Preliminary Study on Real-Time Phonocardiogram Signal Acquisition and Analysis Using Machine Learning and IoMT for Digital Stethoscope by M. Kalimuthu, C. Hemanth

“…Advanced signal segmentation and classification were performed using machine learning algorithms, including K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). …”
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Monitoring Gypsiferous Soils by Leveraging Advanced Spaceborne Hyperspectral Imagery via Spectral Indices and a Machine Learning Approach by Najmeh Rasooli, Saham Mirzaei, Stefano Pignatti

“…The gypsum content was retrieved by optical data using three approaches: narrowband indices, spectral absorption features, and machine learning (ML) algorithms. Four machine learning algorithms, including PLSR (Partial Least Squares Regression), RF (Random Forest), SVR (Support Vector Regression), and GPR (Gaussian Process Regression), achieved excellent performance (RPD > 2.5). …”
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Harnessing Data-mining Algorithms to Model and Evaluate Factors Influencing Distortion Product Otoacoustic Emission Variations in a Mining Industry by Sajad Zare, Reza Esmaeili, Mojtaba Nakhaei pour

“…In the second phase, the weight of the factors affecting OAEs was investigated using deep learning (DL) and support vector machine (SVM) algorithms. Results: The results of both algorithms showed that sound exposure had the greatest effect (weighting between 36% and 45%) on the changes in OAEs. …”
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A Comparative study on the impact of urbanisation on microclimate in Cairo (Egypt) and London (UK) using remote sensing and Machine Learning by L. Sabobeh, T. Ali, M. Md. Mortula

“…Using Landsat Collection 2 satellite imagery and Google Earth Engine (GEE) for classification, Land Use and Land Cover (LULC) was divided into four categories: water bodies, vegetation, developed areas, and barren land. Several machine learning (ML) algorithms were compared, with Support Vector Machine (SVM) ultimately selected for its superior performance. …”
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Tanzanian sign language recognition system for an assistive communication glove sign tutor based on the inertial sensor fusion control algorithm by Isack Bulugu

“…The attitude information of the tested object in the instantaneous state can be accurately obtained. The algorithm uses the classification methods of support vector machine (SVM), K-nearest neighbor method (KNN) and feedforward neural network (FNN) classifier adaptive model to classify the data collected by the sign language data through data fusion, data preprocessing and feature extraction. …”
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Statistical modeling and application of machine learning for antibiotic degradation using UV/persulfate-peroxide based advanced oxidation process by Musfekur Rahman Dihan, Md. Ashraful Alam, Surya Akter, Md. Abdul Gafur, Md. Shahinoor Islam

“…Pearson correlation and statistical multivariate linear regression (MLR) were applied to model the removal% and pHfinal of both antibiotics, along with the three machine learning algorithms, Artificial neural network (ANN), support vector machine (SVM), and Random Forest (RF), to make the same predictions. …”
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Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning by Rab Nawaz, Hani A. Albalawi, Syed Basit Ali Bukhari, Khawaja Khalid Mehmood, Muhammad Sajid

“…Logistic Regression, Random Forest and Support Vector Machine were the outperforming classifiers and proved their potential for classifying faults in electric power transmission lines.…”
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Integrative bioinformatics and machine learning identify key crosstalk genes and immune interactions in head and neck cancer and Hodgkin lymphoma by Meiling Qin, Xinxin Li, Xun Gong, Yuan Hu, Min Tang

“…Candidate hub genes were selected via machine learning algorithms, including LASSO regression, random forest, and support vector machine-recursive feature elimination (SVM-RFE). …”
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Enhancing stroke prediction models: A mixing of data augmentation and transfer learning for small-scale dataset in machine learning by Imam Tahyudin, Ade Nurhopipah, Ades Tikaningsih, Puji Lestari, Yaya Suryana, Edi Winarko, Eko Winarto, Nazwan Haza, Hidetaka Nambo

“…The classification models employed in this study were four algorithms: Random Forest, Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting. …”
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Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning by Hui Xu, Xingwang Peng, Ziyu Peng, Rui Wang, Rui Zhou, Lianguo Fu

“…Second, ML models were constructed based on the logistic regression (LR), decision tree (DT), support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms, after which random sampling and tenfold cross-validation were separately used to evaluate and compare these models and identify the optimal model. …”
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The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules by Zuhua Song, Qian Liu, Jie Huang, Dan Zhang, Jiayi Yu, Bi Zhou, Jiang Ma, Ya Zou, Yuwei Chen, Zhuoyue Tang

“…Recursive feature elimination was employed for variable selection. Three ML algorithms—support vector machine (SVM), logistic regression (LR), and naive Bayes (NB)—were implemented to construct predictive models. …”
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MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects by Domenico Lofù, Paolo Sorino, Tommaso Colafiglio, Caterina Bonfiglio, Rossella Donghia, Gianluigi Giannelli, Angela Lombardi, Tommaso Di Noia, Eugenio Di Sciascio, Fedelucio Narducci

“…To provide physicians with a valuable tool, MORIX was trained and tested on a dataset of MAFLD subjects, comparing five different models: Random Forest (RF), eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Light Gradient Boosting Model (LGBM) in a 5-fold cross-validation training strategy. …”
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Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study by Xiaoqin Chen, Zhitong Li, Xiaoying Fan, Yuanyuan Yan, Shiwei Liu

