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- W4285229712 abstract "Educational institutions and education system play the important role in any society or country for development in all aspects like agriculture, industrial, economic and political. Teaching methodologies, learning preferences and techniques, socio-economics decide the student learning capabilities. Cognitive Learning (CL) is a kind of learning that is constructive, active, durable, and productive. It employs educates in the learning measures, encouraging them to think effectively to make connections when learning new things. In this paper, we want to demonstrate the student learning capabilities using their cognitive abilities. For this, we demeanor training and examines on different cognitive abilities and also collect the personal and other factors which impact student learning. In this process, we conduct training and examine the 313 engineering educates from AITAM Eng. College, A.P., India. We apply the Principal Component Analysis (PCA) algorithm for feature selection, and for prediction, use the ANN with back-propagation algorithm. The Artificial Neural Network (ANN) model is constructed, as that the hidden layer of ANN neurons initially is two, after that we increment the neurons by one until reach good accurate results. The 5-neurons HL ANN is performed well as per performance parameters like accuracy (100%), AUC (1), R-value (1), recall, and precisions values." @default.
- W4285229712 created "2022-07-14" @default.
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- W4285229712 date "2022-01-01" @default.
- W4285229712 modified "2023-09-27" @default.
- W4285229712 title "Student Cognitive Learning Capability (SCLC) Prediction System using PCA-ANN based Model" @default.
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- W4285229712 doi "https://doi.org/10.1109/ic3p52835.2022.00013" @default.
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