The COVID-19 stress and increased job pressure have largely affected healthcare professionals’ various life domains. This study particularly explores the effect of stress caused by treating COVID-19 patients on medical doctors’ wellbeing. To explore this phenomenon, we interviewed 12 doctors treating COVID-19 patients in hospitals of metropolitan cities in Pakistan. The thematic analysis using NVivo V.12 Plus software of interviews resulted in four major themes, COVID-19 Stressors, Effects of Stress, Nature and Personality, Stress Relievers, and Stress Coping Strategies. Physicians were physically and emotionally stressed as a result of the intense work. Although they were carrying a lot of pain and hurt on their insides, participants demonstrated a sense of professional determination to overcome obstacles. Physicians are currently dealing with their emotional issues, and they should have access to complete professional help to ensure their wellbeing. The COVID-19 pandemic’s mental health effects are anticipated to last far longer than the physical health effects. This study is well-positioned to investigate frontline physicians’ opinions and attitudes concerning the COVID-19 and its impact on their daily lives and mental health. This research will help implement context-specific innovative mental health solutions to help the frontline workers.
Video event detection is a challenging problem in many applications, such as video surveillance and video content analysis. In this paper, we propose a new framework to perceive high-level codewords by analyzing temporal relationship between different channels of video features. The low-level vocabulary words are firstly generated after different audio and visual feature extraction. A weighted undirected graph is constructed by exploring the Granger Causality between low-level words. Then, a greedy agglomerative graph-partitioning method is used to discover low-level word groups which have similar temporal pattern. The high-level codebooks representation is obtained by quantification of low-level words groups. Finally, multiple kernel learning, combined with our high-level codewords, is used to detect the video event. Extensive experimental results show that the proposed method achieves preferable results in video event detection.
This review paper compares the various disk scheduling algorithms that are used to schedule processes in a queue, such as FCFS, SSTF, SCAN, C-SCAN, LOOK, C-LOOK, OTHDSA, and Zone Base Disk Scheduling, and then applies all of these techniques to two data sets to assess the performance of each algorithm. The comparison includes updated and improved techniques that show promising results in collecting data from the digital store. The comparison results were also contrasted with supporting the statement that all disk scheduling algorithms' technique offers much better performance. The comparison shows that among all disk scheduling algorithms, OTHDSA has excellent performance and takes a search time of 250 to complete all requests in a queue.
Nowadays, educational data mining is being employed as assessing tool for study and analysis of hidden patterns in academic databases which can be used to predict student’s academic performance. This paper implements various machine learning classification techniques on students’ academic records for results predication. For this purpose, data of MS(CS) students were collected from a public university of Pakistan through their assignments, quizzes, and sessional marks. The WEKA data mining tool has been used for performing all experiments namely, data pre-processing, classification, and visualization. For performance measure, classifier models were trained with 3- and 10-fold cross validation methods to evaluate classifiers' accuracy. The results show that bagging classifier combined with support vector machines outperform other classifiers in terms of accuracy, precision, recall, and F-measure score. The obtained outcomes confirm that our research provides significant contribution in prediction of students’ academic performance which can ultimately be used to assists faculty members to focus low grades students in improving their academic records.
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