Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)

<Previous Article In Volume
Next Article In Volume>

An Intelligent Nanorobotics System for Prevention & Monitoring of Blood Related Diseases in Precision Medicine with Artificial Intelligence

Authors
Vandana Sharma1, *, Tamizharasi Seetharaman2, Basanagouda Meti3, Chinmay kirankumar Ghatakamble4, Vinay Kukreja5, Ahmed Alkhayyat6
1Amity Institute of Information Technology, Amity University, Noida Campus, Noida, India
2Assistant Professor, Department of AIML, Acharya Institute of Technology, Bengaluru, India
3Department of AIML, Acharya Institute of Technology, Bengaluru, India
4Department of AIML, Acharya Institute of Technology, Bengaluru, India
5Chitkara University Institute of Engineering and Technology, Punjab, India
6College of Technical Engineering, The Islamic University, Najaf, Iraq
*Corresponding author. Email: vandana.juyal@gmail.com
Corresponding Author
Vandana Sharma
Available Online 4 October 2024.
DOI
10.2991/978-94-6463-529-4_22How to use a DOI?
Keywords
nanorobots; hematology; self-supervised learning; blood clot; precision medicine
Abstract

Currently the application of artificial intelligence for nanorobotics gains significant attention worldwide. The effective convergence of this technique helps in earlier diagnosis and treatment of various harmful diseases. Deep vein thrombosis also called as blood clot is a serious healthcare concern among humans that impacts several individuals every year. On the other hand, there are many numbers of researches that attempt to prevent the harmful impacts of the blood coagulation in human. In this regard, the proposed approach makes an effective convergence of the artificial intelligence and nanorobotic technology to provide an efficient solution for earlier detection of blood clot in humans. The major contributions are divided into twofold. First, we define an efficient nanorobot and artificial intelligence assisted framework for earlier blood clot detection. And the next, an efficient self-supervised learning model is proposed for the control of nanorobots. The simulation results of the proposed approach is found to be comparatively better than the conventional approaches.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
Series
Advances in Engineering Research
Publication Date
4 October 2024
ISBN
978-94-6463-529-4
ISSN
2352-5401
DOI
10.2991/978-94-6463-529-4_22How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Vandana Sharma
AU  - Tamizharasi Seetharaman
AU  - Basanagouda Meti
AU  - Chinmay kirankumar Ghatakamble
AU  - Vinay Kukreja
AU  - Ahmed Alkhayyat
PY  - 2024
DA  - 2024/10/04
TI  - An Intelligent Nanorobotics System for Prevention & Monitoring of Blood Related Diseases in Precision Medicine with Artificial Intelligence
BT  - Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV-2023)
PB  - Atlantis Press
SP  - 244
EP  - 252
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-529-4_22
DO  - 10.2991/978-94-6463-529-4_22
ID  - Sharma2024
ER  -