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Borin, Airton, Anne Humeau-Heurtier, Luiz Virgílio Silva, and Luiz Murta. "Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals." Entropy 23, no. 12 (December 1, 2021): 1620. http://dx.doi.org/10.3390/e23121620.
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Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series using long-term MSE as reference. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions—as a function of time series length—present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.
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Adam Haggagy, Mahmoud El-Nouby, Abdel Galeil Abdel Elal Hassan, Badry Noby Mohamed Abd Allah, and Ezzat Ramadan Mahmoud. "Calculation of the Global Solar Radiation in a Subtropical Region (Qena, Egypt)." International Journal of Research Publication and Reviews 04, no. 01 (2022): 1880–84. http://dx.doi.org/10.55248/gengpi.2023.4153.
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Air temperature (T, °C), relative humidity (RH, %), and global solar radiation (G, MJ.m-2 ) have been measured in the meteorological station at South Valley University (SVU) at Qena, Egypt, from 2000 to 2013, while the total column ozone (TCO, DU) is downloaded from Giovanni's website. T, RH, and TCO are important meteorological parameters, and they are useful to estimate the missing data of the global solar radiation (G), as global solar radiation is desirable for electricity generation applications and for agriculture. Qena is a subtropical region in Upper Egypt, as it's characterized by clear weather most days of the year, and it's very hot in the summer and cold in the winter. The meteorological station at South Valley University (SVU) stopped measuring the G. The linear regression equation and the most important statistical indices are used in this paper such as, the determination coefficient (R2 ), the mean absolute error (MAE), the mean absolute bias error (MABE), the mean square error (MSE), the root mean square error (RMSE), the mean percentage error (MPE), the mean bias error (MBE), the model efficiency (ME), and the agreement index (d). For verification of the empirical models' efficiency, the data of a new period has used, 2013, and the results of the models were excellent and valid for estimating the missing data, as R2 was more than 0.92 in all models but it was near one in models 1 and 4. MAE was close to zero for all models. MBE, MPE, and MABE were close to zero for all models except model 3. Model 1 was the best one, as, R2 , MAE, MABE, MSE, RMSE, MAPE, d, ME, and MPE were 0.9883, 0.0193, 0.383, 0.2303, 0.4799, 1.927, 0.9979, 0.992, and 0.2932, respectively
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Xia, Xinran, Disong Fu, Ye Fei, Wei Shao, and Xiangao Xia. "An Improved Assessment Method and Its Application to the Latest IMERG Rainfall Product in Mainland China." Remote Sensing 13, no. 24 (December 16, 2021): 5107. http://dx.doi.org/10.3390/rs13245107.
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Quantification of uncertainties associated with satellite precipitation products is a prior requirement for their better applications in earth science studies. An improved scheme is developed in this study to decompose mean bias error (MBE) and mean square error (MSE) into three components, i.e., MBE and MSE associated hits, missed precipitation, and false alarms, respectively, which are weighted by their relative frequencies of occurrence (RFO). The trend of total MBE or MSE is then naturally decomposed into six components according to the chain rule for derivatives. Quantitative estimation of individual contributions to total MBE and MSE is finally derived. The method is applied to validation of Integrated MultisatellitE Retrievals for GPM (IMERG) in Mainland China. MBE associated with false alarms is an important driver for total MBE, while MSE associated with hits accounts for more than 85% of MSE, except in inland semi-arid area. The RFO of false alarms increases, whereas the RFO of missed precipitation decreases. Both factors lead in part to a growing trend for total MBE. Detection of precipitation should be improved in the IMERG algorithm. More specifically, the priority should be to reduce false alarms.
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Pratelli, Giovanni, Daniela Carlisi, Antonella D’Anneo, Antonella Maggio, Sonia Emanuele, Antonio Palumbo Piccionello, Michela Giuliano, Anna De Blasio, Giuseppe Calvaruso, and Marianna Lauricella. "Bio-Waste Products of Mangifera indica L. Reduce Adipogenesis and Exert Antioxidant Effects on 3T3-L1 Cells." Antioxidants 11, no. 2 (February 11, 2022): 363. http://dx.doi.org/10.3390/antiox11020363.
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Several studies highlighted the beneficial value of natural compounds in the prevention and treatment of obesity. Here, we investigated the anti-obesity effects of extracts of peel and seed of mango (Mangifera indica L.) cultivated in Sicily (Italy) in 3T3-L1 cells. Mango Peel (MPE) and Mango Seed (MSE) extracts at a 100 µg/mL concentration significantly reduced lipid accumulation and triacylglycerol contents during 3T3-L1 adipocyte differentiation without toxicity. HPLC-ESI-MS analysis showed that both the extracts contain some polyphenolic compounds that can account for the observed biological effects. The anti-adipogenic effect of MPE and MSE was the result of down-regulation of the key adipogenic transcription factor PPARγ and its downstream targets FABP4/aP2, GLUT4 and Adipsin, as well SREBP-1c, a transcription factor which promotes lipogenesis. In addition, both MPE and MSE significantly activated AMPK with the consequent inhibition of Acetyl-CoA-carboxylase (ACC) and up-regulated PPARα. The addition of compound C, a specific AMPK inhibitor, reduced the effects of MPE and MSE on AMPK and ACC phosphorylation, suggesting a role of AMPK in mediating MPE and MSE anti-lipogenic effects. Notably, MPE and MSE possess an elevated radical scavenging activity, as demonstrated by DPPH radical scavenging assay, and reduced ROS content produced during adipocyte differentiation. This last effect could be a consequence of the increase in the antioxidant factors Nrf2, MnSOD and HO-1. In conclusion, MPE and MSE possesses both anti-adipogenic and antioxidant potential, thus suggesting that the bio-waste products of mango are promising anti-obesity natural compounds.
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Sofian, Ian Mochamad, Azhar Kholiq Affandi, Iskhaq Iskandar, and Yosi Apriani. "Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function." International Journal of Advances in Intelligent Informatics 4, no. 2 (July 31, 2018): 154. http://dx.doi.org/10.26555/ijain.v4i2.208.
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Two models of Artificial Neural Network (ANN) algorithm have been developed for monthly rainfall prediction, namely the Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN). A total data of 238 months (1994-2013) was used as the input data, in which 190 data were used as training data and 48 data used as testing data. Rainfall data has been tested using architecture BPNN with various learning rates. In addition, the rainfall data has been tested using the RBFNN architecture with maximum number of neurons K = 200, and various error goals. Statistical analysis has been conducted to calculate R, MSE, MBE, and MAE to verify the result. The study showed that RBFNN architecture with error goal of 0.001 gives the best result with a value of MSE = 0.00072 and R = 0.98 for the learning process, and MSE = 0.00092 and R = 0.86 for the testing process. Thus, the RBFNN can be set as the best model for monthly rainfall prediction.
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Liu, Qingyun, Haiyang Pan, Jinde Zheng, Jinyu Tong, and Jiahan Bao. "Composite Interpolation-Based Multiscale Fuzzy Entropy and Its Application to Fault Diagnosis of Rolling Bearing." Entropy 21, no. 3 (March 18, 2019): 292. http://dx.doi.org/10.3390/e21030292.
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Multiscale fuzzy entropy (MFE), as an enhanced multiscale sample entropy (MSE) method, is an effective nonlinear method for measuring the complexity of time series. In this paper, an improved MFE algorithm termed composite interpolation-based multiscale fuzzy entropy (CIMFE) is proposed by using cubic spline interpolation of the time series over different scales to overcome the drawbacks of the coarse-grained MFE process. The proposed CIMFE method is compared with MSE and MFE by analyzing simulation signals and the result indicates that CIMFE is more robust than MSE and MFE in analyzing short time series. Taking this into account, a new fault diagnosis method for rolling bearing is presented by combining CIMFE for feature extraction with Laplacian support vector machine for fault feature classification. Finally, the proposed fault diagnosis method is applied to the experiment data of rolling bearing by comparing with the MSE, MFE and other existing methods, and the recognition rate of the proposed method is 98.71%, 98.71%, 98.71%, 98.71% and 100% under different training samples (5, 10, 15, 20 and 25), which is higher than that of the existing methods.
