Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Efficient Brain Tumor Identification Based on Optimal Support Scaling Vector Feature Selection (OSSCV) Using Stochastic Spin-Glass Model Classification Subject The topic of the resource brain tumor; classification; feature selection; Machine learning; MRI image processing; neural network; stochastic spin model Description An account of the resource Brain tumor detection is a developing defect finding task in medical imaging, as premature and early identification is a critical once for recommending early treatment. The tumor are identified by the laboratory through MRI images by finding the tumor regions. The Artificial intelligence play a vital role for finding, analyzing, the image data to attain the target results in medical image using various learning methodologies. Most of the existing system failed to find the find the feature dimension leads poor accuracy for identifying tumor regions due to low precision, recall rate, lower intensity in image coverage region. To resolve this problem, to propose an Optimal Support Scaling Vector Based Feature Selection (OSSCV) brain tumor identification using Stochastic Spin-Glass Model Classification (SSGM). Initially the preprocessing is done by bilateral filter and segmentation is applied by suing Active Region Slice Window Segmentation (ARSWS). To separate the tumor entity feature projection using Histogram color quantization and the features process are carried by Optimal Support Scaling Vector Based Feature Selection (OSSCV). The selected features get trained using Stochastic Spin-Glass Model Classification (SSGM) to find the tumor region. The proposed system outperforms traditional machine learning methods in brain tumor detection. Finally proposed system of Stochastic Spin-Glass Model (SSGM) performance of recall is 95.5%, the performance of F1-score is 96.1% and the performance of the 96.5%. The proposed approach has the potential to assist radiologists in diagnosing brain tumors more accurately and efficiently, leading to improved patient outcomes. 2024, Ismail Saritas. All rights reserved. Creator An entity primarily responsible for making the resource Veneeswari J.; Sankar Ganesh S.; Krishnasamy L.; Rengaraj T.; Suseela D.; Kumaran N. Source A related resource from which the described resource is derived International Journal of Intelligent Systems and Applications in Engineering, Vol-12, No. 11, pp. 177-187. Publisher An entity responsible for making the resource available Ismail Saritas Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184883200&amp;partnerID=40&amp;md5=039aea9fd306cb13023733840b8e4ec5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184883200&amp;partnerID=40&amp;md5=039aea9fd306cb13023733840b8e4ec5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21476799 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Veneeswari J., Department of Information Technology, iNurture Education Solutions Private Limited, Vels University, Tamil Nadu, Chennai, 600117, India; Sankar Ganesh S., Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Medchal, Telangana, Hyderabad, 501301, India; Krishnasamy L., Department of CSE, School of Engineering and Technology, Christ (Deemed to be) University, Bengaluru, 560074, India; Rengaraj T., Department of Electrical and Electronics Engineering, P. S. R Engineering College, Sevalpatti, Tamil Nadu, Sivakasi, 626140, India; Suseela D., Department of Artificial Intelligence and Machine Learning, Bannari Amman Institute of Technology, Tamil Nadu, Sathyamangalam, 638401, India; Kumaran N., Department of Mathematics, Veltech Rangarajan Dr.Sagunthala R&amp;D institute of science and technology, Avadi, Tamil Nadu, Chennai, 600062, India