MATHEMATICAL MODELING OF PSYCHOLOGICAL DISEASE BY USING ARTIFICIAL INTELLIGENCE TOOLS

Recently, artificial intelligence (AI)-based tools have attracted great attention of researchers. AI-based neural networks (NNs) have been extensively used in recent times to investigate various real-world problems. One of the powerful tools recently applied in some epeiric diseases’ models is deep neural network (DNN). The mentioned DNNs consist of multiple layers which provide the best solutions to many problems like image recognition, natural language processing and speech recognition. Briefly, we can say that DNNs are some of the finest biologically inspired programming tools which enable a machine to learn from the provided observed data. Mathematical modeling is a hot area of research in recent times to investigate real-world problems. As we know, psychological diseases and mental disorder are common worldwide. Hence, worldwide, one of the largest public health issues is related to psychological disorders and their treatment. The World Health Organization (WHO) estimates that in 2017, one billion people struggled with mental illness and drug abuse disorders. Keeping this issue in mind, in this research work, we deduce a mathematical model to investigate the psychological diseases mathematical model by using modified-type fractional order derivative with Mittag-Leffler kernel. On using fixed point theory and numerical analysis, we deduce theoretical and numerical results for the proposed model. On using some real values for the parameters and initial conditions, we simulate the results graphically by using different fractional order values. Finally, we include the analysis based on DNNs by using the Levenberg–Marquardt algorithm to evaluate the prediction of our numerical results. Various graphical illustration have been provided.