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Christian, Yuriko, and I. Dewa Made Bayu Atmaja Darmawan. "Specialty Coffee Cupping Score Prediction with General Regression Neural Network (GRNN)." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 2 (November 22, 2020): 185. http://dx.doi.org/10.24843/jlk.2020.v09.i02.p04.
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Coffee is a plant that can be processed into beverages. The cupping score is a score for a coffee quality graded with an expert called Q grader. The cupping score will decide if a coffee may be called as specialty coffee. In this research, the cupping score will be predicted by the coffee properties and did not involve the Q grader for giving the score. The prediction of the score is obtained by using the GRNN method. The experiment consists of finding when the MAE and the MSE are converged and find the neuron's best number. The model's performance is measured with MSE and MAE with the best MSE value of 0.097 and MAE value 0.245.
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Nwakuya, M. T., and C. C. Nkwocha. "Manly transformation in quantile regression: A comparison of two transformation parameter estimators." Scientia Africana 21, no. 1 (June 1, 2022): 67–76. http://dx.doi.org/10.4314/sa.v21i1.6.
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This study implements the Manly transformations for normalization of variables in quantile regression analysis.The transformation parameter was estimated using two different methods namely; the maximum likelihood estimation (MLE) method and the two-step estimation method by Chamberlain and Buchinsky(CBTS).The transformation parameters obtained using the two different methods were used for the Manly transformation of data with outliers and data without outliers. The methods were applied to a quantile regression analysis at different quantiles (0.25, 0.50, 0.75, 0.95). Based on our findings, for data without outliers, the 25th quantile model was seen to be the best fit model compared to the other quantiles for the CBTS method with AIC=-43.46279, BIC=20.75212 and MSE=0.70956, while for the MLE the 50th quantile model was seen to be the best fit model with AIC=-348.3657, BIC=20.13548, and MSE=0.00864. Considering data with outliers the 25th quantile model was still seen to be the best fit model compared to the other quantiles for the CBTS method with AIC=-48.5671, BIC=21.8321 and MSE=0.92341, while for the MLE the 50th quantile model was still seen to be the best fit model with AIC=988.6763, BIC=710.09, and MSE=690.7965. Comparison of both methods for data without outliers the study concludes that the estimation of the transformation parameter using the MLE produced better results with lower AIC, BIC and MSE at all quantiles and for data with outliers the study concludes that the estimation of the transformation parameter using CBTS produced better results with lower AIC, BIC and MSE results as is shown in table (3.5) and table (3.6) respectively.
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Al-Obedy, Nadia. "Semi- Minimax Estimations on the Exponential Distribution Under Symmetric and Asymmetric Loss Functions." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 13, 2021): 245–70. http://dx.doi.org/10.55562/jrucs.v36i2.257.
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In this paper the semi-minimax estimators of the scale parameter of the exponential distribution are presented by applying the theorem of Lehmann under symmetric (quadratic) loss function and asymmetric (entropy, mlinex , precautionary) loss functions .The results of comparison between these estimators are compared empirically using Monte-Carlo simulation study with respect to the mean square error(MSE) and the mean percentage error(MPE). In general, the results showed that the semi-minimax estimator under quadratic loss function is the best estimator by MSE and MPE for all sample sizes. We can notice that, when the values of the parameters β ,θ increasing the semi-minimax estimator under quadratic loss function is the best estimator by MSE while comparison by MPE showed that the semi-minimax estimator under mlinex loss function when the value of c positive is the best, but they both get worse as α ,θ increases. Also the results showed that when α, β together increase the semi-minimax estimator under entropy loss function is the best by MSE while by MPE the semi-minimax estimator under precautionary loss function is the best estimator.
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Robeson, Scott M., and Cort J. Willmott. "Decomposition of the mean absolute error (MAE) into systematic and unsystematic components." PLOS ONE 18, no. 2 (February 17, 2023): e0279774. http://dx.doi.org/10.1371/journal.pone.0279774.
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When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean absolute error (MAE) are more interpretable than MSE or RMSE. Part of that historical preference for sums-of-squares measures is that they are mathematically amenable to decomposition and one can then form ratios, such as those based on separating MSE into its systematic and unsystematic components. Here, we develop and illustrate a decomposition of MAE into three useful submeasures: (1) bias error, (2) proportionality error, and (3) unsystematic error. This three-part decomposition of MAE is preferable to comparable decompositions of MSE because it provides more straightforward information on the nature of the model-error distribution. We illustrate the properties of our new three-part decomposition using a long-term reconstruction of streamflow for the Upper Colorado River.
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Huda, Didik Nur, and Santy Handayani. "Prediksi Nilai Ujian dengan Artificial Neural Network." remik 7, no. 1 (January 1, 2023): 157–65. http://dx.doi.org/10.33395/remik.v7i1.11983.
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Artificial Neural Network (ANN) dalam pendidikan lebih sering digunakan untuk klasifikasi, akan tetapi ANN dapat digunakan untuk memprediksi suatu nilai. Nilai dari hasil belajar mata kuliah fisika listrik magnet dapat dimodelkan dengan ANN ini. Dataset yang diperoleh dari nilai yang ada di classroom dan quizizz. Dataset setelah diproses terdiri dari 19 fitur (variabel) dan 1 output nilai yang berisi 113 baris. Dataset dibagi menjadi 80% untuk melatih model dan 20% untuk uji coba model. Dalam model ANN untuk memprediksi nilai ini, pendekatan yang digunakan adalah Mean Squared Error (MSE) dan Mean Absolute Error (MAE). Pendekatan MAE lebih baik dibandingkan MSE, karena selisih dengan nilai sebenarnya tidak terlalu jauh. Sedangkan dari hasil akurasi dinyatakan menggunakan rerata Absolute Percent Error (APE). Hasil akurasi pendekatan MSE dan MAE yaitu 88,97% dan 89,99%.
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Khalis Sofi, Aswan Supriyadi Sunge, Sasmitoh Rahmad Riady, and Antika Zahrotul Kamalia. "PERBANDINGAN ALGORITMA LINEAR REGRESSION, LSTM, DAN GRU DALAM MEMPREDIKSI HARGA SAHAM DENGAN MODEL TIME SERIES." SEMINASTIKA 3, no. 1 (November 1, 2021): 39–46. http://dx.doi.org/10.47002/seminastika.v3i1.275.
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Penelitian ini bertujuan untuk memprediksi harga saham dengan membandingkan algoritma Linear Regression, Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) dengan dataset publik kemudian menentukan performa terbaik dari ketiga algoritma tersebut. Dataset yang diuji bersumber dari Indonesia Stock Exchange (IDX), yaitu dataset harga saham KEJU berbentuk time series dari tanggal 15 November 2019 sampai dengan 08 Juni 2021. Parameter yang digunakan untuk pengukuran perbandingan adalah RMSE (Root Mean Square Error), MSE (Mean Square Error), dan MAE (Mean Absolute Error). Setelah dilakukan proses training dan testing, dihasilkan sebuah analisis bahwa dari hasil perbandingan algoritma yang digunakan, algoritma Gated Recurrent Unit (GRU) memiliki performance paling baik dibandingkan Linear Regression dan Long-Short Term Memory (LSTM) dalam hal memprediksi harga saham, dibuktikan dengan nilai RMSE, MSE, dan MAE dari uji coba GRU paling rendah, yaitu nilai RMSE 0.034, MSE 0.001, dan nilai MAE 0.024.
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Zhang, Qian, Xiaoxu Tian, Peng Zhang, Lei Hou, Zhigong Peng, and Gang Wang. "Solar Radiation Prediction Model for the Yellow River Basin with Deep Learning." Agronomy 12, no. 5 (April 29, 2022): 1081. http://dx.doi.org/10.3390/agronomy12051081.
