Authors:
S. Karthik, Edwin Shalom Soji, S. Silvia Priscila, M. Sakthivanitha, S. Suman Rajest, A.Senthil Kumar

Addresses:
1Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. 2,3Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. 4Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Chennai, Tamil Nadu, India. 5Department of Research and Development (R&D) & International Student Affairs (ISA), Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. 6Department of Computer Science, Skyline University, Kano, Nigeria. karthiks1087@gmail.com1, edwinshalomsoji.cbcs.cs@bharathuniv.ac.in2, silviaprisila.cbcs.cs@bharathuniv.ac.in3, sakthivanithamsc@gmail.com4, sumanrajest414@gmail.com5, Senthil.kumar@sun.edu.ng6

Abstract:

This paper introduces a novel foundational architecture incorporating two primary algorithms. The Partially Enhanced Linear Mean Analysis Algorithm for significant vectorization and the Vertically Enhanced Tensor-Load Interface Algorithm for structural vectorization. These algorithms are meticulously designed to optimize the extraction of valuable information from complex data sets, enabling efficient and insightful historical data analysis. The performance of these algorithms has been rigorously compared with existing significant vectorization and structural methods, demonstrating superior accuracy in mean value estimation. Simulated results provide robust evidence of the high-dimensional capability and throughput of the proposed architecture. Incorporating tools such as Microsoft Excel for data organization, MATLAB for advanced computational analysis, and Python for algorithmic implementation and automation further enhances the framework’s efficiency and precision. Imminent work on this architecture could expand its applicability into multi-functional systems, potentially integrating a multi-role tape framework. Such an expansion would be particularly beneficial for identifying brain tumors with greater accuracy and precision, contributing significantly to medical diagnostics. The proposed system represents a promising advancement in applying computational tools for complex data analysis and structural vectorization.

Keywords: Substantial Mining; Structure Mining; Brain Tumor; Accuracy and Prediction; Scalable Algorithms; MRI Image Analysis; Convolutional Neural Network; Support Vector Machines (SVM); Random Forests.

Received on: 09/02/2024, Revised on: 15/04/2024, Accepted on: 11/05/2024, Published on: 01/06/2024

DOI: 10.69888/FTSHSL.2024.000176

FMDB Transactions on Sustainable Health Science Letters, 2024 Vol. 2 No. 2, Pages: 110-127

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