ABSTRACT
Sterilization of hospitals is one of the major concerns when it comes to hygiene and cleanliness especially during a pandemic situation. The existing methodologies include ultraviolet disinfection or hydrochloride spraying for sterilizing hospital rooms and chemical treatment for surgical and medical equipment. However since COVID strains are developing at a rapid rate, it is necessary for more efficacy and accuracy in sterilization. According to the August 2021 census collected by NCBI, 87 percent of virus transmission is only because of improper sterilization. The following paper proposes efficient and proven ultrasonic sterilization methods that can be preferred to ultraviolet and chemical sterilization in sterilizing not only hospital rooms but also any crowded regions like malls and schools. The Cremant's formula helps in determining the appropriate and effective sterilization ultrasonic frequency level. Using machine learning algorithms, the approximate location, and the number of droplets per second present in the room will be calculated and treated with ultrasonic waves. This demonstration is proved using micro silicon balls which are similar in properties of COVID - 19 viruses. Simulation results are displayed to show the working of the same.
ABSTRACT
Sterilization of hospitals is one of the major concerns when it comes to hygiene and cleanliness especially during a pandemic situation. The existing methodologies include ultraviolet disinfection or hydrochloride spraying for sterilizing hospital rooms and chemical treatment for surgical and medical equipment. However since COVID strains are developing at a rapid rate, it is necessary for more efficacy and accuracy in sterilization. According to the August 2021 census collected by NCBI, 87 percent of virus transmission is only because of improper sterilization. The following paper proposes efficient and proven ultrasonic sterilization methods that can be preferred to ultraviolet and chemical sterilization in sterilizing not only hospital rooms but also any crowded regions like malls and schools. The Cremant's formula helps in determining the appropriate and effective sterilization ultrasonic frequency level. Using machine learning algorithms, the approximate location, and the number of droplets per second present in the room will be calculated and treated with ultrasonic waves. This demonstration is proved using micro silicon balls which are similar in properties of COVID - 19 viruses. Simulation results are displayed to show the working of the same. © 2022 IEEE.
ABSTRACT
The large-scale outbreaks of infectious pandemic diseases emerged regularly throughout history and created notable economic, social, and political disruptions. Major pandemics affect a wide geographic area significantly increasing morbidity and mortality. The world has come across numerous remarkable pandemics such as the Black Death, measles, smallpox, influenza, plague, cholera, Spanish flu, severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) and Ebola virus and is now combating the new coronavirus disease 2019 (COVID-19) pandemic affecting humanity greatly. Studies suggest that the likelihood of pandemic threats is due to the diversity of pathogens, changes in the dynamics of disease transmission and severity, human-pathogen interaction, increased globalization, urbanization, huge exploitation of land and natural resources, and global warming. The pandemic risk burden poses serious challenges to humanity and these trends will prolong and intensify over time. For the well-being of humanity, administration of public health measures, techniques to intercept and control infection, pharmaceutical intervention, global surveillance programs, novel technologies to identify disease biomarkers, and vaccine production prove to be effective beneficiary responses to identify and limit emerging outbreaks and to escalate preparedness and health capacity. The extensive amount of data produced during the pandemic has given a lot of chances to the researchers and healthcare providers to evaluate new trends, detect vulnerable groups, and solve long-standing issues in the healthcare industry. The healthcare industry has sought to use the most comprehensive data and predictive analytics software tools employing intelligent data technology, artificial intelligence (AI), machine learning (ML), and deep learning (DL) and has leveraged to gain insight, establish innovative ways to ease sustainable demand and supply, and pitch straight into the prospective benefits to foster the fight against the pandemic. Hence, these predictive models can support hospitals, healthcare settings, state health organizations, and government establishments to speculate the influence of COVID-19 and prepare for the future. In this chapter, a comprehensive investigation of various data analytic tools that are used in expert systems, proposed for pandemic and epidemic diseases, is discussed. The key issues, challenges, and opportunities of the existing and current methods are also discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
ABSTRACT
One of the greatest challenges in regard to the elderly is the autonomous and healthy availability of appropriate beneficial and social functionalities to achieve the primary goal of prolonging independent living at home. In 2050, the forecast population of elderly people in India is about 300 million. The traditional kind of joint family in Indian culture has come under pressure due to family planning awareness, migration to cities, a lack of sustainability, and inadequate guidance from the elders. A virtual vision system (VVS) is a home-based monitoring device that captures the scene continuously to aid the user. The system helps the individual to navigate in a closed environment. The user can provide the system with commands, queries, and/or demands using free form natural language input to receive help. A wearable sensor that can track physiological parameters such as oxygen saturation, temperature, and pulse rate also can be integrated into the system. The risk of falling and potential injury is one reason for the elderly being placed in care facilities. The system also supports users by tracking their mobility, and helps to identify falls. The system autonomously patrols the user's environment without any user activity and checks if the user is well and has not suffered a fall. The VVS emphasis is on an application platform that incorporates distinct technical solutions such as biometric tools, remote doctor visits, emergency call and tracking systems, drug dispensers, and online shopping. In elderly care, continuous monitoring helps not only to focus on improving the senior's safety when a caregiver is not present, but also provides peace of mind to adult children who may be concerned about the welfare of their loved one. Such systems would be helpful for those in self-quarantine/isolation during this COVID-19 pandemic situation. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
The entire world is facing a pandemic after the COVID-19 outbreak reported in Wuhan City in China. The number of newly infected cases and deaths are increasing on an hourly basis. The birth and spread of the n-coronavirus is a mystery to the world. Social distancing, staying home, and washing hands frequently with soap and water are the present norms for not getting infected or spreading it. The symptoms of a COVID-19 infected person are high fever, nasal congestion, aches and pains, difficulty in breathing, loss of smell and taste, and sore throat. The standard approach for screening any COVID-19 patient is to measure the body temperature, usually by infrared temperature sensors. This primary indication makes the person to be required to undergo the COVID-19 test. In most of the cases, the test results provide false positive and true negative kind of misclassification. Delay in finding the COVID-19 carriers makes it a challenging task for any healthcare administration to reduce the growth of positive cases. Few more vital signs like heart rate, oxygen saturation, respiratory rate, and body temperature are more relevant to make the person take the COVID test. In most of the screening tests at crowded places like airports, railways stations, and industries, the primary signs vital in detecting COVID are missed. Moreover, the measurement of all these physiological parameters requires dedicated measuring devices and skilled healthcare professionals. The cost of implementation and procurement gives more financial burden during this economic crisis. An alternate approach to measuring all these vital signs is extracting feature points from the thermal and or visible light reflected from the face of subjects. These feature sets are given to convolutional neural network (CNN) models for training and the trained model can predict the signs from the test inputs. The preliminary readings would be instrumental in suggesting the person undergo the COVID-19 test. It will also act as a continuous monitoring system to read the health condition of vulnerable and treatment undergoing persons. Such systems can be incorporated in any surveillance system and immigration zones to find overseas travelers’ health conditions. The risk of affecting healthcare field workers can be reduced. The possible implementation of health drones creates a pathway to Tele-diagnosis. © 2022 Elsevier Inc. All rights reserved.