robustness with a rapid learning speed compared to representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP, and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.

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Gruber, Leonhard, Maria Ledinek, Franz Steininger, Birgit Fuerst-Waltl, Karl Zottl, Martin Royer, Kurt Krimberger, Martin Mayerhofer, and Christa Egger-Danner. "Body weight prediction using body size measurements in Fleckvieh, Holstein, and Brown Swiss dairy cows in lactation and dry periods." Archives Animal Breeding 61, no. 4 (October 30, 2018): 413–24. http://dx.doi.org/10.5194/aab-61-413-2018.

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Abstract. The objective of this study was to predict cows' body weight from body size measurements and other animal data in the lactation and dry periods. During the whole year 2014, 6306 cows (on 167 commercial Austrian dairy farms) were weighed at each routine performance recording and body size measurements like heart girth (HG), belly girth (BG), and body condition score (BCS) were recorded. Data on linear traits like hip width (HW), stature, and body depth were collected three times a year. Cows belonged to the genotypes Fleckvieh (and Red Holstein crosses), Holstein, and Brown Swiss. Body measurements were tested as single predictors and in multiple regressions according to their prediction accuracy and their correlations with body weight. For validation, data sets were split randomly into independent subsets for estimation and validation. Within the prediction models with a single body measurement, heart girth influenced relationship with body weight most, with a lowest root mean square error (RMSE) of 39.0 kg, followed by belly girth (39.3 kg) and hip width (49.9 kg). All other body measurements and BCS resulted in a RMSE of higher than 50.0 kg. The model with heart and belly girth (ModelHG BG) reduced RMSE to 32.5 kg, and adding HW reduced it further to 30.4 kg (ModelHG BG HW). As RMSE and the coefficient of determination improved, genotype-specific regression coefficients for body measurements were introduced in addition to the pooled ones. The most accurate equations, ModelHG BG and ModelHG BG HW, were validated separately for the lactation and dry periods. Root mean square prediction error (RMSPE) ranged between 36.5 and 37.0 kg (ModelHG BG HW, ModelHG BG, lactation) and 39.9 and 41.3 kg (ModelHG BG HW, ModelHG BG, dry period). Accuracy of the predictions was evaluated by decomposing the mean square prediction error (MSPE) into error due to central tendency, error due to regression, and error due to disturbance. On average, 99.6 % of the variance between estimated and observed values was caused by disturbance, meaning that predictions were valid and without systematic estimation error. On the one hand, this indicates that the chosen traits sufficiently depicted factors influencing body weight. On the other hand, the data set was very heterogeneous and large. To ensure high prediction accuracy, it was necessary to include body girth traits for body weight estimation.

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Torres, Benjamin, and David Fuertes. "Characterization of aerosol size properties from measurements of spectral optical depth: a global validation of the GRASP-AOD code using long-term AERONET data." Atmospheric Measurement Techniques 14, no. 6 (June 17, 2021): 4471–506. http://dx.doi.org/10.5194/amt-14-4471-2021.

