ABSTRACT
Recognizing patient activity in real-time from video or images collected by a CCTV camera available in the hospital during a Covid-19 situation has proven challenging. The dilemma of patient activity recognition is identifying and recognizing a patient's various actions in a series of videos. The process presented in our paper needs to achieve unrestricted, generic behavior in videos. Detecting events in any video is often difficult because we use Bidirectional ConvLSTM to create a robust patient in the sense behaviors (PSB) framework capable of eliminating certain barriers. To begin this paper by proposing a new Bidirectional ConvLSTM for establishing a stable PSB scheme. Our proposed model is capable of accurately predicting patient's behaviors like seated, standing, and so on. Using Bidirectional ConvLSTM, learning information from a pre-trained model is an excellent place to start for rapidly developing a new PSB system using a current PSB database, as both the source and target datasets are critical. All parameters are frozen in a pre-trained PSB device. Then, using the UCI and HMDB51 dataset to train the model, variables and local relations are progressively fixed. A novel PSB framework is developed using the target dataset. Relevant tests are conducted using commonly used research indices to assess prediction precision accuracy. They acknowledge six patient's behavior with a weighted accuracy rate of 92%. For recognizing novel activity, laying, the precision of a corresponding prediction is the best, 91%, of all six test results. The proposed work uses bidirectional ConvLSTM with modified activation layers to sense the patients' behavior. This article may be a patient activity recognition system to identify an individual. It takes a clip of COVID-19 patients as input and looks for matches inside the hold-on images.
ABSTRACT
Background: COVID-19 is a deadly viral infection that kills many people throughout the globe. The goal of this study was to find out how people in Pakistan felt about the COVID-19 vaccine.Method: Convenience and respondent-driven sampling method was used to conduct an online survey with 15 closed-and open-ended questions to a sample of 330 participants. The proportion of people who had a positive attitude towards vaccination vs. those who had a negative attitude towards vaccination was revealed by the closed-ended questions. The open-ended questions elicited qualitative data on why peo-ple accepted or rejected the vaccination.Results: 62.9% of the total number of respondents, male 1.97 times more likely (OR: 1.97, CI: 1.08-3.58) than female, 80% younger than 50 years, higher age groups, 71.3% married, 69.3% of the working population intended to get vaccinated with COVID-19 vaccine. People who held pro-vaccine health beliefs, had knowledge of, access to the COVID-19 vaccine, were employed, or under government pressure to get vaccinated, or visited public vaccination location, reported a positive attitude towards vaccination. People with safety concerns, social pressure of not getting vaccinated, low levels of awareness, trust and belonging to communities with anti-vaccination beliefs were likely to have negative attitudes towards COVID-19 vaccine.Conclusion: This study helps to identify the attitudes of people and has implications for COVID-19 immunization efforts in Pakistan for various population segments.
ABSTRACT
Purpose: The study aims to develop an in-depth understanding of challenges faced by Indian women professionals during the pandemic and the human resource (HR) initiatives like effective communication, taken by the organizations to mitigate the plight of these professionals. Design/methodology/approach: A mix of two qualitative research methods namely focus groups in-depth and one-to-one in-depth interviews was used. A total of 32 females working with different organizations participated. Findings: The thematic analysis revealed themes related to challenges faced by working women-gendered burnout, mental health issues, increased household responsibilities, job insecurity, work-life conflict, gender inequalities, reduced internal communication and financial independence, domestic violence and exploitation. The major themes that emerged for the organizational initiatives were flexible working hours, equal women representation in response to planning and decision making, driving transformative change for gender equality, paid leaves for family care, caregiving bonus, leadership development seeds, increased female recruitments, transparent communication and counseling sessions. Research limitations/implications: The study establishes a holistic understanding of the plight of Indian women professionals and the consequent organizational interventions accompanied by transparent communication. It adds rigor to the evolving literature on COVID-19 and enriches the theoretical narrative of policy adaptations by industry practitioners for aligning them with employee needs. This helps in routing the policy design and implementation in light of the challenges faced. Originality/value: The study presents an in-depth understanding of challenges faced by women employees;and provides a foundation for identifying human resource management (HRM) interventions customized for working females. It also proposes a framework implementable in the recovery phase, deploying critical strategic shifts like reflection, recommitment and re-engagement of the women workforce in order to maximize their efficacy for rapidly evolving organizational priorities. © 2023, Emerald Publishing Limited.
