Comparative Analysis of Facial Expression Recognition Methods by Denys - Florin COT

“…The research compares the performance of classical machine learning algorithms (such as K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree, and Random Forest) with the modern deep learning methods (such as Convolutional Neural Networks, Deep Neural Networks, and Recursive Neural Networks) using standardized datasets. …”
Get full text

A web-based prediction model for brain metastasis in non-small cell lung cancer patients by Jianing Chen, Li Wang, Li Liu, Qi Wang, Jing Zhao, Xin Yu, Shiji Zhang, Chunxia Su

“…Additionally, 276 NSCLC patient records from Shanghai Pulmonary Hospital (SPH) were used as an external cohort. Subsequently, seven machine learning models were constructed employing diverse algorithms, namely Logistic Regression (LR), Classification and Regression Tree (CART), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGBOOST). …”
Get full text

AI-Driven Optimization of Breakwater Design: Predicting Wave Reflection and Structural Dimensions by Mohammed Loukili, Soufiane El Moumni, Kamila Kotrasova

“…Two datasets of 32,000 data points were generated for underwater and free-surface breakwaters, with an additional 10,000 data points for validation, totaling 42,000 data points per case. Five ML algorithms—Random Forest, Support Vector Regression, Artificial Neural Network, Decision Tree, and Gaussian Process—were applied and evaluated. …”
Get full text

Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

“…Based on linear regression (LR), support vector machine (SVM), decision tree (DT) and random forest (RF) algorithms, four intelligent prediction models of cross screening rate were established. …”
Get full text

A comprehensive survey on customer churn analysis studies by Maryam Shahabikargar, Amin Beheshti, Xuyun Zhang, Jin Foo, Alireza Jolfaei

“…The survey then reviews a range of machine learning algorithms used for churn prediction and explores the various feature types employed in this domain. …”
Get full text

Prediction of risk for acute kidney injury and its progression to mortality in obese patients admitted to ICU postoperatively by LI Qiang, LI Qiang, MU Guo, MU Guo, WANG Wenzhang

“…After data cleaning and preprocessing, Boruta feature selection was applied, followed by the construction of prediction models using 7 machine learning algorithms, that is, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), k-Nearest Neighbors (KNN), Naïve Bayes (NB), Neural Network (NNET), Support Vector Machine (SVM), and XGBoost. …”
Get full text

Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy by Leilei Zheng, Xinyan Wu, Wei Gu, Rui Wang, Jing Wang, Hongying He, Zhao Wang, Bin Yi, Yi Zhang

“…In total, 26 characteristic variables, including demographic information, past medical history, and clinical data of the patients were included, and BorutaShap was used for feature selection. Five machine learning algorithm models, including logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM), were selected. …”
Get full text

A data driven predictive viscosity model for the microemulsion phase by Akash Talapatra, Bahareh Nojabaei, Pooya Khodaparast

“…The data, computed via the Einstein relation and Green-Kubo formula, provides robust training and test datasets for model development. Various machine learning (ML) based regression algorithms are employed on our dataset to train and fit the model. …”
Get full text

An Intelligent Technique for Android Malware Identification Using Fuzzy Rank-Based Fusion by Altyeb Taha, Ahmed Hamza Osman, Yakubu Suleiman Baguda

“…The suggested ANDFRF primarily consists of two steps: in the first step, five machine learning algorithms, comprising K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), XGbooost (XGB) and Light Gradient Boosting Machine (LightGBM), were utilized as base classifiers for the initial identification of Android Apps either as goodware or malware apps. …”
Get full text

The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…Finally, we compare the performance of our proposed DNN model with other machine learning algorithms, such as Random Forest and Support Vector Machines, demonstrating superior predictive accuracy. …”
Get full text

A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs by Ahmed Alabd-Aljabar, Zain Raisan, Mohammed Adnan, Salam Dhou

“…However, the hybrid framework combining AlexNet with a Support Vector Machine achieved an accuracy of 94%, and although it falls short of ViT in terms of performance, it comprises far fewer parameters. …”
Get full text

Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis by S. Jeba Priya, C. Anand Deva Durai, M. S. P. Subathra, S. Thomas George, Andrew Jeyabose

“…This study integrates decomposition techniques with ML algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), decision trees (DTs), and k-nearest neighbors (k-NNs), as well as DL algorithms such as long short-term memory (LSTM), bidirectional long short-term memory (LSTM), and convolutional neural networks (CNNs), for PD classification. …”
Get full text

Water vapor density field estimation using commercial microwave link attenuation combined with temperature measurements by Itay Bragin, Yoav Rubin, Pinhas Alpert, Jonatan Ostrometzky

“…Accurate water vapor density (WVD) measurement is critical for weather models, health risk management, and industrial management among many other applications. A number of machine-learning based algorithms (e.g. support vector machine) for estimating water vapor density at a reference weather station using the received signal level values measured at a commercial microwave link has been proposed in the past, and also was expanded to include a combination of three commercial microwave links with temperature measurements to achieve a higher estimation accuracy (with respect to the root mean square error at a given location). …”
Get full text

Ensemble stacked model for enhanced identification of sentiments from IMDB reviews by Komal Azim, Alishba Tahir, Mobeen Shahroz, Hanen Karamti, Annia Almeyda Vazquez, Angel Rojas Vistorte, Imran Ashraf

“…In this regard, an ensemble model RRLS is proposed that stacks random forest, recurrent neural network, logistic regression (LR), and support vector machine (SVM). The Internet Movie Database (IMDB) movie reviews and Urdu tweets are examined in this study using Urdu sentiment analysis. …”
Get full text

Analysis of mid-infrared spectrum characteristics of sandstone with different acidification degrees based on fusion model by Lu Chen, Longfei Chang, Huiqing Lian, En Wang, Bixing Zhang, Jia Kang

“…Subsequently, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) algorithms were compared, and a fusion model of mid-infrared spectral prediction models for red sandstone samples with varying degrees of acidification was established. …”
Get full text

Potential of Multi-Source Multispectral vs. Hyperspectral Remote Sensing for Winter Wheat Nitrogen Monitoring by Xiaokai Chen, Yuxin Miao, Krzysztof Kusnierek, Fenling Li, Chao Wang, Botai Shi, Fei Wu, Qingrui Chang, Kang Yu

“…Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. …”
Get full text

Data-driven prediction of critical diameter for deterministic lateral displacement devices: an integrated DPD-ML approach by Shuai Liu, Peng Zhang, Anbin Wang, Keke Tang, Shuo Chen, Chensen Lin

“…Four ML models are trained: Random Forest Regression (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR) and Artificial Neural Networks (ANN). …”
Get full text

Detection and monitoring of Melampsora spp. Damage in multiclonal poplar plantations coupling biophysical models and Sentinel-2 time series by Carlos Camino, Alexey Valero-Jorge, Erika García Lima, Ramón Álvarez, Pieter S.A. Beck, Flor Álvarez-Taboada

“…For each DM, three ML algorithms (support vector machines, random forests, and neural networks) were trained using in situ leaf rust inspections as reference data, and the following inputs: (i) inverted plant traits retrieved from the PROSAIL model, (ii) key spectral indices derived from Sentinel-2 time series, and (iii) a combination of both plant traits and indices from Sentinel-2 images. …”
Get full text

Artificial Intelligence Driven Smart Farming for Accurate Detection of Potato Diseases: A Systematic Review by Avneet Kaur, Gurjit S. Randhawa, Farhat Abbas, Mumtaz Ali, Travis J. Esau, Aitazaz A. Farooque, Rajandeep Singh

“…The most widely used algorithms incorporate Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), and MobileNet with accuracy rates between 64.3 and 100%. …”
Get full text

Ferroptosis-related hub genes and immune cell dynamics as diagnostic biomarkers in age-related macular degeneration by Jinquan Chen, Zhao Long, Dandan Shi, Qian Zhang, H. Peng

“…Subsequent screening of these 19 genes using LASSO regression, Support Vector Machine (SVM), and Random Forest algorithms identified four hub genes: FADS1, TFAP2A, AKR1C3, and TTPA. …”
Get full text