Open Access
Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
---|---|---|
Article Number | 01123 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/matecconf/202439201123 | |
Published online | 18 March 2024 |
- Shivani Dere, Maziya Fatima, Rutuja Jagtap and Nikhilkumar B Shardoor “Anomaly Detection in Astronomical Objects of Galaxies Using Deep Learning,”7th International Conference on Advanced Computing & Communication Systems (ICACCS),(2021) [Google Scholar]
- A.Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenetclassification with deep convolutional neuralnetworks,” in Advances in Neural Information Processing Systems, (2012), pp. 1097–1105. [Google Scholar]
- S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised Representation Learning by Predicting Image Rotations,” in International Conference on Learning Representations, (2018). [Google Scholar]
- M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep Clustering for Unsupervised Learning of Visual Features,” in European Conference on Computer Vision, (2018). [Google Scholar]
- C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised Visual Representation Learning by Context Prediction,” (2015). [Google Scholar]
- D. Hendrycks, M. Mazeika, S. Kadavath, and D. Song, “Using SelfSupervised Learning Can Improve Model Robustness and Uncertainty,” in Advances in Neural Information Processing Systems, (2019), pp. 15 663– 15 674 [Google Scholar]
- M. Raghu, C. Zhang, J. Kleinberg, and S. Bengio, “Transfusion: Understanding Transfer Learning for Medical Imaging,” in Advances in Neural Information Processing Systems, (2019). [Google Scholar]
- A. Martinazzo, M. Espadoto, and N. Hirata, “Deep Learning for Astronomical Object Classification: A Case Study,” in VISAPP, (2020) [Google Scholar]
- Venkateswara Reddy, L., Prema, K. (2020). Efficient Spatial Database Keyword Query Search. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA (2019). Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_186 [Google Scholar]
- P. Venkateswarlu Reddy, D. Ganesh, L. S. Azeem, M. Padma, K. D. Lakshmi and L. Chakradhar, “Implementation of Latest Deep Learning Techniques for Brain Tumor Identification from MRI Images,” (2023) 8th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, (2023), pp. 1166-1171, doi: 10.1109/ICCES57224.2023.10192620. [Google Scholar]
- D. Ganesh et al., ”Implementation of Novel Machine Learning Methods for Analysis and Detection of Fake Reviews in Social Media,” 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, (2023), pp. 243-250, doi: 10.1109/ICSCDS56580.2023.10104856. [Google Scholar]
- Venkateswara Reddy, L., Davanam, G., Pavan Kumar, T., Sunil Kumar, M., Narendar, M. (2023). Bio-Inspired Firefly Algorithm for Polygonal Approximation on Various Shapes. In: Rao, B.N.K., Balasubramanian, R., Wang, SJ. , Nayak, R. (eds) Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 315. Springer, Singapore. https://doi.org/10.1007/978-981-19-4162-7_10 [Google Scholar]
- Davanam, G. ;; Singh, N. ; Gunjan, V.K. ; Roy, S. ; Rahebi, J. ; Farzamnia, A. ; Saad, I. Multi-Controller Model for Improving the Performance of IoT Networks. Energies (2022), 15, 8738. https://doi.org/10.3390/en15228738 [CrossRef] [Google Scholar]
- Ganesh, D., et al. “Improving Security in Edge Computing by using Cognitive Trust Management Model.” 2022 International Conference on Edge Computing and Applications (ICECAA). IEEE, (2022). [Google Scholar]
- Davanam, Ganesh, T. Pavan Kumar, and M. Sunil Kumar. “Novel Defense Framework for Cross-layer Attacks in Cognitive Radio Networks.” International Conference on Intelligent and Smart Computing in Data Analytics: ISCDA (2020). Springer Singapore, (2021). [Google Scholar]
- Ganesh, D., Kumar, T. P., & Kumar, M. S. (2021). Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks. IET Communications, 15(2), 245-256. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.