ABSTRACT
In the present COVID-19 situation, it poses a danger to a person's life because of organ infection and other health problems. It is mandatory to research work to find a better COVID-19 infection diagnosis method through scans and contact tracing through the AI method. In this, a novel AI structural model is intended to identify the infection features in the respective regions of human being availability, which makes the infection monitoring easier to identify an infected and non-infected human being from the population identified. The method used for monitoring the multiplicative nature of Coronavirus infections is through contact feature tracing and infection confirmation status and confirms the Coronavirus cases from scans and feature analysis to include real-time contact tracking from the same region and distant regions, providing an efficient method to track the infection spread. The anticipated model is used to forecast coronavirus transmission using feature forecasting data. The performance assessment is compared based on the outcomes of the suggested model and shows an enhanced COVID-19 diagnostic model.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.
ABSTRACT
In the Artificial intelligence (AI) field, intelligent social awareness is a quantifiable analysis that interacts with humans socially with other infected or non-infected COVID-19 (CoV19) humans. However, less importance is given in this direction. Clinically, there is a need for a social-awareness automated model design to quantify the self-awareness of infected patients and develop a social learning system. In this research paper, a new model of self-aware internal learning coronavirus 19 (SIntL-CoV19) model technique is presented with quantification measures to represent model requirements as an individual self-aware automated detection. Through this model, a human can communicate with the social environment and other humans with an accurate CoV19 infection diagnosis. SIntL-CoV19 model framework for implementation of self-aware architecture with this model is proposed making the diagnosis process compared with the existing architecture. The proposed model achieves improved accuracy Feature Classifier, which outperforms other learning algorithms for CoV19 and normal scans. The data from the investigation show that the proposed SIntL-CoV19 model method might be more effective than other methods. © 2023 Peniero Tupas et al.
ABSTRACT
Coronavirus is the deadliest disease globally, and no efficient treatment has been established. The prognosis of illnesses caused by virus outbreaks is a severe medical process that demands a large amount of accurate data comprised of many factors to produce an appropriate analysis. We have researched and analyzed the factors that might affect humans and increase the chances of infection with Covid-19. One of them is the breathing symptoms directly affecting the lungs and chest. To analyze the factors, we have used traditional machine learning and deep learning models to classify and predict the chances of a human getting infected with different SARs variants. So, we used a Cyclic Generative Adversarial Networks (CGANs) model, Convolutional Neural Networks (CNNs), to generate, predict and classify the Covid-19 occurrence through chest x-rays and other attributes like Diabetes and Hypertension. These models are deployed to the cloud with appropriate hypermeter tuning to use the result in real time. This paper proposed CGANs and CNNs, which automatically use ADAM, RMSprop and Bayesian optimizers to identify chest X-ray COVID-19 pneumonia images. Then, using extracted features has increased the performance of the proposed technique. The experiments suggest that the presented ADAM method fits RMSprop and Bayesian optimization achieves better accuracy. Within proposed algorithms, Bayesian optimization effectively predicts the diagnosis of covid-19 patients.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.
ABSTRACT
The COVID-19 epidemic has thrown the globe into chaos as governments struggle to contain the virus's spread. While considerable attention has been paid to health problems, reports of the social-psychological impacts of this pandemic are on the increase, ranging from mental distress and personal disputes to racial assaults and patriotic behaviors. Moreover, these social-psychological impacts may have long-term repercussions that will likely outlast the epidemic. Globally, the physical impacts of COVID-19 may be felt. The influence of Post-COVID-19 on mental health is a subject that has not been well explored. The reaction to the Covid-19 pandemic may resemble the response to natural catastrophes or other comparable catastrophic occurrences affecting a community and may create chronic anguish in the afflicted population. The psychological reaction to the Covid-19 epidemic has been immediately recorded;in communities rarely affected, it resembles symptoms of post-traumatic stress disorder (PTSD). The unfavorable psychological effects of COVID-19, such as anxiety and depression, have been widely expected but have not yet been precisely quantified. Data science enables academics to evaluate the past and future consequences of mental diseases on the United States and worldwide populations. When the consequences are defined and substantiated by statistics, the argument for allocating much-needed resources to mental health research and treatment accessibility is strengthened. Using data science approaches, the book contains chapters describing the influence of post-COVID-19 psychological impacts. © 2022 by Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
COVID pandemic and the subsequent recent emergence of its different variants have posed significant challenges for continuing everyday lifestyle, including any educational institute's campus life. In contrast, educational institutes conduct classes, exams, placement, and other co-curricular activities online, offline, and hybrid modes. Because of this, we have achieved a web-based survey on students about their mental health and other related issues such as anxiety, worry, disturbance, fear of infection, and mental anguish caused by COVID-19 in university undergraduates. 1100 pupils completed a digital survey in this crosssectional study. All these are college graduates from various universities in Bhubaneswar, India, and other universities in Odisha. COVID-19 awareness, nervousness, tension, panic, and mental illness in the past were used to screen the psychological distress. This paper reviews the current scenario of COVID-19 concerning psychological distress and related issues. Students' mental health can be affected by using the development of RST (rough set theory) principles. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
