Phenology-Aware Machine Learning Framework for Chlorophyll Estimation in Cotton Using Hyperspectral Reflectance by Chunbo Jiang, Yi Cheng, Yongfu Li, Lei Peng, Gangshang Dong, Ning Lai, Qinglong Geng

“…Five regression approaches were evaluated, including univariate and multivariate linear models, along with three machine learning algorithms: Random Forest, K-Nearest Neighbor, and Support Vector Regression. …”
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Enhancing Accessibility Through Machine Learning: A Review on Visual and Hearing Impairment Technologies by Pal Patel, Shreyansh Pampaniya, Ananya Ghosh, Ritu Raj, Deepa K, Saravanakumar Kandasamy

“…For hearing impairments, the analysis focuses on advanced models such as Support Vector Machines (SVM), Random Forests (RF), and Multi-Layer Perceptrons (MLP), examining their effectiveness in auditory assistive applications. …”
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Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward by Sudeep Tanwar, Qasim Bhatia, Pruthvi Patel, Aparna Kumari, Pradeep Kumar Singh, Wei-Chiang Hong

“…There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. …”
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A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities by José Luis Uc Castillo, Ana Elizabeth Marín Celestino, Diego Armando Martínez Cruz, José Tuxpan Vargas, José Alfredo Ramos Leal, Janete Morán Ramírez

“…It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). It identified that the selection and application of the algorithms rely on the study objective and the data patterns. …”
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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

“…We tested the classification performances of a support vector machine (SVM) and a random forest classifier (RFC), as representatives of ML algorithms, and two versions of the multi-layer perceptron (MLPC) as representatives of deep learning (DL). …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…In one experiment, Alkhatib et al. used Convolutional neural network (CNN) based deep trackers to track the median and sciatic nerve with a surprising accuracy of 0.87.2 Another study employed the same CNN model to locate and discriminate accurate images of sacrum, vertebral levels and intervertebral gaps during percutaneous spinal needle insertion.3 Another study used a different AI model called SVM (support vector machine) classification, image processing, and template matching to locate lumbar level L3-L4 and the ideal puncture site for epidural anaesthesia in real-time. …”
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Machine learning-based identification of exosome-related biomarkers and drugs prediction in nasopharyngeal carcinoma by Zhengyu Wei, Guoli Wang, Yanghao Hu, Chongchang Zhou, Yuna Zhang, Yi Shen, Yaowen Wang

“…The least absolute shrinkage and selection operator regression, support vector machine, and random forest approaches were utilized to develop NPC diagnostic model. …”
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Explainable machine learning model for predicting compressive strength of CO2-cured concrete by Jia Chu, Bingbing Guo, Taotao Zhong, Qinghao Guan, Yan Wang, Ditao Niu

“…A comprehensive database comprising 198 datasets was collected from published experimental investigations, and four ML algorithms were employed, i.e., RF (random forest), SVR (support vector regression), GBRT (gradient boosting regression tree), and XGB (extreme gradient boosting). …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…Six advanced regression-based algorithms, including support vector regression (SVR), Gaussian process regression (GPR), random forest regression (RFR), extreme gradient boosting regression (XGBR), artificial neural networks (ANN), and K-nearest neighbors (KNN), were benchmarked through rigorous model validation processes including hold-out testing, K-fold cross-validation, sensitivity analysis, and external validation with unseen experimental data. …”
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Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology by Raoof Nopour

“…Materials and methods In this retrospective study, we used 1156 CRC patients referred to the Masoud internal clinic in Tehran City from January 2017 to December 2023. The chosen machine learning algorithms, including LightGBM, XG-Boost, random forest, artificial neural network, support vector machine, decision tree, K-Nearest Neighbor and logistic regression, were utilized to establish prediction models for predicting metastasis among colorectal cancer patients. …”
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An interpretable machine learning approach for predicting and grading hip osteoarthritis using gait analysis by Qing Yang, Xinyu Ji, Yuyan Zhang, Shaoyi Du, Bing Ji, Wei Zeng

