Task prediction on tablet based deep learning method

Abstract

The ability of smartphones and other mobile technology to improve cognitive abilities has long been acknowledged by all. This model includes pre-processing, enhanced higher-order feature extraction, and prediction stages. At first, the collected data from different age groups is pre-processed using a data cleaning approach. The feature extraction stage extracts the crucial features from the pre-processed data, including enhanced higher-order statistical features. The final prediction regarding the automatic prediction of variations in pupil diameter of the left eye, right eye, and reading ability (count of characters read/second) is made using three mathematical models: optimized Deep Convolutional Neural Network 1 (DCNN1), optimized DCNN2, and optimized DCNN3. Furthermore, the weight functions of the three mathematical models are fine-tuned to increase prediction accuracy using a hybrid optimization model called Arithmetically Updated Poor Rich Optimization (AUPRO). The comparative assessment is undergone to verify the efficiency of the projected model in terms of various measures.