Tools and Techniques Adapted for Teaching Software Engineering Topics Remotely during the COVID-19 Pandemic

To stop the spread of the COVID-19 virus, educational institutions abruptly switched from in-person to online, remote mode of teaching without giving educators the necessary tools and training. In this paper, we focus on the Software Engineering Education & Training (SEET) courses at the university levels and address questions like: What tools and techniques did they adapt to handle the modality transition challenges? What lessons they learned and what would they do differently the next time? What are the students' perspective on these, etc.? We interviewed 16 SEET educators from different countries around the world;followed by surveys of more than 300 educator and student participants. Our empirical study found some common themes of challenges, as well as suggestions on tools and techniques to overcome them. © 2022 IEEE Computer Society. All rights reserved.

Diagnose COVID-19 by using hybrid CNN-RNN for chest X-ray

Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. RNN was used to classify data after extracting complicated characteristics from them using CNN. The VGG19-RNN design had the greatest accuracy of all of the networks with 97.8% accuracy. Gradient-weighted the class activation mapping (Grad-CAM) method was then used to show the decision-making areas of pictures that are distinctive to each class. In comparison to other current systems, the system produced promising findings, and it may be confirmed as additional samples become available in the future. For medical personnel, the examination revealed an excellent alternative way of diagnosing COVID-19. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

A Cross-sectional study on effect of Covid 19 infection among smokers in Chamarajanagar district

Background: Tobacco is one of the deadliest public health threats to humankind, killing more than eight million people a year globally. Combined with COVID-19, smoking is even more lethal, in which smoked tobacco damage the lungs tissue and reduces its function drastically. So, comparing to a non-smoker the smoker has more chance of developing severe COVID-19 infection and related complications. Method(s): This cross-sectional study was conducted in a tertiary care center of Chamarajanagar District. All Adult patients who attended the study settings with previous history of Covid 19 infection and history of smoking was administered a pre-tested semi structured questionnaire after meeting inclusion criteria. The questionnaire was structured into 4 parts to meet the expected objectives. The data obtained was entered into MS Excel and analysed. Result(s): The study included 103 participants;out of which 65% belongs to the age group of more than 40 years. Majority of the study subjects were literate and semi-skilled workers which comprise 58% & 64% respectively. 81% of the study subjects were not vaccinated at the time of infection, but in contrast 97% were vaccinated at the time of interview. Majority of the subjects are current smokers (73%), and many of them prefers Beedis to smoke. A proportion of 44% are smokers for more than 15 years and half of total smokers are thinking it has ill effects on health. The major symptoms identified in our study were fever, cough & body ache. Conclusion(s): Cause effect analysis shows direct relationship between number of cigarettes smoked per day and number of days require for institutional care during infection. This leads to the necessity to quit smoked tobacco products as soon as possible in high-risk individuals for better health outcome. Copyright © 2022 Authors. All rights reserved.

Diagnose COVID-19 by using hybrid CNN-RNN for chest X-ray

Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121, InceptionV3, and Inception-ResNetV2. RNN was used to classify data after extracting complicated characteristics from them using CNN. The VGG19-RNN design had the greatest accuracy of all of the networks with 97.8% accuracy. Gradient-weighted the class activation mapping (Grad-CAM) method was then used to show the decision-making areas of pictures that are distinctive to each class. In comparison to other current systems, the system produced promising findings, and it may be confirmed as additional samples become available in the future. For medical personnel, the examination revealed an excellent alternative way of diagnosing COVID-19. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

COVID-19 Outbreak Based Coronary Heart Diseases (CHD) Prediction Using SVM and Risk Factor Validation

Heart disease is a major concern for the medical fraternity under the influence of global pandemic outbreak. According to WHO 17.9 million deaths are reported due to suffering from cardio disease related medical influences and has increased due to comorbidities in outbreak of SARS-CoVID-19 globally. In this research, a focus is on proposing a technique to deal with a predict cognitive approach of classifying and validating the heat diseases risks. The technique is aided with SVM based classifier for decision support via risk factor validation. The technique has provided an improved predictive accuracy and reliability over the risk factor validation caused due to pandemic parameters. © 2021 IEEE.

Tele-robotic recommendation framework using multi-dimensional medical datasets on COVID-19 classification

The development of robotic partners to take care of daily human life has been expanded recently. Mobile robots have spread their presence within the public environment to assist people in a variety of problematic activities. Mobile Robots are developed with the underlying artificial intelligence technology. Adequate training is provided to the mobile robots under the classifications of supervised learning. The interaction of robots is very important to practice everything that is told to the robotic systems from domestic robots to high-risk work environments that threaten the health of the spinal cord, which focuses on robotic support during the COVID-19 epidemic. In the present research work, a mobile agent is trained using Computerized Tomography (CT) scan reports and X-rays under VGG-16 processing standards for classifying covid and non-covid patients. A hybrid model is designed using Deep Learning Network (DNN) and Convolutional Neural Network (CNN). CNN is trained using images collected using a camera and thermal camera with RGB values ranging from 0 to 255. The advantage of the proposed model in training the mobile agent is making use of CT scan and X-ray images and providing recommendations to the victim about the criticality of being affected by covid. In addition to that, the Machine Learning Algorithm like Decision Tree and Random Forest is constructed and achieved a classification accuracy of 95%. The proposed technique has efficiently provided a reliable recommendation system based on ReLu activation. The other evaluation parameters used to estimate the performance of the proposed model are precision, recall, F1-score. The proposed model achieves 0.84 Precision over the inception technique with 0.79 precision. The reason behind the improvement of accuracy in the present work is the filter used to extract the features. © 2022 The Authors.