A Mining Frame Work of CO-PO Attainment using Deep Learning Techniques
P Vasanth Sena1, P Sammulal2, Suresh Pabboju3, D L Srinivasa Reddy4

1P.Vasanth Sena, Assistant Professor, Department of IT, CBIT, Hyderabad (Telangana), India.

2Sammulal Porika, Professor, Department of CSE, JNTU CEJ, Hyderabad (Telangana), India.

3Suresh Pabboju, Professor, Department of IT, CBIT, Hyderabad (Telangana), India.

4Dr. D L Srinivasa Reddy, Department of IT, CBIT, Hyderabad (Telangana), India.

Manuscript received on 25 February 2020 | Revised Manuscript received on 05 March 2020 | Manuscript Published on 15 March 2020 | PP: 60-63 | Volume-9 Issue-4S2 March 2020 | Retrieval Number: D10150394S220/2020©BEIESP | DOI: 10.35940/ijitee.D1015.0394S220

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Student performance measured in CO-PO (Course Outcome and Program Outcome) attainment for OMR based answer sheet automation playing very curtail role in pupil concert analysis in this approach. In the proposed work, marks evaluation sheet is consider as input image, then apply frame cropping technique to extract the marks filled table by subdividing into cells as individual images by frame cropping technique. In order to recognition of hand written digit in each frame, various machine learning models are adopted, trained. Experimental results from proposed work show that convolutional neural network excels higher in identification digits from frames. The outputs are then converted to CSV version, which is used to evaluate CO-PO attainment for each learner. The experiments have been conducted and tested in proposed work on various machine learning techniques and compared the results to pick the optimal model.

Keywords: Hand Written Digit, Object Detection, Classification, Deep Learning.
Scope of the Article: Deep Learning