LAUSR

Emotion plays a significant role in human understanding and generally connected with rational decision making, attitudes, human activity, and human intelligence. To create realistic emotional relations between human beings and machines, the research community’s increasing interests need reliable and deployable solutions to recognize human emotional conditions. Automatic emotion detection is one of the main obstacles in providing innovative methods for more comfortable and more objective diagnosis, communication and analysis. Hence in this paper, an Artificial intelligence-assisted emotion prediction model (AIEPM) has been proposed to evaluate the probability of digital representation, identification and estimation of feelings, their state-of-the-art methods and primary research guidance. The proposed AIEPM analyses the effect on multimodal detection of emotional models. This paper presents emerging works based on language, sound, image, film, and physiological signals using current methods such as machine intelligence for recognizing human emotion. The proposed emphasis on this cutting-edge analysis reflects elements like the form and presentation of emotional stimulation, the sample’s scale. The numerical outcome suggested AIEPM, according to age, behaviour classification according to age (95.2%), emotions state recognition high probability in satisfaction compared to the proposed method, accuracy ratio (89.6%), performance ratio (94.5%) and recognition outcomes of existing and proposed work (98.2%) compared to other existing approaches improve behaviour classification.