SSA Net: Small Scale-Aware Enhancement Network for Human Pose Estimation.

In the field of human pose estimation, heatmap-based methods have emerged as the dominant approach, and numerous studies have achieved remarkable performance based on this technique. However, the inherent drawbacks of heatmaps lead to serious performance degradation in methods based on heatmaps for smaller-scale persons. While some researchers have attempted to tackle this issue by improving the performance of small-scale persons, their efforts have been hampered by the continued reliance on heatmap-based methods. To address this issue, this paper proposes the SSA Net, which aims to enhance the detection accuracy of small-scale persons as much as possible while maintaining a balanced perception of persons at other scales. SSA Net utilizes HRNetW48 as a feature extractor and leverages the TDAA module to enhance small-scale perception. Furthermore, it abandons heatmap-based methods and instead adopts coordinate vector regression to represent keypoints. Notably, SSA Net achieved an AP of 77.4% on the COCO Validation dataset, which is superior to other heatmap-based methods. Additionally, it achieved highly competitive results on the Tiny Validation and MPII datasets as well.

Effective detection of Alzheimer's disease by optimizing fuzzy K-nearest neighbors based on salp swarm algorithm.

Alzheimer's disease (AD) is a typical senile degenerative disease that has received increasing attention worldwide. Many artificial intelligence methods have been used in the diagnosis of AD. In this paper, a fuzzy k-nearest neighbor method based on the improved binary salp swarm algorithm (IBSSA-FKNN) is proposed for the early diagnosis of AD, so as to distinguish between patients with mild cognitive impairment (MCI), Alzheimer's disease (AD) and normal controls (NC). First, the performance and feature selection accuracy of the method are validated on 5 different benchmark datasets. Secondly, the paper uses the Structural Magnetic Resolution Imaging (sMRI) dataset, in terms of classification accuracy, sensitivity, specificity, etc., the effectiveness of the method on the AD dataset is verified. The simulation results show that the classification accuracy of this method for AD and MCI, AD and NC, MCI and NC are 95.37%, 100%, and 93.95%, respectively. These accuracies are better than the other five comparison methods. The method proposed in this paper can learn better feature subsets from serial multimodal features, so as to improve the performance of early AD diagnosis. It has a good application prospect and will bring great convenience for clinicians to make better decisions in clinical diagnosis.