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Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

The purpose of this study is to use the framework of stakeholder analysis in a participatory democracy, used in forest management planning, for arriving at the best management option for selected sacred groves of Kachchh. This is achieved by accounting for economic, cultural and ecological values and the resulting outcomes in the complex institutional mechanism. Additionally, this study provides a framework for complex decision-making that characterizes the management of sacred groves involving multiple criteria and options accounting for multiple stakeholders that involve conflicting interests.

The analytical hierarchy process was used to calculate the global priorities of management options using the relative importance of stakeholders, weights of different decision criteria to arrive at the best management practice for selected groves of Kachchh. The global priorities of management options rank management practices based on stakeholders' values and their effects on the choice of management strategy as well as on the potential to attain a compromise between competing interests. For this purpose, survey responses of 141 individuals belonging to seven different stakeholder categories were analyzed. Along with focus group discussions, and personal interviews, a stratified random sampling technique was used to survey respondents.

Based on the global priority weights of the alternatives, it is determined that the restoration management option (guggal is restored by planting new guggal sapplings, cattle grazing is prohibited and high levels of ecosystem goods and services are provided) had the highest score, followed by the preservation management option (grazing is allowed on the periphery, juvenile guggal is preserved and moderate ecosystem goods and services are provided). Therefore, restoration of sacred groves is the best management practice of sacred groves in West Kachchh, offering a compromise between maximizing provision of ecosystem services and economic benefits in terms of allowing cattle grazing.

Though there are several studies on best management practices for community-owned forests, irrigation systems, and pasture lands, and the role of local institutions in sustaining these common-pool resources; such studies for sacred groves are absent, despite sacred groves being one of the longest surviving common-pool resources that has sustained it over several decades. This is the first study that uses the framework of stakeholder analysis to arrive at the best management practice for sacred groves. The uniqueness of the study lies in a comprehensive evaluation of ecological–economic–cultural interests of multiple stakeholders toward management of sacred groves.