Hybrid statistical and recurrent neural network architecture implementation in FPGA device used for severe acute respiratory syndrome coronavirus detector

The Severe Acute Respiratory Syndrome (SARS) are caused by the strain of the corona virus causes cold and influenza. In recent years, the covid pandemic spread throughout the world killing millions of people. The fatality rate has increased and it also leads to pneumonia for breathing problems. Several methods like wavelet filter banks, time series methods, Neural networks was developed for the diagnosis of severe acute respiratory syndrome coronavirus, still the accuracy can be improved. Less works is carried out for hardware implementation for syndrome detectors. This proposed work represents the FPGA (Field Programmable Gate Array) implementation of the hybrid method using Convolutional Recurrent neural network and Independent Components Analysis (ICA). The architecture extracts the ccomplex features from ECG (Electrocardiogram) samples. The hybrid Statistical and Recurrent Neural Network (RNN) Architecture implementation in a real time hardware detects the Severe Acute Respiratory Syndrome presented. The proposed method can be implemented in MATLAB, Embedded and DSP (Digital Signal Processor). But, the FPGAs consume less power computationally efficient. Since, ICA is an efficient method due to its blind source separation property accumulate the extraction of features accurate described. The mathematical model for the analysis of ECG signal using RNN is analyzed and based on that the proposed model is selected. On investigation the hybrid method using the statistical and neural network model is efficient in the analysis of biomedical signal especially ECG. The proposed ICA based RNN model is mathematically evaluated and tested with real time data. For implementation, Quartus software is used for effectiveness of the proposed model. [ABSTRACT FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse, when patient emotion is fear, cry or sad. The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm. The proposed Convolution Neural Networks (CNN) architecture with DnCNN preprocessing enhances the performance of recognition. The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras. The proposed method accuracy is more when compared to other methods. It detects the chances of suicide attempt based on stress level and emotional recognition. © 2023 CRL Publishing. All rights reserved.

A Pandemic of Webinars in the COVID Era-Can It be the Way Forward?

Introduction Conferences are important and sometimes mandatory to update the clinician with latest knowledge. Attending conferences requires planning, expenditure, and leave from work. Webinars have become the new normal in the coronavirus disease (COVID) era. We surveyed the esteemed medical fraternity on their opinion on webinars. Methods This was conducted as an online survey (Survey Monkey) through personal electronic mails and social media with 24 questions. Details on demographic profile, specialization and affiliation, experience, choice of frequency of webinar sessions, suitable platform, mode of intimation of webinars, number of days for prior intimation, appropriate timing of the day and week, and ideas on payment options were enquired. Need for technical assistance, choice of topic for discussions, methods to make webinars more interactive, availability of recorded content, and impact on clinical practice were also assessed. Results A total of 235 medical professionals voiced their opinion;67% were < 35 years of age and 49% were residents. An average of 2 to 3 webinars per month (33.8%), conducted on weekdays (63%), after 6 p.m. (54%) in the form of case discussion (67.3%) or lectures from experts (55%) with at least 7 days' notice (41.7%) was the most common choice;free webinars were the wish of 56.1% participants and 28% felt webinars would definitely impact practice. Conclusion Webinars are welcoming even after the COVID era and should go hand-in-hand with conventional conferences. Virtual learning experience should be optimized by proper scheduling of multiple simultaneous events and converting them into interlinked or serial events.