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Clustering is a fundamental task in the field of data analysis. With the development of deep learning, deep clustering focuses on learning meaningful representation with neural networks. Ensemble clustering algorithms combine multiple base partitions into a robust and better consensus clustering. Current deep ensemble clustering algorithms usually neglect shallow and original features. Besides, rarel algorithms use graph attention networks to explore clustering structures. This paper proposes a novel Clustering algorithm based on Multi-layer Features and Graph attention Networks (CMFGN). CMFGN obtains multi-layer features through the hierarchical convolutional layers. Moreover, CMFGN combines the co-association matrix with original features as the Graph Attention Networks (GAT) input to obtain consensus clustering, which reuses original information and leverages GAT to inherit a good clustering structure. Extensive experimental results show that CMFGN remarkably outputs competitive methods on four challenging image datasets. In particular, CMFGN achieves the ACC of 82.14% on the Digits dataset, which is almost up to 6% performance improvement compared with the best baseline. [ABSTRACT FROM AUTHOR]

An intrusion detection system is a security system that aims to detect sabotage and intrusions on networks to inform experts of the attack and abuse of the network. Different classification methods have been used in the intrusion detection systems such as fuzzy, genetic algorithms, decision trees, artificial neural networks, and support vector machines. Moreover, ensemble classifiers have shown more robust and effective performance for various tasks in the field. In this paper, we adopt ensemble models in order to improve the performance of intrusion detection and, at the same time, decrease the false alarm rate. We use kNN for multi-class classification, as well as SVM to approach the classification problem in normal-based detection. In order to combine multiple outputs, we use the Dempster–Shafer method in which there is the possibility of explicit retrieval of uncertainty. Moreover, we utilize deep learning for extracting features to train the samples, selected by the sample selection algorithm based on ensemble margin. We compare our results with state-of-the-art methods on benchmarking datasets such as UNSW-NB15, CICIDS2017, and NSL-KDD. Our proposed method indicates the superiority in terms of prominent metrics Accuracy, Precision, Recall, and F-measure. [ABSTRACT FROM AUTHOR]

As a derivative task of object segmentation, camouflage object segmentation has the difficulties of redundant complex information and anti-detection objects. Most object segmentation algorithms are dedicated to improving the structure of the feature extraction and fusion modules, but the processing of complex redundant information is not sufficient, which makes them unable to segment the camouflage objects well. Aiming at the data characteristics of the camouflage object, we propose a novel structure of fully convolutional network called DAFNet, which mainly consists of feature filter module (FFM). FFM is formed by multi-path dilated convolution through multiplication, which "filters" the redundant features flowing in the network to improve the network's segmentation performance on camouflage objects. We also design an attention module based on Gaussian convolution called Gaussian attention module (GAM), which is used to refine the rough predicted map to further improve the output quality. Experiments on existing camouflage object datasets show that the DAFNet can achieve state-of-the-art performance on camouflage object segmentation. [ABSTRACT FROM AUTHOR]

The uniform corpus of untranslated script is a preliminary stage for computational epigraphy. Mechanizing this process through deep learning algorithms will be an essential support to the epigraphical research. Our proposed system based on soft computing techniques focuses on the progression of recognizing the eleventh-century ancient Tamil character and converting them into current-century word form. Initially, the system is implemented by performing preprocessing steps followed by image segmentation. The decomposed image undergoes a hybrid feature extraction technique along with Chi-square test to check whether entire pixel in image of Zernike is bounded inside the unit circle or not, whereas ANOVA method is used for testing the significant difference between HOG feature and zoning feature. These functions are subjected to image classification and proceeded with character recognition using convolutional neural networks. Finally, the identified character is progressed into word form with the help of boggle algorithm. The hybrid feature extraction along with convolutional neural networks is achieved with 92.78% of recognition rate accurately. Our experiment shows a large perspective of deep learning algorithms in automatic epigraphy. [ABSTRACT FROM AUTHOR]