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COVID-19 has altered our lifestyle, communication, employment, and also our emotions. The pandemic and its devastating implications have had a significant impact on higher education, as well as other sectors. Numerous researchers have utilized typical statistical methods to determine the effect of COVID-19 on the psychological wellbeing of young people. Moreover, the primary aspects that have changed in the psychological condition of children and young adults during COVID lockdown is analyzed. These changes are analyzed using machine learning and AI techniques which should be established for the alterations. This research work mainly concentrates on children's and young people's mental health in the first lockdown. There are six processes involved in this work. Initially, it collects the data using questionnaires, and then, the collected data are pre-processed by data cleaning, categorical encoding, and data normalization method. Next, the clustering process is used for grouping the data based on their mood state, and then, the feature selection process is done by chi-square, L1-Norm, and ReliefF. Then, the machine learning classifiers are used for predicting the mood state, and automatic calibration is used for selecting the best model. Finally, it predicts the mood state of the children and young adults. The findings revealed that for a better understanding of the effects of the COVID-19 pandemic on children's and youths' mental states, a combination of heterogeneous data from practically all feature groups is required.

COVID-19 has altered our lifestyle, communication, employment, and also our emotions. The pandemic and its devastating implications have had a significant impact on higher education, as well as other sectors. Numerous researchers have utilized typical statistical methods to determine the effect of COVID-19 on the psychological wellbeing of young people. Moreover, the primary aspects that have changed in the psychological condition of children and young adults during COVID lockdown is analyzed. These changes are analyzed using machine learning and AI techniques which should be established for the alterations. This research work mainly concentrates on children’s and young people’s mental health in the first lockdown. There are six processes involved in this work. Initially, it collects the data using questionnaires, and then, the collected data are pre-processed by data cleaning, categorical encoding, and data normalization method. Next, the clustering process is used for grouping the data based on their mood state, and then, the feature selection process is done by chi-square, L1-Norm, and ReliefF. Then, the machine learning classifiers are used for predicting the mood state, and automatic calibration is used for selecting the best model. Finally, it predicts the mood state of the children and young adults. The findings revealed that for a better understanding of the effects of the COVID-19 pandemic on children’s and youths’ mental states, a combination of heterogeneous data from practically all feature groups is required. [ABSTRACT FROM AUTHOR]

COVID-19 has altered our lifestyle, communication, employment, and also our emotions. The pandemic and its devastating implications have had a significant impact on higher education, as well as other sectors. Numerous researchers have utilized typical statistical methods to determine the effect of COVID-19 on the psychological wellbeing of young people. Moreover, the primary aspects that have changed in the psychological condition of children and young adults during COVID lockdown is analyzed. These changes are analyzed using machine learning and AI techniques which should be established for the alterations. This research work mainly concentrates on children's and young people's mental health in the first lockdown. There are six processes involved in this work. Initially, it collects the data using questionnaires, and then, the collected data are pre-processed by data cleaning, categorical encoding, and data normalization method. Next, the clustering process is used for grouping the data based on their mood state, and then, the feature selection process is done by chi-square, L1-Norm, and ReliefF. Then, the machine learning classifiers are used for predicting the mood state, and automatic calibration is used for selecting the best model. Finally, it predicts the mood state of the children and young adults. The findings revealed that for a better understanding of the effects of the COVID-19 pandemic on children's and youths' mental states, a combination of heterogeneous data from practically all feature groups is required. [ABSTRACT FROM AUTHOR]