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Assistant professor Computer Science
Rathinam college of Arts and Science/ Bharathiar University
Dr. Muniappan Ramaraj is working as an Assistant Professor in the Department of Computer Science at Rathinam College of Arts and Science, Coimbatore. He holds a Ph.D., degree in computer science at Bharathiar University in the year of 2020 with specialization in Data Mining with Image Process and also Fuzzy logic in the image analysis. His research areas are Data Mining, Image Processing, Fuzzy Logic, Pattern Recognition and Deep Learning concept. He has published more research article in the reputed various national and international journals and also filed the patents in the same field. He has a reviewer of many international journals including with IEEE, ASTESJ, and JERS. He can be contacted at email: firstname.lastname@example.org, or email@example.com.
Data Mining, Image Processing
Fibrotic notional brain tumor identification and classification is the process of splitting the tumor from normal and abnormal tissues in the medical field that can provide very useful information for diagnosis and treatment planning. Detecting the type of tumor and preventing it is one of the most challenging aspects of brain tumor categorization. Glioma has been most prominent fundamental stage of brain tumors. For diagnosis, treatment planning, and risk factor identification, accurate and strong tumor segmentation and prediction of patients' overall survival are critical. Here, it presents a deep learning Method-is based on the RNN framework for brain tumor segmentation and LSTM prediction in Glioma, using multimodal MRI scans. RNN (Recurrent Neural Network), the most advanced method in deep learning was used to detect a tumor using brain MRI images. Numerous conventional methods including classifiers should have been used to validate the proposed approach.
Journal of economic entomology, ISSN: 00220493, Pages: 1482-1483, Published: Oct 1972 Oxford University Press (OUP)
Compound IV was more active against the bollworm than against the tobacco budwol'ln. Compound V, methyl parathion, the O,O-dimethyl homologue of 0(p-nitrophenyl) phosphorothioate, was the most toxic of all 8 compounds to both species when it was applied topically (Table 2). Compounds V, VI. and VIII were more toxic to the bollworm than to the tobacco budworm when applied topically: the reverse was true for Compounds 1. II. and III (Tab]e ]). Foliar sprays of Compounds V and VI were equally toxic to the bollworm and the tobacco budworm and were more toxic than Compounds VII and VIII.
1.Paper Published on “A Comparative Study on CN2 Rule and SVM Algorithm for Prediction of Heart Disease Datasets Using Clustering Algorithms” IISTE volume 3, No 10 on 2013.
2.Paper Published on “Plagiarism Detection Paradigm for Web Content Using Similarity Analysis Approach” on IJAICT volume 1, issue 5, on September 2014.
3. Paper Published On “Color Based Image Segmentation Using KNN Classification with Contour Analysis Method” IRJET, volume 03, issue 10, Oct 2016.
4. Paper Published On “An Analysis Of K Means Clustering Algorithm Image Segmentation With IQI” IJSRD, Volume 04, Issue 09, Nov 2016.
5.Published paper on “Application of Color Based Image Segmentation Paradigm on RgbColor Pixels Using Fuzzy C-Means and K Means Algorithms” IJCSMC, Vol-6, Issue-6, June-2017.
6. Published paper on “Grouping of Color Pixel Based Image Segmentation using on Clustering Techniques” IJEECSE, Vol-4, Issue-6, December 2017.(UGC Refeered Journal).
7. Published paper on “Color Pixel Based Image Classification and Clustering Using Fuzzy Method” International Journal for Research in Engineering Application & Management (IJREAM) ISSN : 2454-9150 Vol-04, Issue-04, July 2018. (UGC Refeered Journal).
8. Published Paper On “Color Pixel Based Image Segmentation Using Enhanced Data Clustering Algorithms Applying On Tiger Image Dataset” International Journal Of Advance And Innovative Research. ISSN: 2394-7780.Volume 5, Issue 3 (VII): July - September, 2018. UGC Approved Journals.
R-CNN Based Smart Healthcare Cloud Based IOT Model For Detection and Prevention.