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Solar radiation is the main source of energy on the Earth’s surface. It is very important for the environment and ecology, water cycle and crop growth. Therefore, it is very important to obtain accurate solar radiation data. In this study, we use the highest temperature Tmax, lowest temperature Tmin, average temperature Tavg, wind speed U, relative humidity RH, sunshine duration H and maximum sunshine duration Hmax as input variables to construct a deep learning prediction model of solar radiation in the Yellow River Basin. It is compared with the recommended and corrected values of the widely used Å-P method. The results show that: (1) The correction results of the Å-P equation are better in the upstream and downstream of the Yellow River Basin but worse in the midstream. (2) The prediction result of the deep learning model in the Yellow River Basin is far better than that of the Å-P equation using the FAO-56 recommended value. It is the best in the downstream of the Yellow River Basin: R2 increases from 0.894 to 0.934; MSE, RMSE and MAE decrease by 43.12%, 27.73% and 25.80%, respectively. The upstream prediction result comes in second: R2 increases from 0.888 to 0.921; MSE, RMSE and MAE decrease by 33.27%, 20.02% and 19.04%, respectively. The midstream result is the worst: R2 increases from 0.869 to 0.874; MSE, RMSE and MAE decrease by −0.50%, 0.07% and 3.82%, respectively. (3) The prediction results of the deep learning model in the upstream and downstream of the Yellow River Basin are far better than those of the Å-P equation using correction. The R2 in the upstream of the Yellow River Basin increases from 0.889 to 0.921. MSE, RMSE and MAE decrease by 22.11%, 11.84% and 8.94%, respectively. R2 in the downstream of the Yellow River Basin increases from 0.900 to 0.934, and MSE, RMSE and MAE decrease by 13.21%, 11.40% and 5.55%, respectively. In the midstream of the Yellow River Basin, the prediction results of the deep learning model are worse than those of the Å-P equation using correction: R2 increases from 0.870 to 0.874, but MSE, RMSE and MAE decrease by −24.93%, −10.83% and −11.56%, respectively.
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Lee, Hye-Jeong, Jae Hyun Park, Hye Young Seo, Sung-Kwon Choi, Na-Young Chang, Kyung-Hwa Kang, and Jong-Moon Chae. "A CBCT Evaluation of Nasal Septal Deviation and Related Nasofacial Structures after Maxillary Skeletal Expansion." Applied Sciences 12, no. 19 (October 3, 2022): 9949. http://dx.doi.org/10.3390/app12199949.
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Background: The aim of this study was to evaluate three-dimensional (3D) changes in nasal septal deviation (NSD) and related nasofacial structures after maxillary skeletal expansion (MSE). Methods: This retrospective study evaluated 28 patients aged 12.0–48.4 years (mean age, 20.4 ± 7.3 years; 12 males, 16 females) diagnosed with transverse maxillary deficiency and treated with MSE. Cone-beam computed tomography (CBCT) images were taken at pre-expansion (T1), post-expansion (T2), and 6-months after MSE (T3) and were reoriented. Three-dimensional coordinates (x,y,z) were constructed using nasion (N) as the reference point (0,0,0). A paired-sample t-test and an independent sample t-test were performed to investigate and compare the 3D changes of the NSD and nasofacial structures after MSE, depending on the direction and amount of NSD and the amount of midpalatal expansion (MPE). Results: NSD was alleviated at T3–T1 in the coronal plane; nasal septal length increased significantly. The absolute nasal floor (NF) angle (2.06° at T1) decreased at T2–T1 (p > 0.05) and increased at T3–T2 (p < 0.05). The midface moved forward and downward, and pogonion (Pog) and menton (Me) moved downward and backward. There were no significant differences between the higher and lower NSD groups and greater and lesser MPE groups. Conclusions: Consequently, NSD was alleviated with variable positive nasofacial changes after MSE in both the short and long term. Therefore, MSE can be used to improve or camouflage facial deformities.
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Sim, Taeyong, Seonbin Choi, Yunjae Kim, Su Hyun Youn, Dong-Jin Jang, Sujin Lee, and Chang-Jae Chun. "eXplainable AI (XAI)-Based Input Variable Selection Methodology for Forecasting Energy Consumption." Electronics 11, no. 18 (September 17, 2022): 2947. http://dx.doi.org/10.3390/electronics11182947.
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This research proposes a methodology for the selection of input variables based on eXplainable AI (XAI) for energy consumption prediction. For this purpose, the energy consumption prediction model (R2 = 0.871; MAE = 2.176; MSE = 9.870) was selected by collecting the energy data used in the building of a university in Seoul, Republic of Korea. Applying XAI to the results from the prediction model, input variables were divided into three groups by the expectation of the ranking-score (Fqvar) (10 ≤ Strong, 5 ≤ Ambiguous < 10, and Weak < 5), according to their influence. As a result, the models considering the input variables of the Strong + Ambiguous group (R2 = 0.917; MAE = 1.859; MSE = 6.639) or the Strong group (R2 = 0.916; MAE = 1.816; MSE = 6.663) showed higher prediction results than other cases (p < 0.05 or 0.01). There were no statistically significant results between the Strong group and the Strong + Ambiguous group (R2: p = 0.408; MAE: p = 0.488; MSE: p = 0.478). This means that when considering the input variables of the Strong group (Fqvar: Year = 14.8; E-Diff = 12.8; Hour = 11.0; Temp = 11.0; Surface-Temp = 10.4) determined by the XAI-based methodology, the energy consumption prediction model showed excellent performance. Therefore, the methodology proposed in this study is expected to determine a model that can accurately and efficiently predict energy consumption.
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Ahmad, Ayaz, Furqan Farooq, Pawel Niewiadomski, Krzysztof Ostrowski, Arslan Akbar, Fahid Aslam, and Rayed Alyousef. "Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm." Materials 14, no. 4 (February 8, 2021): 794. http://dx.doi.org/10.3390/ma14040794.
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Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.
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Hussain, Lal, Sharjil Saeed, Adnan Idris, Imtiaz Ahmed Awan, Saeed Arif Shah, Abdul Majid, Bilal Ahmed, and Quratul-Ain Chaudhary. "Regression analysis for detecting epileptic seizure with different feature extracting strategies." Biomedical Engineering / Biomedizinische Technik 64, no. 6 (December 18, 2019): 619–42. http://dx.doi.org/10.1515/bmt-2018-0012.
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Abstract Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.
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Megantara, Sandra, Mutakin Mutakin, and Jutti Levita. "PREDICTION OF LOG P AND SPECTRUM OF QUERCETINE, GLUCOSAMINE, AND ANDROGRAPHOLIDE AND ITS CORRELATION WITH LABORATORY ANALYSIS." International Journal of Pharmacy and Pharmaceutical Sciences 8, no. 11 (October 28, 2016): 33. http://dx.doi.org/10.22159/ijpps.2016v8i11.9101.
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Objective: This study was aimed to confirm the result of computational prediction of log P and spectrum (ultraviolet-visible, 1H-NMR, 13C-NMR) of quercetin, glucosamine and andrographolide with laboratory analysis.Methods: Quercetine, glucosamine and andrographolide, were downloaded from ChemSpider and were geometry optimised. Log P and spectrum were calculated and predicted and the data obtained were compared with laboratory results. The correlation was calculated by employing mean absolute deviation (MAD), mean square error (MSE), mean forecast error (MFE), and mean absolute percentage error (MAPE) parameters.Results: The smallest energy value of geometry optimisation was provided by ab initio method. Log P prediction showed good accuracy, with r-value 0.995 and p-value 0.05 respectively. The error parameters were: MAD 0.19; MSE 0.06; MFE 0.16, and MAPE 8.62%, respectively. Prediction of λ maximum by ab initio, semiempirical, and molecular mechanics were respectively: MAD 2.67, 6.67, and 28.67; MSE 8.67, 45.33, and 830; MFE 2.67, 6.67, and 28.67; and MAPE 1.10%, 2.79%, and 11.99%; r-value 0.997, 0.997, and 0.979; and p-value 0.044, 0.043, and 0.129. 1H-NMR and 13C-NMR spectra prediction were: MAD 0.73 and 1.58; MSE 1.15 and 7.41; MFE 0.27 and 0.69; MAPE 18.35% and 2.68%; r-value 0.942 and 0.986; and p-value 0.001 and 0.001.Conclusion: There is a positive correlation between computational ab initio calculation method with experimental results in predicting log P and spectrum of quercetine, glucosamine, and andrographolide.
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Prabaswara, Aditya, Jens Birch, Muhammad Junaid, Elena Alexandra Serban, Lars Hultman, and Ching-Lien Hsiao. "Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy." Applied Sciences 10, no. 9 (April 27, 2020): 3050. http://dx.doi.org/10.3390/app10093050.
