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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Shrinivasa, Shrinivasa
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Computer Science & IT Engineering

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Automated diagnosis of brain tumor classification and segmentation of magnetic resonance imaging images B. Muddaraju, Chandrakala; Shrinivasa, Shrinivasa; Narasimhamurthy, Shobha; Sontakke, Vaishali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4833-4842

Abstract

Brain tumors are one of the most prevalent disorders of the central nervous system and are dangerous. For patients to receive the best treatment, early diagnosis is crucial. For radiologists to correctly detect brain tumor images, an automated approach is required. The identification procedure can be time-consuming and prone to mistakes. In this work, the issue of fully automated brain tumor classification and segmentation of magnetic resonance imaging (MRI) including meningioma, glioma, pituitary, and no tumor is taken into consideration. In this study, convolutional neural network (CNN) and mask region-based convolutional neural network (R-CNN) are proposed for classification and segmentation problems respectively. This study employed 3,200 images as a training set and the system achieved an accuracy of 96% for classifying the tumors and 94% accuracy in segmentation of tumors.
Advancing integrity and privacy in cloud storage: challenges, current solutions, and future directions Shrinivasa, Shrinivasa; Beturpalya Muddaraju, Chandrakala; Prashanth Patil, Annapurna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp12-18

Abstract

The rapid expansion of cloud computing has steered in an era where cloud storage is increasingly prevalent, offering significant advantages in terms of reducing local storage burden. However, this technological shift has also introduced complex security challenges, including data integrity and privacy concerns. In response to these challenges, various data integrity auditing (DIA) protocols have been developed, aiming to enable efficient and secure verification of data stored in cloud environments. This survey paper provides a comprehensive analysis of existing DIA mechanisms, focusing on methods like homomorphic linear authentication, dynamic hash tables, and watermarking techniques for integrity and privacy preservation. It critically evaluates these methods in terms of their advantages, limitations, and the unique challenges they face in practical applications, such as scalability, efficiency in multi-owner contexts, and real-time auditing. Furthermore, the paper identifies key research gaps, including the need for optimizing largescale data handling, balancing watermarking imperceptibility with embedding capacity, and developing comprehensive solutions for decentralized public auditing. The survey serves as a critical resource for researchers to understand the current background of cloud data integrity auditing and the future directions in this evolving field.
Co-Authors B. Muddaraju, Chandrakala Beturpalya Muddaraju, Chandrakala Narasimhamurthy, Shobha Prashanth Patil, Annapurna Sontakke, Vaishali