“…Various clinical, demographic, and laboratory variables were analyzed using univariate and multivariate logistic regression, complemented by LASSO regression for feature selection. Additionally, eight machine learning algorithms—logistic regression (LR), decision tree (DT), random forests (RF), k-nearest neighbors (KNN), support vector machine (SVM), neural networks (NNET), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)—were employed to predict carotid atherosclerosis. …”
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Application of Machine Learning in the Prediction of the Acute Aortic Dissection Risk Complicated by Mesenteric Malperfusion Based on Initial Laboratory Results by Zhechuan Jin, Jiale Dong, Jian Yang, Chengxiang Li, Zequan Li, Zhaofei Ye, Yuyu Li, Ping Li, Yulin Li, Zhili Ji

“…Key preoperative predictive variables were identified through the least absolute shrinkage and selection operator (LASSO) regression. Subsequently, six machine learning algorithms were used to develop and validate an MMP risk identification model: logistic regression (LR), support vector classification (SVC), random forest (RF), extreme gradient boosting (XGBoost), naive Bayes (NB), and multilayer perceptron (MLP). …”
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Machine learning based on alcohol drinking-gut microbiota-liver axis in predicting the occurrence of early-stage hepatocellular carcinoma by Yi Yang, Zhiyuan Bo, Jingxian Wang, Bo Chen, Qing Su, Yiran Lian, Yimo Guo, Jinhuan Yang, Chongming Zheng, Juejin Wang, Hao Zeng, Junxi Zhou, Yaqing Chen, Gang Chen, Yi Wang

“…Alcohol drinking-gut microbiota-liver axis was established through the mediation/moderation effect analyses. Eight ML algorithms including Classification and Regression Tree (CART), Gradient Boosting Machine (GBM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) were applied. …”
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Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks by Chalita Jainonthee, Phutsadee Sanwisate, Panneepa Sivapirunthep, Chanporn Chaosap, Raktham Mektrirat, Sudarat Chadsuthi, Veerasak Punyapornwithaya

“…This classification was performed using machine learning (ML) algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost). …”
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Machine learning-based prediction of carotid intima–media thickness progression: a three-year prospective cohort study by An Zhou, Kui Chen, Kui Chen, Yonghui Wei, Qu Ye, Qu Ye, Yuanming Xiao, Rong Shi, Jiangang Wang, Wei-Dong Li

“…The participants were categorized into CIMT thickening and nonthickening groups on the basis of a final CIMT ≥1.0 mm or an increase ≥0.1 mm across consecutive measurements. We evaluated seven machine learning algorithms: logistic regression, random forest, XGBoost, support vector machine (SVM), elastic net, decision tree, and neural network. …”
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Machine learning applications in forecasting patient satisfaction and clinical outcomes after carpal tunnel release: a retrospective study by Zohreh Manoochehri, Sara Manoochehri, Seyed Reza Bagheri, Alireza Abdi, Ehsan Alimohammadi

“…Based on BCTQ scores, patients were categorized into desirable and undesirable groups: for the symptom severity scale (SSS), scores of 11–24 were classified as desirable, while scores of 25–55 were classified as undesirable; for the functional status scale (FSS), scores of 8–16 were considered desirable, and scores of 17–40 as undesirable. Four machine learning algorithms—random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and k-nearest neighbors (k-NN)—were used to predict these outcomes. …”
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Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques by Liuqing Yang, Liuqing Yang, Liuqing Yang, Rui Xuan, Rui Xuan, Rui Xuan, Dawei Xu, Dawei Xu, Dawei Xu, Aming Sang, Aming Sang, Aming Sang, Jing Zhang, Jing Zhang, Jing Zhang, Yanfang Zhang, Xujun Ye, Xinyi Li, Xinyi Li, Xinyi Li

“…Following this, we integrated the DEGs with the genes from key modules as determined by Weighted Gene Co-expression Network Analysis (WGCNA), identifying 262 overlapping genes. 12 core genes were subsequently selected using three machine-learning algorithms: random forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVW-RFE). …”
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Resisting bad mouth attack in vehicular platoon using node-centric weight-based trust management algorithm (NC-WTM) by R. Priya, N. Sivakumar

“…Abbreviations: VANETs: vehicular ad hoc networks; IVC: inter-vehicular communication; NC-WTM: node-centric weight-based trust management algorithm; WTM: weight-based trust management algorithm; RPRep: robust and privacy-preserving reputation management scheme; ART: attack-resistant trust management scheme; MANET: mobile ad hoc network; DSRC: dedicated short-range communication; WAVE: wireless access in vehicular environment; IVC: inter-vehicular communication; I2V: infrastructure-to-vehicle; V2I: vehicle-to-infrastructure; V2V: vehicle-to-vehicle; TA: trust authority; RSU: road side unit; OBU: on-board unit; GPS: global positioning system; WSN: wireless sensor network; VASNETs: vehicular sensor networks; CCW: cooperative collision warning; BMA: bad mouth attack; TDMA: time division multiple access; GDVAN: greedy detection for VANETs; SMTS: spider monkey time synchronization; SVM: support vector machine; DST: Dempster-Shafer theory of evidence; TA: trust authority; PCA: puzzle-based co-authentication; VLC: visible light communication; NE: Nash equilibrium; RTB: request-to-broadcast; CTB: clear-to-broadcast; RREQ: route request message; RREP: route reply; DDR: data disseminate ratio; Dir: direct trust; IDir: indirect trust; TCE: trust computation error; PDR: packet delivery ratio…”
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