Department of Computer Science and Information Systems

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Author: search.filters.author.Narang, PratikSubject: search.filters.subject.Neural networks

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    MCNNet: Generalizing Fake News Detection with a Multichannel Convolutional Neural Network using a Novel COVID-19 Dataset
    (ACM Digital Library, 2021-01) Narang, Pratik
    During the pandemic of COVID-19, the propagation of fake news is spreading like wildfire on social media. Such fake news articles have created confusion among people and serious social disruptions as well. To detect such news articles effectively, we propose a generalized classification model (MCNNet) having the power of learning across different kernel-sized convolutional layers in different parallel channel network. The capability of MCNNet is lucrative towards any real-world fake news dataset. Experimental results have demonstrated the performance of our model with different real-world fake news datasets.
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    FNDNet – A deep convolutional neural network for fake news detection
    (Elsevier, 2020-06) Narang, Pratik
    With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.
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    DeepFakE: improving fake news detection using tensor decomposition-based deep neural network
    (Springer, 2020-05) Narang, Pratik
    Social media platforms have simplified the sharing of information, which includes news as well, as compared to traditional ways. The ease of access and sharing the data with the revolution in mobile technology has led to the proliferation of fake news. Fake news has the potential to manipulate public opinions and hence, may harm society. Thus, it is necessary to examine the credibility and authenticity of the news articles being shared on social media. Nowadays, the problem of fake news has gained massive attention from research communities and needed an optimal solution with high efficiency and low efficacy. Existing detection methods are based on either news-content or social-context using user-based features as an individual. In this paper, the content of the news article and the existence of echo chambers (community of social media-based users sharing the same opinions) in the social network are taken into account for fake news detection. A tensor representing social context (correlation between user profiles on social media and news articles) is formed by combining the news, user and community information. The news content is fused with the tensor, and coupled matrix-tensor factorization is employed to get a representation of both news content and social context. The proposed method has been tested on a real-world dataset: BuzzFeed. The factors obtained after decomposition have been used as features for news classification. An ensemble machine learning classifier (XGBoost) and a deep neural network model (DeepFakE) are employed for the task of classification. Our proposed model (DeepFakE) outperforms with the existing fake news detection methods by applying deep learning on combined news content and social context-based features as an echo-chamber.
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    A hybrid approach for search and rescue using 3DCNN and PSO
    (ACM Digital Library, 2021-09) Narang, Pratik
    Search and rescue are essential applications of disaster management in which people are evacuated from the disaster-prone area to a safer place. This overall process of search and rescue can be more efficient if an automated system can quickly locate the human or area where rescue is required. To provide a faster and accurate search of those places, this paper proposes a novel approach to search and rescue using automated drone surveillance. In this paper, a complex scene classification problem is solved using the proposed 3DCNN model. The proposed model uses spatial as well as temporal features of the video for the classification of the scene as help or non-help in the natural disaster. Due to the unavailability of such kind of dataset, it is impossible to train the model. Therefore, it is essential to develop a dataset for search and rescue. The proposed dataset is a first and unique dataset for scene classification using drone surveillance. The major contribution of this paper is (1) a novel 3DCNN powered model for scene classification in drone surveillance, (2) to develop the required dataset for the training of scene classification model, and (3) particular swarm optimization (PSO)-based hyper-parameter tuning for getting the best value of multiple parameters used for training the model. Our hybridization of parameter tuning with PSO helps for the convergence of parameter values of proposed 3DCNN model, and the proposed scene classification model (3DCNN+PSO) is applied to the dataset. The proposed model gives an impressive performance to help situation identification with 98% training and 99% validation accuracy.