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MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms. [ABSTRACT FROM AUTHOR]

MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms.

Network community detection with higher-order features has become a hot research topic recently since higher-order features that are captured at the level of small network subgraphs can help to gain new insights into the higher-order organizations of the overall network. However, most of the existing higher-order community detection methods only leverage the structural information of networks, yet fail to consider the rich information of node attributes, which typically makes them incapable of discovering semantically meaningful communities. To address this problem, we propose a novel method for community detection based on a newly designed higher-order feature termed Attribute Homogenous Motif (AHMotif), which integrates both node attributes and higher-order structure of the network in a seamless way. Specifically, the statistically significant motifs are identified for the network in terms of the topological structure, and homogeneity values are computed for structural motifs according to the attributes of the nodes that are involved in the motif. Then, the given attributed network can be further represented via a newly designed AHMotif adjacency matrix. The AHMotif adjacency matrix encodes both structures and attribute information and can characterize the network from a higher-order perspective. Afterward, several proximity based methods are utilized to obtain the final community detection results. Extensive experiments on several real-world data sets have shown the superiority of our AHMotif-based method in community detection.

• The current study extracts higher-order features from fire images. • A new set of data is correctly labelled for classifying fire and non-fire. • Information-theoretic feature selection is adopted to minimize computational cost. • The SVM performs classification with an RBF kernel. • The model draws an overall accuracy of 96,21%, and a specificity of 97,99%. • The model draws an f-measure and g-mean values of 96,13% and 96,19% respectively. In recent years, wildfires and forest fires have ravaged millions of hectares of the forest all over the world. Recent technological breakthroughs have increased interest in computer vision-based fire classification that classifies fire and non-fire pixels from image or video datasets. Fire pixels from an image or video can be classified using either a traditional machine learning approach or a deep learning approach. Presently, the deep learning approach is the mainstream in forest fire detection studies. Although deep learning algorithms can handle vast amounts of data, they ignore the variation in complexity among training samples and as a result, their training model performance is limited. Furthermore, deep learning approaches with little data and features perform poorly in real-world challenging fire scenarios. As a result, the current study adopts a machine learning technique to extract higher-order features from the processed images from the publicly available datasets: Corsican dataset and FLAME, and a private dataset: Firefront_Gestosa, for classifying fire and non-fire pixels. It should be emphasized that in machine learning applications, handling multidimensional data to train a model is challenging. Feature selection is used to overcome this problem by removing redundant or irrelevant data that has an impact on the model's performance. In this paper, information-theoretic feature selection approaches are used to choose the most important features for classification while minimizing the computational cost. The traditional machine classifier, Support Vector Machine (SVM) is adopted in the present work, that works on the discriminative features input selected from the feature selection technique. The SVM performs the classification of fire and non-fire pixels with a Radial Basis Function (RBF) kernel, and the model's performance is measured using assessment measures such as overall accuracy, sensitivity, specificity, precision, recall, F-measure, and G-mean. The model draws an overall accuracy of 96,21%, a sensitivity of 94,42%, a specificity of 97,99%, a precision of 97,91%, a recall of 94,42%, an f-measure and g-mean values of 96,13% and 96,19% respectively. [ABSTRACT FROM AUTHOR]

A generally applicable discretization method is proposed to approximate a continuous distribution on a real line with a discrete one, supported by a finite set. The method adopts a criterion which is shown to be flexible in approximating higher order features of the underlying continuous distribution while automatically preserving mean and variance. To illustrate the effectiveness of the method, several examples covering a wide-range continuous distributions are analyzed. A computer implementation (using R) of the proposed procedure is provided. [ABSTRACT FROM AUTHOR]

Recently, many deep supervised hashing has been developed for multi-label image retrieval applications and has already achieved good effects. However, current methods quantify the similarities between image pairs by counting the number of shared labels roughly, totally ignoring the semantic relationships of image pairs. Besides, the sample imbalance of different classes could bias the training process toward majority categories, deteriorating the model performance. To solve the above-mentioned problems, we propose a novel deep supervised hashing framework, named Deep Multi-Similarity Hashing with semantic-aware preserving (DMSH), to generate binary codes with high-level semantic similarity. Specifically, to obtain distinguishing features, Convolutional Neural Networks (CNN) with correlation operators are introduced to capture discriminative semantic features of the input images, including auto-correlation characteristics and cross-correlation characteristics. By jointly exploiting label-level and semantic-level similarity, the semantic-aware similarity module is developed to quantify the high-level semantic similarity of image pairs, distinguishing the differences beyond categories. Furthermore, the multi-similarity loss is incorporated into deep hashing to collect informative pairs efficiently, which could alleviate the impact of imbalanced sample pairs during model training. Extensive comparison experiments on three benchmark datasets demonstrate that our DMSH achieves promising performance with respect to different evaluation metrics. • CNNs with correlation operators are introduced to capture higher-order features. • A semantic-aware method is proposed to quantify the similarity of image pairs. • Multi-similarity loss is developed to collect informative pairs efficiently. • The state-of-the-art results are reported on three benchmark datasets. [ABSTRACT FROM AUTHOR]