A Modified New Two-Parameter Estimator in a Linear Regression Model by Adewale F. Lukman, Kayode Ayinde, Sek Siok Kun, Emmanuel T. Adewuyi

“…Furthermore, the superiority of the new estimator over OLSE, RRE, LE, MRE, MLE, and the two-parameter estimator proposed by Ozkale and Kaciranlar (2007) was obtained by using the mean squared error matrix criterion. In conclusion, a numerical example and a simulation study were conducted to illustrate the theoretical results.…”
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Issues of qualification of crimes against life and health by O. Artyushina

“…Yarovaya in March 2017, is overviewed and practical recommendationsfor improving the criminal legislation of Russia are formulated.Conclusions. After the analyzing the most difficult cases of competition, the conclusion isbased on the decisive significance of the expert's conclusion about the presence or absenceof affect for the qualification of these crimes. …”
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Estimation of Manning\'s Roughness Coefficient by the Inverse Solving Method using Observational Data (Sanij River-Yazd, Iran) by Mahtab Alimoradi, Mohammad Reza Ekhtesasi, Arash malekian

“…Despite many efforts, the inability to accurately estimate the roughness coefficient and the use of Manning's constant value (n) are the main error factors in flood simulation and flow depth calculation. …”
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Improved High Frequency Pulse Injection Control Inlow Speed Domain of Permanent Magnet Synchronous Motor by LU De-gang, JIANG Guo-wei, JI Tang-long

“…Selecting DSP minimum system board as panel, in combination with the driven plate and motor set up experimental platform experiment, by comparing the results before and after improvement, it is concluded that the improved control method can better reduce the speed and rotor position information error, making the motor estimated speed and rotor position closer to a given value conclusion.…”
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In-situ real-time monitoring of flavonoids extraction from propolis extracts based on near infrared spectroscopy by WANG Wuli, MA Haile, LI Han, ZHANG Yong, XIA Hong

“…The Rp (prediction correlation coefficient) and RMSEP (the root mean square prediction error) were 0.929 1 and 0.242 7 respectively, which showed that the in-situ monitoring of the model had good regression and prediction ability.ConclusionNear-infrared spectroscopy can be used to quickly and accurately monitor the flavonoid content in the extraction process of propolis residue, and realize real-time monitoring in situ.…”
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On the Optimum Linear Soft Fusion of Classifiers by Luis Vergara, Addisson Salazar

“…To this end, we propose an optimal linear combiner based on a minimum mean-square-error class estimation approach. This solution allows us to define a post-fusion mean-square-error improvement factor relative to the best fused classifier. …”
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In Vitro Analysis of the Accuracy of Intraoral Scanning in Children by Eloá Cristina Passucci Ambrosio, Aliny Bisaia, Thiago Cruvinel, Natalino Lourenço Neto, Thais Marchini Oliveira, Maria Aparecida Andrade Moreira Machado

“…Paired t-test, independent t-test, intraclass correlation coefficient (r), Mean Absolute Difference (MAD), Technical Error of Measurement (TEM), and Relative Percentage Error (RPE) were used. …”
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Artificial neural network model for predicting water inflow into a reservoir by A. N. Shilin, M. A. Bogale, L. A. Konovalova

“…Statistical analysis (mean square error (MSE) and R-squared (R2)) was used to verify the validity of the model by comparing the actual values of water inflow with the predicted values. results. …”
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Inter-examiner repeatability and validity of static retinoscopy by Nabeela Hasrod

“…Results: Stereo-pair scatter plots for the three refractive samples from both student examiners obtained for the right eye clustered within the same region, which suggested minimal variation in refractive error between the different samples. Bland-Altman plots for mean differences () were less than or equal to one clinical step (0.25 dioptre [D]) for all refractive error variables although 95% Limits of Agreement (LoA) widths were larger for the spherical equivalent coefficients (M). …”
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Accuracy of 10 Intraocular Lens Power Calculation Formulas by D. F. Belov, V. P. Nikolaenko, D. E. Dmitrieva

“…To compare refractive results of formulas, mean calculation error (ME), mean absolute calculation error (MAE) and formula performance index (FPI) were assessed.Results. …”
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Transfer learning for accelerated failure time model with microarray data by Yan-Bo Pei, Zheng-Yang Yu, Jun-Shan Shen

