Predicting response to anti-VEGF therapy in neovascular age-related macular degeneration using random forest and SHAP algorithms by Peng Zhang, Jialiang Duan, Caixia Wang, Xuejing Li, Jing Su, Qingli Shang

“…Methods: In this retrospective study, we extracted data including demographic characteristics, laboratory test results, and imaging features from optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA). Eight machine learning methods, including Logistic Regression, Gradient Boosting Decision Tree, Random Forest, CatBoost, Support Vector Machine, XGboost, LightGBM, K Nearest Neighbors were employed to develop the predictive model. …”
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Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy by Qingsong Chen, Tao Li, Tao Zhang, Yue Zhou, Weifeng Huang, Hui Li, Li Shi, Jianxiao Li, Qi Zhang, Man Ma, Pan Wang, Hui Hu, Gongbin Wei, Jiangxia Xiang, Yuan Cheng, Jun Yang, Guangbin Huang, Yongming Li, Dingyuan Du

“…This approach included principal component analysis, differential gene expression analysis using DESeq2, Gene Set Enrichment Analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and machine learning (ML) algorithms (support vector machine-recursive feature elimination, least absolute shrinkage and selection operator, and random forest) for feature gene identification. …”
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Comparing supervised classification algorithm–feature combinations for Spartina alterniflora extraction: a case study in Zhanjiang, China by Qiujie Chen, Qiujie Chen, Chunyan Shen, Hong Du, Danling Tang

“…Here, we combined five supervised classification algorithms—random forest (RF), support vector machine, maximum likelihood classification (MLC), minimum distance classification, and Mahalanobis distance classification—with spectral bands, spectral indices, and the gray-level co-occurrence matrix (GLCM) derived from Sentinel-2 imagery to identify the optimal combination for monitoring the spatial distribution of S. alterniflora on Donghai Island, Zhanjiang. …”
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A Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches by Sam Ansari, Soliman Mahmoud, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny

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A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

“…This study aims to compare the performance of six classification algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) in detecting fall incidents using wearable sensor data such as accelerometers and gyroscopes. …”
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Extreme high accuracy prediction and design of Fe-C-Cr-Mn-Si steel using machine learning by Hao Wu, Jianyuan Zhang, Jintao Zhang, Chengjie Ge, Lu Ren, Xinkun Suo

“…The results show that the trained support vector regression (SVR) model demonstrated the highest prediction precision for microhardness. …”
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Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at the herd level using mid-infrared spectral information f... by A. Birkinshaw, M. Sutter, M. Nussbaum, M. Kreuzer, B. Reidy

“…Three of these models have not yet been tested for herd-level dietary proportion classification, and all 4 follow completely different approaches: least absolute shrinkage and selection operator (LASSO), partial least squares discriminant analysis (PLS-DA), random forest (RF), and support vector machines (SVM). Seasonality has been a missing element from previous dietary herbage proportion classification models. …”
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A HYBRID APPROACH FOR MALARIA CLASSIFICATION USING CNN-BASED FEATURE EXTRACTION AND TRADITIONAL MACHINE LEARNING CLASSIFIERS by omar Mohammed Alzakholi, Walat A. Ahmed, Bafreen N. Mohammed, Asaad Kh. Ibrahim

“…For our study, we utilize VGG16 CNN with a weight pre-trained on ImageNet to extract the features from non-infected and infected blood cell images from malaria. Five classical machine learning algorithms, such as Random Forest, Logistic Regression, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), & Gradient Boosting, are used to classify the extracted features. …”
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Enhancing revenue generation in Bangladesh’s FinTech sector: a comprehensive analysis of real-time predictive customer behavior modeling in AWS using a hybrid OptiBoost-EnsembleX m... by Avijit Chowdhury

“…The investigation utilized a range of machine learning algorithms, such as random forest, support vector machine (SVM), XGBoost, CatBoost, and LightGBM, to develop models. …”
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Optimizing machine learning methods for groundwater quality prediction: Case study in District Bagh, Azad Kashmir, Pakistan by Usman Basharat, Wenjing Zhang, Cuihong Han, Shoukat Husain Khan, Arshad Abbasi, Sehrish Mahroof, Shuxin Li

“…The aim was to establish a reliable method for predicting groundwater quality classification. Six supervised machine learning classifiers were utilized, namely Logistic Regression (LR), K-Nearest Neighbours (KNN), Decision Trees (DT), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB). …”
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Non-Destructive Methods Based on Machine Learning for the Prediction of Sweet Potato Leaf Area: A Comparative Approach by Joao Everthon Da Silva Ribeiro, Ester Dos Santos Coelho, Antonio Gideilson Correia Da Silva, Pablo Henrique De Almeida Oliveira, Elania Freire Da Silva, Gisele Lopes Dos Santos, Anna Kezia Soares De Oliveira, John Victor Lucas Lima, Walter Esfrain Pereira, Lindomar Maria Da Silveira, Aurelio Paes Barros

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Prediction of R1234yf flow boiling behavior in horizontal, vertical, and inclined tubes using machine learning techniques by Farzaneh Abolhasani, Behrang Sajadi, Mohammad Ali Akhavan-Behabadi

