FD-YOLO11: A Feature-Enhanced Deep Learning Model for Steel Surface Defect Detection by Zichen Dang, Xingshuo Wang

“…To address this challenge, FD-YOLO11, which is a YOLO11-based deep learning model with enhanced feature extraction and fusion mechanisms for attaining improved detection performance, is proposed in this paper. …”
Get full text

Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models by Ayoob Almotairi, Samer Atawneh, Osama A. Khashan, Nour M. Khafajah

“…The integration of these components harnesses information from selected features and leverages the collective strength of individual models to enhance classification performance. …”
Get full text

Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction by Leilei Kang, Guojing Hu, Hao Huang, Weike Lu, Lan Liu

“…In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. …”
Get full text

Hybrid feature selection framework for enhanced credit card fraud detection using machine learning models. by Al Mahmud Siam, Pankaj Bhowmik, Md Palash Uddin

“…To address this, we propose a novel hybrid feature selection framework designed to enhance the performance of machine learning models in credit card fraud detection. …”
Get full text

Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features by Zhen Wang, Yingzhe Song, Lei Pang, Shanjun Li, Gang Sun

“…Stacking both attentions (spatio-temporal) yielded <i>R</i>² = 0.960, slightly below the value for spatial attention alone, implying that added complexity does not guarantee better performance. Across all models, prediction errors grew during high-intensity bouts, highlighting a bottleneck in capturing non-linear physiological responses under heavy load. …”
Get full text

Machine learning model for diagnosing salivary gland adenoid cystic carcinoma based on clinical and ultrasound features by Huan-Zhong Su, Zhi-Yong Li, Long-Cheng Hong, Yu-Hui Wu, Feng Zhang, Zuo-Bing Zhang, Xiao-Dong Zhang

“…Abstract Objective To develop and validate machine learning (ML) models for diagnosing salivary gland adenoid cystic carcinoma (ACC) in the salivary glands based on clinical and ultrasound features. …”
Get full text

Model Klasifikasi Dengan Logistic Regression Dan Recursive Feature Elimination Pada Data Tidak Seimbang by Sutarman, Rimbun Siringoringo, Dedy Arisandi, Edi Kurniawan, Erna Budhiarti Nababan

“…The ridge regression technique (L2-regularization) is applied to prevent overfitting during the validation stage of the linear regression model. The model performance evaluation is based on confusion matrices and ROC graphs. …”
Get full text

Multi-machine learning model based on radiomics features to predict prognosis of muscle-invasive bladder cancer by Bin Wang, Zijian Gong, Peide Su, Guanghao Zhen, Tao Zeng, Yinquan Ye

“…Furthermore, the combined model, which incorporates clinical features, demonstrates enhanced performance and is beneficial for clinical decision-making.…”
Get full text

Deep learning and support vector machine-recursive feature elimination-based network intrusion detection model by YE Qing, ZHANG Yannian, WU Hao

“…However, there are a lot of redundant information and unbalanced distribution problems in network intrusion data, therefore, deep learning and support vector machine-recursive feature elimination-based network intrusion detection model (DLRF) was proposed. …”
Get full text

Comprehensive Performance Assessment of Multi-Neural Ensemble Model for Mortality Prediction in ICU by M. Fathima Begum, Subhashini Narayan

“…Wrapper-based genetic feature selection method is used for the feature selection. …”
Get full text

Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models by Hamzeh F. Assous

“…Finally, we choose the best prediction model with the highest R2 in the training and the testing phases with/out feature selection that is the CHAID model. …”
Get full text

Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) by Siddharth Shankar, Leigh A. Stearns, C. J. van der Veen

“…We show that the Segment Anything Model performs well for different features (icebergs, glacier termini, supra-glacial lakes, crevasses), in different environmental settings (open water, mélange, and sea ice), with different sensors (Sentinel-1, Sentinel-2, Planet, timelapse photographs) and different spatial resolutions. …”
Get full text

Improved Deep Support Vector Data Description Model Using Feature Patching for Industrial Anomaly Detection by Wei Huang, Yongjie Li, Zhaonan Xu, Xinwei Yao, Rongchun Wan

“…This model integrates a feature-patching technique with the Deep SVDD framework. …”
Get full text

Application of supervised machine learning models in human emotion classification using Tsallis entropy as a feature by Pragati Patel, Sivarenjani B., Ramesh Naidu Annavarapu

“…Additionally, the ensemble models KNN-DT and DT-LDA are also analyzed. The SEED dataset is employed for this study, and performance is evaluated through holdout cross-validation, considering accuracy, F1 score, precision, and recall metrices. …”
Get full text

Speech emotion recognition with light weight deep neural ensemble model using hand crafted features by Jaher Hassan Chowdhury, Sheela Ramanna, Ketan Kotecha

“…Despite its promise, SER research faces challenges such as data scarcity, the subjective nature of emotions, and complex feature extraction methods. In this paper, we seek to investigate whether a lightweight deep neural ensemble model (CNN and CNN_Bi-LSTM) using well-known hand-crafted features such as ZCR, RMSE, Chroma STFT, and MFCC would outperform models that use automatic feature extraction techniques (e.g., spectrogram-based methods) on benchmarked datasets. …”
Get full text

Doppler Stretch and Delay Statistical Performance Comparison for Wideband and Narrowband Signal Model by I. V. Gogolev

“…Also, estimation variances in narrowband signal model differ from wideband parameter variances by magnitude of spectrum width to central frequency ratio.…”
Get full text

Comparative Analysis of Machine Learning Models for CO Emission Prediction in Engine Performance by Beytullah Eren, İdris Cesur

“…Four models—Linear Regression, Decision Tree, Random Forest, and Support Vector Regression—were evaluated using a dataset of engine performance parameters and emission measurements. …”
Get full text

Early Detection of Lung Cancer Using Predictive Modeling Incorporating CTGAN Features and Tree-Based Learning by Abdulrahman Alzahrani

Get full text

MSCD-YOLO: A Lightweight Dense Pedestrian Detection Model with Finer-Grained Feature Information Interaction by Qiang Liu, Zhongmin Li, Lei Zhang, Jin Deng

“…Existing methods suffer from low detection accuracy, high miss rates, large model parameters, and poor robustness. In this paper, to address these issues, we propose a lightweight dense pedestrian detection model with finer-grained feature information interaction called MSCD-YOLO, which can achieve high accuracy, high performance and robustness with only a small number of parameters. …”
Get full text

Enhanced Interpretable Forecasting of Cryptocurrency Prices Using Autoencoder Features and a Hybrid CNN-LSTM Model by Wajeeha Badar, Shabana Ramzan, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…Deep variational autoencoders (VAE) are used in the stage of preprocessing to determine noticeable patterns in datasets by learning features from historical Bitcoin price data. The CNN-LSTM model additionally implies Shapley additive explanations (SHAP) to promote interpretability and clarify the role of various features. …”
Get full text