Objective Since December 2019, the COVID-19 pandemic has posed a substantial threat with its associated high mortality, infection, and risk of psychological stress. A large number of students are affected because of a prolonged break from academic activities and staying at home. The focus of this study is to understand the stress levels of Indian students, any psychological imbalances, and their major hurdles during the COVID-19 lockdown. Methods Using a snowball sampling method, an online survey of the Perceived Stress Scale (PSS) was conducted on students across India. Along with their demographic details, the participants also reported their study patterns and challenges during their confinement period. The statistical scores for the responses were calculated and the demographic variables analysed. The levels indicated by the PSS were compared, and variance and regression analyses were performed. Results We observed that students were generally stressed during lockdown and the pandemic. Females (mean = 3.03) were more stressed than males (mean = 2.61) as they were constantly under pressure because of stressful life events (OR = 0.752, 95% CI = 2.425–310.642) and apprehensive about their studies (RII = 0.67, OR = 2.168, 95% CI = 0.332–6.691). Conclusion During the pandemic, students’ mental health needs to be continually monitored as they are stressed owing to fear as well as about their studies and future careers.

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Background: A menace case of drug & narcotics abuse has been in prime focus of the society nowadays. Therefore, the need of technological intervention is primary concern to examine the prevalence, severity and outcome to the drug menace and its consequences. Objective: This study is to suffice clinical decisions through behaviour observatory data through preliminary screening of prevalence, correlation and severity of illness. Method: The model has been proposed to check for General Anxiety Disorder and Depression of a subject abusing any of the drug/marijuana/alcohol. In this model data set of Sikkim's youth has been considered to find relation of addiction leading to mental disorder. Result: This proposed system has been successful to associate any form of substance abuse to to some of illness to a limit of .83 accuracy scored by Support Vector Machine over the other machine learning models. The model has been deployed and being observed in few of the rehabilitation centre.

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