Publications by authors named "Anurag Goswami"

3 Publications

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A transfer learning with structured filter pruning approach for improved breast cancer classification on point-of-care devices.

Comput Biol Med 2021 Apr 30;134:104432. Epub 2021 Apr 30.

Missouri University of Science and Technology, Rolla, MO, 65409, USA. Electronic address:

Background And Objective: A significant progress has been made in automated medical diagnosis with the advent of deep learning methods in recent years. However, deploying a deep learning model for mobile and small-scale, low-cost devices is a major bottleneck. Further, breast cancer is more prevalent currently, and ductal carcinoma being its most common type. Although many machine/deep learning methods have already been investigated, still, there is a need for further improvement.

Method: This paper proposes a novel deep convolutional neural network (CNN) based transfer learning approach complemented with structured filter pruning for histopathological image classification, and to bring down the run-time resource requirement of the trained deep learning models. In the proposed method, first, the less important filters are pruned from the convolutional layers and then the pruned models are trained on the histopathological image dataset.

Results: We performed extensive experiments using three popular pre-trained CNNs, VGG19, ResNet34, and ResNet50. With VGG19 pruned model, we achieved an accuracy of 91.25% outperforming earlier methods on the same dataset and architecture while reducing 63.46% FLOPs. Whereas, with the ResNet34 pruned model, the accuracy increases to 91.80% with 40.63% fewer FLOPs. Moreover, with the ResNet50 model, we achieved an accuracy of 92.07% with 30.97% less FLOPs.

Conclusion: The experimental results reveal that the pre-trained model's performance complemented with filter pruning exceeds original pre-trained models. Another important outcome of the research is that the pruned model with reduced resource requirements can be deployed in point-of-care devices for automated diagnosis applications with ease.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104432DOI Listing
April 2021

FakeBERT: Fake news detection in social media with a BERT-based deep learning approach.

Multimed Tools Appl 2021 Jan 7:1-24. Epub 2021 Jan 7.

Departement of CSIS, BITS Pilani, Pilani, Rajasthan India.

In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale. In recent researches, many useful methods for fake news detection employ sequential neural networks to encode news content and social context-level information where the text sequence was analyzed in a unidirectional way. Therefore, a bidirectional training approach is a priority for modelling the relevant information of fake news that is capable of improving the classification performance with the ability to capture semantic and long-distance dependencies in sentences. In this paper, we propose a BERT-based (Bidirectional Encoder Representations from Transformers) deep learning approach (FakeBERT) by combining different parallel blocks of the single-layer deep Convolutional Neural Network (CNN) having different kernel sizes and filters with the BERT. Such a combination is useful to handle ambiguity, which is the greatest challenge to natural language understanding. Classification results demonstrate that our proposed model (FakeBERT) outperforms the existing models with an accuracy of 98.90%.
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http://dx.doi.org/10.1007/s11042-020-10183-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7788551PMC
January 2021

EchoFakeD: improving fake news detection in social media with an efficient deep neural network.

Neural Comput Appl 2021 Jan 2:1-17. Epub 2021 Jan 2.

Department of CSIS, BITS Pilani, Pilani, Rajasthan India.

The increasing popularity of social media platforms has simplified the sharing of news articles that have led to the explosion in fake news. With the emergence of fake news at a very rapid rate, a serious concern has produced in our society because of enormous fake content dissemination. The quality of the news content is questionable and there exists a necessity for an automated tool for the detection. Existing studies primarily focus on utilizing information extracted from the news content. We suggest that user-based engagements and the context related group of people (echo-chamber) sharing the same opinions can play a vital role in the fake news detection. Hence, in this paper, we have focused on both the content of the news article and the existence of echo chambers in the social network for fake news detection. Standard factorization methods for fake news detection have limited effectiveness due to their unsupervised nature and primarily employed with traditional machine learning models. To design an effective deep learning model with tensor factorization approach is the priority. In our approach, the news content is fused with the tensor following a coupled matrix-tensor factorization method to get a latent representation of both news content as well as social context. We have designed our model with a different number of filters across each dense layer along with dropout. To classify on news content and social context-based information individually as well as in combination, a deep neural network (our proposed model) was employed with optimal hyper-parameters. The performance of our proposed approach has been validated on a real-world fake news dataset: BuzzFeed and PolitiFact. Classification results have demonstrated that our proposed model (EchoFakeD) outperforms existing and appropriate baselines for fake news detection and achieved a validation accuracy of 92.30%. These results have shown significant improvements over the existing state-of-the-art models in the area of fake news detection and affirm the potential use of the technique for classifying fake news.
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http://dx.doi.org/10.1007/s00521-020-05611-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776294PMC
January 2021