Major symptoms during coronavirus 2019 are dangerous, and the mass of patients recover a vital division of patients at the present more experience for long-term health care. In this context, spotlight on health-related measures after severe illness and hospitalization. This chapter is a theoretical study of health issues in initially obtainable patients with subtle symptoms of severe respiratory conditions and coronavirus infections. We mainly focus on moderate and severe COVID-19 in hospitalized patients. Almost 600 patients with respiratory syndrome and coronavirus infections were observed last year, with fatigue, anemia, and breathing problems. Among these symptoms, breathing is the most common symptom. These symptoms are projected on lengthy-term health issues post-COVID-19. This type of symptom is measured by uni or multi-logistic regression model techniques. We observed more than 600 patients over one year after COVID-19. So many issues like breathing issues, anosmia, and long-time symptoms in hospitalized patients were experiential one-year post-infection. The evaluation will be continued for post-COVID-19 conditional patients. It will become a critical mission to describe and lessen the socioeconomic and long-term medical effects of COVID-19. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
Many institutions worldwide, such as education, corporate, and business firms, have concluded that pandemic illness is as critical as climate change. An epidemic sweeps the globe in a few months. COVID-19, an illness that started to form in China in December 2019, could bring a long-term pandemic to the world depending on its spread rate, which also affects the world's educational systems. Many educational institutions had no option but to change the teaching mode and learn to be online or virtual. The main objective of this analysis was to determine the impact of psychiatric issues on learner progress during the COVID-19 epidemic. Furthermore, the effects of quarantine, self-stigma, and loneliness on students' stress and behavioral health will be evaluated. The research was carried out at ITMO University, where a student was asked to complete a questionnaire via an online survey. The current machine learning approach was used to analyze the data gathered. The study found that 59% of the participants could not pass all their courses within the pandemic frame when studying online, while 41% of the participants could pass all their courses. The study clearly shows that students from Russia had bad results compared with students already in Russia. This leads to more international students quitting their graduate studies. Due to this, we propose that colleges and universities offer flexible ways to teach and learn to meet the needs of international students during the pandemic. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
The current epidemic of severe coronavirus illness has led to global medical issues. Furthermore, the ongoing and protracted post-COVID-19 or chronic COVID imposes a significant burden on health care professionals because of inadequate medical facilities. Heart disease has been identified as the most prevalent enduring post-COVID-19 consequence of coronavirus persistence. COVID-19's undesirable psychological repercussions, such as anxiety and depression, have been generally anticipated but have yet to be fully assessed. COVID-19 has several physiological conditions and diseases. However, it is unknown whether there are possible psychological causes. The severe acute respiratory pandemic was also related to a rise in PTSD, tension, and emotional anguish among victims and doctors. In the context of certain occurrences, the effects might be rapid and then last for a significant period. COVID-19 may induce severe psychological issues like disorientation, restlessness, and dementia. Patients who have had psychological, cognitive, or medication abuse issues are significantly more susceptible to SARS-CoV-2 infection and may have a higher risk of devastating outcomes, even fatality. During one year post-acute, victims of the devastating disease have ongoing psychological disturbance, with considerable psychological distress, sadness, and chronic posttraumatic depression. Most people with symptomatic respiratory failure have neuropsychological problems, such as poor concentration, cognition, remembering, and intellectual information processing. This chapter addressed the post-COVID psychological issues and their consequences. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
Data science has improved healthcare as new data sources and analytic techniques have become available. Data science applications are valuable technologies, which could powerfully improve health care service delivery, especially in mental illness. COVID-19's undesired psychological health repercussions, such as depression symptoms, have been widely anticipated. COVID-19 has several physical health risk factors;however, it is unknown if there are any psychological risk factors. Data science enables statistical cooperation, data analytic technique development, and data administration for psychiatric and mental health research. Although data science is not commonly employed in the mental health profession, there is hopeful evidence that it can substantially affect it. Researchers may use data science to evaluate mental diseases' consequences in the US and worldwide. When the consequences are measured and backed up by statistics, it makes a stronger argument for much-needed provisions. When the consequences are defined and validated by evidence, it provides a robust discussion for allocating critical resources to accessible psychological health diagnosis and care. Diagnostic reports, pharmaceutical sales, physicians' observations, and diagnostic testing are all examples of data. Data from health applications and monitoring tools can be used to gain valuable insights and, ultimately, break the stigma of psychiatric conditions in enhancing health care. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
The pandemic raises several concerns for medical providers. Speedy screening and therapy, susceptibility categorization, effective utilization of acute medical utilities, proper drugs, observation, and quick release are vital for protecting as many victims as feasible. We discussed several ML (machine learning) classification strategies for analyzing skin-related concerns. This chapter tries to give appropriate skin specialists a strategy plan to comprehend its potential and difficulties. In this study, we employ several classification approaches and multi-model classification methods-design autonomous assessment assistance to reliably detect a specific type of allergic rhinitis ailment for deployment. © 2022 Nova Science Publishers, Inc. All rights reserved.