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Magnetron sputter epitaxy (MSE) offers several advantages compared to alternative GaN epitaxy growth methods, including mature sputtering technology, the possibility for very large area deposition, and low-temperature growth of high-quality electronic-grade GaN. In this article, we review the basics of reactive sputtering for MSE growth of GaN using a liquid Ga target. Various target biasing schemes are discussed, including direct current (DC), radio frequency (RF), pulsed DC, and high-power impulse magnetron sputtering (HiPIMS). Examples are given for MSE-grown GaN thin films with material quality comparable to those grown using alternative methods such as molecular-beam epitaxy (MBE), metal–organic chemical vapor deposition (MOCVD), and hydride vapor phase epitaxy (HVPE). In addition, successful GaN doping and the fabrication of practical devices have been demonstrated. Beyond the planar thin film form, MSE-grown GaN nanorods have also been demonstrated through self-assembled and selective area growth (SAG) method. With better understanding in process physics and improvements in material quality, MSE is expected to become an important technology for the growth of GaN.
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Chen, Yupeng. "Interactive Model of Rural Tourism and New Socialist Countryside Construction Using Deep Learning Technology." Journal of Environmental and Public Health 2022 (July 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/2620548.
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China has proposed two major measures to address the “three rural issues”: the first is to abolish the agricultural tax, which has been in place for over 2000 years; the second is to propose the construction of a new socialist countryside, which would mark the end of the old era and the beginning of the new. As a result, this paper employs in-depth learning technology to enhance rural tourism development and the creation of a new socialist countryside. This paper investigates deep-learning-based rural tourism and the creation of a new socialist countryside. Because MSE and MAE reflect the prediction error score, the lower the value, the better the recommendation accuracy. The MSE value of the machine learning algorithm is 2.456, the MSE value of the data mining algorithm is 2.324, and the MSE value of the convolution neural network algorithm is 2.102, when the number reaches 80. It can be concluded that the convolution neural network algorithm proposed in this paper has the lowest MSE and MAE values of the three methods, implying that the convolution neural network algorithm is the best of the three. The use of the convolution neural network algorithm to implement the scientific concept of development and the construction of a new socialist countryside is an important part of creating a harmonious society that fully meets the central government’s objectives and requirements for the construction of a new socialist countryside.
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Huang, Huajian, Dasheng Wu, Luming Fang, and Xinyu Zheng. "Comparison of Multiple Machine Learning Models for Estimating the Forest Growing Stock in Large-Scale Forests Using Multi-Source Data." Forests 13, no. 9 (September 13, 2022): 1471. http://dx.doi.org/10.3390/f13091471.
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The forest growing stock is one of the key indicators in monitoring forest resources, and its quantitative estimation is of great significance. Based on multi-source data, including Sentinel-1 radar remote sensing data, Sentinel-2 optical remote sensing data, digital elevation model (DEM), and inventory data for forest management planning and design, the Lasso feature selection method was used to remove the non-significant indicators, and three machine learning algorithms, GBDT, XGBoost, and CatBoost, were used to estimate forest growing stock. In addition, four category features, forest population, dominant tree species, humus thickness, and slope direction, were involved in estimating forest growing stock. The results showed that the addition of category features significantly improved the performance of the models. To a certain extent, radar remote sensing data also could improve estimating accuracy. Among the three models, the CatBoost model (R2 = 0.78, MSE = 0.62, MAE = 0.59, MAPE = 16.20%) had the highest estimating accuracy, followed by XGBoost (R2 = 0.75, MSE = 0.71, MAE = 0.62, MAPE = 18.28%) and GBDT (R2 = 0.72, MSE = 0.78, MAE = 0.68, MAPE = 20.28%).
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Chen, Ching-Mu, Yung-Fa Huang, and You-Ting Jheng. "An Efficient Indoor Positioning Method with the External Distance Variation for Wireless Networks." Electronics 10, no. 16 (August 12, 2021): 1949. http://dx.doi.org/10.3390/electronics10161949.
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This study strengthens the external distance variation for the indoor positioning performance. With the received signal strength (RSS) of the unknown node, a localization is performed to positioning its coordinates. The mean square error (MSE) of localization is deteriorated by the shadowing effect and the MSE depends on the location of reference nodes. Moreover, the minimum mean square error (MMSE) algorithm is also used with the RSS. The amount of variation in the distance between the reference point and the positioning node will also affect the accuracy. Therefore, this paper considers the distance between the reference point and the positioning node and also the distance variation between the reference points. MSE is used to estimate positioning performance and Monte Carlo is also used to simulate the average error of different shadowing and decay environments. When reference nodes have known distances, the distance is obtained separately and the estimated distances are identified by the MMSE method. In order to reduce the number of reference nodes and calculation cost, this paper uses adaptive reference node selection to improve the accuracy of positioning. Simulation results show that the external distance variation mechanism strengthens the indoor positioning performance. Moreover, this paper investigates the performance of several reference nodes (three, four, five, and six reference nodes) through 3D graphs to estimate the small range area. The differences are more clearly observed with fewer reference nodes and lower MSE. Finally, simulation results show that the MSE value of fixed three reference nodes is almost 100% better with external distance variation method compared to the random selected three reference nodes.
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Apriliza, Fayza, Darmansah Darmansah, Annisa Oktavyani, and Dzakiyyah Al Kaazhim. "Perbandingan Metode Linear Regression dan Exponential Smoothing Dalam Peramalan Penerimaan Mahasiswa Baru." JURIKOM (Jurnal Riset Komputer) 9, no. 3 (June 30, 2022): 726. http://dx.doi.org/10.30865/jurikom.v9i3.4334.
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In improving the quality of higher education, the Telkom Purwokerto Institute of Technology needs to plan and make decisions about what strategies and policies can support teaching and learning activities or other activities within the institution. To plan these strategies and policies, the institution needs reference data such as data on predictions of the number of students that will be accepted in the next five years. With this predictive data, the institution can also consider various things that must be improved and plan a better marketing strategy so that new student admissions in the following year can increase. This study aims to predict the number of students who will be accepted at the Telkom Purwokerto Institute of Technology in the next five years. The overall comparison of the linear regression and exponential smoothing methods is carried out to test the accuracy of the forecasting results. The forecasting accuracy used is MAE, MSE, and MAPE. Forecasting accuracy testing was carried out using the linear regression method with the MAE, MSE, and MAPE values of 115.28 each; 15238.46; and 0.1216054793, and the exponential smoothing method with = 0.1 has the value of the results of the MAE, MSE, and MAPE forecasting accuracy tests of 327.2938; 137036,2639; and 0.2875524468. The best method for forecasting new student admissions at the Telkom Purwokerto Institute of Technology. namely the linear regression method because it has the smallest MAE, MSE, and MAPE forecasting accuracy values. The results of forecasting new student admissions at the Telkom Purwokerto Institute of Technology for the next 5 years, from 2022 to 2026, respectively, are 1369.7; 1479.4; 1589.1;1698,8; and 1808.5.
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Kang, Ziyang, Zhiliang Liu, Xinnian Guo, and Liu Liu. "Cavitation Noise Signal Classification of Hydroturbine Based on Improved Multi-Scale Symbol Dynamic Entropy." International Journal of Acoustics and Vibration 27, no. 4 (December 24, 2022): 326–33. http://dx.doi.org/10.20855/ijav.2022.27.41871.
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Cavitation is a phenomenon in the operation of hydroturbine, which is related to the operation efficiency and service life of the turbine. To identify both the cavitation noise signal and the non-cavitation noise signal, prevent damage as soon as possible, and avoid irreversible damage to the hydroturbine, a new paradigm based on multi-scale information entropy is proposed in this paper. The new proposed classification model combines improved multi-scale symbol dynamic entropy (IMSDE) and least square support vector machine (LSSVM). Improved multi-scale symbol dynamic entropy is utilized to learn features from the cavitation noise signal, and then the classifier of the least square support vector machine is used to classification. Multi-scale sample entropy (MSE), multi-scale permutation entropy (MPE) and multi-scale symbol dynamic entropy (MSDE) are selected as the contrast algorithms. According to the experimental results of four different operating conditions, IMSDE has the highest recognition rate. The average recognition rate of IMSDE is higher than that of MSDE, MSE and MPE. There is no significant difference in computational efficiency of IMSDE, MSDE and MPE. In conclusion, the IMSDE method proposed in this paper is superior to MSDE, MSE and MPE, for meeting the requirements of cavitation noise signal classification.
