Published on September 23rd, 2022
Customising ANN on Emojis for User Behaviour Prediction: An Unconscious Attitude towards Mathematical Problem
Fatima Isiaka
Safiya Al Sharji
Ken John
Abstract
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The major contributions of AI in modern technology are the application of emotion recognition tools which is mostly based on eye movement, facial expression and modification of its inference engine. These systems are schemes that are mostly built to understand user expression in an online E-learning environment or a Cooperate environment with limited ability to recognise exclusive expressions that determines the transmission of appropriate solutions. The basic emotions are expressed when involved with online surfing or interrelating with other personals online. At most times studying how to understand user expression is often a most tedious task, especially the subtle expressions. An emotion recognition system can be used to optimise and reduce complexity in understanding users’ subconscious thoughts and reasoning through their pupil changes that automatically give a methodical strategy to a complex solution. This paper demonstrates the use of a PC webcam to read in eye movement data that includes pupil changes. A custom eye movement algorithm (CEMA) is used to capture users’ activity to detect stress and record the data which is served as an input model to an inference engine (artificial neural network (ANN)) this helps to predict user emotional response conveyed as emoticons on the complex steps detected for a given problem this is directed to automatically display stress suppresser in form of a window based prototypical model. The window contains explicit steps for an average user‘s conceptual limit. This bridges the gap between high knowledge assimilation and an average comprehensive limit of a student.
Keywords: AI, E-learning, Artificial neural network, Emotion recognition system, Pupil changes, User expression, Eye movement behaviour
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