“…In the present study, the utilization of machine learning algorithms (MLAs) is proposed for the prediction of the heat transfer coefficient and pressure drop in horizontal, vertical, and inclined tubes during flow boiling of R1234yf. …”
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ML-based approach to potato diseases diagnosis using image processing and whale optimization algorithm for feature selection by Zhongxu Li, Seyed Mohamad Javidan

“…After extracting texture, color, and shape features using GLCM, Color Moments, and Zernike moments, Support Vector Machine (SVM) was utilized for classification. …”
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Predicting student retention: A comparative study of machine learning approach utilizing sociodemographic and academic factors by Reymark D. Deleña, Norniña J. Dia, Redeemtor R. Sacayan, Joseph C. Sieras, Suhaina A. Khalid, Amer Hussien T. Macatotong, Sacaria B. Gulam

“…A total of 482 student records and 146 variables were preprocessed using Power BI and prepared via the CRISP-DM methodology before being modeled in Jupyter Notebook. Ten ML algorithms such asExtreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) were evaluated using both train-test split and 5-fold cross-validation to ensure robustness and generalizability. …”
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Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR by Sackdavong Mangkhaseum, Yogesh Bhattarai, Sunil Duwal, Akitoshi Hanazawa

“…This study utilizes remote sensing geo-datasets and machine learning algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, and Long Short-Term Memory) to generate comprehensive flood susceptibility maps. …”
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A simulation-driven computational framework for adaptive energy-efficient optimization in machine learning-based intrusion detection systems by Ripal Ranpara, Osamah Alsalman, Om Prakash Kumar, Shobhit K. Patel

“…In the proposed research paper study, by integrating advanced machine learning techniques such as random forest classifier and support vector machines classifier with knowledge distillation and adaptive energy-aware optimization, GreenMU achieves a balanced trade-off between computational efficiency and cybersecurity accuracy. …”
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Groundwater estimation and determination of its probable recharge source in the Lower Swat District, Khyber Pakhtunkhwa, Pakistan, using analytical data and multiple machine learni... by Imran Ahmad, Ibrar Ul Haq, Mansoor Ahmad, Iram Gul, Mursaleen Khan, Khushnuma Khushnuma, Ubaid Ullah, Maqsood Ur Rehman, Mohamed Metwaly

“…The study applied six ML models, including random forest, support vector machine (SVM), and ridge Regression, to predict groundwater zones, with random forest achieving the highest accuracy (R2 = 0.95, root mean square error (RMSE) = 8.49, and mean absolute error (MAE) = 4.03), followed by decision tree (R2 = 0.93). …”
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AI-enhanced automation of building energy optimization using a hybrid stacked model and genetic algorithms: Experiments with seven machine learning techniques and a deep neural net... by Mohammad H. Mehraban, Samad ME Sepasgozar, Alireza Ghomimoghadam, Behrouz Zafari

“…This approach balances multiple objectives, such as energy consumption and thermal comfort, to streamline the identification of optimal building configurations. Seven machine learning (ML) models, including Linear Regression (LR), Decision Trees (DT), Random Forest Regressor (RFR), Gradient Boosting Machines (GBM), Support Vector Regressor (SVR), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGB), and a deep Feedforward Neural Network (FNN) are developed and assessed in predicting three key performance metrics: Energy Use Intensity (EUI), Predicted Percentage Dissatisfied (PPD), and Heating Load.A hybrid stacked model, combining FNN with XGB, using GBM meta learner, emerged as the top performer, achieving an impressive Coefficient of Determination (R²) of 0.99 and Mean Absolute Percentage Error (MAPE) of 0.02 across all targets. …”
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A web-based tool for predicting gastric ulcers in Chinese elderly adults based on machine learning algorithms and noninvasive predictors: A national cross-sectional and cohort stud... by Xingjian Xiao, Xiaohan Yi, Zumin Shi, Zongyuan Ge, Hualing Song, Hailei Zhao, Tiantian Liang, Xinming Yang, Suxian Liu, Bo Sun, Xianglong Xu

“…Results Noninvasive predictors such as demographic, behavioral, nutritional, and physical examination factors were utilized to predict the current and future occurrence of gastric ulcers. In our study, Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LGBM) achieved an accuracy of 0.97 for predicting gastric ulcers over seven years; Logistic Regression, Adaptive Boosting, SVM, RF, Gradient Boosting Machine, LGBM, and K-Nearest Neighbors reached 0.98 for three-year predictions; and SVM, Extreme Gradient Boosting, RF, and LGBM attained 0.95 for current gastric ulcer prediction. …”
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The Application of Machine Learning Algorithms to Predict HIV Testing Using Evidence from the 2002–2017 South African Adult Population-Based Surveys: An HIV Testing Predictive Mode... by Musa Jaiteh, Edith Phalane, Yegnanew A. Shiferaw, Haruna Jallow, Refilwe Nancy Phaswana-Mafuya

“…The study employed four SML algorithms, namely, decision trees, random forest, support vector machines (SVM), and logistic regression, across the five cross-sectional cycles of the South African National HIV Prevalence, Incidence, and Behavior and Communication Survey (SABSSM) datasets. …”
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