An Intelligent Prediction System of Students Academic Performance based on Deep Learning and FPSOPCNN 
E.M.N. Sharmila
1E.M.N Sharmila*, Arts and Crafts Instructor, Alagappa University College of Education, Alagappa University, Karaikudi.
Manuscript received on March 27, 2020. | Revised Manuscript received on April 20, 2020. | Manuscript published on April 30, 2020. | PP: 2478-2484 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6621049420/2020©BEIESP | DOI: 10.35940/ijeat.D6621.049420
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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: This paper proposes a new method based on text extraction techniques for predicting student outcomes using cognitive computation. Predicting student academic achievement is most helpful in helping educators and learners improve their teaching and learning processes. This shows that these students have different experiences that influence their level of information capture in the classroom as they have the potential to use different lenses for training. This document provides a predictive examination of student academic performance in Tamil Nadu College in India during the academic year 2018 and 2019. First, this work applies statistical examination to gain insights from the data. Then, two datasets were obtained. The first dataset contains variables obtained before the beginning of the school year and the second includes study variables collected two months after the beginning of the semester. Convolution Neural Network and Fuzzy Particle Swarm Optimization Pulse Coupled Neural Network (FPSOPCNN) are designed to predict the end-of-year student performance for each dataset. .
Keywords: Cognitive Computing, Efficient Prediction System; Educational Data Mining; Learning Analytics model; CNN; FPSOCNN.