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Abstract. A validation study is conducted regarding aerosol optical size property retrievals from measurements of the direct sun beam only (without the aid of diffuse radiation). The study focuses on using real data to test the new GRASP-AOD application, which uses only spectral optical depth measurements to retrieve the total column aerosol size distributions, assumed to be bimodal lognormal. In addition, a set of secondary integral parameters of aerosol size distribution and optical properties are provided: effective radius, total volume concentration and fine-mode fraction of aerosol optical depth (AOD). The GRASP-AOD code is applied to almost 3 million observations acquired over 20 years (1997–2016) at 30 AERONET (Aerosol Robotic Network) sites. These validation sites have been selected based on known availability of an extensive data record, significant aerosol load variability throughout the year, wide worldwide coverage and diverse aerosol types and source regions. The output parameters are compared to those coming from the operational AERONET retrievals. The retrieved fine-mode fractions at 500 nm (τf(500)) obtained by the GRASP-AOD application are compared to those retrieved by the spectral deconvolution algorithm (SDA) and by the AERONET aerosol retrieval algorithm. The size distribution properties obtained by the GRASP-AOD are compared to their equivalent values from the AERONET aerosol retrieval algorithm. The analysis showed the convincing capacity of the GRASP-AOD approach to successfully discriminate between fine- and coarse-mode extinction to robustly retrieve τf(500). The comparisons of 2 million results of τf(500) retrieval by the GRASP-AOD and SDA showed high correlation with a root mean square error (RMSE) of 0.015. Also, the analysis showed that the τf(500) values computed by the AERONET aerosol retrieval algorithm agree slightly better with the GRASP-AOD (RMSE = 0.018, from 148 526 comparisons) than with the SDA (RMSE = 0.022, from 127 203 comparisons). The comparisons of the size distribution retrieval showed agreement for the fine-mode median radius between the GRASP-AOD and AERONET aerosol retrieval algorithm results with an RMSE of 0.032 µm (or 18.7 % in relative terms) for the situations when τ(440)>0.2 occur for more than 80 000 pairs of the study. For the cases where the fine mode is dominant (i.e., α>1.2), the RMSE is only of 0.023 µm (or 13.9 % in relative terms). Major limitations in the retrieval were found for the characterization of the coarse-mode details. For example, the analysis revealed that the GRASP-AOD retrieval is not sensitive to the small variations of the coarse-mode volume median radius for different aerosol types observed at different locations. Nonetheless the GRASP-AOD retrieval provides reasonable agreement with the AERONET aerosol retrieval algorithm for overall coarse-mode properties with with RMSE = 0.500 µm (RMSRE = 20 %) when τ(440)>0.2. The values of effective radius and total volume concentration computed from the GRASP-AOD retrieval have been compared to those estimated by the AERONET aerosol retrieval algorithm. The RMSE values of the correlations were 30 % for the effective radius and 25 % for the total volume concentration when τ(440)>0.2. Finally, the study discusses the importance of employing the assumption of bimodal lognormal size distribution. It also evaluates the potential of using ancillary data, in particular aureole measurements, for improving the characterization of the aerosol coarse-mode properties.

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Sharma, Megha, Namita Mittal, Anukram Mishra, and Arun Gupta. "Machine Learning-Based Electricity Load Forecast for the Agriculture Sector." International Journal of Software Innovation 11, no. 1 (January 1, 2023): 1–21. http://dx.doi.org/10.4018/ijsi.315735.

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A large section of the population has a source of income from the agriculture sector, but their share in the Indian GDP is low. Thus, there is a need to forecast energy to improve and increase productivity. The main sources of energy in agriculture are electricity, coal, and diesel. Among them, electricity plays an important role in land irrigation. Power forecasting is also essential for demand response management. Thus, any process that dissolves future consumption is favorable. This article presents a time series-based technique for forecasting medium-term load in agriculture. The aim is to find the peak periods of power consumption by months and seasons using statistical and machine learning-based techniques. The result shows that SARIMA has lower RMSE and exponential smoothing has lower RMSPE error than random forest and LSTM, which makes the statistical approach more efficient than intelligent approach for historical datasets. The season-wise peak demand occurs during the Rabi season. Finally, five-year ahead load in the agriculture sector was determined using the best models.

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Lappalainen, Kari, Michel Piliougine, and Giovanni Spagnuolo. "Experimental comparison between various fitting approaches based on RMSE minimization for photovoltaic module parametric identification." Energy Conversion and Management 258 (April 2022): 115526. http://dx.doi.org/10.1016/j.enconman.2022.115526.

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Barnston, Anthony G. "Correspondence among the Correlation, RMSE, and Heidke Forecast Verification Measures; Refinement of the Heidke Score." Weather and Forecasting 7, no. 4 (December 1992): 699–709. http://dx.doi.org/10.1175/1520-0434(1992)007<0699:catcra>2.0.co;2.

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Bedendo, Andre, Manuel Reimbold, and Airam Sausen. "Evaluation of Model ARX For Elastic Masses MEMS Using the Indexes RMSE, AIC, and BIC." Journal of Control, Automation and Electrical Systems 25, no. 2 (December 24, 2013): 195–205. http://dx.doi.org/10.1007/s40313-013-0103-5.

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Zhou, Shuang, Jianhua Ren, Qiang Chen, and Zhuopeng Zhang. "Dynamic Change Patterns of Soil Surface Roughness and Influencing Factors under Different Tillage Conditions in Typical Mollisol Areas of Northeast China." Agronomy 13, no. 7 (July 8, 2023): 1817. http://dx.doi.org/10.3390/agronomy13071817.

