Parameter Extraction of Photovoltaic Cells and Panels Using a PID-Based Metaheuristic Algorithm by Aseel Bennagi, Obaida AlHousrya, Daniel T. Cotfas, Petru A. Cotfas

“…This paper presents a new metaheuristic algorithm for extracting parameters from photovoltaic cells using the functionality of the PID-based search algorithm (PSA). …”
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Image information optimization processing based on fractional order differentiation and WT algorithm. by Qiong Long

“…Therefore, the study is based on wavelet transform algorithm and fractional order differentiation to perform edge detection and image fusion. …”
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On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams by Thuy-Anh Nguyen, Hai-Bang Ly, Hai-Van Thi Mai, Van Quan Tran

“…The performance evaluation of the models is performed using statistical criteria, including the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). …”
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Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment by Saiqa Khan, Meera Narvekar

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Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation by Eluã Ramos Coutinho, Jonni G.F. Madeira, Robson Mariano da Silva, Angel Ramon Sanchez Delgado, Alvaro L.G.A. Coutinho

“…The findings are assessed based on the coefficient of correlation (r), mean absolute error (MAE), root mean square error (RMSE), and mean percentage error (MPE), and are contrasted with the Hargreaves-Samani, Jensen-Haise, Linacre, Benavides & Lopez, and Hamon methods, along with the Multilayer Perceptron (MLP) neural network, which is conventionally trained and employs hyperparameter tuning techniques such as Grid Search (MLP-GRID) and Random Search (MLP-RD). …”
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Frost Resistance Prediction of Concrete Based on Dynamic Multi-Stage Optimisation Algorithm by Xuwei Dong, Jiashuo Yuan, Jinpeng Dai

“…To predict the frost resistance of concrete more accurately, based on the four ensemble learning models of random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost), this paper optimises the ensemble learning models by using a dynamic multi-stage optimisation algorithm (DMSOA). These models are trained using 7090 datasets, which use nine features as input variables; relative dynamic elastic modulus (RDEM) and mass loss rate (MLR) as prediction indices; and six indices of the coefficient of determination (R²), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and standard deviation ratio (SDR) are selected to evaluate the models. …”
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The Simultaneous Interpolation of Target Radar Cross Section in Both the Spatial and Frequency Domains by Means of Legendre Wavelets Model-Based Parameter Estimation by Yongqiang Yang, Yunpeng Ma, Lifeng Wang

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Research on IGBT Sequentially Prediction Algorithm Based on Improved Wavelet Neural Network by Kexun HUANG, Songrong WU, Binan XIANG, Rui XU, Zhenwei TU

“…Aiming at the aging failure of IGBT, an improved wavelet neural network sequentially prediction method based on genetic algorithm was proposed. Based on the analysis of IGBT failure mechanism, with the IGBT aging data, the instantaneous collector emitter peak voltage was selected as the failure characteristic parameter, the training set and test set were constructed by the sliding time window method, and then the wavelet neural network prediction model improved by genetic algorithm was built in MATLAB for prediction, which was compared with the traditional wavelet neural network prediction model. …”
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Key Technology of the Medical Image Wise Mining Method Based on the Meanshift Algorithm by Jinli Cui, Yadong Wang, Ke Wang

“…Mean-shift originally refers to the mean vector of the offset. …”
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Tighter bounds on the Gaussian Q-function based on wild horse optimization algorithm by Reza Etesami, Mohsen Madadi

“…The new lower and upper bounds achieve significantly lower maximum absolute error and mean absolute error values compared to previous approaches. …”
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Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection by Omar Farhan Al-Hardanee, Hüseyin Demirel

“…According to the findings, the LSTM model attained an accuracy of 99.55%, a mean square error (MSE) of 0.0072, and a mean absolute error (MAE) of 0.0053.…”
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Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine by Ting Lan, Yanan Bie, Dong Hai, Jun Zhong

“…The results of the experiment show that compared with other dominant algorithms, the proposed algorithm estimates heart rate with a smaller mean absolute error and can extract heart rate more effectively.…”
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Intelligent Layout and Optimization of EV Charging Stations: Initial Configuration via Enhanced K-Means and Subsequent Refinement through Integrated GCN by H. Yang, M. Liu, J. Zou, R. Xu, J. Huang, P. Geng

“…Through an in-depth study of the deployment optimization of EV charging stations, a layout algorithm based on K-Means and simulated annealing is first introduced to determine the optimal locations for new charging stations. …”
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AI-based algorithms for estimating hydrochar properties in terms of biomass ultimate analysis by Raouf Hassan, Ali sharifzadegan, Alireza Baghban

“…The Decision Tree model achieved the highest accuracy in yield prediction, with an R² of 0.9445, mean squared error (MSE) of 16.43, and mean relative deviation (MRD) of 2.66 %. …”
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Algorithm study of digital HPA predistortion using one novel memory type BP neural network by Chun-hui HUANG, Yong-jie WEN

“…Based on the characteristic analysis of the high power amplifier (HPA) in wide-band CMMB repeater stations,a novel neural network was proposed which can respectively process the memory effect and the nonlinear of power amplifier.The novel model based on real-valued time-delay neural networks(RVTDNN) uses the Levenberg-Marquardt (LM) optimization to iteratively update the coefficients of the neural network.Due to the new parameters w⁰in the novel NN model,the modified formulas of LM algorithm were provided.Next，in order to eliminate the over-fitting of LM algorithm,the Bayesian regularization algorithm was applied to the predistortion system.Additionally,the predistorter of CMMB repeater stations based on the indirect learning method was constructed to simulate the nonlinearity and memory effect of HPA.Simulation results show that both the NN models can improve system performance and reduce ACEPR (adjacent channel error power ratio ) by about 30 dB.Moreover,with the mean square error less than 10⁻⁶,the coefficient of network for FIR-NLNNN is about half of that for RVTDNN.Similarly,the times of multiplication and addition in the iterative process of FIR-NLNNN are about 25% of that for RVTDNN.…”
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MODELLING FLUCTUATIONS OF GROUNDWATER LEVEL USING MACHINE LEARNING ALGORITHMS IN THE SOKOTO BASIN by Samson Alfa, Haruna Garba, Augustine Odeh

“…Hyperparameters for the XGBoost model were fine-tuned using grid search techniques, resulting in optimal settings that significantly enhanced predictive accuracy with Mean Absolute Error (MAE) ranging from 0.016 – 0.757m and Root Mean Square Error (RMSE) ranging from 0.051 - 2.859m. …”
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Detection of Potentially Anomalous Cosmic Particle Tracks Acquired with CMOS Sensors: Validation of Rough k–Means Clustering with PCA Feature Extraction by Hachaj Tomasz, Piekarczyk Marcin, Wąs Jarosław

“…The analysis of the behavior of the rough k-means clustering-based algorithm presented here and the method of selecting its parameters showed that the algorithm performs as expected and demonstrates efficiency, stability, and repeatability of results for the test data set. …”
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Performance and improvement of deep learning algorithms based on LSTM in traffic flow prediction by Wei Xu, Eric Blancaflor, Mideth Abisado

“…The hybrid model is applied to Beijing urban road data with a time granularity (TG) of 10 min and a window size of 30 min, achieving an RMSE (root mean square error) of 4.478, an MAE (mean absolute error) of 3.609, and an R2 of 0.965. …”
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Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

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A novel EEG artifact removal algorithm based on an advanced attention mechanism by Rui Jiang, Shen Tong, Jiawei Wu, Haowei Hu, Ran Zhang, Heng Wang, Yan Zhao, Weixin Zhu, Shuyan Li, Xiao Zhang

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