Blind Feedforward Timing Estimation for Constant-Modulus Signalling in Time-Varying Fading Channels by Shankar Prakriya

“…It also has a Mean-Square-Error (MSE) performance that is superior for short data records and small roll-off factors to that of the Square-Law-(SQL-) and the CycloStationarity-(CS-) based algorithms available to date. …”
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Numerical Solutions of Coupled Systems of Fractional Order Partial Differential Equations by Yongjin Li, Kamal Shah

“…During this process, we need no discretization of the data. We also provide error analysis and some test problems to demonstrate the established technique.…”
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How Islamic Rural Bank Overcomes the Trade-off Between Sustainability and Outreach: Does Market Competition Matter? by Syafaat Muhari, Zulfiqar Ali Jumani

“…Furthermore, the efficiency analysis of Data Envelopment Analysis (DEA) is used to measure efficiency which allows estimation of efficiency performance using multiple inputs and outputs to understand how Islamic Rural Bank deals with sustainability and outreach. …”
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Development of an assessment for responsibility in junior high school swimming lessons by Ermawan Susanto, Subagyo, Devi Tirtawirya, M. Hamid Anwar, Mujriah, Nur Khozanah Ilmah

“…By Confirmatory Factor Analysis first order it was known that the instrument had a p-value = 0.69138 (> 0.05) and Confirmatory Factor Analysis second order data analysis showed that the results had a p-value = 0.09844 (> 0.05) which mean that it was fit with the data. …”
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Multi-Output Regression for the Prediction of World-Class Performances in Women’s Handball by Rayane Elimam, Nicolas NICOLAS, Jacques Prioux, Jacky Montmain, Stephane Perrey

“…We compared 4 single-output models (kNN, regression tree, random forest and(NN) Predictive models inspired by the human brain, used in this study for multi-output prediction in sports performance analysis (neural networks)), their multi-output counterparts and aA baseline model predicting future performance as the average of each player’s past performance, serving as a simple reference for comparison with more complex models (dummy baseline) (predicting the average performance of each player over the last month) in terms of average(Root Mean Squared Error) A measure of the quadratic difference between predicted and actual values in regression models (RMSE) (aRMSE) during aAn evaluation method where past training and game data are used sequentially to predict performance of the next game (chronological evaluation) where previous trainings and games data are used to train models to predict the next game performances. …”
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New method for landslide susceptibility evaluation in alpine valley regions that considers the suitability of InSAR monitoring and introduces deformation rate grading by Dingyi Zhou, Zhifang Zhao, Wenfei Xi, Xin Zhao, Jiangqin Chao

“…Incorporating Interferometric Synthetic Aperture Radar (InSAR) data sources into the landslide susceptibility evaluation process has yielded favorable outcomes in some studies. …”
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A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder by Xingfa Zi, Feiyi Liu, Mingyang Liu, Yang Wang

“…We trained the model using data from 2020 to 2022 from Australia’s Desert Knowledge Australia Solar Centre (DKASC), with 2023 data used for testing. …”
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Hilbert–Huang transform and spectrum-weighted reconstruction integration for millimeter wave radar-based heart rate detection by Hao CUI, Luyao LIU, Hao CHEN, Wendong XIAO

“…The effect of different angles on HR estimation is also analyzed at 0°, 15°, and 30°, showing that the signal-to-noise ratio of radar echo signals decreases as distance and angle are increased, resulting in increased absolute error in HR estimation. Furthermore, a comparative analysis is performed between the proposed method and three commonly used methods: variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and zero-attracting sign exponentially forgetting least mean square (ZA-SEFLMS). …”
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Quality of Snow Cover Characteristics Derived from ERA 5-Land Reanalysis for the Territory of Perm Krai by A. D. Kryuchkov, N. A. Kalinin, I. A. Sidorov

“…For 30 years, the magnitude of the reanalysis error decreased as it was compared with 61% observation points. …”
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A Theoretical Model for a Spray-type Ionic Solutions Dehumidifier Driven by Industrial Waste Heat by Liang Jyun-De, Hung Cheng-Kai

“…Specifically, the average error in the air outlet humidity ratio was only 1.1%, while the average error in the air outlet temperature was 6.1%. …”
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Interactive online learning method for students based on artificial intelligence by Cizhang Li, Wenfen Yin

“…The model was evaluated through experimental analysis using key regression and classification metrics, including Mean Absolute Error, Root Mean Square Error, R2 Score, Accuracy, Precision, Recall, Sensitivity, Specificity, and F1-Score with training time. …”
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Use of synchronized vector measurements technology for perform relay protection functions by R. G. Mustafin, V. A. Kasimov

