Machine learning classifiers to detect data pattern change of continuous emission monitoring system: A typical chemical industrial park as an example by Zhefeng Xu, Xiahong Shi, Wei Shu, Yilu Xin, Xuan Zan, Zhaonian Si, Jinping Cheng

“…By categorizing outlets into 12 datasets based on monitoring parameters, 17 machine learning models were evaluated to identify emission patterns and detect potential data anomalies. …”
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Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study by Zheng Zhang, Jian Wu, Yi Duan, Linwei Liu, Yaru Liu, Jinghan Wang, Li Xiao, Zhifeng Gao

“…We applied four ML algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Logistic Regression (LR)—to classify patients with or without HIBPV. …”
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Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms by Yogita Dubey, Yashraj Tarte, Nikhil Talatule, Khushal Damahe, Prachi Palsodkar, Punit Fulzele

“…The stroke pre-diction using optimized boosting machine learning algorithms is supported with explainable AI using LIME and SHAP. …”
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Analyzing Student Graduation and Dropout Patterns Using Artificial Intelligence and Survival Strategies by Behrouz Alefy, Vahid Babazadeh

“…The study applies state-of-the-art machine learning techniques to establish dominant patterns and offer forecasts using a wide range of student records. …”
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Machine learning approach for 2D abrasion mapping in Sediment Bypass Tunnels: a case study of Koshibu SBT, Japan by Ahmed Emara, Sameh A. Kantoush, Mohamed Saber, Tetsuya Sumi, Vahid Nourani, Emad Mabrouk

“…Overall, this study demonstrates the potential of machine learning algorithms for predicting tunnel abrasion in SBTs.Paper highlightsThis study introduces a validated 2D model for tunnel abrasion based on field data, contributing to improved sediment management in SBTs.ASM Model efficiently predicts abrasion mapping in SBT, achieving 86.4% overall accuracy.High sensitivity and specificity in distinguishing abraded and non-abraded areas.Captures four complex abrasion patterns in straight and curved sections but is limited to relatively small wave-like patterns.Geometric and hydraulic parameters, particularly the elongated distance and flow velocity, exhibit significant impacts in the ASM model.…”
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Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine by Hojin Moon, Lauren Tran, Andrew Lee, Taeksoo Kwon, Minho Lee

“…Objectives: The primary goal of this research is to develop treatment-related genomic predictive markers for non-small cell lung cancer by integrating various machine learning algorithms that recommends near-optimal individualized patient treatment for chemotherapy in an effort to maximize efficacy or minimize treatment-related toxicity. …”
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Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms by Chenhui Feng, Zhimiao Wei, Xiaohui Li, Xiaohui Li

“…Through differential expressed genes (DEG) analysis, high-dimensional Weighted Correlation Network Analysis (hdWGCNA) and machine learning algorithms, we identified signature genes associated with both BAM and FAM. …”
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Development and Internal Validation of Machine Learning Algorithms for Predicting Subsequent Contralateral Slipped Capital Femoral Epiphysis in Patients With Unilateral Slips by David P. VanEenenaam, Jr., BS, Carter Hall, BS, Daniel A. Maranho, MD, PhD, Christopher J. DeFrancesco, MD, Eduardo N. Novais, MD, Wudbhav N. Sankar, MD

“…Background: Controversy remains about whether to pin the contralateral side in cases of unilateral slipped capital femoral epiphysis (SCFE). Machine learning (ML) algorithms can be leveraged to identify complex, nonlinear patterns in data and allow for more accurate predictions on which patients may need a prophylactic pin. …”
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Deep learning-based recognition model of football player’s technical action behavior using PCA–LBP algorithm by Hongtao Chen, Zhengbai Lin, Quan Xu

“…When the number of recognition times was 300, the recognition accuracy of the PCA–LBP algorithm was 24% higher than that of the LBP algorithm. …”
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Search Guidance Network Assisted Dynamic Particle Swarm Optimization Algorithm by LIU Zhi, SONG Wei

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Histopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients by Tuğba Şentürk, Fatma Latifoğlu, Çiğdem Gülüzar Altıntop, Arzu Yay, Zeynep Burçin Gönen, Gözde Özge Önder, Özge Cengiz Mat, Yusuf Özkul

“…Classification was performed using ML algorithms, including decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and Artificial Neural Network (ANN). …”
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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

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SwinDenoising: A Local and Global Feature Fusion Algorithm for Infrared Image Denoising by Wenhao Wu, Xiaoqing Dong, Ruihao Li, Hongcai Chen, Lianglun Cheng

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Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015–2018 by Danhui Mao, Junfang Mu, Yajing Li, Lu He, Qianhui Chai, Xin Zhao, Xiaojun Ren, Hui Cheng

“…This study used Louvain and machine learning algorithm for CMM pattern detection. …”
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Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach by Nilayam Kumar Kamila, Jaroslav Frnda, Subhendu Kumar Pani, Rashmi Das, Sardar M.N. Islam, P.K. Bharti, Kamalakanta Muduli

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Rapid classification of rice according to storage duration via near-infrared spectroscopy and machine learning by Chen Zhai, Wenxiu Wang, Man Gao, Xiaohui Feng, Shengjie Zhang, Chengjing Qian

“…Therefore, we investigated the ability of near-infrared spectroscopy combined with machine learning algorithms to distinguish rice storage duration. …”
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Reinventing the Trochoidal Toolpath Pattern by Adaptive Rounding Radius Loop Adjustments for Precision and Performance in End Milling Operations by Santhakumar Jayakumar, Sathish Kannan, Poongavanam Ganeshkumar, U. Mohammed Iqbal

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Towards Automated Cadastral Map Improvement: A Clustering Approach for Error Pattern Recognition by Konstantinos Vantas, Vasiliki Mirkopoulou

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3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology by Chongyang YAO, Yongxin CHOU, Zhiwei LIANG, Haiping YANG, Jicheng LIU, Dongmei LIN

“…On this basis, nine features are extracted from the 3D pulse signals and features selection is performed using a two-sample Kolmogorov-Smirnov test. Finally, machine learning algorithms such as decision trees and random forests are used to identify the five types of pulse conditions: deep pulse, intermittent pulse, flooding pulse, slippery pulse, and rapid pulse. …”
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Diel and Annual Patterns of Vocal Activity of Three Neotropical Wetland Birds Revealed via BirdNET by Cristian Pérez-Granados, Karl-L. Schuchmann

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