Title: Early prediction of mental health using SqueezeR_MobileNet

Authors: Vanita Ganesh Kshirsagar; Sunil Kumar Yadav; Nikhil Karande; Pramod Patil

Addresses: Department of computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Jaipur, Rajasthan, India; Dr. D.Y. Patil Institute of Technology, Pimpri, Pune, India ' Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Jaipur, Rajasthan, India ' SISA Information Security Pvt. Ltd., Bangalore, India ' Dr. D. Y. Patil Institue of Technology, Pune, India

Abstract: Mental illnesses are common among college students as well as their non-student peers, and the number and severity of these problems are increasing. It can be difficult to identify people suffering from mental illness and get the help they need early. So in this paper, the SqueezeR_MobileNet method is proposed. It performs feature fusion and early mental health prediction. Initially, outliers in the input data are detected and removed. After that, using missing data imputation and Z-score normalisation the pre-processing phase is executed. Next to this, for feature fusion, a combination of the Soergel metric and deep Kronecker network (DKN) is used. By utilising bootstrapping data augmentation is performed. Finally, early mental health prediction is done using SqueezeR_MobileNet, which is the incorporation of residual SqueezeNet and MobileNet. The devised approach has reached the highest specificity of 0.937, accuracy of 0.911 and sensitivity of 0.907.

Keywords: mental health; residual SqueezeNet; MobileNet; deep Kronecker network; DKN; Soergel metric.

DOI: 10.1504/IJAHUC.2024.142169

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.47 No.3, pp.158 - 175

Received: 20 Feb 2024
Accepted: 30 May 2024
Published online: 10 Oct 2024 *

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