“…When solving the problem, the method of calculating the total measurement error of the synchrophasor TVE (Total Vector Error) was used - a value that characterizes the deviation of the amplitude and phase of the measured vector from their specified values.   …”
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Fibre tracing in biomedical images: An objective comparison between seven algorithms. by Youssef Arafat, Cristina Cuesta-Apausa, Esther Castellano, Constantino Carlos Reyes-Aldasoro

“…To compare the algorithms, four biomedical data sets with vastly distinctive characteristics were selected. …”
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Evaluation of Contemporary Computational Techniques to Optimize Adsorption Process for Simultaneous Removal of COD and TOC in Wastewater by Areej Alhothali, Hifsa Khurshid, Muhammad Raza Ul Mustafa, Kawthar Mostafa Moria, Umer Rashid, Omaimah Omar Bamasag

“…The study showed that ANFIS outperformed the ANN and RSM in terms of fast convergence, minimum MSE, and sum of square error for prediction of adsorption data. The adsorption parameters were optimized using ANFIS-surface plots, ANN-GA hybrid, RSM-GA hybrid, and RSM optimization tool in design expert (DE) software. …”
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Thermodynamic and nutritional properties and drying kinetics of pequi (Caryocar brasiliense Cambess) mesocarp by Jéssica L. F. Souza, Daniel E. C. Oliveira, Geovana R. Plácido, Mariana B. Egea, Márcio Caliari, Marco A. P. da Silva

“…The mathematical models were adjusted by non-linear regression analysis using the Gauss-Newton method, considering the magnitude of the coefficient of determination (R2), the mean relative error (P) and the estimated mean error (SE). …”
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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

“…For the improvement in prediction accuracy of monthly runoff time series,a prediction model is proposed,which combines the wavelet decomposition (WD),African vultures optimization algorithm (AVOA),and deep extreme learning machine (DELM),and it is applied to the monthly runoff prediction of Yale Hydrological Station in Yunnan Province.Specifically,WD decomposes the time-series data of monthly runoff to obtain highly regular subsequence components,and AVOA is employed to optimize the number of neurons in the hidden layers of DELM;then,the WD-AVOA-DELM model is built to predict each subsequence component,and the prediction results are summated and reconstructed to produce the final prediction results of monthly runoff.Meanwhile,models based on the support vector machine (SVM) and BP neural networks are constructed for comparative analysis,including WD-AVOA-SVM,WD-AVOA-BP,AVOA-DELM,AVOA-SVM,and AVOA-BP models.The results reveal that the average absolute percentage error of the WD-AVOA-DELM model for the monthly runoff prediction of Yale Hydrological Station is 3.02%;the prediction error is far less than that of WD-STOA-SVM and WD-AVOA-BP models,and the prediction accuracy is more than one order of magnitude higher than that of AVOA-SVM,AVOA-SVM,and AVOA-BP models.The result indicates that the proposed model has good prediction performance.In this model,WD can scientifically reduce the complexity of runoff series and raise the prediction accuracy;AVOA can effectively optimize the key parameters of DELM and improve the performance of DELM networks.…”
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Intelligent Modeling; Single (Multi-layer perceptron) and Hybrid (Neuro-Fuzzy Network) Method in Forest Degradation (Case Study: Sari County) by somayeh mehrabadi

“…Also, the MSE value for the neuro-fuzzy model in the optimization and hybrid algorithms was 0.0190 and 0.0102, respectively. The analysis of the results showed the optimal performance of the neuro-fuzzy method both in reducing the error and in generalizing the model. …”
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Dosimetry Study of a VARIAN 600 C/D Linear Accelerator Head Model using MCNP5 Monte Carlo Code by Claubia Pereira, Jorge L. Batista Cancino, Carlos E. Cabrera, Lorena C. Fernandes, Clarysson A. M. da Silva, Rômulo V. Sousa

“…The results show the experimentally measured data comparison to the Monte Carlo results, where the measurements of PDD are inside the margin of error for buildup region and the flat region for the beam profile dose according to reference criteria. …”
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Coupled vibration characteristics of a drive system during automatic transmission power shifting by Chengyun Su, Meitao Wang, Yanling Yuan, Wenguan Li, Xiao Liu

“…Through vehicle-based vibration analysis and testing, a comparison is made between optimized gear parameters’ vibrational response characteristics and noise test data; revealing significant improvement in vibration performance by 3%–6%. …”
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Utilizing soil–water characteristic curve parameters in custom artificial neural network models to predict the unsaturated hydraulic conductivity by Bashar Alibrahim, Maan Habib, Ahed Habib

“…The ANN model was trained and tested on a diverse soil texture dataset, achieving high predictive accuracy, as indicated by R² values for both training and testing sets. Analysis of mean absolute error (MAE) and root mean square error (RMSE) values revealed variability in predictive performance across soil textures, with sandy soils showing higher errors due to their unique hydraulic properties and testing method discrepancies. …”
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