Realtime Risk Monitoring System using Convolutional Neural Network for COVID19 Pandemic

The COVID19 pandemic has significantly changed the lifestyle of billions of people across the globe. It has greatly affected almost all sectors of business, industry and public life. As per the WHO's guidelines, wearing a face mask has become the new compulsory and precautionary measures for everyone. Currently, all the public and private service providers will expect their stakeholders to wear face mask in an appropriate way to avail any services. Therefore, detection of face mask at public places is a crucial task to help the society to overcome current pandemic. This paper presents a unique approach to not only detect face mask but also calculate the risk of getting infected by COVID-19 using machine learning algorithms. The proposed model detects the various faces present in an input video, identifies if it has a mask present or not. If the mask is not detected, the model calculates the risk of human being getting infected based on their age. Finally, the model generates the output and provides analysis based on the real time data it has processed. As a real-time surveillance system, the model can also classify a face when a person is moving in the live video. The proposed method attained a highest accuracy of 99.57 % against standard datasets under study. The authors experimented and explored various Convolutional Neural Network models like DenseNet, MobileNet_V2, Inception_V3 and YOLO_V4 find the best model, detecting the presence of masks accurately without causing over-fitting. © 2022 IEEE.

A thematic analysis-based model for identifying the impacts of natural crises on a supply chain for service integrity: a text analysis approach.

Numerous studies have been conducted to identify the effects of natural crises on supply chain performance. Conventional analysis methods are based on either manual filter methods or data-driven methods. The manual filter methods suffer from validation problems due to sampling limitations, and data-driven methods suffer from the nature of crisis data which are vague and complex. This study aims to present an intelligent analysis model to automatically identify the effects of natural crises such as the COVID-19 pandemic on the supply chain through metadata generated on social media. This paper presents a thematic analysis framework to extract knowledge under user steering. This framework uses a text-mining approach, including co-occurrence term analysis and knowledge map construction. As a case study to approve our proposed model, we retrieved, cleaned, and analyzed 1024 online textual reports on supply chain crises published during the COVID-19 pandemic in 2019-2021. We conducted a thematic analysis of the collected data and achieved a knowledge map on the impact of the COVID-19 crisis on the supply chain. The resultant knowledge map consists of five main areas (and related sub-areas), including (1) food retail, (2) food services, (3) manufacturing, (4) consumers, and (5) logistics. We checked and validated the analytical results with some field experts. This experiment achieved 53 crisis knowledge propositions classified from 25,272 sentences with 631,799 terms and 31,864 unique terms using just three user-system interaction steps, which shows the model's high performance. The results lead us to conclude that the proposed model could be used effectively and efficiently as a decision support system, especially for crises in the supply chain analysis.

COVID-19 Contagion Risk Estimation Model for Indoor Environments.

COVID-19 is an infectious disease mainly transmitted through aerosol particles. Physical distancing can significantly reduce airborne transmission at a short range, but it is not a sufficient measure to avoid contagion. In recent months, health authorities have identified indoor spaces as possible sources of infection, mainly due to poor ventilation, making it necessary to take measures to improve indoor air quality. In this work, an accurate model for COVID-19 contagion risk estimation based on the Wells-Riley probabilistic approach for indoor environments is proposed and implemented as an Android mobile App. The implemented algorithm takes into account all relevant parameters, such as environmental conditions, age, kind of activities, and ventilation conditions, influencing the risk of contagion to provide the real-time probability of contagion with respect to the permanence time, the maximum allowed number of people for the specified area, the expected number of COVID-19 cases, and the required number of Air Changes per Hour. Alerts are provided to the user in the case of a high probability of contagion and CO2 concentration. Additionally, the app exploits a Bluetooth signal to estimate the distance to other devices, allowing the regulation of social distance between people. The results from the application of the model are provided and discussed for different scenarios, such as offices, restaurants, classrooms, and libraries, thus proving the effectiveness of the proposed tool, helping to reduce the spread of the virus still affecting the world population.

Hair cortisol level might be indicative for a 3PM approach towards suicide risk assessment in depression: comparative analysis of mentally stable and depressed individuals versus individuals after completing suicide.

Depression and suicidal behavior are interrelated, stress-associated mental health conditions, each lacking biological verifiability. Concepts of predictive, preventive, and personalized medicine (3PM) are almost completely missing for both conditions but are of utmost importance. Prior research reported altered levels of the stress hormone cortisol in the scalp hair of depressed individuals, however, data on hair cortisol levels (HCL) for suicide completers (SC) are missing. Here, we aimed to identify differences in HCL between subject with depression (n = 20), SC (n = 45) and mentally stable control subjects (n = 12) to establish the usage of HCL as a new target for 3PM. HCL was measured in extracts of pulverized hair (1-cm and 3-cm hair segments) using ELISA. In 3-cm hair segments, an average increase in HCL for depressed patients (1.66 times higher; p = .011) and SC (5.46 times higher; p = 1.65 × 10-5) compared to that for controls was observed. Furthermore, the average HCL in SC was significantly increased compared to that in the depressed group (3.28 times higher; p = 1.4 × 10-5). A significant correlation between HCL in the 1-cm and the 3-cm hair segments, as well as a significant association between the severity of depressive symptoms and HCL (3-cm segment) was found. To conclude, findings of increased HCL in subjects with depression compared to that in controls were replicated and an additional increase in HCL was seen in SC in comparison to patients with depression. The usage of HCL for creating effective patient stratification and predictive approach followed by the targeted prevention and personalization of medical services needs to be validated in follow-up studies.