ABSTRACT
Coronavirus disease-2019 (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), leading to a pandemic and traumatizing the world. To prevent the spread of virus, infected people were isolated as per the strict protocols. Due to the genome sequence of the virus being strikingly similar to that of SARS-CoV, many antiviral medicines previously approved to treat SARS and MERS are now being repurposed for the plausible treatment COVID-19. To combat SARS-CoV-2, a slew of experimental and clinical medicine and vaccine trials are currently underway worldwide. In the fight against COVID-19 infection, a variety of natural substances are also being searched extensively. Coumarins and chalcones are two important natural chemical classes. They can be found in a wide range of natural products and have many pharmacological effects. SARS-CoV-2 and other coronaviruses were successfully treated with these drugs, which showed significant antiviral activity. This chapter discusses the possible role of coumarins and chalcones in SARS-CoV-2 infection treatment. © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
In light of the ongoing COVID-19 pandemic, it is important to analyse the ventilation system of an AC coach for safer as well as comfortable ride. In this study we have simulated the airflow, temperature distribution and velocity distribution inside the cabin, to find out the best layout for comfortable temperature as well as reduced chances of airborne infection. We have simulated various ventilation layouts of the 2 tier AC train coach of Indian Railways, to study the effect of the position of the inlet and outlet ports on the temperature and velocity distribution inside the cabin. CFD analysis was done using the Ansys Fluent solver employing the realizable k-ε model to solve the turbulence problem. Herein, a total of 12 layouts were simulated with 6 heated manikins sitting inside the cabin. The results of the study suggested that the temperature distribution inside the cabin changes significantly with a change in the inlet port position. Further, the layout with the above window and/or roof outlet has a relatively lower cabin temperature. This study forms the basis for further investigations to analyse the transmission of infection via cough droplets inside the cabin (unreported here). The results of this research are important for finding the optimum position of the inlet and outlet ports in AC coaches to enhance the overall thermal comfort and reduce infection transmission inside the cabin. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Corona virus disease 2019 (COVID-19) is an infectious disease. We have proposed a COVID-19 disease detection using deep learning method in this paper. Novel disease coronavirus bring forth diverse effect on population. Exponential growth of virus and lack of knowledge of treatment was the biggest challenge for doctors to save patient's life. Due to less availability of ventilator and ICU clinical trial and testing overloaded of COVID-19 health status. Lung infection diagnosed by Chest X-ray found as best and fastest approach to detect severity of COVID-19. The work presents an AI model to detect the COVID-19 by diagnoses of chest X-ray report. Chest X-ray report finding has been conducted using CNN (convolution neural network) model with ResNet50 and VGG 19 model. The model classify the patients into four category COVID-19, normal, pneumonia, lung obesity. AI model train the X-ray image through image processing methods with an accuracy of 99.3%. The efficacy of proposed model also has been analyzed in terms of accuracy, specificity, and sensitivity, precision. © 2022 IEEE.
ABSTRACT
Artificial intelligence (AI), deep learning (DL), and neural networks (NN), though these words sound flashy and may leave you perplexed, represent powerful technologies that have the capabilities to transform the world. It is just now emerging how valuable these machine learning-based techniques are and how they can solve many real-world problems ranging from fraud detection, resource management to driver-less cars.One such field where the application of AI systems is progressively growing is in medical diagnosis. A lot of research is going on to enhance computer-Aided diagnosis and detection of diseases. Recent world events have tested the healthcare systems all around the world. Suppose we have sophisticated deep learning systems (DLS) that could help in faster and efficient disease detection and diagnosis;how beneficial it would be to assist both medical professionals and patients.This study explores how AI and machine learning techniques could be used for disease detection, giving COVID-19 and Diabetic Retinopathy detection examples. We present two deep learning (DL) models, one to detect COVID-19 from chest x-ray image scans and the other to detect Diabetic Retinopathy at various stages of the disease from retinal fundus images. With reasonably high accuracy, >95% for the COVID-19 detection model and >80% for the Diabetic Retinopathy detection model, these results highlight AI and deep learning potential to assist general practitioners. © 2022 IEEE.