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Chen, Ming-Shu, and Bernard C. Jiang. "Resistance Training Exercise Program for Intervention to Enhance Gait Function in Elderly Chronically Ill Patients: Multivariate Multiscale Entropy for Center of Pressure Signal Analysis." Computational and Mathematical Methods in Medicine 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/471356.
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Falls are unpredictable accidents, and the resulting injuries can be serious in the elderly, particularly those with chronic diseases. Regular exercise is recommended to prevent and treat hypertension and other chronic diseases by reducing clinical blood pressure. The “complexity index” (CI), based on multiscale entropy (MSE) algorithm, has been applied in recent studies to show a person’s adaptability to intrinsic and external perturbations and widely used measure of postural sway or stability. The multivariate multiscale entropy (MMSE) was advanced algorithm used to calculate the complexity index (CI) values of the center of pressure (COP) data. In this study, we applied the MSE & MMSE to analyze gait function of 24 elderly, chronically ill patients (44% female; 56% male; mean age,67.56±10.70years) with either cardiovascular disease, diabetes mellitus, or osteoporosis. After a 12-week training program, postural stability measurements showed significant improvements. Our results showed beneficial effects of resistance training, which can be used to improve postural stability in the elderly and indicated that MMSE algorithms to calculate CI of the COP data were superior to the multiscale entropy (MSE) algorithm to identify the sense of balance in the elderly.
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Adriono, Erwin, Maman Somantri, and Chrisna Adhi Suryono. "Model Prediksi Jumlah Pakan menggunakan Algoritma Evolusi Pikiran - Jaringan Syaraf Tiruan Rambatan Balik untuk Budidaya Udang." Jurnal Kelautan Tropis 25, no. 2 (May 20, 2022): 266–78. http://dx.doi.org/10.14710/jkt.v25i2.14256.
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Menentukan jumlah pakan yang sesuai merupakan hal penting dalam kegiatan budidaya udang berjenis Litopenaeus Vannamei. Jumlah pakan dapat dipengaruhi oleh banyak faktor antara lain Jumlah Udang, Umur udang, DO, Salinitas, Alkalinitas, Suhu dan PH. Hubungan antar faktor tersebut dengan jumlah pakan sulit dibuatkan dalam persamaan matematis maupun dengan metode statisik. Permasalahan tersebut dapat diselesaikan menggunakan Neural network. Neural network menjadi solusi untuk memodelkan hubungan input dan output yang kompleks. Hubungan Jumlah pakan dan faktorlainnya akan dimodelkan menggunakan metode Backpropagation NN yang dikombinasikan dengan algoritma optimasi seperti Genetic Algoritm dan Mind Evotionary Algoritm. Model BPNN, BPNN – GA dan BPNN MEA akan dibandingkan performa menggunakan MSE, RSME, MAE dan MAPE. Dari ketiga metode yang digunakan didapatkan hasil paling baik adalah pada BPNN MEA yaitu nilai MSE, RSME, MAE dan MAPE berturut-turut adalah 40,92; 6,39; 6,51 dan 20,29. Determining the appropriate amount of feed is important in the aquaculture of Litopenaeus Vannamei shrimp. The amount of feed can be influenced by many factors including the number of shrimp, shrimp age, DO, salinity, alkalinity, temperature and PH. The relationship between these factors and the amount of feed is difficult to make in mathematical equations or with statistical methods. These problems can be solved using a neural network. Neural network is a solution for modeling complex input and output relationships. The relationship between the amount of feed and other factors will be modeled using the Backpropagation NN method combined with optimization algorithms such as Genetic Algorithm and Mind Evotionary Algorithm. The BPNN, BPNN – GA and BPNN MEA models will be compared using MSE, RSME, MAE and MAPE. Of the three methods used, the best results were obtained on BPNN MEA, with values of MSE, RSME, MAE and MAPE respectively 40,92; 6,39; 6,51 and 20,29.
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Amanda Santana Lins Bispo, Lian Filipe Santana Nascimento, Yuri de Jesus Gomes, and Flávio Santos Conterato. "Comparison of Models for Wind Speed Prediction Through Neural Networks in Lençóis, Bahia." JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH 5, no. 2 (May 30, 2022): 118–22. http://dx.doi.org/10.34178/jbth.v5i2.209.
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This study aims to compare forecasting models using artificial intelligence to conclude which is the best one for the forecasting wind speed for 1 hour in Lençóis, BA, using a data source from the Instituto Nacional de Meteorologia (INMET). Furthermore, an Artificial Neural Network (ANN) was developed using TensorFlow and Keras libraries, it was compared with other forecasting models, which showed to be the most efficient among the options for this purpose. Moreover, the principal metric used to evaluate this study was Mean Absolute Error (MAE), and the auxiliary ones were Mean Squared Error (MSE) and R². The RNA obtained the following values for each metric: 0.421 for MAE, 0.389 for MSE, and 0.523 for the R² metric.
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Liu, Yongzhi, Wenting Zhang, Ying Yan, Zhixuan Li, Yulin Xia, and Shuhong Song. "An Effective Rainfall–Ponding Multi-Step Prediction Model Based on LSTM for Urban Waterlogging Points." Applied Sciences 12, no. 23 (December 2, 2022): 12334. http://dx.doi.org/10.3390/app122312334.
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With the change in global climate and environment, the prevalence of extreme rainstorms and flood disasters has increased, causing serious economic and property losses. Therefore, accurate and rapid prediction of waterlogging has become an urgent problem to be solved. In this study, Jianye District in Nanjing City of China is taken as the study area. The time series data recorded by rainfall stations and ponding monitoring stations from January 2015 to August 2018 are used to build a ponding prediction model based on the long short-term memory (LSTM) neural network. MSE (mean square error), MAE (mean absolute error) and MSLE (mean squared logarithmic error) were used as loss functions to conduct and train the LSTM model, then three ponding prediction models were built, namely LSTM (mse), LSTM (mae) and LSTM (msle), and a multi-step model was used to predict the depth of ponding in the next 1 h. Using the measured ponding data to evaluate the model prediction results, we selected rmse (root mean squared error), mae, mape (mean absolute percentage error) and NSE (Nash–Sutcliffe efficiency coefficient) as the evaluation indicators. The results showed that LSTM (msle) was the best model among the three models, with evaluation indicators as follows: rmse 5.34, mae 3.45, mape 53.93% and NSE 0.35. At the same time, we found that LSTM (mae) has a better prediction effect than the LSTM (mse) and LSTM (msle) models when the ponding depth exceeds 30 mm.
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Gupta, Priyanka, Nakul Gupta, Kuldeep K. Saxena, and Sudhir Goyal. "Random Forest Modeling for Fly Ash-Calcined Clay Geopolymer Composite Strength Detection." Journal of Composites Science 5, no. 10 (October 13, 2021): 271. http://dx.doi.org/10.3390/jcs5100271.
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Geopolymer is an eco-friendly material used in civil engineering works. For geopolymer concrete (GPC) preparation, waste fly ash (FA) and calcined clay (CC) together were used with percentage variation from 5, 10, and 15. In the mix design for geopolymers, there is no systematic methodology developed. In this study, the random forest regression method was used to forecast compressive strength and split tensile strength. The input content involved were caustic soda with 12 M, 14 M, and 16 M; sodium silicate; coarse aggregate passing 20 mm and 10 mm sieve; crushed stone dust; superplasticizer; curing temperature; curing time; added water; and retention time. The standard age of 28 days was used, and a total of 35 samples with a target-specified compressive strength of 30 MPa were prepared. In all, 20% of total data were trained, and 80% of data testing was performed. Efficacy in terms of mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and MSE (mean squared error) is suggested in the model. The results demonstrated that the RFR model is likely to predict GPC compressive strength (MAE = 1.85 MPa, MSE = 0.05 MPa, RMSE = 2.61 MPa, and R2 = 0.93) and split tensile strength (MAE = 0.20 MPa, MSE = 6.83 MPa, RMSE = 0.24 MPa, and R2 = 0.90) during training.
