Operator Medical

RESUME

KALIMUTHU A

Personal Profile

Date of Birth :

**-**-****

Qualification :

BE- COMPUTER SCIENCE

&ENGINEERING

Address for Communication :

***,*** ***************** ****,

Avarampatti,Rajapalayam(Tk),

Virudhunagar(dist)-626117

E-mail :

**************@*****.***

Contact No :

Mobile No: 867-***-****

Languages Known :

To speak : Tamil, English.

To write : Tamil, English.

Objective

Seeking an opportunity to associate myself with

professionally driven organization in a techno functional role and put my capabilities and qualifications to be an asset to the organization and enhance my skills.

Academic Details

• BE-Computer Science and Engineering

Kalusalingam Institute of Technology, Krishnankovil 6.78% (CGPA)

2016-2020

• Higher Secondary Education

N.A.Annapparaja Memorial Higher Secondary School,

Rajapalayam

71%

2015-2016

• SSLC

N.A.Annapparaja Memorial Higher Secondary School,

Rajapalayam

85%

2013-2014

Hard Skills

Programming Languages: C,C++, JAVA(basic),

Python(basic),C#(basic)

Packages known : Microsoft Office

Operating System : Windows

Platform Software : Net Beans, Code Blocks,

Turbo C, Android Studio,

Argo UML, Telnet,Visual

Studio

Project Wallet

Title : Prediction Of Brain Tumor Using Fuzzy C-Means Algorithm Summary : Probabilistic Neural Network with image and data processing techniques was employed to implement an automated brain tumor classification. The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques for instant, neural networks, and fuzzy logic shown great potential in this field. Hence, in this paper the Probabilistic Neural Network was applied for the purposes. Decision making was performed in two stages: feature extraction using the principal component analysis and the Probabilistic Neural Network (PNN). The performance of the PNN classifier was evaluated in terms of training performance and classification accuracies. Probabilistic Neural Network gives fast and accurate classification and is a promising tool for classification of the tumors. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques for instant, neural networks, and fuzzy logic shown great potential in this field. Declaration

I hereby declare that the information furnished above is true to the best of my knowledge. Place: Yours truly,

Date:

(Kalimuthu A)

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