Advancing sustainable renewable energy: XGBoost algorithm for the prediction of water yield in hemispherical solar stills by Salwa Ahmad Sarow, Hasan Abbas Flayyih, Maryam Bazerkan, Luttfi A. Al-Haddad, Zainab T. Al-Sharify, Ahmed Ali Farhan Ogaili

“…The current work extends these experimental insights through XG-Boost to predict productivity, employing evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Variation of the Root Mean Squared Error (CVRMSE), and the determination coefficient (R2), with resulted values denoted as 0.43708%, 0.95879%, 0.2780%, 0.05290%, 12.2078%, and 0.88144% respectively. …”
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A novel watermarking algorithm for three-dimensional point-cloud models based on vertex curvature by Jing Liu, Yajie Yang, Douli Ma, Wenjuan He, Yinghui Wang

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Performance evaluation on extended neural network localization algorithm on 5 g new radio technology by Deebalakshmi R, S Markkandan, Vinodh Kumar Arjunan

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Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adil Noor

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Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms by Ö. Şengül, Ş. Çelik

“…<span class="inline-formula"><i>R</i>²=0.633</span>, 0.633, 0.721, 0.637, 0.768, and 0.609), coefficient of variation (CV % <span class="inline-formula">=</span> 6.35 and 5.14, <span class="inline-formula"><i>P</i><i>&lt;</i>0.01</span>), mean square error (MSE <span class="inline-formula">=</span> 3.296, 3.296, 2.904, 4.461, 2.277, and 4.121), root mean square error (RMSE <span class="inline-formula">=</span> 1.815, 1.815, 1.704, 2.112, 1.509, and 2.030), mean absolute error (MAE <span class="inline-formula">=</span> 1.409, 1.409, 1.279, 1.702, 1.193, and 1.628), and mean absolute percentage error (MAPE <span class="inline-formula">=</span> 3.925, 3.925, 3.578, 4.002, 3.335, and 3.967), between actual and predicted values of live body weight. …”
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The Performance Analysis of Diffusion LMS Algorithm in Sensor Networks Based on Quantized Data and Random Topology by Junlong Zhu, Mingchuan Zhang, Changqiao Xu, Jianfeng Guan, Hongke Zhang

“…We further analyze the stability and convergence of the proposed algorithm and derive the closed-form expressions of the MSD (Mean-Square Deviation) and EMSE (Excess Mean-Square Errors), which characterize the steady-state performance of the proposed algorithm. …”
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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…Abstract This research examines the application of eight different machine learning (ML) algorithms for predicting the compressive strength of high-performance concrete (HPC). …”
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E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances by Ali Agheli, Kayvan Aghabayk, Matin Sadeghi, Subasish Das

“…Further insights from the XGBoost-SHAP analysis and heterogeneity in means and variances of random parameters revealed nuanced patterns. …”
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Subway Platform Passenger Flow Counting Algorithm Based on Feature-Enhanced Pyramid and Mixed Attention by Jing Zuo, Guoyan Liu, Zhao Yu

“…On the ShanghaiTech Part_A dataset, the mean absolute error (MAE) and mean square error (MSE) of the proposed algorithm are 2.3% and 1.4% higher than those of the comparison algorithm, respectively. …”
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Infrared Aircraft Detection Algorithm Based on High-Resolution Feature-Enhanced Semantic Segmentation Network by Gang Liu, Jiangtao Xi, Chao Ma, Huixiang Chen

“…Experiments conducted on a self-built infrared dataset show that the proposed algorithm achieves a mean intersection over union (mIoU) of 92.74%, a mean pixel accuracy (mPA) of 96.34%, and a mean recall (MR) of 96.19%, all of which outperform classic segmentation algorithms such as DeepLabv3+, Segformer, HRNetv2, and DDRNet. …”
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Improved interacting multiple model algorithm airport surface target tracking based on geomagnetic sensors by Xinmin Tang, Wenjie Zhao, Shangfeng Gao

“…In this algorithm, the weighted sum of the mean values of the residual errors, which is used to reconstruct the model probabilistic likelihood function, and the Markov model transition probability are updated using posterior information. …”
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Application of quantum-inspired evolutionary algorithm in the analysis of near infrared diffuse transmission spectroscopy of apples by LI Jun-liang, WANG Cong-qing

“…The results showed that the GA-PLS model had 110 variables, with RMSEC (root mean standard error of calibration) of 0.582 0, RMSEP (root mean standard error of prediction) of 0.612 3, but the QEA-PLS model had 194 variables, with RMSEC of 0.492 7, RMSEP of 0.526 0. …”
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Optimisation of Ensemble Learning Algorithms for Geotechnical Applications: A Mathematical Approach to Relative Density Prediction by Mahdy Khari, Ali Dehghanbanadaki, Danial Jahed Armaghani, Manoj Khandelwal

“…A novel approach to optimise ensemble learning algorithms is presented, with a focus placed on the mathematical foundations of these methods. …”
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Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. by Huu Nam Nguyen, Quoc Thanh Tran, Canh Tung Ngo, Duc Dam Nguyen, Van Quan Tran

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An intelligent algorithm for optimizing player positioning using circular intuitionistic fuzzy COCOSO with Einstein aggregation by ZhiXin Fang, XinLi Yao, Man Song

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Research on prediction algorithm of effluent quality and development of integrated control system for waste-water treatment by JianWun Lai

“…The present study presented a prediction algorithm and an Integrated Control System (ICS) to address the problems of conventional methods. …”
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State of Charge Estimation of Lithium Battery Utilizing Strong Tracking H-Infinity Filtering Algorithm by Tianqing Yuan, Yang Liu, Jing Bai, Hao Sun

“…Simulation experiments indicate that, compared to the HIF algorithm, the STF-HIF algorithm achieves a maximum absolute SOC estimation error (MaxAE) of 0.69%, 0.72%, and 1.22%, with mean absolute errors (MAE) of 0.27%, 0.25%, and 0.38%, and root mean square errors (RMSE) of 0.33%, 0.30%, and 0.46% under dynamic stress testing (DST), federal urban driving schedules (FUDS), and Beijing dynamic stress testing (BJDST) conditions, respectively.…”
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Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data by Silvia Elaine Cardozo Macedo, Marina de Borba Oliveira Freire, Oscar Schmitt Kremer, Ricardo Bica Noal, Fabiano Sandrini Moraes, Mauro André Barbosa Cunha

“…Epidemiological, clinical, and laboratory data were processed by machine learning algorithms in order to identify patterns. Mean AUC values were calculated for each combination of model and oversampling/undersampling techniques during cross-validation. …”
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Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…Methods We developed a comprehensive algorithm set comprising eighty-one distinct configurations of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm within AutoDock Vina. …”
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A new approach to error inequalities: From Euler-Maclaurin bounds to cubically convergent algorithm by Miguel Vivas-Cortez, Usama Asif, Muhammad Zakria Javed, Muhammad Uzair Awan, Yahya Almalki, Omar Mutab Alsalami

“…With the help of a new auxiliary result and some well-known ones, like Hölder's, the power mean, improved Hölder, improved power mean, convexity, and bounded features of the function, we obtained new bounds for Euler-Maclaurin's inequality. …”
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