ABSTRACT
Analyzing the emotions about the vaccines and vaccination will help to successfully carry forward the vaccination trials and government policies towards epidemic control. The tweets featured information on the most common immunizations has recently been available all around the world. The method of natural language processing is the successful tool to investigate the reactions of the people to various immunizations. This paper proposes a ensemble learning model making use of the VADER lexicon, logistic regression, and random forest algorithm for sentiment analysis to understand and interpret the people's sentiments through the tweets. We utilize a collection of tweets in April to May 2021 to extract inferences about public views on vaccinations as they become more widely available during the COVID-19 pandemic. The classification output of the VADER algorithm is used as one more feature that helps to achieve better accuracy using the random forest algorithm. One more feature is added with the available features using logistic regression. Hence, the classification outputs of VADER and logistic regression improve the classification accuracy to 88% for positive-negative outputs and 84% for positive, neutral, and negative outputs. © 2022 IEEE.
ABSTRACT
The COVID -19 pandemic has transformed many aspects of life and one of the hard hit sector is Education. The situation has forced us to build virtual alliances and e-learning system which never happened before to fill the formal learning void. Many colleges and universities are relying on tailor-made and existing online courses from other institutions to ensure academic continuity for students. In this reference, this paper tends to explore the changing trends in Higher Education due to the massification of online courses called Massive Open Online Courses (MOOCs) in India. Also the study highlights the comparison between students remote learning and online learning from parents perspective. For this purpose data is collected via both open and closed ended questionnaire and is statistically analysed. The findings reveals that MOOCs and online education have certainly breached the presential learning regulations and acts as a catalyst for higher education reform by offering an array of opportunities and competencies at a relatively low cost. However, they are nowhere close to replacing face to face instructor led learning. The study recommends that online education can be seen as a step in the right direction towards democratizing education but cannot be a cure for all the global education problems or a replacement for campus-based learning. The insights from this study can be helpful in developing a blended system of learning which brings in positive energy and supports teachers, students and professionals learning towards sustainable development.
ABSTRACT
In light of the ongoing COVID-19 pandemic, it is important to analyse the ventilation system of an AC coach for safer as well as comfortable ride. In this study we have simulated the airflow, temperature distribution and velocity distribution inside the cabin, to find out the best layout for comfortable temperature as well as reduced chances of airborne infection. We have simulated various ventilation layouts of the 2 tier AC train coach of Indian Railways, to study the effect of the position of the inlet and outlet ports on the temperature and velocity distribution inside the cabin. CFD analysis was done using the Ansys Fluent solver employing the realizable k-ε model to solve the turbulence problem. Herein, a total of 12 layouts were simulated with 6 heated manikins sitting inside the cabin. The results of the study suggested that the temperature distribution inside the cabin changes significantly with a change in the inlet port position. Further, the layout with the above window and/or roof outlet has a relatively lower cabin temperature. This study forms the basis for further investigations to analyse the transmission of infection via cough droplets inside the cabin (unreported here). The results of this research are important for finding the optimum position of the inlet and outlet ports in AC coaches to enhance the overall thermal comfort and reduce infection transmission inside the cabin. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
COVID-19 has had a lasting effect on the human population around the globe. originating from Wuhan, China, in December 2019, the virus managed to spread worldwide in a short time. Huge waiting time between the detection of symptoms and clinical confirmation of the virus being present in the body has made the virus more fatal;thus, rapid screening of large numbers of suspected patients is essential. Due to inefficiency in pathological testing, alternate ways must be devised to combat these issues. Due to advancements in CAD, integrating radiological images with Artificial Intelligence (AI) can detect the disease accurately. This study proposes a deep learning model for automatic COVID-19 detection using raw Chest X-ray (CXR) images. With 17 convolutional layers, the proposed model is trained to diagnose COVID-19 with an 96.67% accuracy. The model can be used to help the world in numerous ways. © 2022 IEEE.
ABSTRACT
In this current COVID-19 scenario, an effective face mask detection application. The project's major purpose is to put this system in place at college entrances, airlines, hospitals, and offices where the risk of COVID-19 spreading through contagion is highest. According to reports, having a face mask while at work significantly minimizes the chance of transmission. It's an issue of object detection and classification with two classes (Mask and Without Mask). For recognizing face masks, a hybrid model combining deep and traditional machine learning will be shown. This face mask detector is built with Python, OpenCV, TensorFlow, and Keras and is based on a dataset. Everyone should inspect their face before entering the building and make sure they have a mask with them. A beep alert will be triggered if somebody is found without a face mask. As a result, all of the workplaces are reopening, the number of instances of COVID-19 being reported around the country is steadily rising. It can be brought to a close if everyone observes the safety precautions. As a result, we expect that this research will assist in detecting people wearing masks to work. © 2021 IEEE.
