Diabetic Retinopathy Detection Using DL-Based Feature Extraction and a Hybrid Attention-Based Stacking Ensemble by Sanjana Rajeshwar, Shreya Thaplyal, Anbarasi M., Siva Shanmugam G.

“…Classification employs a decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), and a modified convolutional neural network (CNN) with a spatial attention layer. …”
Get full text

Twitter User Account Classification to Gain Insights into Communication Dynamics and Public Awareness During Tampa Bay's Red Tide Events by Andrey Skripnikov, Tania Roy, Fehmi Neffati, Melvin Adkins, Marcus Beck

“…In the initial tier, we employ predefined dictionaries for account groups to establish preliminary class designations, streamlining the subsequent labeling tiers, one of which is aided by preliminary machine learning classification. Having used several text classification algorithms and feature preprocessing approaches, Support Vector Machine with Bidirectional Encoder Representations from Transformers (BERT) yielded the best cross-validation performance in both accuracy (90%) and versatility (unweighted F1 score of 0.67). …”
Get full text

Anomaly classification in IIoT edge devices by Danny Alexandro Múnera-Ramírez, Diana Patricia Tobón-Vallejo, Martha Lucía Rodríguez-López

“…A k-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and Multilayer Perceptron (MLP) algorithms were trained. …”
Get full text

Sentiment Analysis of User Reviews of the AdaKami Online Loan App from the App Store Using SVM and Naive Bayes by Wava Lativa Azzahra, Jamaludin Indra, Rahmat Rahmat, Sutan Faisal

“…The classification model was built using two machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). …”
Get full text

Credit Rating Model Based on Improved TabNet by Shijie Wang, Xueyong Zhang

“…To further enhance classification and predictive performance, a stacked ensemble learning approach is implemented: the improved TabNet serves as the feature extractor, while XGBoost (Extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Categorical Boosting), KNN (K-Nearest Neighbors), and SVM (Support Vector Machine) are selected as base learners in the first layer, with XGBoost acting as the meta-learner in the second layer. …”
Get full text

Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images by R. J. Hemalatha, V. Vijaybaskar, T. R. Thamizhvani

“…For further analysis, classification of the segmentation techniques using support vector machine (SVM) classifier is performed to determine the absolute method for synovial region detection. …”
Get full text

Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer by Rui Zhang, Chunfan Jiang, Feng Li, Lin Li, Xiaomin Qin, Jiang Yang, Huabing Lv, Tao Ai, Lei Deng, Chencui Huang, Hui Xing, Feng Wu

“…Radiomic features were extracted from the whole-lesion tumor region of interest (ROItumor) and from peritumoral regions with 3 mm and 5 mm margins (ROI3mm and ROI5mm, respectively). Various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, AdaBoost, and Decision Tree, were used to construct radiomics models based on different ROIs, and diagnostic performances were compared to identify the optimal approach. …”
Get full text

Real-Time Football Match Prediction Platform by An Zhongqi

“…The platform employs machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, combined with feature engineering techniques, to generate accurate predictions. …”
Get full text

Computational intelligence in the identification of Covid-19 patients  by using KNN-SVM Classifier by shaymaa adnan

“…We applied Principal Component Analysis (PCA) and Histogram of Gradients (HOG) as extract features. while we conducted a classification process using K nearest neighbors (KNN) and Support Vector Machine (SVM) algorithms .  Results showed that the classification accuracy with SVM for Covid-19 identification is 88.54% while with KNN is 82.31% …”
Get full text

TATPat based explainable EEG model for neonatal seizure detection by Turker Tuncer, Sengul Dogan, Irem Tasci, Burak Tasci, Rena Hajiyeva

“…In this EFE model, there are four essential phases and these phases: (i) automaton and transformer-based feature extraction, (ii) feature selection deploying cumulative weight-based neighborhood component analysis (CWNCA), (iii) the Directed Lobish (DLob) and Causal Connectome Theory (CCT)-based explainable result generation and (iv) classification deploying t algorithm-based support vector machine (tSVM). In the first phase, we have used a channel transformer to get channel numbers and these values have been divided into three levels and these levels are named (1) high, (2) medium and (3) low. …”
Get full text

