Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)

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Threats and Vulnerabilities in Social Media: A Review of Cyber Security Perspectives

Authors
Saurabh Shandilya1, *, Sachin Jain2, Gaurav Sharma1, Devendar Nath Pathak1, Shalini Singhal3, Priyanka Sharma4
1Department of Advance Computing, Poornima College of Engineering, Jaipur, India
2Department of Computer Science, Poornima College of Engineering, Jaipur, India
3Department of Information Technology, SKIT Engineering College, Jaipur, India
4Department of Humanities, University of Technology, Jaipur, India
*Corresponding author. Email: saurabh.shandilya@poornima.org
Corresponding Author
Saurabh Shandilya
Available Online 19 April 2025.
DOI
10.2991/978-94-6463-700-7_19How to use a DOI?
Keywords
Brain Tumor; Segmentation; Modality; Deep Learning; Federated Learning
Abstract

Brain tumors are a severe medical problem due to their high mortality rate, necessitating improved diagnostic and therapeutic procedures. Radiologists must manually segment patients, which is expensive, time-consuming, and prone to error. Deep learning-based automated segmentation has recently demonstrated the potential to address these challenges, especially in the areas of image segmentation and classification. Brain tumor segmentation is essential in medical imaging to evaluate the size, location, and shape of tumors. This paper starts with an overview of multi-modal brain tumor segmentation techniques. After that, it reviews pertinent literature to evaluate how deep learning and machine learning are applied in diverse modalities. In addition, the review offers an overview of federated learning strategies that enhance global segmentation efficiency while maintaining data privacy. Lastly, the paper assesses the level of multi-modal brain tumor segmentation algorithms at the moment and looks ahead to potential developments in this area.

Copyright
© 2025 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.

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Volume Title
Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
Series
Advances in Intelligent Systems Research
Publication Date
19 April 2025
ISBN
978-94-6463-700-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-700-7_19How to use a DOI?
Copyright
© 2025 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  - Saurabh Shandilya
AU  - Sachin Jain
AU  - Gaurav Sharma
AU  - Devendar Nath Pathak
AU  - Shalini Singhal
AU  - Priyanka Sharma
PY  - 2025
DA  - 2025/04/19
TI  - Threats and Vulnerabilities in Social Media: A Review of Cyber Security Perspectives
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 241
EP  - 250
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_19
DO  - 10.2991/978-94-6463-700-7_19
ID  - Shandilya2025
ER  -