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1

  • OPTIMIZATION SEGMENTATION AND CLASSIFICATION FROM MRI OF BRAIN TUMOR AND ITS LOCATION CALCULATION USING MACHINE LEARNING AND DEEP LEARNING APPROACH

    DOI:DOI:18.A003.aarf.J14I01.004540

TIRUVEEDULA GOPI KRISHNA1, MOHAMED ABDELDAIEM ABDELHADI2

Abstract:
The manual detection and classification finding correct location and identifying type of tumor becomes a rigorous and hectic task for the radiologists. Medical diagnosis via image processing and machine learning is considered one of the most important issues of artificial intelligence systems. Deep learning has been used successfully in supervised classification tasks in order to learn complex patterns. The main contributions of this paper are as create a more generalized method for brain tumor classification using deep learning a variety of neural networks were constructed based on the preprocessing of image data., analyze the application of tumorless brain images on brain tumor classification and empirically evaluate neural networks on the given datasets with per image accuracy and per patient accuracy. And also presents an efficient image segmentation using machine learning algorithm with some optimization techniques to detect brain tumors


1-13
2

Janne Umapathi1, Dr. Vishal Khatri2

Abstract:
This paper helps to develop an evaluation based on the web based application in the creation of number framework and tool that facilitated the development process. Web application is mainly referred to make faster web development that has a huge effect on the CSS animation. Ruby on rails is a particular framework which plays a significant role in the CSS animation and the web designing.


14-21
3

Ms. Suman Choudhary Prof(Dr.)Mahaveer Kumar Sain

Abstract:
Clustering is a powerful unsupervised machine learning technique for grouping similar occurrences together. This Clustering is primarily used to generate clusters of high quality, enabling the discovery of hidden patterns and information inside massive datasets. It has extensive applications in several fields, including medical, gene expression, image processing, healthcare, agriculture, image processing, fraud detection, and profitability analysis, among others.


22-30
4

Shipra

Abstract:
Converting real-world issues into mathematical language is the process of creating ordinary differential equations. They can facilitate problem solving and make problem processing simpler. They play a crucial role in bridging the gap between theory and practice in mathematics. This paper presents the method steps for creating an ordinary differential equation model based on a brief overview of mathematical modelling. It also integrates the practical investigation of the use of ordinary differential equations in mathematical modelling to offer direction for future research in this area.


31-42
5

Ashok Kumar Srivastava

Abstract:
This paper presents a theoretical framework for understanding the emergence of self-organization in complex adaptive systems. Drawing from concepts in dynamical systems theory, information theory, and statistical physics, we develop a formalism that characterizes how local interactions between individual agents can give rise to global patterns without centralized control. We introduce a generalized measure of emergent complexity and demonstrate its application across diverse systems including biological networks, social organizations, and computational models. Our framework reveals that self-organizing systems typically operate in a critical regime between order and chaos, where information transfer and processing are optimized. These theoretical insights offer new perspectives on how complex systems maintain stability while adapting to changing environments, with implications for designing resilient artificial systems and understanding natural emergent phenomena.


43-52
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  • Rashtriya Research Institute
    Of New Medical Sciences

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