Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia by Ayesh Dushmantha, Ruixuan Zhang, Yilin Gui, Jinjiang Zhong, Chaminda Gallage

“…Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, K-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. …”
Get full text

Differentiation of relapsing-remitting and secondary progressive multiple sclerosis: a magnetic resonance spectroscopy study based on machine learning by Ziya EKŞİ, Murat ÇAKIROĞLU, Cemil ÖZ, Ayse ARALAŞMAK, Hasan Hüseyin KARADELİ, Muhammed Emin ÖZCAN

“…Lastly, binary classifications (healthy controls-RRMS and RRMS-SPMS) were carried out according to features obtained by the Support Vector Machine algorithm. Results: RRMS cases were differentiated from healthy controls with 85% accuracy, 90.91% sensitivity, and 77.78% specificity. …”
Get full text

ANN-SVM-IP: An Innovative Method for Rapidly and Efficiently Detecting and Classifying of External Defects of Apple Fruits by Nashaat M. Hussain Hassan, Mohamed M. Hassan Mahmoud, Mohamed A. Ismeil, M. Mourad Mabrook, A. A. Donkol, A. M. Mabrouk

“…The second phase is designed to accurately and effectively classify five Apple fruit defects (Healthy, Full-Damage, Bloch, Rot, and Scab) an optimized ML (Machine Learning) algorithm, which relied combining ANN (Artificial neural network) and SVM (Support Vector Machine) techniques. …”
Get full text

Development of hybrid robust model based on computational modeling and machine learning for analysis of drug sorption onto porous adsorbents by S. Tasqeeruddin, Shaheen Sultana, Abdulrhman Alsayari

“…., Kernel Ridge Regression (KRR), nu-Support Vector Regression ( $$\:{\upnu\:}$$ -SVR), and Polynomial Regression (PR) for the purpose of forecasting the concentration (C) of a drug within a specified environment, relying on the coordinates (x and y). …”
Get full text

Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response by Sha-Zhou Li, Hai-Ying Sun, Yuan Tian, Liu-Qing Zhou, Tao Zhou

“…Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.MethodTo develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. …”
Get full text

Predictive modeling of ultimate tensile strength in dissimilar friction stir welded aluminum alloys via machine learning approach by Meghavath Mothilal, Atul Kumar

“…The purpose of this study is to evaluate the effectiveness of various machine learning algorithms in predicting the ultimate tensile strength (UTS) of friction stir welded joints. …”
Get full text

A novel machine learning architecture to improve classification of intermediate cases in health: workflow and case study for public health by Bassel Hammoud, Aline Semaan, Lenka Benova, Imad H. Elhajj

“…Its effectiveness is tested, using 8 ML algorithms (Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Random Forest, XGBoost, CatBoost, and Artificial Neural Network) to predict the feeling of protection among healthcare workers during the COVID-19 pandemic, based on a global online survey, then validated on two other outputs. …”
Get full text

Edge vs. Cloud: Empirical Insights into Data-Driven Condition Monitoring by Chikumbutso Christopher Walani, Wesley Doorsamy

“…The tested induction machine fault diagnosis models are developed using popular algorithms, namely support vector machines, k-nearest neighbours, and decision trees. …”
Get full text

Machine learning for predicting metabolic-associated fatty liver disease including NHHR: a cross-sectional NHANES study. by Liyu Lin, Yirui Xie, Zhuangteng Lin, Cuiyan Lin, Yichun Yang

“…Finally, a metabolic - associated fatty liver disease (MAFLD) prediction model was developed using seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and logistic regression. …”
Get full text

Construction and evaluation of a machine learning-based predictive model for enteral nutrition feeding intolerance risk in ICU patients by Gaimei Wang, Cendi Lu, Owusu Mensah Solomon, Yujia Gu, Yijing Ling, Fanchi Xu, Yumin Tao, Yehong Wei

“…The patients were randomly divided into a training set and a test set in an 8:2 ratio. Three machine learning algorithms—logistic regression (LR), support vector machine (SVM), and random forest (RF)—were used to construct the risk prediction model for ENFI in ICU patients. …”
Get full text

Modeling sunflower yield and soil water–salt dynamics with combined fertilizers and irrigation in saline soils using APSIM and deep learning by Qingfeng Miao, Dandan Yu, Haibin Shi, Zhuangzhuang Feng, Weiying Feng, Zhen Li, José Manuel Gonçalves, Isabel Maria Duarte, Yuxin Li

“…Based on sunflower field experiments, four machine learning models (regression trees, random forest, support vector machines, and XGBoost) and two deep learning models (deep neural networks and neural networks) were developed to predict soil salinity. …”
Get full text

Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome by Weirong Xue, Bing Xu, Hui Wang, Xiaoxiao Zhu, Jiajia Qin, Guangshuang Zhou, Peilin Yu, Shengli Li, Yingliang Jin

“…When benchmarked against classical machine learning models, our proposed MKSVRB (Multi-Kernel Support Vector Machine Model for Three-Year Recurrence Prediction of Budd-Chiari Syndrome) demonstrated superior performance. …”
Get full text

A Synergistic Framework for Coupling Crop Growth, Radiative Transfer, and Machine Learning to Estimate Wheat Crop Traits in Pakistan by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Aamir Ali, Syed Roshaan Ali Shah, Cheng Jiang, Zhongqi Ma, Kang Sun, Hongzhi Jiang

“…PROSAIL-HLS has an RMSE of 0.67 for leaf area index (LAI), 5.66 µg/cm² for chlorophyll ab (Cab), 0.0003 g/cm² for dry matter content (Cm), and 0.002 g/cm² for leaf water content (Cw) against the HLS only, with an RMSE of 0.40 for LAI, 3.28 µg/cm² for Cab, 0.0002 g/cm² for Cm, and 0.001 g/cm² for Cw. Optimized machine learning models, namely Extreme Gradient Boost (XGBoost) for LAI, Support Vector Machine (SVM) for Cab, and Random Forest (RF) for Cm and Cw, were deployed for temporal mapping of traits to be used for wheat productivity enhancement.…”
Get full text

Integration of machine learning and bulk sequencing revealed exosome-related gene FOSB was involved in the progression of abdominal aortic aneurysm by Xianlu Ma, Xianlu Ma, Hongjie Zhou, Hongjie Zhou, Ren Wang, Ren Wang

“…However, the key genes involved in the occurrence and progression of AAA remains unclear.MethodsWe applied Weighted Gene Co-expression Network Analysis (WGCNA) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) to screen for significant genes from the Gene Expression Omnibus (GEO) dataset. …”
Get full text

EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer by Shuangyang Mo, Shuangyang Mo, Nan Yi, Fengyan Qin, Huaying Zhao, Huaying Zhao, Yingwei Wang, Haiyan Qin, Haixiao Wei, Haixing Jiang, Shanyu Qin

“…Among the six radiomic models, the support vector machine (SVM) model had the highest performance with AUCs of 0.853 in the training cohort and 0.755 in the test cohort. …”
Get full text

CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses by Qian L, Fu B, He H, Liu S, Lu R

“…Radiomic features were extracted from the arterial, venous and delayed phases and further analysed by dimensionality reduction and selection. Four mainstream machine learning algorithm training models, namely, support vector machine (SVM), k-nearest neighbour (kNN), light gradient boosting (LightGBM) and logistic regression (LR), were constructed to determine the best classifier model. …”
Get full text

Real-Time Detection and Localization of Force on a Capacitive Elastomeric Sensor Array Using Image Processing and Machine Learning by Peter Werner Egger, Gidugu Lakshmi Srinivas, Mathias Brandstötter

“…A Python-based processing pipeline filters and visualizes the data with real-time clustering and adaptive thresholding. Machine learning models such as linear regression, Support Vector Machine, decision tree, and Gaussian Process Regression were evaluated to correlate force with capacitance values. …”
Get full text

Geoinformatics and Machine Learning for Shoreline Change Monitoring: A 35-Year Analysis of Coastal Erosion in the Upper Gulf of Thailand by Chakrit Chawalit, Wuttichai Boonpook, Asamaporn Sitthi, Kritanai Torsri, Daroonwan Kamthonkiat, Yumin Tan, Apised Suwansaard, Attawut Nardkulpat

“…This study analyzes 35 years (1988–2023) of shoreline changes using geoinformatics, machine learning algorithms (Random Forest, Support Vector Machine, Maximum Likelihood, Minimum Distance), and the Digital Shoreline Analysis System (DSAS). …”
Get full text

Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…Once the model fine-tunes the eGFR estimations, it feeds them into various algorithms for CKD stage classification, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). …”
Get full text

Machine Learning Performance Analysis for Bagging System Improvement: Key Factors, Model Optimization, and Loss Reduction in the Fertilizer Industry by Ari Primantara, Udisubakti Ciptomulyono, Berlian Al Kindhi

“…Four algorithms were used: an Artificial Neural Network (ANN), Random Forest Regression (RFR), Linear Regression (LR), and Support Vector Regression (SVR). …”
Get full text