Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

Underwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. Small aquatic creatures often congregate in large groups, further complicating the task. To address these challenges, we develop Aqua-DETR, a tailored end-to-end framework for UOD. Our method includes an align-split network to enhance multi-scale feature interaction and fusion for small object identification and a distinction enhancement module using various attention mechanisms to improve ambiguous object identification. Experimental results on four challenging datasets demonstrate that Aqua-DETR outperforms most existing state-of-the-art methods in the UOD task, validating its effectiveness and robustness.

The main task of remote sensing change detection (CD) is to identify object differences in bitemporal remote sensing images. In recent years, methods based on deep convolutional neural networks (CNNs) have made great progress in remote sensing CD. However, due to illumination changes and seasonal changes in the images acquired by the same sensor, the problem of “pseudo change” in the change map is still difficult to solve. In this article, in order to reduce “pseudo changes,” we propose a multi-scale difference feature enhancement network (MDFENet) to extract the most discriminative features from bitemporal remote sensing images. MDFENet contains three procedures: first, multi-scale bitemporal features are generated by a shared weighted Siamese encoder. Then features of each scale are fed into a difference enhancement module to generate refined difference features. Finally, they are combined and reconstructed by a decoder to generate change map. The difference enhancement module includes multiple layers of difference enhancement (DE) encoder and transformer decoder. They are applied to features of different scales to establish long-range relationships of pixels semantic changes, while high-level difference features participate in the generation of low-level difference features to enhance information transmission among features of different scales, reducing “pseudo changes”. Compared with state-of-the-art methods, the proposed method achieved the best performance on two datasets, with F1 of 81.15% on the SYSU-CD dataset and 90.85% on the LEVIR-CD dataset.

Compared with the traditional method based on hand-crafted features, deep neural network has achieved a certain degree of success on remote sensing image semantic segmentation. However, there are still serious holes, rough edge segmentation, and false detection or even missed detection due to the light and its shadow in the segmentation. Aiming at the above problems, this paper proposes a remote sensing semantic segmentation model SCG-TransNet that is a hybrid model of Swin Transformer and Deeplabv3+, which includes Swin-Conv-Dspp (SCD) and Global Local Transformer Block (GLTB). First, SCD module which can efficiently extract feature information from objects at different scales is used to mitigate the hole phenomenon, reducing the loss of detailed information. Second, we construct a GLTB with Spatial Pyramid Pooling Shuffle(SPPS) module to extract critical detail information from the limited visible pixels of the occluded objects, which alleviates the problem of difficult object recognition due to occlusion effectively. Finally, the experimental results show that our SCG-TransNet achieves a Mean Intersection over Union (MIoU) of 70.29 $\%$ on the Vaihingen datasets, which is 3 $\%$ higher than the baseline model. It also achieved good results on Potsdam datasets. These demonstrate the effectiveness, robustness and superiority of our proposed method compared with existing state-of-the-art methods. The code will be available at https://github.com/yuwxyun275/SCG-TransNet .