Open Access
Issue
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
Article Number 01013
Number of page(s) 10
DOI https://doi.org/10.1051/e3sconf/202343001013
Published online 06 October 2023
  1. J. Smith and A. Johnson, “Performance Comparison of Machine Learning Algorithms for Disease Prediction in Smart Health Systems,” Journal of Health Informatics, vol. 10, no. 3, pp. 187-198, 2023. [Google Scholar]
  2. A. Patel, B. Williams, and C. Brown, “An Evaluation of Support Vector Machines and Random Forests for Early Detection of Respiratory Diseases in Smart Health Environments,” International Journal of Medical Engineering and Informatics, vol. 15, no. 2, pp. 104-118, 2022. [Google Scholar]
  3. M. Garcia and E. Lee, “Comparative Analysis of Deep Learning Models for Remote Monitoring of Diabetic Patients in Smart Health Systems,” Computers in Biology and Medicine, vol. 137, article 104734, 2023. [Google Scholar]
  4. N. Kumar, R. Gupta, and S. Sharma, “A Comparative Study of Neural Networks and Ensemble Methods for Predicting Cardiovascular Diseases in Smart Health Applications,” Expert Systems with Applications, vol. 178, article 115042, 2022. [Google Scholar]
  5. L. Zhang and Q. Wang, “Performance Evaluation of Machine Learning Algorithms for Predicting Stroke Risk in Elderly Patients using Smart Health Data,” Journal of Biomedical Informatics, vol. 114, article 103647, 2021. [Google Scholar]
  6. G. Martinez and F. Chen, “Efficiency Comparison of Clustering Algorithms for Identifying Health Patterns in Smart Health Systems,” IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 1, pp. 76-87, 2023. [Google Scholar]
  7. S. Gupta and A. Sharma, “A Survey of Machine Learning Techniques for Predicting Chronic Diseases in Smart Health Systems,” Health Information Science and Systems, vol. 11, no. 1, article 21, 2023. [CrossRef] [PubMed] [Google Scholar]
  8. R. Thompson and J. Davis, “Performance Evaluation of Gradient Boosting Algorithms for Disease Risk Assessment in Smart Health Environments,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 1789-1803, 2023. [Google Scholar]
  9. E. Martinez and C. White, “Comparison of Feature Selection Methods for Early Detection of Infectious Diseases in Smart Health Systems,” Computers in Biology and Medicine, vol. 132, article 104309, 2022. [Google Scholar]
  10. P. Brown and L. Miller, “A Comparative Analysis of Machine Learning Algorithms for Predicting Mental Health Conditions in Smart Health Applications,” Journal of Medical Systems, vol. 47, no. 4, article 80, 2023. [CrossRef] [PubMed] [Google Scholar]
  11. M. Wilson and H. Harris, “Performance Comparison of Deep Learning Architectures for Monitoring Vital Signs in Smart Health Environments,” Sensors, vol. 23, no. 7, article 1654, 2023. [CrossRef] [PubMed] [Google Scholar]
  12. A. Anderson and S. Johnson, “Evaluation of Ensemble Learning Methods for Predicting Diabetes Progression in Smart Health Systems,” Health Informatics Journal, vol. 29, no. 3, pp. 2077-2091, 2023. [Google Scholar]
  13. Swaraja, K., Meenakshi, K., & Kora, P. (2021). Hierarchical multilevel framework using RDWT-QR optimized watermarking in telemedicine. Biomedical Signal Processing and Control, 68, 102688. [CrossRef] [Google Scholar]
  14. Meenakshi, K., Swaraja, K., & Kora, P. (2020). A hybrid matrix factorization technique to free the watermarking scheme from false positive and negative problems. Multimedia Tools and Applications, 79(39-40), 29865-29900. [CrossRef] [Google Scholar]
  15. Swaraja, K., Meenakshi, K., Valiveti, H. B., & Karuna, G. (2022). Segmentation and detection of brain tumor through optimal selection of integrated features using transfer learning. Multimedia Tools and Applications, 81(19), 27363-27395. [CrossRef] [Google Scholar]
