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 control algorithms for posture and height control of four-leg hydraulic supports by Yihui Pang, Yaoyu Shi

“…This approach transforms the traditional geometric relationship solutions of the main structure of the hydraulic supports into solutions based on the coordinate relationships of the main hinge points of the support, resulting in a mathematical expression for solving the support posture and height of the hydraulic supports. …”
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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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Algorithmic Failure? The Weimar Triangle as a Subregional Emulation of Global Governance Structures by Marek Rewizorski, Beata Przybylska-Maszner

“…The study is based on the assumption that the main reason for the disturbances in the continuity of the Weimar Triangle is the suboptimal emulation of the operating algorithms developed in the G7, that is, the sequences of principles, practices, and knowledge that Germany and France had internalized through their long-term socialization in the G7, and then transferred to the subregional level in order to ensure adaptability and receptiveness in the functioning of the Weimar Triangle.…”
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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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Evaluation of Cluster Algorithms for Radar-Based Object Recognition in Autonomous and Assisted Driving by Daniel Carvalho de Ramos, Lucas Reksua Ferreira, Max Mauro Dias Santos, Evandro Leonardo Silva Teixeira, Leopoldo Rideki Yoshioka, João Francisco Justo, Asad Waqar Malik

“…Our analysis covered a variety of current methods, the mathematical process of these methods, and presented a comparison table between these algorithms, including Hierarchical Clustering, Affinity Propagation Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Mini-Batch K-Means, K-Means Mean Shift, OPTICS, Spectral Clustering, and Gaussian Mixture. …”
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Robustness of machine learning algorithms to generate flood susceptibility maps for watersheds in Jordan by Mohanned S. Al-Sheriadeh, Mohammad A. Daqdouq

“…The study examined three machine learning algorithms (MLAs): random forest (RF), support vector machine (SVM), and artificial neural networks (ANN) for generating flood susceptibility maps in two watersheds in Jordan. …”
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Fractional time delay estimation algorithm based on the fractional lower order moment by LIU Wen-hong1, 2, QIU Tian-shuang1, HU Ting-ting1, LUAN Lian-yi3

“…According to the noise characteristics in signal, a fractional time delay estimation algorithm referred to as LMPFTDE was proposed based on the least mean p-norm. …”
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Internet Tourism Resource Retrieval Using PageRank Search Ranking Algorithm by Hui Li

“…Experimental results show that the algorithm can successfully extract the main content of the article from a wide variety of web pages. …”
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Online blind equalization algorithm with echo state network based on prediction principle by Ling YANG, Qin HAN, Li CHENG, Aonan ZHAO, Juan DU

“…In view of the nonlinear channel,the online blind equalization algorithm with echo state network was proposed based on prediction principle.In the proposed algorithm,the traditional linear prediction error filter was replaced by the ESN with good nonlinear mapping ability,and recursive least square (RLS) algorithm was used to calculate the output weight of the network to minimize the network prediction error.Then,the amplitude and phase were adjusted.Simulation results show that the proposed algorithm can effectively reduce the distortion caused by nonlinear channel to the transmitted signal for 16QAM signal,which has lower mean square error and faster convergence speed in comparison with other blind equalization algorithms based on prediction principle.…”
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Fractional time delay estimation algorithm based on the fractional lower order moment by LIU Wen-hong1, 2, QIU Tian-shuang1, HU Ting-ting1, LUAN Lian-yi3

“…According to the noise characteristics in signal, a fractional time delay estimation algorithm referred to as LMPFTDE was proposed based on the least mean p-norm. …”
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Online blind equalization algorithm with echo state network based on prediction principle by Ling YANG, Qin HAN, Li CHENG, Aonan ZHAO, Juan DU

“…In view of the nonlinear channel,the online blind equalization algorithm with echo state network was proposed based on prediction principle.In the proposed algorithm,the traditional linear prediction error filter was replaced by the ESN with good nonlinear mapping ability,and recursive least square (RLS) algorithm was used to calculate the output weight of the network to minimize the network prediction error.Then,the amplitude and phase were adjusted.Simulation results show that the proposed algorithm can effectively reduce the distortion caused by nonlinear channel to the transmitted signal for 16QAM signal,which has lower mean square error and faster convergence speed in comparison with other blind equalization algorithms based on prediction principle.…”
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Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms by Lamiae Azouggagh, Noelia Ibáñez-Escriche, Marina Martínez-Álvaro, Luis Varona, Joaquim Casellas, Sara Negro, Cristina Casto-Rebollo

“…ML models exploring maternal, paternal and heterosis effects showed varying levels of classification performance, with the paternal effect scenario being the best, achieving a mean Area Under the ROC curve (AUROC) of 0.74 using the Catboost (CB) algorithm. …”
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