“…We use a Leave-One-Out cross validation based procedure to evaluate the relative stability of selected genes and overall predictive power. Conclusion In simulation studies, the transfer learning method for the AFT model can correctly identify a small number of genes, its estimation error is smaller than the estimation error obtained without using the source cohorts. …”
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Safety and efficacy of simultaneous photorefractive keratectomy and corneal cross-linking in managing suspected keratoconus by Ali Dal, Mehmet Canleblebici, Murat Erdağ

“…Eligibility criteria included stable refractive error for at least 1 year, spherical equivalent refractive error not exceeding −4.0 D, and central corneal thickness between 470 and 500 µm. …”
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POWER, METALLURGICAL AND CHEMICAL MECHANICAL ENGINEERING DEVICE OF PIECEWISE-LINEAR APPROXIMATION OF TRANSIENT RESPONSE OF CONTROLLED ELECTRONIC COMPONENTS by T. A. Ismailov, Kh. M. Gajiyev, D. A. Chelushkin

“…The minimum value of the systematic error will be obtained with a time constant of the input circuit equal to 0.001 sec.Conclusion. …”
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Dynamic graph structure and spatio-temporal representations in wind power forecasting by Zang Peng, Dong Wenqi, Wang Jing, Fu Jianglong

“…Finally, comprehensive experiments are performed on the collected Xuji Group Wind Power (XGWP) dataset, and the results show that DSTG outperforms the state-of-the-art spatio-temporal methods by 10.12% on the average of root mean square error and mean absolute error, demonstrating the effectiveness of DSTG. …”
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Estimation of Ambient Air PM2.5 Concentration Using MLP and RBF by Ali Mohammadi Bardshahi, Nematollah Jaafarzadeh, Tayebeh Tayebeh, Fazel Amiri

“…The dataset was divided into three subsets: 70% for training, 15% for testing, and 15% for validation.Results: The results showed that the average concentration of PM2.5 was 26.5 μg/m3. The root mean square error (RMSE) was estimated as 6.49 μg/m3. Increasing the input data resulted in a slight reduction in network error, with the RBF model, utilizing 1450 inputs and an RMSE of 6.47, achieving the same accuracy as the MLP model with 10 inputs.Conclusion: Given that the PM2.5 concentration estimates from the RBF and MLP models deviated by less than 23 and 25%, respectively, compared to the observed concentrations, both MLP and RBF can be regarded as reliable tools for predicting PM2.5 levels.…”
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Calibrated muscle models improve tracking simulations without enhancing gait predictions. by Filippo Maceratesi, Míriam Febrer-Nafría, Josep M Font-Llagunes

“…Including gait in the calibration problems improved the knee torques accuracy (normalised root mean square error: 0.31 [Formula: see text] 0.11), compared to the baseline calibration (normalised root mean square error: 0.70 [Formula: see text] 0.21). …”
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OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING by Nur Alamsyah, Venia Restreva Danestiara, Budiman Budiman, Reni Nursyanti, Elia Setiana, Acep Hendra

“…Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. …”
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Influence of Wheel-Rail Contact Point Changes on Force-Measuring Wheelset Measurement and the Compensation Method by CHEN Chunjun, DENG Qian

“…The validity of the wheel-rail force measurement compensation scheme considering wheel-rail contact point position changes is verified through simulation analysis. ［Result & Conclusion］After adding the U.S Grade V track irregularity spectrum excitation, the maximum offset of the wheel-rail contact point position is 17.53 mm, and the maximum relative error of the vertical force reaches 22.85%; after adopting the proposed measuring compensation method, the maximum relative error of the wheel-rail vertical force decreases to 8.02%, and the measurement accuracy is significantly improved. …”
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Comparison of Deep Learning and Gradient Boosting: ANN Versus XGBoost for Climate‐Based Dengue Prediction in Bangladesh by Arman Hossain Chowdhury

“…XGBoost outperformed other models, achieving the lowest root mean square error (918.83) and mean absolute error (479.44). …”
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Artificial Neural Network and Ensemble Models for Flood Prediction in North-Central Region of Nigeria by Sikiru Abdulganiyu Siyanbola, Aisha Olabisi Sowemimo, Zaid Habibu, Timothy Ebuka Eberechukwu

“…The metrics used in evaluating the performance of the models were accuracy score, mean absolute error (MAE), and root mean squared error (RMSE). …”
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