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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A Real Time Coordinated Hydro Governor With Dual STATCOM for Low Frequency Oscillation Damping Using Hybrid Mean DE & TVAPSO by Samarjeet Satapathy, Narayan Nahak, Renu Sharma

“…A new multi-objective mean Differential Evolution with time varying accelerated Particle Swarm Optimization algorithm (MeDETVAPSO) has been implemented to tune controller parameters. …”
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Investigating the Association Between Mean Arterial Pressure on 28-Day Mortality Risk in Patients With Sepsis: Retrospective Cohort Study Based on the MIMIC-IV Database by Qimin Chen, Wei Li, Ying Wang, Xianjun Chen, Dehua He, Ming Liu, Jia Yuan, Chuan Xiao, Qing Li, Lu Chen, Feng Shen

“…The association between mean arterial pressure (MAP) and prognosis among patients with patients is yet to be demonstrated. …”
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Research on intra-day multi-objective operation control of distribution network system under quantum communication optimization by QIAN Hao, DONG Zhihui, XIANG Jundong, ZHENG Jiewen, TANG Zhipeng, GUO Qiang

“…Quantum states have the property of superposition, meaning that the same quantum bit can be in multiple states simultaneously. …”
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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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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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Algorithmic indexing in MEDLINE frequently overlooks important concepts and may compromise literature search results by Alexandre Amar-Zifkin, Taline Ekmekjian, Virginie Paquet, Tara Landry

“…Indexing was performed by the Medical Text Indexer-Auto (MTIA) algorithm. The primary outcome measure is the number of records for which the MTIA algorithm assigned subject headings that represented the main concepts of the publication.  …”
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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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