Research on traffic representation in network anomaly detection by SUN Jianwen, ZHANG Bin, CHANG Heyu

“…Subsequently, the evolution of traffic representation in network anomaly detection was systematically reviewed, providing a comprehensive analysis of its forms, feature learning, and application in anomaly detection both globally and domestically. …”
Get full text

Ensemble Network Graph-Based Classification for Botnet Detection Using Adaptive Weighting and Feature Extraction by Muhammad Aidiel Rachman Putra, Tohari Ahmad, Dandy Pramana Hostiadi, Royyana Muslim Ijtihadie

Get full text

Lightweight multidimensional feature enhancement algorithm LPS-YOLO for UAV remote sensing target detection by Yong Lu, Minghao Sun

Get full text

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

Get full text

TW-YOLO: An Innovative Blood Cell Detection Model Based on Multi-Scale Feature Fusion by Dingming Zhang, Yangcheng Bu, Qiaohong Chen, Shengbo Cai, Yichi Zhang

“…However, due to the diversity and complexity of blood cell images, traditional models still exhibit deficiencies in blood cell detection. To address blood cell detection, we developed the TW-YOLO approach, leveraging multi-scale feature fusion techniques. …”
Get full text

Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis by Yalin Wu

“…This study proposes an innovative congestion prediction approach using dynamic big data analysis of vessel trajectories and multiscale feature analysis. …”
Get full text

A lightweight vulnerability detection method for long smart contracts based on bimodal feature fusion by Chen Yang Lin, Hui Zhao, Jing Han Liu

“…This method can combine the source code and opcode of smart contracts for analysis, and use a hierarchical attention network composed of bidirectional GRU and attention mechanism for vulnerability feature extraction. …”
Get full text

PFRNet: A Small Object Detection Method Based on Parallel Feature Extraction and Attention Mechanism by Hai Lin, Ji Wang, Jingguo Li

“…Visualization analysis reveals that the PFR module effectively captures richer and more comprehensive visual features in images, providing robust input for subsequent detection tasks, which is crucial for PFRNet’s superior performance. …”
Get full text

The Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI by Fatih Atesoglu, Harun Bingol

“…Therefore, the early detection and classification of grape diseases with the latest artificial intelligence techniques and feature reduction techniques was carried out within the scope of this study. …”
Get full text

Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques by S. Vijayalakshmi, Binay Kumar Pandey, Digvijay Pandey, Mesfin Esayas Lelisho

“…This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical features and deep learning techniques. …”
Get full text

FAMHE-Net: Multi-Scale Feature Augmentation and Mixture of Heterogeneous Experts for Oriented Object Detection by Yixin Chen, Weilai Jiang, Yaonan Wang

“…These deficiencies can lead to insufficient feature representation and reduced detection accuracy. …”
Get full text

Chronic liver disease detection using ranking and projection-based feature optimization with deep learning by Sumaiya Noor, Salman A. AlQahtani, Salman Khan

“…The approach integrates multiple ranking and projection techniques for features, utilizing deep learning to detect early signs of liver disease. …”
Get full text

Precise Crop Pest Detection Based on Co-Ordinate-Attention-Based Feature Pyramid Module by Chenrui Kang, Lin Jiao, Kang Liu, Zhigui Liu, Rujing Wang

Get full text

Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks by P. Valarmathi, Y. Suganya, K. R. Saranya, S. Shanmuga Priya

“…The present study introduces a potentially effective approach for the automated and non-intrusive identification of Parkinson’s disease through the analysis of audio data. The integration of Autoencoder-based feature extraction with Deep Neural Networks (DNN) presents a dependable and easily accessible solution for the early detection and continuous monitoring of Parkinson’s disease. …”
Get full text

A complex roadside object detection model based on multi-scale feature pyramid network by Zhihao Zheng, Jianguang Zhao, Jingjing Fan, Ruirui Bai, Jiana Zhao, Jianan Liu

“…Combining the weighted Bi-directional Feature Pyramid Network (BCFPN) for feature fusion incorporates deep, shallow, and original features, reinforces feature integration, minimizes information loss during convolution processes, and enhances target detection accuracy. …”
Get full text

SFG-Net: A Scattering Feature Guidance Network for Oriented Aircraft Detection in SAR Images by Qingyang Ke, Youming Wu, Wenchao Zhao, Qingbiao Meng, Tian Miao, Xin Gao

“…Benefiting from the powerful capacity of feature extraction and analysis of deep learning, aircraft detection performance has been improved by most traditional general-purpose visual intelligence methods. …”
Get full text

UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism by Shaodan Lin, Deyao Huang, Libin Wu, Zuxin Cheng, Dapeng Ye, Haiyong Weng

“…This design aims to enhance feature extraction and focus on critical image regions, addressing the challenges of detecting small and complex disease patterns in UAV-captured imagery. …”
Get full text

Multi-Level Intertemporal Attention-Guided Network for Change Detection in Remote Sensing Images by Shuo Liu, Qinyu Zhang, Yuhang Zhang, Xiaochen Niu, Wuxia Zhang, Fei Xie

Get full text

Prostate cancer detection with transrectal in-bore MRI biopsies: impact of prostate volume and lesion features by Alexander Schaudinn, Harald Busse, Constantin Ehrengut, Nicolas Linder, Jonna Ludwig, Toni Franz, Lars-Christian Horn, Jens-Uwe Stolzenburg, Timm Denecke

“…Features were then compared for statistically significant differences in the cancer detection rate (CDR) of clinically significant (cs-PCa) and any prostate cancer (any-PCa) using categorical and continuous variables. …”
Get full text

Innovative Approach to Detecting Autism Spectrum Disorder Using Explainable Features and Smart Web Application by Mohammad Abu Tareq Rony, Fatama Tuz Johora, Nisrean Thalji, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

Get full text