ABSTRACT
The COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (THCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. Therefore, the THCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. The study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. The program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. This study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%. © 2023 IEEE.
ABSTRACT
Anthropogenic activities on the land side cause microbial pollution in coastal waters. The primary culprits in changing coastal water quality are industrial effluents, urban discharges, and agricultural runoff. The current study provides a comparative overview of microbial abundance during the pre (July 2019) and post-lockdown (July 2020) periods. Microbial densities were significantly higher during July 2019. Total heterotrophic bacteria (THB) and E. coli (ECLO) like organisms were about 53 % higher during pre-lockdown, while Fecal coliform (FC) counts were approximately seven× higher than post-lockdown. FC levels surpassed the standard safe limits (100 FC/100 ml) prescribed by Central Pollution Control Board (CPCB) at many locations. Physiochemical variables are significantly high during pre-lockdown. Total suspended matter levels were higher by 40.1 %, Total nitrogen (69.2 %), Total phosphorus (7×), Biological oxygen demand (45.6 %), and pCO2 (20.4%). Although nutrients are not limiting (high TN & TP), the phytoplankton biomass (Chl-a) was relatively low in pre-lockdown due to higher TSM restricting light penetration and affecting photosynthetic activities. Significant reductions in microbial contamination during July 2020 corroborated lesser anthropogenic activities associated with the lockdown, demonstrating the positive impact of lockdown on the coastal water quality. © 2022 IEEE.
ABSTRACT
Over the last decade, there has been a quantum leap in terms of the evolution of new methodologies to better our quest to understand artificial intelligence and machine learning. One such field, where there has been an unparalleled advancement, is computer vision. The paper aims to design and structure an automated monitoring system that automates the monitoring of the number of people in this COVID-19 scenario in a designated enclosure. We have deployed the system on Raspberry Pi module and integrated a HOG detector which transcends ordinary Haar cascades in terms of performance. This model can then subsequently be connected and integrated with other modules to further enhance its applicability and spectrum of usage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.