Nonverbal communication is important in everyday interactions, and its contribution to communication can be as high as 93%. Video surveillance, expression analysis, paralinguistic communication, and detection all benefit from the application of facial emotion analysis. We provide a thorough description of FER (Facial Emotion Recognition), which is based on traditional machine learning (ML), in our suggested system. In-depth research is required to make learning problems detection simpler because it is still laborious and time-consuming. Dyscalculia is characterised by difficulties counting, comparing numbers, and adding mathematical operations. This learning disability is thought to affect between 3 and 6% of school-aged youngsters. One of the special learning disabilities (SLD) with a mathematical impairment is dyscalculia. As the results of these individual tests alone are insufficient for identification, a variety of tests must be administered and analysed manually in order to discover dyscalculia. When analysing complex medical data, artificial intelligence (AI) for healthcare uses Random Forest algorithms to simulate human cognition. The screening procedure for these particular learning problems makes use of machine learning techniques. Counting accuracy, time spent per question during the counting phase, number comparison accuracy, time spent per question during the number comparison phase, arithmetic addition accuracy, and time spent per question during the addition phase were the six inputs used to develop the model. The model, which categorises children as dyscalculic or not, was constructed using the Random Forest method.

Facial emotion recognition (FER), Artificial intelligence, Random Forest, and dyscalculia.

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