Open Access
Issue
ITM Web Conf.
Volume 56, 2023
First International Conference on Data Science and Advanced Computing (ICDSAC 2023)
Article Number 05018
Number of page(s) 11
Section Machine Learning & Neural Networks
DOI https://doi.org/10.1051/itmconf/20235605018
Published online 09 August 2023
  1. Baskar, S., Shakeel, P.M., Kumar, R., Burhanuddin, M.A. and Sampath, R., (2020). A dynamic and interoperable communication framework for controlling the operations of wearable sensors in smart healthcare applications. Computer Communications, 149, pp. 17-26. [CrossRef] [Google Scholar]
  2. Salih, A.S.M. and Abraham, A., (2015). Intelligent decision support for real time health care monitoring system. In Afro-European Conference for Industrial Advancement: Proceedings of the First International Afro-European Conference for Industrial Advancement AECIA (2014) (pp. 183-192). Springer International Publishing. [Google Scholar]
  3. Ali, F., El-Sappagh, S., Islam, S.R., Kwak, D., Ali, A., Imran, M. and Kwak, K.S., (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, pp. 208-222. [CrossRef] [Google Scholar]
  4. S. Aruna, Prediction of Leaf Disease Utilizing Internet of Things, ISSN: 2277-4998 / DOI: https://doi.org/10.31032/IJBPAS/2021/10.11.1047, Vol. No. 10, Issue 11 and pg. no. 540-550, International Journal of Biology, Pharmacy, and Allied Science, November (2021). [Google Scholar]
  5. Al-Khafajiy, M., Baker, T., Chalmers, C., Asim, M., Kolivand, H., Fahim, M. and Waraich, A., (2019). Remote health monitoring of elderly through wearable sensors. Multimedia Tools and Applications, 78(17), pp. 24681-24706. [CrossRef] [Google Scholar]
  6. Awotunde, J.B., Chakraborty, C. and Folorunso, S.O., (2022). A secured smart healthcare monitoring systems using Blockchain Technology. In Intelligent Internet of Things for Healthcare and Industry (pp. 127-143). Cham: Springer International Publishing. [CrossRef] [Google Scholar]
  7. Ba, T., Li, S. and Wei, Y., (2021). A data-driven machine learning integrated wearable medical sensor framework for elderly care service. Measurement, 167, p. 108383. [CrossRef] [Google Scholar]
  8. Velrani, K.S. and Geetha, G., (2016), July. Sensor based healthcare information system. In 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (pp. 86-92). IEEE. [CrossRef] [Google Scholar]
  9. Sarangi, L., Mohanty, M.N. and Patnaik, S., (2017). Design of ANFIS based e- health care system for cardio vascular disease detection. In Recent Developments in Intelligent Systems and Interactive Applications: Proceedings of the International Conference on Intelligent and Interactive Systems and Applications (IISA2016) (pp. 445-453). Springer International Publishing. [Google Scholar]
  10. Boursalie, O., Samavi, R. and Doyle, T.E., (2015). M4CVD: Mobile machine learning model for monitoring cardiovascular disease. Procedía Computer Science, 63, pp. 384-391. [CrossRef] [Google Scholar]
  11. Padma, T. and Balasubramanie, P., (2011). A fuzzy analytic hierarchy processing decision support system to analyze occupational menace forecasting the spawning of shoulder and neck pain. Expert Systems with Applications, 38(12), pp. 15303-15309. [CrossRef] [Google Scholar]
  12. Rajeswari C., Sathiyabhama B., Devendiran S., Manivanan K. Bearing fault diagnosis using wavelet packet transform, hybrid PSO and support vector machine, Procedia Engineering., Vol 97 (1) PP: 1772-1783, (2014) (CSE). [CrossRef] [Google Scholar]
  13. Xu, X., Li, W., Ran, Q., Du, Q., Gao, L. and Zhang, B., (2017). Multisource remote sensing data classification based on convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 56(2), pp. 937-949. [Google Scholar]
  14. Haut, J.M., Paoletti, M.E., Plaza, J., Li, J. and Plaza, A., (2018). Active learning with convolutional neural networks for hyperspectral image classification using a new Bayesian approach. IEEE Transactions on Geoscience and Remote Sensing, 56 (11), pp. 6440-6461. [CrossRef] [Google Scholar]
  15. Albahar, M.A., (2019). Skin lesion classification using convolutional neural network with novel regularizes IEEE Access, 7, pp. 38306-38313. [Google Scholar]
  16. Gao, H., Cheng, B., Wang, J., Li, K., Zhao, J. and Li, D. (2018). Object classification using CNN-based fusion of vision and LIDAR in autonomous vehicle environment. IEEE Transactions on Industrial Informatics, 14(9), pp. 4224-4231. [CrossRef] [Google Scholar]
  17. Sultana, F., Sufian, A. and Dutta, P. (2018), November. Advancements in image classification using convolutional neural network. In 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) (pp. 122-129). IEEE. [Google Scholar]
  18. Huang, Y., Zheng, H., Liu, C., Ding, X. and Rohde, G.K., (2017). Epithelium- stroma classification via convolutional neural networks and unsupervised domain adaptation in histopathological images. IEEE journal of biomedical and health informatics, 21(6), pp. 1625-1632. [CrossRef] [Google Scholar]
  19. Sharawi, M., Zawbaa, H.M. and Emary, E., (2017), February. Feature selection approach based on whale optimization algorithm. In 2017 Ninth international conference on advanced computational intelligence (ICACI) (pp. 163-168). IEEE. [CrossRef] [Google Scholar]
  20. Xu, H., Fu, Y., Fang, C., Cao, Q., Su, J. and Wei, S., (2018), September. An improved binary whale optimization algorithm for feature selection of network intrusion detection. In 2018 IEEE 4th International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS) (pp. 10-15). IEEE. [Google Scholar]
  21. Kundu, R., Chattopadhyay, S., Cuevas, E. and Sarkar, R., (2022). AltWOA: Altruistic Whale Optimization Algorithm for feature selection on microarray datasets. Computers in biology and medicine, 144, p. 105349. [CrossRef] [Google Scholar]
  22. Hussien, A.G., Hassanien, A.E., Houssein, E.H., Bhattacharyya, S. and Amin, M., 2019. S-shaped binary whale optimization algorithm for feature selection. In Recent Trends in Signal and Image Processing: ISSIP (2017) (pp. 79-87). Springer Singapore. [CrossRef] [Google Scholar]
  23. Inbarani H.H., Azar A.T., Jothi G. Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis, Computer Methods and Programs in Biomedicine, Vol. 113(1) pp: 175-185 DOI: 10.1016/j.cmpb.2013.10.007, (2014) (IT). [CrossRef] [Google Scholar]
  24. Agrawal, R.K., Kaur, B. and Sharma, S., (2020). Quantum based whale optimization algorithm for wrapper feature selection. Applied Soft Computing, 89, p. 106092. [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.