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Naser, Jinan Abbas. "Under Different Priors &Two Loss Functions To Compare Bayes Estimators With Some of Classical Estimators For the Parameter of Exponential Distribution." Journal of Economics and Administrative Sciences 23, no. 99 (October 1, 2017): 1. http://dx.doi.org/10.33095/jeas.v23i99.267.
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المستخلص:
في هذا البحث , استعملنا طرائق مختلفة لتقدير معلمة القياس للتوزيع الاسي كمقدر الإمكان الأعظم ومقدر العزوم ومقدر بيز في ستة أنواع مختلفة عندما يكون التوزيع الأولي لمعلمة القياس : توزيع لافي (Levy) وتوزيع كامبل من النوع الثاني وتوزيع معكوس مربع كاي وتوزيع معكوس كاما وتوزيع غير الملائم (Improper) وتوزيع Non-informative. وفقا لدالتي الخسارة هي : دالة الخسارة التربيعية و دالة الخسارة التربيعية الموزونة. استعمل أسلوب المحاكاة في مقارنة اداء كل مقدر, بافتراض عدة حالات لمعلمة التوزيع الاسي استعملت لتوليد البيانات ولأحجام مختلفة من العينات ( صغيرة , متوسطة , كبيرة). وقد أظهرت نتائج المحاكاة بان طريقة بيز الأفضل وفقا لمقياس اقل قيمة متوسط مربع الأخطاء (MSE) , متوسط مربع الأخطاء الموزونة (MWSE) مقارنة بطريقتي الإمكان الأعظم (MLE) وطريقة العزوم (ME) . وفقا للنتائج المستحصلة , نرى بانه عندما يكون التوزيع الاولي لـ توزيع معكوس كاما عند قيم معينة لمعلمتي التوزيع الاولي , أعطى نتائج أفضل وفقا لاقل قيمة لـ MSE ولـ MWSE مقارنة بنفس القيم المستحصلة بطريقتي MLE و ME,عندما تكون القيمة الحقيقة المفترضة لـ ولكل حجوم العينات (n). وعندما يكون التوزيع الاولي لـ هو غير الملائم (Improper) عند قيم معينة لمعلمتي التوزيع الاولي, اعطى نتائج أفضل وفقا لاقل قيمة لـ MSE ولـ MWSE مقارنة بنفس القيم المستحصلة بطريقتي MLE و ME, للقيم الحقيقة المفترضة لـ ولكل حجوم العينات (n) .
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Tan, Woon Yang, Sai Hin Lai, Fang Yenn Teo, Danial Jahed Armaghani, Kumar Pavitra, and Ahmed El-Shafie. "Three Steps towards Better Forecasting for Streamflow Deep Learning." Applied Sciences 12, no. 24 (December 8, 2022): 12567. http://dx.doi.org/10.3390/app122412567.
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Elevating the accuracy of streamflow forecasting has always been a challenge. This paper proposes a three-step artificial intelligence model improvement for streamflow forecasting. Step 1 uses long short-term memory (LSTM), an improvement on the conventional artificial neural network (ANN). Step 2 performs multi-step ahead forecasting while establishing the rates of change as a new approach. Step 3 further improves the accuracy through three different kinds of optimization algorithms. The Stormwater and Road Tunnel project in Kuala Lumpur is the study area. Historical rainfall data of 14 years at 11 telemetry stations are obtained to forecast the flow at the confluence located next to the control center. Step 1 reveals that LSTM is a better model than ANN with R 0.9055, MSE 17,8532, MAE 1.4365, NSE 0.8190 and RMSE 5.3695. Step 2 unveils the rates of change model that outperforms the rest with R = 0.9545, MSE = 8.9746, MAE = 0.5434, NSE = 0.9090 and RMSE = 2.9958. Finally, Stage 3 is a further improvement with R = 0.9757, MSE = 4.7187, MAE = 0.4672, NSE = 0.9514 and RMSE = 2.1723 for the bat-LSTM hybrid algorithm. This study shows that the δQ model has consistently yielded promising results while the metaheuristic algorithms are able to yield additional improvement to the model’s results.
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Jorswieck, E. A., and H. Boche. "Transmission strategies for the MIMO mac with MMSE receiver: average MSE optimization and achievable individual MSE region." IEEE Transactions on Signal Processing 51, no. 11 (November 2003): 2872–81. http://dx.doi.org/10.1109/tsp.2003.818207.
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Tarigan, Imelda Alvionita, I. Putu Agung Bayupati, and Gusti Agung Ayu Putri. "Comparison of support vector machine and backpropagation models in forecasting the number of foreign tourists in Bali province." Jurnal Teknologi dan Sistem Komputer 9, no. 2 (February 26, 2021): 90–95. http://dx.doi.org/10.14710/jtsiskom.2021.13847.
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Tourism in Bali is one of the major industries which play an important role in developing the global economy in Indonesia. Good forecasting of tourist arrival, especially from foreign countries, is needed to predict the number of tourists based on past information to minimize the prediction error rate. This study compares the performance of SVM and Backpropagation to find the model with the best prediction algorithm using data from foreign tourists in Bali Province. The results of this study recommend the best forecasting using the SVM model with the radial kernel function. The best accuracy of the SVM model obtained the lowest error values of MSE 0.0009, MAE 0.0186, and MAPE 0.0276, compared to Backpropagation which obtained MSE 0.0170, MAE 0.1066, and MAPE 0.1539.
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Shi, Junjun, Jingfang Shen, and Yaohui Li. "High-Precision Kriging Modeling Method Based on Hybrid Sampling Criteria." Mathematics 9, no. 5 (March 4, 2021): 536. http://dx.doi.org/10.3390/math9050536.
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Finding new valuable sampling points and making these points better distributed in the design space is the key to determining the approximate effect of Kriging. To this end, a high-precision Kriging modeling method based on hybrid sampling criteria (HKM-HS) is proposed to solve this problem. In the HKM-HS method, two infilling sampling strategies based on MSE (Mean Square Error) are optimized to obtain new candidate points. By maximizing MSE (MMSE) of Kriging model, it can generate the first candidate point that is likely to appear in a sparse area. To avoid the ill-conditioned correlation matrix caused by the too close distance between any two sampling points, the MC (MSE and Correlation function) criterion formed by combining the MSE and the correlation function through multiplication and division is minimized to generate the second candidate point. Furthermore, a new screening method is used to select the final expensive evaluation point from the two candidate points. Finally, the test results of sixteen benchmark functions and a house heating case show that the HKM-HS method can effectively enhance the modeling accuracy and stability of Kriging in contrast with other approximate modeling methods.
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Yu, Lijun, Shuhui Wu, and Zhanhong Ma. "Evaluation of Moist Static Energy in a Simulated Tropical Cyclone." Atmosphere 10, no. 6 (June 12, 2019): 319. http://dx.doi.org/10.3390/atmos10060319.
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The characteristics of moist static energy (MSE) and its budget in a simulated tropical cyclone (TC) are examined in this study. Results demonstrate that MSE in a TC system is enhanced as the storm strengthens, primarily because of two mechanisms: upward transfer of surface heat fluxes and subsequent warming of the upper troposphere. An inspection of the interchangeable approximation between MSE and equivalent potential temperature (θe) suggests that although MSE is capable of capturing overall structures of θe, some important features will still be distorted, specifically the low-MSE pool outside the eyewall. In this low-MSE region, from the budget analysis, the discharge of MSE in the boundary layer may even surpass the recharge of MSE from the ocean. Unlike the volume-averaged MSE, the mass-weighted MSE in a fixed volume following the TC shows no apparent increase as the TC intensifies, because the atmosphere becomes continually thinner accompanying the warming of the storm. By calculating a mass-weighted volume MSE budget, the TC system is found to export MSE throughout its lifetime, since the radial outflow overwhelms the radial inflow. Moreover, the more intensified the TC is, the more export of MSE there tends to be. The input of MSE by surface heat fluxes is roughly balanced by the combined effects of radiation and lateral export, wherein a great majority of the imported MSE is reduced by radiation, while the export of MSE from the TC system to the environment accounts for only a small portion.
