An Improved Machine Learning Approach to Detect Real Time Face Mask

Several nations have implemented health protocols like maintaining a particular measure of distance from each other and use of face masks when going out in public, in an effort to stop or at least reduce the spread of Covid-19. However, manually checking whether each person have put on a mask or not is a tiring job, and is possible only if there is a particular person assigned specially for that. This paves way for the need of an electronic device or a machine that would identify whether a person has worn mask or not. Thus, this research proposes a face mask detection system using a machine learning algorithm known as Support Vector Machine (SVM). After creating and preprocessing the dataset, training the model, and evaluating the final model, an accuracy of 98% has been obtained. The model can further be developed and used in real time scenarios to detect faces without a mask and pass those faces separately into a neural network with the help of CNN to easily find out his/her identity, and punish accordingly. © 2023 IEEE.

IoT Secure Framework for Wearable Sensor Data for E-health System

The risk of infection among people may be reduced dramatically with the reduction in patient movement especially during the pandemic situation like the COVID-19 virus. In such scenarios, a dramatic increase is observed among people in availing tele-healthcare services. The Internet of Things and Cloud Computing technologies are leveraged in this paper while the patients are provided with clinical exams through tele-medical laboratory services by technicians in a hospital. Consultation or validation of the results obtained from the IoT devices is enabled by transferring this data to the hospitals and doctors to the cloud environment. A virtual health team is set up with the hospital clouds by integration of various hospital data, medical doctors, technicians, and nurses on a distributed scenario along with blockchain-based intrinsic security to carry out the workflow of the healthcare system. Ethereum platform is used for assessment of the public, private and hybrid blockchain scenarios.

Is an HPV negative test by self sampling truly negative?

Objectives: Self-sampling using the HPV test is being increasingly used as a means of increasing cervical cancer coverage. Various studies have found that women find self-sampling convenient and satisfied with it as a screening option. Self-sampling can either be a sample of cervical cells but more often are of vaginal cells. To determine the compliance of women to the instructions given prior to obtaining a self-sample and to determine the adequacy of the selfsampling technique. Methods: A cohort of seventy women who attended HPV screening was divided into two equal groups by simple randomization;one group was subjected to self-sampling with cobas® HPV kit, and the second group had cobas® HPV test performed by the healthcare worker. The choice of cobas® was made as this has an inbuilt quality control mechanism which would denote whether no cervical/vaginal cells were made out on the cervical brush that was sent for analysis. Results: Among 70 women screened with cobas® HPV test, 5 (3 out of 35 self-samples and 2 out of 35 healthcare worker collected samples) were HPV positive, and none of the sample was invalid. Out of 5 positive cases, 2 were HPV-16, 1 was HPV-18, and 2 were other hrHPV positive. Conclusions: This simple study shows that self-sampling provides adequate cellular material for HPV DNA testing. Self-sampling is an efficacious technique that has to be included in cervical cancer screening programmes to increase screening coverage rates. It is an ideal screening mechanism, especially during the Covid pandemic.