The Relationship Between Surface Meteorological Variables and Air Pollutants in Simulated Temperature Increase Scenarios in a Medium-Sized Industrial City by Ronan Adler Tavella, Daniele Feijó das Neves, Gustavo de Oliveira Silveira, Gabriella Mello Gomes Vieira de Azevedo, Rodrigo de Lima Brum, Alicia da Silva Bonifácio, Ricardo Arend Machado, Letícia Willrich Brum, Romina Buffarini, Diana Francisca Adamatti, Flavio Manoel Rodrigues da Silva Júnior

“…This study utilized five years of daily meteorological data (from 1 January 2019 to 31 December 2023) to model atmospheric conditions and two years of daily air pollutant data (from 21 December 2021 to 20 December 2023) to simulate how pollutant levels would respond to annual temperature increases of 1 °C and 2 °C, employing a Support Vector Machine, a supervised machine learning algorithm. …”
Get full text

Identification and Validation of Key Genes Related to Lipophagy in Osteoporosis by Hu YX, Zuo ML, Wu Y, Yang Y, Shi XB, Zhang Q, Wu J, Xie RQ, Bi Y, Lin B, Mo C

“…The minimum absolute contraction selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and Boruta algorithm are used to identify candidate genes for OP-related feature genes, and the expression of key genes is analyzed. …”
Get full text

Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…The detection of corporate accounting fraud is a critical challenge in the financial industry, where traditional models such as neural networks, logistic regression, and support vector machines often fall short in achieving high accuracy due to the complex and evolving nature of fraudulent activities. …”
Get full text

A Systematic Mapping Study on State Estimation Techniques for Lithium-Ion Batteries in Electric Vehicles by Carolina Tripp-Barba, José Alfonso Aguilar-Calderón, Luis Urquiza-Aguiar, Aníbal Zaldívar-Colado, Alan Ramírez-Noriega

“…For estimating SoH, prevalent data-driven techniques include support vector regression (SVR) and Gaussian process regression (GPR), alongside hybrid models merging machine learning with conventional estimation techniques to heighten predictive accuracy. …”
Get full text

Leveraging artificial intelligence to assess the impact of COVID-19 on the teacher-student relationship in higher education. by Md Juwel Ahmed Sarker, Mahmudul Hasan, Alamgir Kabir, Abdullah Haque

“…COVID-19 disrupted teacher-student interaction and hindered the flow of teacher's support to students. The damage caused by the pandemic to the higher education sector has mostly recovered. …”
Get full text

An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction by Songpeng Zhao, Zeyuan Li, Changshuai Yin, Zhaofu Zhang, Teng Long, Jingjing Yang, Ruyue Cao, Yuzheng Guo

“…Key physicochemical features were extracted, and a hierarchical clustering model-driven hybrid feature selection strategy (HC-MDHFS) was employed to identify the most relevant descriptors. Three machine learning algorithms-Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (Gradient Boosting)-were integrated into a multi-level stacking ensemble, with Support Vector Regression serving as the meta-learner. …”
Get full text

Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson’s disease by Wee-Shin Lim, Sung-Pin Fan, Shu-I Chiu, Meng-Ciao Wu, Pu-He Wang, Kun-Pei Lin, Yung-Ming Chen, Pei-Ling Peng, Jyh-Shing Roger Jang, Chin-Hsien Lin

“…An integrated multimodal model using a support vector machine improved performance to 0.86 and achieved 0.82 for identifying early-stage PD during the “off” phase. …”
Get full text

Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification by Xiaojuan Cai, Ya Zhou, Jie Ren, Jinrong Wei, Shiyu Lu, Hanbing Gu, Weizhe Xu, Xun Zhu

“…We developed five AI models using distinct classification algorithms (Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine) that integrate demographic data, cytological findings, and an AI-assisted ultrasound diagnostic system for thyroid nodule assessment. …”
Get full text

Early Breast Cancer Prediction Using Thermal Images and Hybrid Feature Extraction-Based System by Doaa Youssef, Hanan Atef, Shaimaa Gamal, Jala El-Azab, Tawfik Ismail

“…The back end of the methodology uses support vector machine (SVM) and extreme gradient boosting (XGB) classification algorithms to establish the relationship between the retrieved feature vector and breast functionality. …”
Get full text

Adaptive ensemble techniques leveraging BERT based models for multilingual hate speech detection in Korean and english by Seohyun Yoo, Eunbae Jeon, Joonseo Hyeon, Jaehyuk Cho

“…PMF test data are calculated using Majority Voting Integration or Weighted Probabilistic Averaging. Popular machine learning algorithms such as Random Forest, Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine are employed as meta-learners for PMF. …”
Get full text