ABSTRACT
Present study points out the impact of Lockdown on the health of the Yamuna river at Delhi stretch by comparing prelockdown and Post-lockdown period by studying the reports of pollution monitoring agencies. Delhi segment of the Yamuna is highly polluted, where alongwith domestic sewage a huge quantity of industrial waste is being discharged continuously without proper treatment. Pre lockdown (March 2020) water quality parameters at three sampling stations named as Palla, Nizammuddin Bridge and Okhla barrage U/s in Delhi were, pH were 8.7, 7.3 and 7.2, DO were 17.1 mg/L, not detected in later two sites, BOD were 7.9 mg/L, 57 mg/L and 27 mg/L and COD were 28 mg/L, 90 mg/L and 95 mg/L respectively and postlockdown period (April 2020) the pH was 7.8, 7.2 and 7.1, DO was 8.3 mg/L, 2.4 mg/L and 1.2 mg/L BOD was 2 mg/L, 5.6 mg/L and 6.1 mg/L and COD were 6 mg/L, 16 mg/L and 18 mg/L respectively. The study of these parameters at three sampling stations reveals that the lack of industrial pollutants discharging due to nationwide lockdown for COVID-19 pandemic had positive effect on water quality of this river. Water quality could be maintained by planned establishment of industries and setup of ETP with without gap between generation and treatment.
ABSTRACT
Purpose Over recent years, brand semiotics have been gaining the marketing practitioners' attention for designing their brand strategy. Hence, to address this gap, the current study investigates the effect of semiotic product packaging on brand experience dimensions, brand trust and purchase intent of reputed major brands of fast-moving consumer good (FMCG) products. Design/methodology/approach The data for this study were collected by administering a questionnaire-based survey from 254 respondents from the Delhi National Capital Region (NCR) of India, using systematic sampling. Structural equation modeling has been used to test the conceptual model and examine the hypotheses developed in the study. Findings The results present evidence of the growing influence of semiotic product packaging upon consumer brand trust and purchase intentions. The study suggests that brand semiotics positively influence customer brand experience, brand trust and purchase intention of FMCG products. Practical implications The research findings will benefit FMCG companies to identify how to apply semiotics in packaging to improve consumers' brand experience and influence intent to purchase. Originality/value Research in brand semiotics on product packaging is limited, as most prior studies focus on brand semiotics in advertising, product design improvement and retail design. The present study has investigated the impact of semiotics on brand experience dimensions in product packaging, which is emerging as a critical concern for the FMCG sector particularly in the post-COVID period.
ABSTRACT
We sequenced the genomes of 5,085 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains causing two coronavirus disease 2019 (COVID-19) disease waves in metropolitan Houston, TX, an ethnically diverse region with 7 million residents. The genomes were from viruses recovered in the earliest recognized phase of the pandemic in Houston and from viruses recovered in an ongoing massive second wave of infections. The virus was originally introduced into Houston many times independently. Virtually all strains in the second wave have a Gly614 amino acid replacement in the spike protein, a polymorphism that has been linked to increased transmission and infectivity. Patients infected with the Gly614 variant strains had significantly higher virus loads in the nasopharynx on initial diagnosis. We found little evidence of a significant relationship between virus genotype and altered virulence, stressing the linkage between disease severity, underlying medical conditions, and host genetics. Some regions of the spike protein-the primary target of global vaccine efforts-are replete with amino acid replacements, perhaps indicating the action of selection. We exploited the genomic data to generate defined single amino acid replacements in the receptor binding domain of spike protein that, importantly, produced decreased recognition by the neutralizing monoclonal antibody CR3022. Our report represents the first analysis of the molecular architecture of SARS-CoV-2 in two infection waves in a major metropolitan region. The findings will help us to understand the origin, composition, and trajectory of future infection waves and the potential effect of the host immune response and therapeutic maneuvers on SARS-CoV-2 evolution.