A deep learning semantic segmentation-based document classification method

Authors

  • Harshini R School of Computer Science and Engineering, REVA University, Bengaluru, India.
  • Nihar Gaokar School of Computer Science and Engineering, REVA University, Bengaluru, India.
  • Nagashree N Deptartment of Computer Science and Engineering, Sai Vidhya Institute of Technology, India

Keywords:

Deep learning, segmentation, document classification

Abstract

The introduction of the internet and internet-based existence has had a significant impact on people's lives. Additionally, mass media has an impact on everyday public life. Due to political power, many people take use of these rights to indulge in luxuries and elevate their social status. After COVID 19, People deliberately spread fake information through web-based social networking sites. This has an impact on how internet news sites originally operated and were intended. Therefore, we need some tools to automate the process and identify effective ways to classify it in order to stop the spread of such bad news. A computer vision based semantic segmentation method is proposed on a deep learning platform to perform muti-document analysis.

References

Debnath, K., & Kar, N. (2022, May). Email Spam Detection using Deep Learning Approach. In 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) (Vol. 1, pp. 37-41). IEEE.

Junnarkar, A., Adhikari, S., Fagania, J., Chimurkar, P., & Karia, D. (2021, February). E-mail spam classification via machine learning and natural language processing. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 693-699). IEEE.

Nagashree, N., Patil, P., Patil, S., & Kokatanur, M. (2021, June). Alpha beta pruned UNet-a modified unet framework to segment MRI brain image to analyse the effects of CNTNAP2 gene towards autism detection. In 2021 3rd International Conference on Computer Communication and the Internet (ICCCI) (pp. 23-26). IEEE.

Cheng, Q., Xu, A., Li, X., & Ding, L. (2022, March). Adversarial Email Generation against Spam Detection Models through Feature Perturbation. In 2022 IEEE International Conference on Assured Autonomy (ICAA) (pp. 83-92). IEEE.

Al-Shammari, N. K., Syed, T. H., & Syed, M. B. (2021). An Edge–IoT framework and prototype based on blockchain for smart healthcare applications. Engineering, Technology & Applied Science Research, 11(4), 7326-7331.

Nagesh, N., Patil, P., Patil, S., & Kokatanur, M. (2022). An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm. International Journal of Electrical & Computer Engineering (2088-8708), 12(2).

Ahmed, S. T., Sreedhar Kumar, S., Anusha, B., Bhumika, P., Gunashree, M., & Ishwarya, B. (2018, November). A Generalized Study on Data Mining and Clustering Algorithms. In International Conference On Computational Vision and Bio Inspired Computing (pp. 1121-1129). Springer, Cham.

Gibson, S., Issac, B., Zhang, L., & Jacob, S. M. (2020). Detecting spam email with machine learning optimized with bio-inspired metaheuristic algorithms. IEEE Access, 8, 187914-187932.

Liu, X., Lu, H., & Nayak, A. (2021). A spam transformer model for SMS spam detection. IEEE Access, 9, 80253-80263.

Ahmed, S. T., Kumar, V. V., Singh, K. K., Singh, A., Muthukumaran, V., & Gupta, D. (2022). 6G enabled federated learning for secure IoMT resource recommendation and propagation analysis. Computers and Electrical Engineering, 102, 108210.

Agboola, O. (2022). Spam Detection Using Machine Learning and Deep Learning.

Nagashree, N., Patil, P., Patil, S., & Kokatanur, M. (2022). InvCos curvature patch image registration technique for accurate segmentation of autistic brain images. In Soft Computing and Signal Processing (pp. 659-666). Springer, Singapore.

Downloads

Published

2023-01-11

How to Cite

Harshini R, Nihar Gaokar, & Nagashree N. (2023). A deep learning semantic segmentation-based document classification method. International Journal of Computational Learning & Intelligence, 2(1), 14–16. Retrieved from https://milestoneresearch.in/JOURNALS/index.php/IJCLI/article/view/54

Issue

Section

RESEARCH ARTICLES