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Soil surface roughness is an important factor affecting hydrology and soil erosion processes, and its development is influenced by precipitation, topography, and tillage practices. In this study, the typical mollisol area in northeast China was taken as the research object. Then, the variation in soil surface roughness with time was analyzed under different terrains, as well as different tillage methods, and the effect of the precipitation condition on roughness was also discussed in detail. Through the design of field experiments, the height information of the soil surface was measured using a probe-type roughness plate. Two parameters, the root-mean-square height (RMSH) and the correlation length (CL), were selected to quantitatively characterize the soil surface roughness. In addition, the dynamic change patterns of surface roughness resulting from five tillage methods, including rotary tillage, combined tillage, no tillage, conventional tillage, and reduced tillage, under both sloping and flat land, were compared and analyzed throughout the soybean growing season, under the influence of rainfall. The results show that with the increase in rainfall, the RMSH of the soil surface, under different tillage methods, showed a trend of first decreasing, and then increasing. The results also showed that the RMSHs under rotary tillage, combined tillage, conventional tillage, and reduced tillage in flat land were greater than those in sloping land, and that the CLs of the soil surface under different tillage methods in flat land were smaller than those in sloping land. In addition, the degree of variation in the soil surface roughness was greater in flat land than that in sloping land under all tillage practices, indicating that this study is of great practical importance in the rational selection of tillage methods, and in the scientific quantification of soil erosion, which also show obvious significance for soil and water conservation.

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Hodson, Timothy O. "Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not." Geoscientific Model Development 15, no. 14 (July 19, 2022): 5481–87. http://dx.doi.org/10.5194/gmd-15-5481-2022.

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Abstract. The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.

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Somappa, Laxmeesha, Adarsh G. Menon, Ajay K. Singh, Ashwin A. Seshia, and Maryam Shojaei Baghini. "A Portable System With 0.1-ppm RMSE Resolution for 1–10 MHz Resonant MEMS Frequency Measurement." IEEE Transactions on Instrumentation and Measurement 69, no. 9 (September 2020): 7146–57. http://dx.doi.org/10.1109/tim.2020.2978588.

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Odhiambo, Joab, Patrick Weke, Philip Ngare, Raphael Naryongo, and Stanley Sewe. "Poisson Incorporated Credibility Regression Modelling of Systematic Mortality Risk for Populations with Finite Data." Mathematical Problems in Engineering 2022 (October 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/1753542.

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This study considered the modeling of systematic mortality risk for populations with finite data using the Poisson incorporated Credibility regression model. For novelty, we have included the credibility regression approach to modelling mortality by assuming the number of annual deaths follow a Poisson distribution. Our model shows improvement in precision levels when estimating mortality risk compared to classical models used in European countries. We have illustrated that our model works optimally when using Kenyan mortality data, comparing male and female lives under the different strategies, thus making better predictions than the classical Lee–Carter (LC) and Cairns–Blake–Dowd (CBD) models. The mean absolute forecast error (MAFE), mean absolute percentage forecast error (MAPFE), root mean square error (RMSE), and root mean square forecast error (RMSFE) under the incorporated credibility regression model are much lower than the values obtained without incorporation of the Buhlmann credibility approach. The findings of this research will help insurance companies, pension firms, and government agencies in sub-Saharan countries model and forecast systematic mortality risks accurately. Finally, the results are essential in actuarial modelling and pricing, thus making life assurance products affordable for most people in low-income African countries.

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Al–Taee, Mohammed. "Using Surveying and Computer Techniques to Calculate (R.A) & (RMSE) for Digital map of Technical Institute/Mosul." Iraqi National Journal of Earth Sciences 19, no. 2 (December 30, 2019): 1–14. http://dx.doi.org/10.33899/earth.2019.170273.

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Vates, U. K., Nirmal Kumar Singh, and R. V. Singh. "Modelling and optimisation of wire electrical discharge machining process on D2 steel using ANN and RMSE approach." International Journal of Computational Materials Science and Surface Engineering 6, no. 3/4 (2016): 161. http://dx.doi.org/10.1504/ijcmsse.2016.081679.