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Castiglioni, Paolo, Giampiero Merati, Gianfranco Parati, and Andrea Faini. "Sample, Fuzzy and Distribution Entropies of Heart Rate Variability: What Do They Tell Us on Cardiovascular Complexity?" Entropy 25, no. 2 (February 2, 2023): 281. http://dx.doi.org/10.3390/e25020281.
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Distribution Entropy (DistEn) has been introduced as an alternative to Sample Entropy (SampEn) to assess the heart rate variability (HRV) on much shorter series without the arbitrary definition of distance thresholds. However, DistEn, considered a measure of cardiovascular complexity, differs substantially from SampEn or Fuzzy Entropy (FuzzyEn), both measures of HRV randomness. This work aims to compare DistEn, SampEn, and FuzzyEn analyzing postural changes (expected to modify the HRV randomness through a sympatho/vagal shift without affecting the cardiovascular complexity) and low-level spinal cord injuries (SCI, whose impaired integrative regulation may alter the system complexity without affecting the HRV spectrum). We recorded RR intervals in able-bodied (AB) and SCI participants in supine and sitting postures, evaluating DistEn, SampEn, and FuzzyEn over 512 beats. The significance of “case” (AB vs. SCI) and “posture” (supine vs. sitting) was assessed by longitudinal analysis. Multiscale DistEn (mDE), SampEn (mSE), and FuzzyEn (mFE) compared postures and cases at each scale between 2 and 20 beats. Unlike SampEn and FuzzyEn, DistEn is affected by the spinal lesion but not by the postural sympatho/vagal shift. The multiscale approach shows differences between AB and SCI sitting participants at the largest mFE scales and between postures in AB participants at the shortest mSE scales. Thus, our results support the hypothesis that DistEn measures cardiovascular complexity while SampEn/FuzzyEn measure HRV randomness, highlighting that together these methods integrate the information each of them provides.
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Bowler, Alexander, Josep Escrig, Michael Pound, and Nicholas Watson. "Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning." Fermentation 7, no. 1 (March 4, 2021): 34. http://dx.doi.org/10.3390/fermentation7010034.
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Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2 = 0.952, mean absolute error (MAE) = 0.265, mean squared error (MSE) = 0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2 = 0.948, MAE = 0.283, MSE = 0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.
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Viatkin, Dmitrii, Maxim Zakharov, and Dmitrii Zhuro. "Prediction of reduced glass transition temperature of metallic alloys based on a neural network." Journal of Physics: Conference Series 2373, no. 8 (December 1, 2022): 082016. http://dx.doi.org/10.1088/1742-6596/2373/8/082016.
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Abstract The reduced glass transition temperature Trg is an important glass forming ability parameter. Trg describes the glass formation in materials and the behaviour of materials at the transition between solid and liquid states and is an important parameter for materials analysis, development, and production process. This article describes the process and results of research on the development of a system for prediction of the reduced glass transition temperature Trg of metallic alloys based on recurrent neural network algorithms. The developed system can predict the reduced glass transition temperature Trg of metallic alloys based on the analysis of its chemical formula with high accuracy. The accuracy was evaluated using the 3 metrics: MSE, RMSE, MAE. Obtained values are: MSE value is 0.000678, RMSE value is 0.0260, MAE value is 0.01835.
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KRAMARENKO, A. "SMALL BUSINESS: DIRECTIONS OF BUSINESS ACTIVITY GROWTH." Vestnik of Polotsk State University Part D Economic and legal sciences 62, no. 12 (November 14, 2022): 28–34. http://dx.doi.org/10.52928/2070-1632-2022-62-12-28-34.
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The article presents the conclusions of the research of the micro and small enterprises (MSE) development in EU and Belarus for 2005–2020. The dynamics of the main MSE indicators are given in the context of the business conditions. The grouping of MSE regulation measures from the standpoint of their interconnection with the main areas of MSE participation in solving regional economic problems is presented. According to the survey of MSE heads, the author highlighted the problems of MSE. The method calculation of the economic development MSE and assessment of its influ-ence on the economic growth from the position of the created product is proposed to clarify the existing methodological tools for forecasting the development MSE. The research is aimed at maximizing the MSE potential in Belarus.
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Estrada, Ma del Rocío Castillo, Marco Edgar Gómez Camarillo, María Eva Sánchez Parraguirre, Marco Edgar Gómez Castillo, Efraín Meneses Juárez, and M. Javier Cruz Gómez. "Evaluation of Several Error Measures Applied to the Sales Forecast System of Chemicals Supply Enterprises." International Journal of Business Administration 11, no. 4 (June 30, 2020): 39. http://dx.doi.org/10.5430/ijba.v11n4p39.
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The objective of the industry in general, and of the chemical industry in particular, is to satisfy consumer demand for products and the best way to satisfy it is to forecast future sales and plan its operations.Considering that the choice of the best sales forecast model will largely depend on the accuracy of the selected indicator (Tofallis, 2015), in this work, seven techniques are compared, in order to select the most appropriate, for quantifying the error presented by the sales forecast models. These error evaluation techniques are: Mean Percentage Error (MPE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Symmetric Mean Absolute Percentage Error (SMAPE) and Mean Absolute Arctangent Percentage Error (MAAPE). Forecasts for chemical product sales, to which error evaluation techniques are applied, are those obtained and reported by Castillo, et. al. (2016 & 2020).The error measuring techniques whose calculation yields adequate and convenient results, for the six prediction techniques handled in this article, as long as its interpretation is intuitive, are SMAPE and MAAPE. In this case, the most adequate technique to measure the error presented by the sales prediction system turned out to be SMAPE.
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Appati, Justice Kwame, Ismail Wafaa Denwar, Ebenezer Owusu, and Michael Agbo Tettey Soli. "Construction of an Ensemble Scheme for Stock Price Prediction Using Deep Learning Techniques." International Journal of Intelligent Information Technologies 17, no. 2 (April 2021): 72–95. http://dx.doi.org/10.4018/ijiit.2021040104.
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This study proposes a deep learning approach for stock price prediction by bridging the long short-term memory with gated recurrent unit. In its evaluation, the mean absolute error and mean square error were used. The model proposed is an extension of the study of Hossain et al. established in 2018 with an MSE of 0.00098 as its lowest error. The current proposed model is a mix of the bidirectional LSTM and bidirectional GRU resulting in 0.00000008 MSE as the lowest error recorded. The LSTM model recorded 0.00000025 MSE, the GRU model recorded 0.00000077 MSE, and the LSTM + GRU model recorded 0.00000023 MSE. Other combinations of the existing models such as the bi-directional LSTM model recorded 0.00000019 MSE, bi-directional GRU recorded 0.00000011 MSE, bidirectional LSTM + GRU recorded 0.00000027 MSE, LSTM and bi-directional GRU recorded 0.00000020 MSE.
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Pap, Jozsef, Csaba Mako, Miklos Illessy, Norbert Kis, and Amir Mosavi. "Modeling Organizational Performance with Machine Learning." Journal of Open Innovation: Technology, Market, and Complexity 8, no. 4 (September 28, 2022): 177. http://dx.doi.org/10.3390/joitmc8040177.
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Identifying the performance factors of organizations is of utmost importance for labor studies for both empirical and theoretical research. The present study investigates the essential intra- and extra-organizational factors in determining the performance of firms using the European Company Survey (ECS) 2019 framework. The evolutionary computation method of genetic algorithm and the machine learning method of Bayesian additive regression trees (BART), are used to model the importance of each of the intra- and extra-organizational factors in identifying the firms’ performance as well as employee well-being. The standard metrics are further used to evaluate the accuracy of the proposed method. The mean value of the evaluation metrics for the accuracy of the impact of intra- and extra-organizational factors on firm performance are MAE = 0.225, MSE = 0.065, RMSE = 0.2525, and R2 = 0.9125, and the value of these metrics for the accuracy of the impact of intra- and extra-organizational factors on employee well-being are MAE = 0.18, MSE = 0.0525, RMSE = 0.2275, and R2 = 0.88. The low values of MAE, MSE and RMSE, and the high value of R2, indicate the high level of accuracy of the proposed method. The results revealed that the two variables of work organization and innovation are essential in improving firm performance well-being, and that the variables of collaboration and outsourcing, as well as job complexity and autonomy, have the greatest role in improving firm performance.
