Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection by Ruo-Fei Xu, Zhen-Jing Liu, Shunan Ouyang, Qin Dong, Wen-Jing Yan, Dong-Wu Xu

“…Model performance was systematically evaluated through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. …”
Get full text

Among Artificial Intelligence/Machine Learning Methods, Automated Gradient-Boosting Models Accurately Score Intraoral Plaque in Non-Standardized Images by Eric Coy, William Santo, Bonnie Jue, Helen Betts, Francisco Ramos-Gomez, Stuart A. Gansky

“…Average and dominant hue, saturation, and brightness values were features for training plaque-scoring algorithms.Results Best performing models were: Support Vector Machine-Gaussian for image selection, 5-CV AUC-ROC of 0.99 and 0.76s of training time; Gradient-Boosting classification and regression models for individual teeth (5-CV AUC-ROC of 0.99 with 105s training); and mean plaque-scoring algorithms (5-CV R2 of 0.72 with 1415s training).Conclusions Accurate automated plaque-scoring is attainable without the high computational and financial costs of deep learning (DL) models. …”
Get full text

Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N₂O Emissions in China by Yuan Zeng, Sujuan Chen, Yunpeng Li, Li Xiong, Cheng Liu, Muhammad Azeem, Xiaoting Jie, Mei Chen, Longjiang Zhang, Jianfei Sun

“…The artificial neural network (ANN) model outperformed random forest (RF) and support vector machine (SVM) in predicting N₂O emissions (R²: 0.99; EF: 0.99), while all models showed high accuracy for crop yields (R², EF: 0.98–0.99). …”
Get full text

Factors influencing the response to periodontal therapy in patients with diabetes: post hoc analysis of a randomized clinical trial using machine learning by Nidia CASTRO DOS SANTOS, Arthur MANGUSSI, Tiago RIBEIRO, Rafael Nascimento de Brito SILVA, Mauro Pedrine SANTAMARIA, Magda FERES, Thomas VAN DYKE, Ana Carolina LORENA

“…We tested seven different algorithms: K-Nearest Neighbors, Decision Tree, Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Logistic Regression. …”
Get full text

Human footprint with machine learning identifies risks of the invasive weed Conyza sumatrensis across land-use types under climate change by Hua Cheng, Kasper Johansen, Baocheng Jin, Shiqin Xu, Xuechun Zhao, Liqin Han, Matthew F. McCabe

“…This study compares the predictive performance of 10 machine learning algorithms, including random forests, maximum entropy, support vector machines, and others, by integrating global occurrence records with climatic, edaphic, and human activity variables to identify the most robust model for predicting the global distribution of the invasive weed, Conyza sumatrensis (Retz.) …”
Get full text

Research on civil aircraft cockpit display interface availability considering multidimensional indicators clustering and reduction by CHEN Dengkai, XIAO Yao, XIAO Jianghao, ZHOU Yao, YANG Cong

“…Finally, the support vector machine(SVM) classification model was employed to verify performance and reliability of both algorithms. …”
Get full text

Machine learning approach effectively discriminates between Parkinson’s disease and progressive supranuclear palsy: Multi-level indices of rs-fMRI by Weiling Cheng, Xiao Liang, Wei Zeng, Jiali Guo, Zhibiao Yin, Jiankun Dai, Daojun Hong, Fuqing Zhou, Fangjun Li, Xin Fang

“…Results: The classification performance of logistic regression (LR) and support vector machine (SVM) models, based on multiple index combinations, was significantly superior to that of other machine learning models and combinations when utilizing automatic anatomical labeling (AAL) templates. …”
Get full text

Monitoring and Assessment of Slope Hazards Susceptibility Around Sarez Lake in the Pamir by Integrating Small Baseline Subset InSAR with an Improved SVM Algorithm by Yang Yu, Changming Zhu, Majid Gulayozov, Junli Li, Bingqian Chen, Qian Shen, Hao Zhou, Wen Xiao, Jafar Niyazov, Aminjon Gulakhmadov

Get full text

Raising a Child to Live in Society – Personality Traits Parents Develop and Prevent from Developing in their Preschool Children by Agnieszka Szymańska, Elżbieta Aranowska

“…Analyses were carried out using two data mining algorithms: (a) text mining algorithms, (b) support vector machine and (c) social network analysis, and (d) Aranowska's λ judge agreement coefficient.The results revealed that parents of preschool children care mainly about the development of competency traits, especially self-reliance. …”
Get full text

BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes by Thanadol Tuntiwongwat, Sippawit Thammawiset, Thongchai Rohitatisha Srinophakun, Chawalit Ngamcharussrivichai, Somboon Sukpancharoen

“…A methodology involving K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), Random Forest (RF), and CatBoost (CB) algorithms was employed to predict H2 yields in the BCLpro, utilizing 10-fold cross-validation for robust model evaluation. …”
Get full text

Predicting Severe Knee Arthritis Based on Two Inertial Measurement Unit Sensors as a Dynamic Coordinate System Using Classical Machine Learning by Erfan Azizi, Mohammadsadegh Darbankhalesi, Amirhossein Zare, Zahra Sadat Rezaeian, Saeed Kermani

“…These features were employed in four different classifiers: (1) naive Bayes, (2) K-nearest neighbors (KNNs), (3) support vector machine, and (4) random forest. Each classifier was evaluated using the 10-fold cross-validation method (K = 10). …”
Get full text

Fast evaluation on the fatigue level of copper contact wire based on laser induced breakdown spectroscopy and supervised machine learning for high speed railway by Wenfu Wei, Langyu Xia, Zefeng Yang, Huan Zhang, Like Pan, Jian Wu, Guangning Wu

“…Results have shown that the standard normal variable transform–principal component analysis–genetic algorithm improve support vector machine (SNV‐PCA‐GASVM) model have presented a most satisfactory performance than the others. …”
Get full text

Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

“…Saliva samples were subjected to sequencing of the V3–V4 region of the 16S rRNA gene to assess microbial diversity and differential abundance. Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. …”
Get full text

Research on dynamic allocation of network slicing resources based on OS-MBRL by YAN Jiahui, ZHONG Weixuan, DONG Ligang, JIANG Xian, WANG Guangchang, LU Lingrong

“…Considering that traditional modelless reinforcement learning methods require a longer model training time, a dynamic resource allocation method based on OS-MBRL was proposed. The online support vector machines algorithm was utilized to construct a system model that could handle dynamically changing data streams and continuously update the model to adapt to new data, ensuring a lower number of SLA violations when allocating fewer resources. …”
Get full text

AI-Driven predicting and optimizing lignocellulosic sisal fiber-reinforced lightweight foamed concrete: A machine learning and metaheuristic approach for sustainable construction by Mohamed Sahraoui, Aissa Laouissi, Yacine Karmi, Abderazek Hammoudi, Mostefa Hani, Yazid Chetbani, Ahmed Belaadi, Ibrahim M.H. Alshaikh, Djamel Ghernaout

“…Six predictive models were assessed for accuracy and generalization: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), Linear Model (LM), Dragonfly Algorithm-based Deep Neural Network (DNN-DA), and Improved Grey Wolf Optimizer-based Deep Neural Network (DNN-IGWO). …”
Get full text

Intelligent classification models for food products basis on morphological, colour and texture features by Narendra Veernagouda Ganganagowder, Priya Kamath

“…The best prediction accuracy is obtained for the Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Simple Logistic (SLOG) and Sequential Minimal Optimization (SMO) classifiers (more than 80% of the success rate for the training/test set and 80% for the validation set). …”
Get full text

Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals by Liuyi Ling, Zhu Lin, Bin Feng, Liyu Wei, Li Jin, Yiwen Wang

“…The performance of the proposed model was compared with those of traditional backpropagation neural network, support vector machine regression, and random forest models. …”
Get full text

Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters by Luca Quagliato, Jiin Seo, Jiheun Hong, Taeyong Lee, Yoon-Sok Chung

“…To model the association of the bone’s current health status with prevalent FXs, three prediction algorithms—extreme gradient boosting (XGB), support vector machine, and multilayer perceptron—were trained using two-dimensional dual-energy X-ray absorptiometry (2D-DXA) analysis results and subsequently benchmarked. …”
Get full text

Enhancing stone matrix asphalt performance with sugarcane bagasse ash: Mechanical properties and machine learning-based predictions using XGBoost and random forest by Hamed Khani Sanij, Rezvan Babagoli, Reza Mohammadi Elyasi

“…In parallel, the study applied machine learning (ML) models—Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—to predict the mechanical properties of SMA based on input mix parameters. …”
Get full text

Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

“…The applied Machine Learning techniques include Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Multiple Linear Regression (MLR). …”
Get full text