“…Afterwards, the Shapley Additive exPlanations (SHAP) method is applied for feature selection and dimensionality reduction, providing detailed explanations of each feature’s contribution to classification performance. Second, a support vector machine (SVM) is used to classify gait patterns between unilateral hip OA patients and HCs. …”
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Effect of phosphorus fractions on benthic chlorophyll-a: Insight from the machine learning models by Yuting Wang, Sangar Khan, Zongwei Lin, Xinxin Qi, Kamel M. Eltohamy, Collins Oduro, Chao Gao, Paul J. Milham, Naicheng Wu

“…Additionally, support vector regression (SVR) was used to predict chl-a concentrations across upstream, midstream, and downstream sections. …”
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The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma by Binyu Wang, Di Liu, Danfei Shi, Xinmin Li, Yong Li

“…To identify critical biomarkers, machine learning algorithms including Random Forest, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Support Vector Machine (SVM) were employed. …”
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Non-invasive detection of Parkinson’s disease based on speech analysis and interpretable machine learning by Huanqing Xu, Wei Xie, Mingzhen Pang, Ya Li, Luhua Jin, Fangliang Huang, Xian Shao

“…To address class imbalance, synthetic minority oversampling technique (SMOTE) was applied. Several machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forests, and Neural Networks, were implemented and evaluated. …”
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Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis by Hamed Hajishah, Danial Kazemi, Ehsan Safaee, Mohammad Javad Amini, Maral Peisepar, Mohammad Mahdi Tanhapour, Arian Tavasol

“…The neural network model achieved the highest overall AUC for mortality prediction (0.808), while the support vector machine performed best for readmission prediction (AUC 0.733). …”
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Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction by Shuo Zhang, Biao Chen, Chaoyang Chen, Maximillian Hovorka, Jin Qi, Jie Hu, Gui Yin, Marie Acosta, Ruby Bautista, Hussein F. Darwiche, Bryan E. Little, Carlos Palacio, John Hovorka

“…Seven healthy subjects were recruited with their EMG signals collected from eight muscles of the lower limbs during walking with normal and abnormal gaits. Four basic ML algorithms including support vector machine (SVM), K-nearest neighbor (kNN), decision tree (DT), and naive Bayes (NB), and five deep learning models including convolutional neural network (CNN), long-short term memory (LSTM), bidirectional long short-term memory (BiLSTM), and CNN-BiLSTM were used to process the EMG signals recorded under different gaits. …”
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Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach by Zhengyuan Su, Huadong Jiang, Ying Yang, Xiangqing Hou, Yanli Su, Li Yang

“…Basic acoustic features (eg, Mel Frequency Cepstral Coefficients, formant frequencies, jitter, shimmer) and high-level statistical function (HSF) features (using OpenSMILE [Open-Source Speech and Music Interpretation by Large-Space Extraction] with the ComParE 2016 configuration) were extracted. Four supervised machine learning algorithms (logistic regression, support vector machine, random forest, and extreme gradient boosting) were trained and evaluated using grouped 5-fold cross-validation and a test set, with performance metrics, including accuracy, F1-score, recall, and false negative rate. …”
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Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features by You Wu, Ke Tang, Chunzheng Wang, Hao Song, Fanfan Zhou, Ying Guo

“…In this study, by integrating cellular transcriptome and cell viability data using four machine learning algorithms (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and two ensemble algorithms (voting and stacking), highly accurate prediction models of 50% and 80% cell viability were developed with area under the receiver operating characteristic curve (AUROC) of 0.90 and 0.84, respectively; these models also showed good performance when utilized for diverse cell lines. …”
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Construction and interpretation of tobacco leaf position discrimination model based on interpretable machine learning by Ranran Kou, Cong Wang, Jinxia Liu, Ran Wan, Zhe Jin, Le Zhao, Youjie Liu, Junwei Guo, Feng Li, Hongbo Wang, Song Yang, Cong Nie

“…Based on the 70 tobacco leaf chemical components obtained using near-infrared rapid analysis technology, tobacco leaf position discrimination models were built using Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Random Forest (RF). …”
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Classification of chest radiographs into healthy/pneumonia using Harris-Hawks Algorithm optimized deep-features by K. Vijayakumar, Mohammad Nazmul Hasan Maziz, Swaetha Ramadasan, Seifedine Kadry, S. Arunmozhi

“…This work provided an accuracy of 99.3750% during healthy/pneumonia detection with FFV and Support Vector Machine (SVM), and detection accuracy of 88.5417% during viral/bacterial pneumonia detection with FFV and SVM.…”
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