Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning anal... by Abhijeet Das

“…This study sought to evaluate the region's surface water quality and sources of contamination using machine learning (ML) methods such as Logistic Regression (LOR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN). …”
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Measuring Online Battery AC Resistance Using a DC-DC Converter With Time and Frequency Hybrid Method by Seong-Jong Kim, Hwa-Pyeong Park, Tae-Hwan Jin, In-Jun Lee

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Optimisation of Aluminium Alloy Variable Diameter Tubes Hydroforming Process Based on Machine Learning by Yong Xu, Xuewei Zhang, Wenlong Xie, Shihong Zhang, Yaqiang Tian, Liansheng Chen

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Chinese Massage (Tuina) Attenuates Knee Osteoarthritis by Modulating Autophagy-Related Cytokines: A Multidisciplinary Methodological Investigation by Wang Z, Zhao C, Li M, Zhang L, Diao J, Wu Y, Yang T, Shi M, Lei Y, Wang Y, Li M, Bian Y, Zhou Y, Xu H

“…Zhen Wang,1,* Chi Zhao,1,2,* Mengmeng Li,1,2,* Lili Zhang,1,* Jieyao Diao,1 Yiming Wu,2 Tao Yang,2 Mingwei Shi,2 Yang Lei,2 Yu Wang,3 Miaoxiu Li,4 Yanqin Bian,5 Yunfeng Zhou,1 Hui Xu1,2 1College of Acupuncture and Massage, Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 2Acupuncture and Massage Department, The Third Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, People’s Republic of China; 3College of Computer Science, Xidian University, Xian, People’s Republic of China; 4College of Acupuncture and Massage, Shanghai University of Chinese Medicine, Shanghai, People’s Republic of China; 5Orthopaedic Research Laboratory, University of California, Davis, CA, USA*These authors contributed equally to this workCorrespondence: Hui Xu, Email 15036065036@163.comBackground: Tuina therapy has demonstrated its potential in modulating autophagy-related factors in knee osteoarthritis (KOA); however, its core therapeutic targets and specific mechanisms require systematic elucidation through interdisciplinary research.Objective: This study investigated the mechanism by which Tuina alleviates KOA progression using multidimensional approaches, including Mendelian randomization (MR), in vivo experiments, and machine learning.Methods: Genetic data from genome-wide association studies of 60 cytokines and KOA were analyzed using MR analysis to identify autophagy-related factors significantly associated with KOA. …”
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Applications of Multi-Robotic Arms to Assist Agricultural Production: A Review by Xiaojian Gai, Chang Xu, Yajia Liu, Qingchun Feng, Shubo Wang

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A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa) by Li-Tang Qin, Xue-Fang Tian, Jun-Yao Zhang, Yan-Peng Liang, Hong-Hu Zeng, Ling-Yun Mo

“…To address this gap, the application of machine learning (ML) algorithms has emerged as an effective strategy. In this study, we applied 12 algorithms, namely, k-nearest neighbor (KNN), kernel k-nearest neighbors (KKNN), support vector machine (SVM), random forest (RF), stochastic gradient boosting (GBM), cubist, bagged multivariate adaptive regression splines (Bagged MARS), eXtreme gradient boosting (XGBoost), boosted generalized linear model (GLMBoost), boosted generalized additive model (GAMBoost), bayesian regularized neural networks (BRNN), and recursive partitioning and regression trees (CART) to build ML models for 225 mixture toxicity of azole fungicides towards Auxenochlorella pyrenoidosa. …”
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Streamlining ICI Transformed as a Nonnegative System by David Hyland

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Missing values imputation using Fuzzy K-Top Matching Value by Azza Ali, Mervat Abu-Elkheir, Ahmed Atwan, Mohammed Elmogy

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Robotic Hand–Eye Calibration Method Using Arbitrary Targets Based on Refined Two-Step Registration by Zining Song, Chenglong Sun, Yunquan Sun, Lizhe Qi

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Multi-objective Windy Postman Problem in a Fuzzy Transportation Network by Debosree PAL, Haresh Kumar SHARMA, Olegas PRENTKOVSKIS, Falguni CHAKRABORTY, Lijana MASKELIŪNAITĖ

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Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques by Manjog Padhy, Umar Muhammad Modibbo, Rasmita Rautray, Subhranshu Sekhar Tripathy, Sujit Bebortta

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Enhanced Viral Genome Classification Using Large Language Models by Hemalatha Gunasekaran, Nesaian Reginal Wilfred Blessing, Umar Sathic, Mohammad Shahid Husain

“…Among these are traditional algorithms such as Random Forest (RF), K-nearest neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB), each offering unique advantages in handling genetic data. …”
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Finding high posterior density phylogenies by systematically extending a directed acyclic graph by Chris Jennings-Shaffer, David H. Rich, Matthew Macaulay, Michael D. Karcher, Tanvi Ganapathy, Shosuke Kiami, Anna Kooperberg, Cheng Zhang, Marc A. Suchard, Frederick A. Matsen

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Comparison of machine learning models for coronavirus prediction by B. K. Amos, I. V. Smirnov, M. M. Hermann

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Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households by Seok Min Ji, Jeewuan Kim, Kyu Min Kim

“…The classification model was developed using four machine learning algorithms: Random Forest, Gradient boosting, Decision tree, Ridge regression, Neural network, and AdaBoost. …”
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Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Pro... by Zixiang Ye, Zhangyu Lin, Enmin Xie, Chenxi Song, Rui Zhang, Hao-Yu Wang, Shanshan Shi, Lei Feng, Kefei Dou

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Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning by Seyum Abebe, Irene Poli, Roger D. Jones, Debora Slanzi

“…Our study aims to evaluate the performance and feasibility of such algorithms: tree-based reinforcement learning (T-RL), DTR-Causal Tree (DTR-CT), DTR-Causal Forest (DTR-CF), stochastic tree-based reinforcement learning (SL-RL), and Q-learning with Random Forest. …”
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Predicting Livestock Farmers’ Attitudes towards Improved Sheep Breeds in Ahar City through Data Mining Methods by Jabraeil Vahedi, Masoumeh Niazifar, Mohammad Ghahremanzadeh, Akbar Taghizadeh, Soheila Abachi, Valiollah Palangi, Maximilian Lackner

“…Next, we employed data mining-based methods, including multilayer perceptron neural networks, random forest, and random tree algorithms. These helped identify essential variables affecting ranchers’ attitudes. …”
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The coverage method of unmanned aerial vehicle mounted base station sensor network based on relative distance by Taifei Zhao, Hua Wang, Qianwen Ma

“…The simulation results show that the coverage of the proposed algorithm is 22.4% higher than that of random deployment, and it is 9.9%, 4.7% and 2.1% higher than similar virtual force-oriented node, circular binary segmentation and hybrid local virtual force algorithms.…”
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Impact of climate change over distribution and potential range of chestnut in the Iberian Peninsula by Pedro Álvarez-Álvarez, Adrián Aviñoa-Arias, Adrián Aviñoa-Arias, Emilio Díaz-Varela, José Vicente López-Bao, José Carlos Pérez-Girón

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