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This study aims to identify the potential association of mental health and social media forum during the outbreak of COVID-19 pandemic. COVID-19 brings a lot of challenges to government globally. Among the different strategies the most extensively adopted ones were lockdown, social distancing, and isolation among others. Most people with no mental illness history have been found with high risk of distress and psychological discomfort due to anxiety of being infected with the virus. Panic among people due to COVID-19 spread faster than the disease itself. Them is information and excessive usage of social media in this pandemic era have adversely affected mental health across the world. Due to limited historical data, psychiatrists are finding it difficult to cure the mental illness of people resulting from the pandemic repercussion, fueled by social media forum. In this study the methodology used for data extraction is by considering the implications of social network platforms (such as Reddit) and levering the capabilities of a semi-supervised co-training technique-based use of Naïve Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) classifiers. The experimental results shows the efficacy of the proposed methodology to identify the mental illness level (such as anxiety, bipolar disorder, depression, PTSD, schizophrenia, and OCD) of those who are in anxious of being infected with this virus. We observed 1 to 5% improvement in the classification decision through the proposed method as compared to state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

Context: Since the end of 2019, the COVID-19 pandemic had a worst impact on world's economy, healthcare, and education. There are several aspects where the impact of COVID-19 could be visualized. Among these, one aspect is the productivity of researcher, which plays a significant role in the success of an organization. Problem: There are several factors that could be aligned with the researcher's productivity of each domain and whose analysis through researcher's feedback could be beneficial for decision makers in terms of their decision making and implementation of mitigation plans for the success of an organization. Method: We perform an empirical study to investigate the substantial impact of COVID-19 on the productivity of researchers by analyzing the relevant factors through their perceptions. Our study aims to find out the impact of COVID-19 on the researcher's productivity that are working in different fields. In this study, we conduct a questionnaire-based analysis, which included feedback of 152 researchers of certain domains. These researchers are currently involved in different research activities. Subsequently, we perform a statistical analysis to analyze the collected responses and report the findings. Findings: The results indicate the substantial impact of COVID-19 pandemics on the researcher's productivity in terms of mental disturbance, lack of regular meetings, and field visits for the collection of primary data. Conclusion: Finally, it is concluded that researcher's daily or weekly meetings with their supervisors and colleagues are necessary to keep them more productive in task completion. These findings would help the decision makers of an organization in the settlement of their plan for the success of an organization. [ABSTRACT FROM AUTHOR]

One of the profitable strategies in construction of portfolio is pair trading strategy. To capture the profitability through pair trading, co-integration approach is used in this study. The formation of pairs take place from three sectors textile, chemical and banking using the daily data from year 2011 to year 2019. Different parameterizations are used for trading system algorithm. Positive and significant returns are generated from co-integration approach. Further, positive risk adjusted returns are also be observed in all the three sectors. The results also validate the market neutrality, mean revision and challenge to EMH. Investors and fund managers can get positive risk adjusted returns while applying pair trading strategies at PSX. [ABSTRACT FROM AUTHOR]

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier’s performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains. [ABSTRACT FROM AUTHOR]