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Vates, U. K., Nirmal Kumar Singh, and R. V. Singh. "Modelling and optimisation of wire electrical discharge machining process on D2 steel using ANN and RMSE approach." International Journal of Computational Materials Science and Surface Engineering 6, no. 3/4 (2016): 161. http://dx.doi.org/10.1504/ijcmsse.2016.10002548.

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Melinda, Maulana Imam Muttaqin, Yudha Nurdin, and Al Bahri. "Implementation of Word Recommendation System Using Hybrid Method for Speed Typing Website." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 1 (February 1, 2023): 7–14. http://dx.doi.org/10.29207/resti.v7i1.4518.

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Typing is one of the most frequently done activities in society therefore a medium is necessary to help train typing words that are often mistyped. Methods used in this research are the Content-Based Filtering Algorithm to gather the words that have a similar pattern to the words that are often mistyped based on the user's previous typing records and the Collaborative Filtering Algorithm that uses other users typing pattern to recommend the words. The result of this study shows the Collaborative Filtering Algorithm was able to gather words that are hard to type by the user with an accuracy of 49.2%, dan the Collaborative Filtering able to predict the score on how difficult for the user to type a word with the result of Root Mean Square Error (RMSE) value of 0.82 and with the Root Mean Square Percentage Error (RMSPE) value of 30% from the actual value, and a website which is the combination of the two algorithms with the result of 28% of the total word that is recommended was indeed difficult to type by the user with the typing speed of 103 WPM, and 72.3% for the user that has a typing speed of 39 WPM.

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Lia Hananto, April, Sarina Sulaiman, Sigit Widiyanto, and Aviv Yuniar Rahman. "Evaluation comparison of wave amount measurement results in brass-plated tire steel cord using RMSE and cosine similarity." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (April 1, 2021): 207. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp207-214.

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<span lang="IN">In the production process, quality checking is very important, one of which is on the wire. In the process of making brass-coated steel tire straps sometimes produce quality goods not in accordance with the desired standard values. Checks that are carried out manually have low efficiency and quite high errors occur. So it is necessary to check by measuring the wavelength on the brass plated steel cord automatically. In this study, used 3 automatic measurement methods using 2 evaluations, namely RMSE and Cosine Similarity. The results showed the best measurement using RMSE with method 2. Whereas the worst method uses RMSE with method 1. The smallest RMSE value is 0.0098 and the largest RMSE is 0.0966. The lowest Cosine Similarity value is 0.1253, while the highest Cosine Similarity value is 0.2079.</span>

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Wang, Weijie, and Yanmin Lu. "Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model." IOP Conference Series: Materials Science and Engineering 324 (March 2018): 012049. http://dx.doi.org/10.1088/1757-899x/324/1/012049.

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Uddin, Syed Farid, Ayan Alam Khan, Mohd Wajid, Mahima Singh, and Faisal Alam. "Performance evaluation of direction-finding techniques of an acoustic source with uniform linear array." Frontiers in Engineering and Built Environment 1, no. 2 (October 22, 2021): 230–42. http://dx.doi.org/10.1108/febe-09-2021-0045.

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PurposeThe purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).Design/methodology/approachThe relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.FindingsVariations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.Originality/valueDAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.

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Chalermwongphan, Karn, and Prapatpong Upala. "The Analysis of Dynamic O/D Adjustment for Bicycle Traffic Demand Estimation with AIMSUN Simulation Model: A Case Study of Nakhon Sawan Municipality in Thailand." Open Transportation Journal 12, no. 1 (December 31, 2018): 352–65. http://dx.doi.org/10.2174/1874447801812010352.

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Aim: This research aimed to present the process of estimating bicycle traffic demand in order to design bike routes that meet the daily transportation needs of the people in Nakhon Sawan Municipality. Methods: The primary and secondary traffic data were collected to develop a virtual traffic simulation model with the use of the AIMSUN simulation software. The model validation method was carried out to adjust the origin and destination survey data (O/D matrix) by running dynamic O/D adjustment. The 99 replication scenarios were statistically examined and assessed using the goodness-of-fit test. The 9 measures, which were examined, included: 1) Root Mean Square Error (RMSE), 2) Root Mean Square Percentage Error (RMSPE%), 3) Mean Absolute Deviation (MAD), 4) Mean Bias Error (MBE), 5) Mean Percentage Error (MPE%), 6) Mean Absolute Percentage Error (MAPE%), 7) Coefficient of Determination (R2), 8) GEH Statistic (GEH), and 9) Thiel’s U Statistic (Theil’s U). Results: The resulting statistical values were used to determine the acceptable ranges according to the acceptable indicators of each factor. Conclusion: It was found that there were only 8 scenarios that met the evaluation criteria. The selection and ranking process was consequently carried out using the multi-factor scoring method, which could eliminate errors that might arise from applying only one goodness-of-fit test measure.

