Colonoscopy images exhibit multi-frequency features, with polyp boundaries residing in a mid-frequency range, which are critical for accurate polyp segmentation. However, current deep learning models tend to prioritize low-frequency features, leading to reduced segmentation performance. To address this challenge, we propose a novelboundary-aware network(BAN) that integrates trainable Gabor filters into the polyp segmentation process through a dedicated module calledGabor-driven feature extraction(GFE). By developing and using atrajectory-directed frequency learningapproach, Gabor filters are trained along adamping sinusoidalpath, dynamically optimizing their frequency parameters within a proper mid-frequency range. This enhances boundary feature representation and significantly improves polyp segmentation accuracy. Extensive experiments demonstrate that our BAN outperforms existing state-of-the-art methods.

Recently, deep learning methods are often used in wafer map failure pattern classification. CNN requires less feature engineering but still needs preprocessing, e.g., denoising and resizing. Denoising is used to improve the quality of the input data, and resizing is used to transform the input into an identical size when the input data sizes are various. However, denoising and resizing may distort the original data information. Nevertheless, CNN-based applications are focusing on studying different feature map architectures and the input data manipulation is less attractive. In this study, we proposed an image hash layer triggered CNN framework for wafer map failure pattern retrieval and classification. The motivation and novelty are to design a CNN layer that can play as a resizing, information retrieval-preservation method in one step. The experiments proved that the proposed hash layer can retrieve the failure pattern information while maintaining the classification performance even though the input data size is decreased significantly. In the meantime, it can prevent overfitting, false negatives, and false positives, and save computing costs to a certain extent.

Recent research applying deep learning to the field of defect pattern recognition in wafer maps has greatly accelerated the process of defect detection. However, when different defects are mixed on the same wafer, the mixed type is very complex, and it is still difficult to recognize the defect pattern. In this article, we propose a new framework to segment different defect patterns on the wafer map by using a semantic segmentation approach. This method works well on single and known and unknown mixed types. First, we extract the defects from the single-defect wafer map of the MixedWM38 dataset to generate single-defect pixel-level labels. Then, a mixed defect pattern dataset suitable for semantic segmentation is generated using single-defect wafer maps and single-defect pixel-level labels. The average accuracy of the test set on our synthetic dataset reaches over 97%, and using the trained model for testing on MixedWM38, we can get an average accuracy of 95.8%.

In electricity customer classification, the most important task is to avoid the curse of dimensionality problem, as the consumption diagrams have a large number of dimensions. To avoid the curse of dimensionality problem, field indices (load shape factor) are often used instead of consumption diagrams. Field indices are directly extracted from consumption diagrams according to a predefined formula. Previous studies show that the most important thing for defining such a formula is to find meaningful time intervals from consumption diagrams. However, the inconvenient thing is that there are still a lack of details to explain how to define such time intervals. In our study, we propose a data mining--based method named SFATIE to support the extraction of field indices. The performance of the proposed method is evaluated by comparing it with other dimensionality reduction methods during the classification. For the classification, most often we have used classification methods like C5.0, SVM, Neural Net, Bayes Net, and Logistic. The experimental results show that our method is better or close to other dimensionality reduction methods. In addition, the experimental results show that our proposed method can produce the good quality of field indices and that these indices can improve the performance of electricity customer classification.

MOTIVATION: Gene selection for cancer classification is one of the most important topics in the biomedical field. However, microarray data pose a severe challenge for computational techniques. We need dimension reduction techniques that identify a small set of genes to achieve better learning performance. From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target. RESULTS: In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine. In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper. The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature.