Dr. Subhani Shaik, Dr. K. Vijayalakshmi, K Gowri Priya, V. Vismitha, J. Saiteja


The COVID-19 epidemic has been causing chaos on the society, rendering it in ruins. WHO predicts that in March 2020, the world has taken a significant toll on people, leading to a lot of distortion in their lifestyle? Mental health has gone for a toss and overlooked. People in India do not have the privilege to expect support and deal with their mental health. It is high time to bridge this gap, and we have attempted to do using unconventional approaches like the booming technologies. The proposed model is validated against other baseline techniques like naive-bayes, gradient-boosting, xgboost, catboost, lightgbm and optimization techniques like simulated annealing using svm. The proposed method outperforms other baseline techniques for attaining better accuracy. They are particularly suited to predicting psychological problems. For implementation purposes, choose features like age, family history, seek_help, employment, and a few other features. The proposed model is evaluated with a COVID-19 dataset based on various performance matrices to show its effectiveness.


pandemic, classification, model, age, psychological, health, consequences, optimization, techniques, conventional approaches.

Full Text:



Aarts EHL, Korst JHM (1989) Simulated annealing and Boltzmann machines. Wiley, ChichesterzbMATHGoogle Scholar

. Aarts EHL, Van Laarhoven PJM (1985) Statistical cooling: a general approach to combinatorial optimisation problems. Philips J Res 40:193–226MathSciNetGoogle Scholar

. Aarts EHL, Korst JHM, Michiels W (2005) Simulated annealing. In: Burke EK, Kendall G (eds) Search methodologies. Springer, New York, pp 187–210Google Scholar

. Abramson D (1991) Constructing school timetables using simulated annealing: sequential and parallel algorithms. Manag Sci 37:98–113Google Scholar

. Alrefaei MH, Andradottir S (1999) A simulated annealing algorithm with constant temperature for discrete stochastic optimisation. Manag Sci 45:748–764zbMATHGoogle Scholar

. Altiparmak F, Karaoglan I (2008) An adaptive tabu-simulated annealing for concave cost transportation problems. J Operational Res Soc 59:331–341zbMATHGoogle Scholar

. Anagnostopoulos A, Michel L, Van Hentenryck P, Vergados YA (2006) Simulated annealing approach to the traveling tournament problem. J Scheduling 9:177–193zbMATHGoogle Scholar.

. Arai K, Sakakibara J (2006) Estimation of sea surface temperature, wind speed and water vapour with microwave radiometer data based on simulated annealing. Adv Space Res 37(12):2202–2207Google Scholar

. Azizi N, Zolfaghari S (2004) Adaptive temperature control for simulated annealing: a comparative study. Comput Operations Res 31(4):2439–2451MathSciNetzbMATHGoogle Scholar.

. Bai R, Burke EK, Kendall G, McCollum B (2006) A simulated annealing hyper-heuristic for university course timetabling. In: Burke EK, Rudova H (eds) In: Proceedings of PATAT 2006, Brno, Czech Republic, August–September 2006. Lecture notes in computer science, vol 3867. Springer, HeidelbergGoogle Scholar.

. Bianci L, Dorigo M, Gambardella LM, Gutjahr WJ (2008) A survey on metaheuristics for stochastic combinatorial optimisation. Nat Comput. DOI 101007, Online September 2008Google Scholar.




  • There are currently no refbacks.

Copyright (c) 2022 International Journal of Advanced Research in Computer Science