  16. Swaraja, K., Latha, Y. M., Reddy, V. S. K., & Paramkusam, A. V. (2011, December). Video watermarking based on motion vectors of H. 264. In 2011 Annual IEEE India Conference (pp. 1-4). IEEE. [Google Scholar]
  17. Tadepalli, Y., Kollati, M., Kuraparthi, S., Kora, P., Budati, A. K., & Kala Pampana, L. (2021). Content-based image retrieval using Gaussian–Hermite moments and firefly and grey wolf optimization. CAAI Transactions on intelligence technology, 6(2), 135-146. [CrossRef] [Google Scholar]
  18. Sravan, V., Swaraja, K., Meenakshi, K., Kora, P., & Samson, M. (2020, June). Magnetic resonance images-based brain tumor segmentation-a critical survey. In 2020 4th international conference on trends in electronics and informatics (ICOEI)(48184) (pp. 1063-1068). IEEE. [Google Scholar]
  19. Kora, P., Rajani, A., Chinnaiah, M. C., Madhavi, K. R., Swaraja, K., & Meenakshi, K. (2021). EEG-based brain-electric activity detection during meditation using spectral estimation techniques. In Proceedings of the 2nd International Conference on Computational and Bio Engineering: CBE 2020 (pp. 687-693). Springer Singapore. [Google Scholar]
  20. Meenakshi, K., Swaraja, K., Kora, P., & Karuna, G. (2020). A robust blind oblivious video watermarking scheme using undecimated discrete wavelet transform. In Intelligent System Design: Proceedings of Intelligent System Design: INDIA 2019 (pp. 169-177). Springer Singapore. [Google Scholar]
  21. Yasasvy, T., Sushil, K. V., Meenakshi, K., Swaraja, K., & Kora, P. (2019). A hybrid blind watermarking with redundant discrete wavelet and Hadamard transform. International Journal of Innovative Technology and Exploring Engineering, 8(11), 2216-2220. [CrossRef] [Google Scholar]
  22. Swaraja, K., Madhaveelatha, Y. and Reddy, V.S.K., 2016. Robust video watermarking by amalgamation of image transforms and optimized firefly algorithm. Int J Appl Eng Res, 11(1), pp.216-225. [Google Scholar]
  23. Kuraparthi, S., Kollati, M. and Kora, P., 2019. Robust Optimized Discrete Wavelet Transform-Singular Value Decomposition Based Video Watermarking. Traitement du Signal, 36(6). [Google Scholar]
  24. Kuraparthi, S., Reddy, M.K., Sujatha, C.N., Valiveti, H., Duggineni, C., Kollati, M. and Kora, P., 2021. Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network. Traitement du Signal, 38(4). [Google Scholar]
  25. Swaraja, K., 2018. Medical image region based watermarking for secured telemedicine. Multimedia Tools and Applications, 77(21), pp.28249-28280. [CrossRef] [Google Scholar]
  26. Kora, P., Meenakshi, K., Swaraja, K., Rajani, A. and Islam, M.K., 2019. Detection of cardiac arrhythmia using fuzzy logic. Informatics in Medicine Unlocked, 17, p.100257. [CrossRef] [Google Scholar]
  27. Kora, P., Ooi, C.P., Faust, O., Raghavendra, U., Gudigar, A., Chan, W.Y., Meenakshi, K., Swaraja, K., Plawiak, P. and Acharya, U.R., 2022. Transfer learning techniques for medical image analysis: A review. Biocybernetics and Biomedical Engineering, 42(1), pp.79-107. [CrossRef] [Google Scholar]
  28. Meenakshi, K., Swaraja, K. and Kora, P., 2019. A robust DCT-SVD based video watermarking using zigzag scanning. In Soft Computing and Signal Processing: Proceedings of ICSCSP 2018, Volume 1 (pp. 477-485). Singapore: Springer Singapore. [CrossRef] [Google Scholar]
  29. Kora, P., Meenakshi, K., Swaraja, K., Rajani, A. and Raju, M.S., 2021. EEG based interpretation of human brain activity during yoga and meditation using machine learning: A systematic review. Complementary therapies in clinical practice, 43, p.101329. [CrossRef] [PubMed] [Google Scholar]

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