As the traditional rumor detection method is concerned, reduce in the data accuracy may influence the observation directivity and lack in the extraction of feature datasets. In order to overcome the above problem, here the modified rumor prediction model is proposed using deep learning with the integration of CNN in order to improve the data accuracy with the help of neural networks. First, the continuous bag of words (CBOW) model is modified to define the window context based on the definition of the weighted module. Then, CNN gets modified with the association of the attention module added along with the output of the weighted module as it helps to identify the feature incompleteness in the long sequence of online social networks. The attention module along with CNN with the function of max pooling and upsampling layer in order to extract the features in the text effectively. Then, the extracted feature is given into the softmax layer for performing text classification to identify and predict the rumor in the online social network. Based on the online samples, the analysis is conducted on the proposed model based on certain parameter such as, data accuracy, precision, recall and F1-Score as it shows better performance as compared to the other existing models like LSTM based Fuzzy deep learning, deep recurrent Q-learning and adaptive deep transfer learning.

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This paper introduces a novel hybrid filter-based ensemble multi-class classification model for distributed privacy-preserving applications. The conventional privacy-preserving multi-class learning models have limited capacity to enhance the true positive rate, mainly due to computational time and memory constraints, as well as the static nature of metrics for parameter optimization and multi-class perturbation processes. In this research, we develop the proposed model on large medical and market databases with the aim of enhancing multi-party data confidentiality through a security framework during the privacy-preserving process. Moreover, we also introduce a secure multi-party data perturbation process to improve computational efficiency and privacy-preserving performance. Experimental results were evaluated on different real-time privacy-preserving datasets, such as medical and market datasets, using different statistical metrics. The evaluation results demonstrate that the proposed multi-party-based multi-class privacy-preserving model performs statistically better than conventional approaches.

The collection of nodes is termed as community in any network system that are tightly associated to the other nodes. In network investigation, identifying the community structure is crucial task, particularly for exposing connections between certain nodes. For community overlapping, network discovery, there are numerous methodologies described in the literature. Numerous scholars have recently focused on network embedding and feature learning techniques for node clustering. These techniques translate the network into a representation space with fewer dimensions. In this paper, a deep neural network-based model for learning graph representation and stacked auto-encoders are given a nonlinear embedding of the original graph to learn the model. In order to extract overlapping communities, an AEOCDSN algorithm is used. The efficiency of the suggested model is examined through experiments on real-world datasets of various sizes and accepted standards. The method outperforms various well-known community detection techniques, according to empirical findings.