Today, due to the widespread outbreak of the deadly coronavirus, popularly known as COVID-19, the traditional classroom education has been shifted to computer-based learning. Students of various cognitive and psychological abilities participate in the learning process. However, most students are hesitant to provide regular and honest feedback on the comprehensiveness of the course, making it difficult for the instructor to ensure that all students are grasping the information at the same rate. The students’ understanding of the course and their emotional engagement, as indicated via facial expressions, are intertwined. This paper attempts to present a three-dimensional DenseNet self-attention neural network (DenseAttNet) used to identify and evaluate student participation in modern and traditional educational programs. With the Dataset for Affective States in E-Environments (DAiSEE), the proposed DenseAttNet model outperformed all other existing methods, achieving baseline accuracy of 63.59% for engagement classification and 54.27% for boredom classification, respectively. Besides, DenseAttNet trained on all four multi-labels, namely boredom, engagement, confusion, and frustration has registered an accuracy of 81.17%, 94.85%, 90.96%, and 95.85%, respectively. In addition, we performed a regression experiment on DAiSEE and obtained the lowest Mean Square Error (MSE) value of 0.0347. Finally, the proposed approach achieves a competitive MSE of 0.0877 when validated on the Emotion Recognition in the Wild Engagement Prediction (EmotiW-EP) dataset.
SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.
Objective: A high mortality rate is associated with anesthesia in low and middle income countries. The provision of basic and emergency surgical services in developing countries includes safe anesthetic care. We sought to determine the resources available to deliver anesthesia care in low and middle income countries.
Methods:A standard World Health Organization tool was used to collect data from 34 Low and Middle-Income Countries (LMICs) regarding infrastructure and capacity of facilities. We then performed a database query to extract information on anesthesia-related capacity.Findings: Twelve countries were excluded for providing data on less than four facilities, leaving 22 countries in our results, with a total of 590 facilities surveyed. Thirty five percent of hospitals had no access to oxygen and 40% had no anaesthesia machines; despite this, 58.5% of hospitals offered general inhalational anesthesia. All facilities reported presence of an anaesthesia provider: a nurse or clinical assistant was present in all 590 facilities. Hospitals with > 200 beds reported a range of 2-10 providers; the average number of anesthesia physicians increased from one to four as the hospital size increased from less than to greater than 300 beds. The majority of facilities were district/rural/community hospitals (34.7%), followed by health centres (23.2%), private/NGO/missions hospitals (16.6%), provincial hospitals (11.7%), and general hospitals (13.1%).
Conclusion:The delivery of anesthesia is limited by deficiencies in human resources, equipment availability and system capacity in many low and middle income countries.
This work present new approach eye tracking with very less expensive IR sensor & IR led based apparatus connected to the computer. Photodiode & phototransistor are light reflection based component, the system design by use these components can be called as light reflection based system or nonimaging sensor base system. Basically this system is having very low complexity & very low computational approach. The infrared light sensitive apparatus is eye mounted spectacle, is used to measure the different voltages reflected from iris for the computation of cursor position in the computer screen. For the accurate position of cursor highly efficient microcontroller based hardware system is used, which is accurately sample time varying signal & digitized in digital. Based on the movement of predefined eye wings the system is able to perform left click, right click and double click with perfect accuracy on computer system. As it is Infrared light based system the computational approach is very low as compared to video or camera based system, so the physical size of system is very small and manufacturing cost of system is also very less.
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