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Kuan, Chin-Hung, Yungho Leu, Wen-Shin Lin, and Chien-Pang Lee. "The Estimation of the Long-Term Agricultural Output with a Robust Machine Learning Prediction Model." Agriculture 12, no. 8 (July 22, 2022): 1075. http://dx.doi.org/10.3390/agriculture12081075.

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Recently, annual agricultural data have been highly volatile as a result of climate change and national economic trends. Therefore, such data might not be enough to develop good agricultural policies for stabilizing agricultural output. A good agricultural output prediction model to assist agricultural policymaking has thus become essential. However, the highly volatile data would affect the prediction model’s performance. For this reason, this study proposes a marriage in honey bees optimization/support vector regression (MBO/SVR) model to minimize the effects of highly volatile data (outliers) and enhance prediction accuracy. We verified the performance of the MBO/SVR model by using the annual total agricultural output collected from the official Agricultural Statistics Yearbook of the Council of Agriculture, Taiwan. Taiwan’s annual total agricultural output integrates agricultural, livestock and poultry, fishery, and forest products. The results indicated that the MBO/SVR model had a lower mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), and relative root mean squared error (r-RMSE) than those of the models it was compared to. Furthermore, the MBO/SVR model predicted long-term agricultural output more accurately and achieved higher directional symmetry (DS) than the other models. Accordingly, the MBO/SVR model is a robust, high-prediction-accuracy model for predicting long-term agricultural output to assist agricultural policymaking.

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Scotto, Carlo, and Dario Sabbagh. "The Accuracy of Real-Time hmF2 Estimation from Ionosondes." Remote Sensing 12, no. 17 (August 19, 2020): 2671. http://dx.doi.org/10.3390/rs12172671.

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A total of 4991 ionograms recorded from April 1997 to December 2017 by the Millstone Hill Digisonde (42.6°N, 288.5°E) were considered, with simultaneous Ne(h)[ISR] profiles recorded by the co-located Incoherent Scatter Radar (ISR). The entire ionogram dataset was scaled with both the Autoscala and ARTIST programs. The reliability of the hmF2 values obtained by ARTIST and Autoscala was assessed using the corresponding ISR values as a reference. Average errors Δ and the root mean square errors RMSE were computed for the whole dataset. Data analysis shows that both the Autoscala and ARTIST systems tend to underestimate hmF2 values with |Δ| in all cases less than 10 km. For high magnetic activity ARTIST offers better accuracy than Autoscala, as evidenced by RMSE[ARTIST] < RMSE[Autoscala], under both daytime and nighttime conditions, and considering all hours of the day. Conversely, under low and medium magnetic activity Autoscala tends to estimate hmF2 more accurately than the ARTIST system for both daytime and nighttime conditions, when RMSE[Autoscala] < RMSE[ARTIST]. However, RMSE[Autoscala] slightly exceeds RMSE[ARTIST] for the day as a whole. RMSE values are generally substantial (RMSE > 16 km in all cases), which places a limit on the results obtainable with real-time models that ingest ionosonde data.

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Amadou Oury Sow, Thierno, Samba Thiapato Faye, Mory Sangaré, Mohamed Habiboulah Moustapha Ova, Ibrahima Diallo, Abdoulaye Sadjo Diallo, Alhassane Ismael Touré, and Cheikh Diouf. "Paratesticular Embryonal Rhabdomyosarcoma (RMSE-PT): A Case Study in the Surgery Department of the Regional Hospital of Ziguinchor (Senegal)." International Journal of Clinical Urology 6, no. 1 (2022): 63. http://dx.doi.org/10.11648/j.ijcu.20220601.24.

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