Feature Fusion and Early Prediction of Mental Health Using Hybrid Squeeze-MobileNet

Mental health is the main factor which is affected by stress, disease and sarcastic statements or people comments. It effects on persons health directly or indirectly. People cannot share or discuss about their mental condition, even they can’t talk about it. Firstly, they cannot accept that they are suffering mental illness. It is very necessary to predict the mental health of a Pearson in early stage. There is the need to use new strategies for diagnosis and daily monitoring of the mental health conditions. The goal of our research is to develop a module based on feature fusion, which will be performed based on Soergel metric and Deep Kronecker Network (DKN) and early prediction of mental health utilizing Squeeze-MobileNet. It improves accuracy without sacrificing the model efficiency. Particle swarm cuckoo search (PS-CS) is effective and capable to capture the unpredictability of data. We got F1 score and validation score of NN is good as compare to ML.