Ensemble Learning-Based Metamodel for Enhanced Surface Roughness Prediction in Polymeric Machining by Elango Natarajan, Manickam Ramasamy, Sangeetha Elango, Karthikeyan Mohanraj, Chun Kit Ang, Ali Khalfallah

Get full text

Development of a Student Depression Prediction Model Based on Machine Learning with Algorithm Performance Evaluation by Penni Wintasari Simarmata, Putri Taqwa Prasetyaningrum

Get full text

Research on prediction model of adolescent suicide and self-injury behavior based on machine learning algorithm by Yao Gan, Li Kuang, Xiao-Ming Xu, Ming Ai, Jing-Lan He, Wo Wang, Su Hong, Jian mei Chen, Jun Cao, Qi Zhang

“…Performance of the random forest, multi-level perceptron, and extreme gradient models was acceptable, while the K-nearest neighbor algorithm and support vector machine performed poorly.ConclusionThe detection rate of suicidal and self-injurious behaviors is higher in women than in men. …”
Get full text

Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study by Mahmoud B Almadhoun, MA Burhanuddin

“…MethodsMultiple ML models are evaluated in this study, including random forest, extreme gradient boosting (XGBoost), support vector machine (SVM), and k. ResultsA cross-validated ROC-AUC (receiver operating characteristic area under the curve) score of 0.9117 highlighted the robustness of random forest in generalizing across datasets among the models tested. …”
Get full text

A mathematical PAPR estimation of OTFS network using a machine learning SVM algorithm by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…The article presents a Support Vector Machine (SVM) algorithm to lower the peak-to-average power ratio (PAPR) in networks that work in orthogonal time frequency space (OTFS). …”
Get full text

Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…The performance of the models and data quality improved when emphasizing the impact of feature selection and data pre-processing approaches. Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). …”
Get full text

Anomaly-Based Intrusion Detection System in Wireless Sensor Networks Using Machine Learning Algorithms by Belal Al-Fuhaidi, Zainab Farae, Farouk Al-Fahaidy, Gawed Nagi, Abdullatif Ghallab, Abdu Alameri

“…This model applied mutual information (MI) for feature selection and the synthetic minority oversampling technique (SMOTE) for solving the imbalanced dataset problem. It used different machine learning (ML) algorithms, random forest (RF), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNNs) to analyze network traffic and binary classification or multiclass classification. …”
Get full text

The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports. by Guomei Cui, Chuanjun Wang

“…Moreover, the superiority of the model is verified by comparing with the commonly used algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). …”
Get full text

A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection by Ahmed Adil Nafea, Manar AL-Mahdawi, Khattab M Ali Alheeti, Mustafa S. Ibrahim Alsumaidaie, Mohammed M AL-Ani

“…This study introduces an approach that utilizes 1D CNN as feature extraction and employs machine learning (ML) algorithms such as XGBoost, random forests (RF), decision trees (DT) support vector machines (SVM) and k nearest neighbor (KNN) to classify samples as either benign or malignant aiming to enhance accuracy. …”
Get full text

Machine learning algorithms to predict stroke in China based on causal inference of time series analysis by Qizhi Zheng, Ayang Zhao, Xinzhu Wang, Yanhong Bai, Zikun Wang, Xiuying Wang, Xianzhang Zeng, Guanghui Dong

“…Multiple classic classification algorithms were compared, including Random Forest, Logistic Regression, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi-Layer Perceptron (MLP). …”
Get full text

Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications by Ashfakul Karim Kausik, Adib Bin Rashid, Ramisha Fariha Baki, Md Mifthahul Jannat Maktum

“…However, selecting the most effective ML model remains challenging due to performance variability, scalability constraints, and inconsistent evaluation metrics across manufacturing sectors. This systematic review analyzes over 300 peer-reviewed studies over the last two decades (mostly analyzing the recent works) to evaluate the effectiveness of widely used ML algorithms—Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forests (RFs), Decision Trees (DTs), and K-Nearest Neighbors (KNN)—in QA applications. …”
Get full text

The Use of Machine Learning Algorithms for Water Quality Index Prediction in the Sai Gon River, Vietnam by Thuy Nguyen Thi Diem, Mai Nguyen Thi Huynh, Tra Tran Quang

“…The present study leverages the predictive performance of several ML algorithms, including extreme gradient boosting (XGB), the gradient boosting model (GBM), support vector regression (SVR), and the radial basic function (RBF), to predict the WQI at three monitoring sites on the Sai Gon River from 2015–2019. …”
Get full text

Biomarker identification and gene-drug interaction prediction for breast cancer using machine learning algorithms by Raja Abhavya, Pragya Pragya, Sabitha R., Kumar Brijesh, Agastinose Ronickom Jac Fredo

“…Initially, RNA-sequencing data of normal and malignant BC tissues publicly available in the NCBI GEO database were pre-processed using a standard pipeline. Further, machine learning algorithms, such as logistic regression, support vector machine, and random forest, were used to identify the differentially expressed genes (DEGs). …”
Get full text

Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…Abstract This research examines the application of eight different machine learning (ML) algorithms for predicting the compressive strength of high-performance concrete (HPC). …”
Get full text

Machine learning algorithms to predict depression in older adults in China: a cross-sectional study by Yan Li Qing Song, Lin Chen, Haoqiang Liu, Yue Liu

“…Thereafter, the dataset was classified into training and testing sets at a 6:4 ratio. Six ML algorithms, namely, logistic regression, k-nearest neighbors, support vector machine, decision tree, LightGBM, and random forest, were used in constructing a predictive model for depression among the older adult. …”
Get full text

Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms by Nikolaos Servos, Xiaodi Liu, Michael Teucke, Michael Freitag

“…Despite that, only a minority of recent publications use ML for travel time prediction in multimodal transports. We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times. …”
Get full text

Evaluating Label Encoding and Preprocessing Techniques for Breast Cancer Prediction Using Machine Learning Algorithms by Mukesh Kumar, Vivek Bhardwaj

Get full text

Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adil Noor

Get full text

Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics by Franck Morais de Oliveira, Patrícia Ferreira Ponciano Ferraz, Gabriel Araújo e Silva Ferraz, Marcos Neves Pereira, Matteo Barbari, Giuseppe Rossi

Get full text

Miniaturized NIRS Coupled with Machine Learning Algorithm for Noninvasively Quantifying Gluten Quality in Wheat Flour by Yuling Wang, Chen Zhang, Xinhua Li, Longzhu Xing, Mengchao Lv, Hongju He, Leiqing Pan, Xingqi Ou

“…Five different algorithms were employed to mine the relationship between the full-range spectra (900–1700 nm) and three parameters, with support vector regression (SVR) demonstrating the best prediction performance for all gluten parameters (R_P = 0.9370–0.9430, RMSEP = 0.3450–0.4043%, and RPD = 3.1348–3.4998). …”
Get full text