Diagnosis of Covid-19 from Chest X-Ray Images Using Wavelet-Based Depth Wise Convolution Network

There is a great need to create and put in place a method of automatic detection as a substitute for conventional diagnosis for COVID-19 detection that can be employed on a commercialscale because there aren't as many COVID-19 test kits availablein medical institutions. In particular, chest X-Ray scans can beexamined to assess whether a patient has COVID. Due to the availability of numerous big annotated picture datasets, convolutional neural networks have achieved remarkable success in image analysis and classification. Input is obtained in the form of chest x-rays images. Output results are acquired instantly in real-time which predicts if the person suffers from Covid or not. Modern technique use the RCNN algorithm, which makes them less precise and time-consuming. We suggest an automated deep learning-base method for extracting COVID-19 from chest X-ray pictures. For analysing the chest X-Ray pictures, suggested method offers enhanced depth-wise convolution neural network. Through wavelet decomposition, multiresolution analysis is incorporatedinto the network. In order to identify the condition, the network is given the frequency sub-bands that were recovered from the input pictures. The network's goal is to determine whether the input image belongs to the Covid-19 class or not. The Advantage of the proposed system are that it could be the very first-of its kind, cost-efficient, and highly accurate application that provide complete and accurate covid - 19 diagnosis. © 2022 IEEE.

WITHDRAWN: Micro blogging supported classroom during COVID19 pandemic situation for classification of relevant and irrelevant questions

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.

A SURVEY AMONG AYURVEDIC PHYSICIANS IN KERALA REGARDING POST-COVID

Design of IoT Enabled Cloud Assisted Health Monitoring System for Covid-19 Patients

Covid-19 is one of the life-threatening diseases which requires intensifying attention to combat disease by designing a smart and effective healthcare system for patients towards diagnosing and managing the Covid-19 disease. Various systems have been developed for diagnosing patient with diseases, but intelligent and feasible solution to explore and monitor the accurate predictive health conditions of affected patients has not been provided yet. In this paper, a new Contactless IoT-enabled cloud-assisted health monitoring system has been designed and developed. The system is made up of unobtrusive sensors, a data acquisition unit, a microcontroller, wi-fi Module, Web server, and Web application or mobile application. It illustrates the design of the system to monitor and detect the severity of the coronavirus in the patients using various unobtrusive sensors to measure disease-specific vital parameters such as heart rate, temperature, oxygen level and pulse rate as main symptoms of the coronavirus are high fever, fatigue, and difficult breathing. Sensor acquired patient data is transformed using the HTTP protocol to cloud server using microcontroller and wi-fi module in real-time. Transformed data of patient condition is processed in the cloud server using data predictive algorithms such as Severity Defined Convolution Neural Network with respect to data collected and severity specific data thresholds and severity class predicted patient information will alarm the healthcare provider on the abnormalities detected in the patient health. A particular model is capable of forecasting the health situation of the patients. Experimental analysis of the proposed architectures finds it effective in monitoring the status of the severity of breathing on the patients. Finally, the performance of the architecture is validated over accuracy and scalability measures. © 2022 IEEE.

Face Mask Detection using CNN

Face recognition is an important feature of computer vision. It is used to detect a face and recognize a person and verify the person correctly. Face recognition technology plays an essential role in our everyday lives like in passport checking, smart door, access control, voter verification, criminal investigation, and system to secure public places such as parks, airports, bus stations, and railway stations, etc and many other purposes. While going through the pandemic and the post pandemic situations wearing a mask are compulsory for everyone in order to prevent the transmission of corona virus. This resulted in ineffectiveness of the existing conventional face recognition systems. Hence it is required to improvise the existing systems to get the desired results to detect the masked face at the earliest. This system works in three processes that are image pre-processing, image detection, and image classification. The main aim is to identify that whether a person’s face is covered with mask or not as per the CCTV camera surveillance or a webcam recording. It keeps on checking if a person is wearing mask or not. For classification, feature extraction and detection of the masked faces, Convolutional Neural Network (CNN) and Caffe models are used. These help in easy detection of masked faces with higher accuracy in a very less time and with high security. © 2021 IEEE.

Micro blogging supported classroom during COVID19 pandemic situation for classification of relevant and irrelevant questions

Miniature publishing content to a blog is a well-known innovation in web-based media applications that permits clients to distribute short instant messages on the web (for instance, under 200 characters) progressively through the Web, SMS, texting customers, and so on Little scope writing for a blog can be an effective gadget in the investigation corridor and has starting late got impressive enthusiasm from the instructive network. The report proposes another content arrangement application for two kinds of miniature writing for a blog addresses presented in a class, for example pertinent (for example questions that the teacher needs to glance in class) and irrelevant requests that usage personalization along with the content. of the inquiry prompts a superior exactness of arrangement contrasted with the utilization of the content of the inquiry just It is also significant to use the association among's requests and available getting materials, just as the connection between's inquiries posed during a gathering. Furthermore, eliminating void words prompts a superior gauge of the relationship among's inquiries and better arrangement precision.