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Alslman, Majd, and Amal Helu. "Estimation of the stress-strength reliability for the inverse Weibull distribution under adaptive type-II progressive hybrid censoring." PLOS ONE 17, no. 11 (November 15, 2022): e0277514. http://dx.doi.org/10.1371/journal.pone.0277514.
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In this article, we compare the maximum likelihood estimate (MLE) and the maximum product of spacing estimate (MPSE) of a stress-strength reliability model, θ = P(Y < X), under adaptive progressive type-II progressive hybrid censoring, when X and Y are independent random variables taken from the inverse Weibull distribution (IWD) with the same shape parameter and different scale parameters. The performance of both estimators is compared, through a comprehensive computer simulation based on two criteria, namely bias and mean squared error (MSE). To demonstrate the effectiveness of our proposed methods, we used two examples of real-life data based on Breakdown Times of an Insulated Fluid by (Nelson, 2003) and Head and Neck Cancer Data by (Efron, 1988). It is concluded that the MPSE method outperformed the MLE method in terms of bias and MSE values.
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Akram, Muhammad Nauman, Muhammad Amin, Ahmed Elhassanein, and Muhammad Aman Ullah. "A new modified ridge-type estimator for the beta regression model: simulation and application." AIMS Mathematics 7, no. 1 (2021): 1035–57. http://dx.doi.org/10.3934/math.2022062.
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<abstract> <p>The beta regression model has become a popular tool for assessing the relationships among chemical characteristics. In the BRM, when the explanatory variables are highly correlated, then the maximum likelihood estimator (MLE) does not provide reliable results. So, in this study, we propose a new modified beta ridge-type (MBRT) estimator for the BRM to reduce the effect of multicollinearity and improve the estimation. Initially, we show analytically that the new estimator outperforms the MLE as well as the other two well-known biased estimators i.e., beta ridge regression estimator (BRRE) and beta Liu estimator (BLE) using the matrix mean squared error (MMSE) and mean squared error (MSE) criteria. The performance of the MBRT estimator is assessed using a simulation study and an empirical application. Findings demonstrate that our proposed MBRT estimator outperforms the MLE, BRRE and BLE in fitting the BRM with correlated explanatory variables.</p> </abstract>
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Abdaljabbar, Luay Adel, and Qutaiba Nabeel Nayef. "مقارنة بين طريقة الامكان الاعظم والطريقة البيزية في تقدير انحدار كاما مع تطبيق عملي." Journal of Economics and Administrative Sciences 27, no. 125 (January 1, 2021): 477–92. http://dx.doi.org/10.33095/jeas.v27i125.2088.
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يتناول البحث انموذج انحدار كاما على افتراض أن المتغير التابع (Y) يتبع توزيع كاما بمتوسط مرتبط من خلال تركيبة خطية بواسطة دالة الربط الطبيعية . ويحتوي أيضًا على معلمة الشكل ، والتي تكون غير ثابتة وتعتمد ايضاً على تركيبة خطية بواسطة دالة الربط اللوغارتمية ، حيث سيتم تقدير معلمات انحدار كاما باستعمال طريقتين للتقدير هما طريقة الامكان الاعظم (Maximu Likelihood Method ) والطريقة البيزية ((Bayesian Method واجراء المقارنة بين هذه الطريقتين بأستعمال معيار المقارنة متوسط مربعات الخطأ (MSE) ، حيث تم تطبيق الطريقتين على بيانات حقيقة حول مرض يرقان الاطفال (ابو صفار في الدم) حديثي الولادة وكانت افضل طريقة للتقدير هي طريقة الامكان الاعظم (MLE) لانها اعطت اقل متوسط مربعات الخطأ (MSE)
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Marmolin, Hans. "Subjective MSE Measures." IEEE Transactions on Systems, Man, and Cybernetics 16, no. 3 (1986): 486–89. http://dx.doi.org/10.1109/tsmc.1986.4308985.
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Marsden, Kevin, and James J. Robinson. "MSE Progress Report." JOM 39, no. 5 (May 1987): 12–14. http://dx.doi.org/10.1007/bf03258983.
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Keller, Thomas F. "BioMat@MSE 2010." Advanced Engineering Materials 12, no. 12 (December 2010): B657. http://dx.doi.org/10.1002/adem.201080142.
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Ihsan, Hisyam, Rahmat Syam, and Fahrul Ahmad. "Peramalan Penjualan dengan Metode Exponential Smoothing (Studi Kasus : Penjualan Bakso Kemasaan/Kiloan Rumah Bakso Bang Ipul)." Journal of Mathematics, Computations, and Statistics 1, no. 1 (May 17, 2019): 1. http://dx.doi.org/10.35580/jmathcos.v1i1.9168.
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Abstrak. Peramalan penjualan memungkinkan sebuah perusahan memilih kebijakan yang optimal untuk membuat keputusan yang sesuai dan mempertahankan efisiensi dari kegiatan operasional. Rumah Bakso Bang Ipul adalah salah satu usaha yang melakukan penjualan yakni penjualan bakso kemasaan/kiloan. Oleh sebab itu,. Rumah Bakso Bang Ipul sangat memerlukan peramalan penjualan untuk meningkatkan keuntungan dan menghindari terjadinya kelebihan atau kekurangan persedian bakso kemasaan/kiloan. Penelitian ini dilakukan peramalan dengan metode exponential smoothing. Adapun parameter atau a yang digunakan dalam meramalkan penjualan adalah a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, dan 0.9. Singel exponential smoothing melakukan perbandingan dalam menentukan nilai a, dengan mencari nilai a tersebut secara trial and error sampai menemukan a yang memiliki error minimum dengan pencarian menggunakan metode mean absolute error (MAE) dan metode Mean Squaered error (MSE). Sehingga dipilih a = 0.1 dengan nilai MAE = 6.23 dan nilai MSE = 58.32. berdasarkan hasil ini, dengan menggunakan metode singel exponential smoothing dan a =0.1 diperoleh hasil peramalan penjualan bakso bang ipul pada bulan juni 2018 sebanyak 48 kilogram.Kata Kunci: Peramalan, Metode Exponential Smoothing, Metode Singel Exponential SmoothingAbstract. Sales forecasting enables an optimal policy of the company had to make the appropriate decision and maintain the efficiency of operational activities. Rumah Bakso Bang Ipul is a business that sells packaged meatballs. Therefore, Rumah Bakso Bang Ipul is in need of sales forecasting to increase profit and avoid the occurrence or lack of supply of packaged meatballs. This research was conducted by the method of exponential smoothing forecasting. As for parameter or a used predicting sales is a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, and 0.9. single exponential smoothing do a comparison in determining the value of a, by searching for the value of such a trial and error to find a that has minimum error with search method using the mean absolute error (MAE) and mean squared error (MSE). So that selected a = 0.1 with MAE value = 6.23 and MSE Value = 58.32. Based on these results, using the method of single exponential smoothing and retrieved results forecasting Rumah Bakso Bang Ipul in July 2018 as much as 48 kilograms.Keywords: Forecasting, Method of exponential smoothing, Method of single exponential smoothing.
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Chao, Hsuan-Hao, Chih-Wei Yeh, Chang Francis Hsu, Long Hsu, and Sien Chi. "Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure." Applied Sciences 9, no. 17 (August 24, 2019): 3496. http://dx.doi.org/10.3390/app9173496.
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Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure (CHF) and healthy individuals has remained controversial. Few studies have been published on the discrimination between them, using only MSE with machine learning for automatic multidimensional analysis, with reported testing accuracies of less than 86%. In this study, we determined the optimal MSE scales for discrimination by using a low-dimensional exhaustive search along with three classifiers—linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). In younger people (<55 years), the results showed an accuracy of up to 95.5% with two optimal MSE scales (2D) and up to 97.7% with four optimal MSE scales (4D) in discriminating between young CHF and healthy participants. In older people (≥55 years), the discrimination accuracy reached 90.1% using LDA in 2D, SVM in 3D (three optimal MSE scales), and KNN in 5D (five optimal MSE scales). LDA with a 3D exhaustive search also achieved 94.4% accuracy in older people. Therefore, the results indicate that MSE analysis can differentiate between CHF and healthy individuals of any age.