spam-classification-with-support-vector-machines-using-van-der

Spam Classification with Support Vector Machines Using Van der Waerden Rank Score Attention

As email usage expands, spam has become a critical challenge, threatening network security and reducing communication efficiency. Conventional detection ...

Xiamen University of Technology

Antenna selection in Multiple-Input Multiple-Output (MIMO) systems has attracted increasing attention due to the challenge of keeping a balance between communication performance and computational complexity. Recently, deep learning based methods have achieved promising performance in many application fields. This paper proposed a deep learning (DL) based antenna selection technique. First, we generated the label of training antenna systems by maximizing the channel capacity. Then, we adopted the deep convolutional neural network (CNN) on the channel matrices to explicitly exploit the massive latent cues of attenuation coefficients. Finally, we used the adopted CNN to assign the class label and then select the optimal antenna subset. Experimental results demonstrate that our method can achieve better performance than the state-of-the-art baselines for data-driven based antenna selection.

Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks.

Lung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.

Lung image segmentation via generative adversarial networks.

The traditional diagnosis of chronic hepatitis C usually relies on liver biopsy. Diagnosing chronic hepatitis C based on serum indices provides a non-invasive way to determine the stage of chronic hepatitis C without liver biopsy. In this paper, we proposed two automatic diagnosis systems for non-invasive diagnosis of chronic hepatitis C based on serum indices, an extreme learning machine (ELM) based auto-diagnosis method and a hybrid method using k-means clustering and ELM. The two proposed systems were used to predict the fibrosis stage and inflammatory activity grade of patients with chronic hepatitis C by analyzing their serum index observations. ELM has superiorities such as simple structure and fast calculation speed and can provide good diagnosis performance. To overcome the problem of class-imbalance, outliers and small sample size, we also proposed a method hybridizing k-means and ELM. It employed the k-means clustering to generate new robust training samples and then employed the new generated training samples to train an ELM for chronic hepatitis C diagnosis. The proposed methods were tested on 123 real clinical cases. Experimental results show that the proposed methods outperform the state-of-the-art methods for the fibrosis stage and inflammatory activity grade diagnosis tasks.

Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine.

Accurate identification of fetal torso ultrasound planes is essential in pre-natal examinations, as it plays a critical role in the early detection of severe fetal malformations and this process is heavily dependent on the clinical expertise of health care providers. However, the limited number of medical professionals skilled at identification and the complexity of fetal plane screening underscore the need for efficient diagnostic support tools. Clinicians often encounter challenges such as image artifacts and the intricate nature of fetal planes, which require adjustments to image gain and contrast to obtain clearer diagnostic information. In response to these challenges, we propose the contrast and gain-aware attention mechanism. This method generates images under varying gain and contrast conditions, and utilizes an attention mechanism to mimic the clinician's decision-making process. The system dynamically allocates attention to images based on these conditions, integrating feature fusion through a lightweight attention module. Positioned in the first layer of the model, this module operates directly on images with different gain and contrast settings. Here we integrated this attention mechanism into ResNet18 and ResNet34 models to predict key fetal torso planes: the transverse view of the abdomen, the sagittal view of the spine, the transverse view of the kidney and the sagittal view of the kidney. Our experimental results showed that this approach significantly enhances performance compared with traditional models, with minimal addition to model parameters, ensuring both efficiency and effectiveness in fetal torso ultrasound plane identification. Our codes are available at https://github.com/sysll/CCGAA.

Article	Year
Antenna selection for multiple-input multiple-output systems based on deep convolutional neural networks. PloS one\| PubMed ID: 31042772	2019
Lung image segmentation via generative adversarial networks. Frontiers in physiology\| PubMed ID: 39219839	2024
Fibrosis and inflammatory activity diagnosis of chronic hepatitis C based on extreme learning machine. Scientific reports\| PubMed ID: 39747413	2025
Contrast and Gain-Aware Attention: A Plug-and-Play Feature Fusion Attention Module for Torso Region Fetal Plane Identification. Ultrasound in medicine & biology\| PubMed ID: 40915866	2025
Artificial Intelligence in Proteins and Peptides: A Comprehensive Overview from Hybrid Modeling to Deep Learning. Current protein & peptide science\| PubMed ID: 41051038	2025

Jiaxin Cai

Biography

JoVE Journal Publications

Other Publications