ABSTRACT
Internet hoaxes like COVID-19 pose a danger to the population in southern India. Studying the relevance of COVID's traits has helped academics debunk the bogus news spread on social media. A plethora of concerns were voiced when a small number of unverified reports made headlines recently;such stories may have implications across a wide range of topics, including religion, politics, health, and beyond. More than half of all health-related bogus news is itself fraudulent. Vaccine side effects, drug interactions, outdated medical technology, new viruses, and other factors all contribute to people's declining health. The fake news includes text content, audio files, video files, and photos—most of the fake news is in the form of video content 55%. Social media websites like YouTube, WhatsApp, Twitter, and Facebook, produce false news content. The COVID pandemic is universal, so more than 65% of news is connected internationally. Finally, 75% are treated as fake news;it could be a genuine risk to public health—fake news understanding the social media during the present and future situation. Unfortunately, internet users complain about being shown a flood of similar content that is either misleading or entirely false when searching for relevant and dependable information. Several worries about what has been termed an "infodemic" of misleading material being spread online, including possibly bad advice on some subjects. In this paper, the main objective is to identify fake news in online media and classify the fake news.
ABSTRACT
The most dangerous and infectious disease, COVID-19, affecting millions of people is by an enveloped RNA virus known as SARS-COV-2 or Coronavirus, and the disease is unknown before the epidemic commenced in Wuhan, China, in December 2019. Many researchers are busy finding the vaccine for the pandemic. Here, we analyze the diagnostic methods by using mathematical modeling. The majority probable corona patient category with an enhanced AUC characterizes the SVM’s optimal diagnostics model in this chapter. Experimental and computational analyses demonstrate that the diagnosis of potentially COVID-19 can be supported by adopting ML algorithms that learn linguistic diagnostics from the interpretation of elderly persons. Highlight the collection of significant semantic, lexical, and top n-gram properties with the better ML method to estimate diseases. But diagnostics methods must be trained on massive datasets, leading to improved AUC and medical diagnoses of COVID-19 probability. A significant use resulting from mathematical modeling is that it claims transparency and accurateness about our model. These techniques can help in decision-making by useful predictions about substantial issues such as treatment protocols and interfere and minimize the spread of COVID-19. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
COVID-19 is an expanding social, economic, and health epidemic. Present COVID-19, as it has led to tremendous increases in psychiatric problems, has a leading national influence on this secondary disease. COVID-19 caused widespread hysteria, socioeconomic injury, and a high infection rate and mortality to the psychosocial effect. The virus is predictable to pose a big global mental health problem that already has a huge impact on millions of people’s physical health. Emotional and cognitive risks. Discussed various perceived risks when the perceived surveillance reduced health risk chance. The prediction model incorporates biological, psychological, and social variables in diagnosis, prognosis, and treatment of COVID-19 by logistic regression, decision tree, random forest, RNN (Recurrent Neural Network), and PNN (Probability Neural Network). In order to provide doctors and patients with information about their use, efficacy, and deficiencies, this research includes a design evaluation for the topic, diagnosis, and assessment of moderate or extreme neurocognitive impairments. The adverse effects of fear, cold, and depression increase the health risk;obsession raises the health risk, risks between individuals and mental health, and uncertainty;Finally, positive mental states enhance health risk perception. Further, positive survivor techniques can help ease emotional distress that causes tension, while pessimistic coping mechanisms can intensify emotional symptoms due to stress. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Today entire world is struggling, and significant cases are rising due to Coronavirus, namely COVID-19. Healthcare providers are busy in clinical trials to investigate the vaccine for this pandemic. If this virus attacks the person, nobody can know that person is going to be tested positive. This virus is spreading through the droplets of one person or dirty hands. The primary task of healthcare providers is to provide diagnostic product services at low costs and accurately diagnose patients. Machine learning methods can use for disease identification because they mainly apply to data and prioritize specific tasks’ outcomes. In this work, a multistage fuzzy rule-based algorithm for detection and CART algorithm is utilizing to produce the fuzzy rules. Implementation results exhibit that the proposed method differentiated the development of the disease prediction accuracy. The integration of these two techniques, multistage fuzzy rules and CART algorithms with unrelated data removal methods, could help predict disease. The proposed system can be helpful for healthcare providers in predicting the early stages of COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
With the enormous increase in data size, the complexity of finding duplicate data is recognized as one of the significant challenges. Elimination of duplicate data is an essential step in data cleaning as redundant data can affect a system's performance in the data processing. In order to do this deduplication technique is used to eliminate the duplicated data at the file or content level which helps to only store one copy of the file in the database. In this paper a technique is proposed to solve the storage issues and deduplication where the Hadoop Distributed File System is used to solve the vast amount of data storage issues and to identify the duplicate data a cryptography algorithm SHA 256 is used. Finally, HBase a non-relational distributed database including Hive Integration is used for data retrieval. The dataset containing counts of tests and results for COVID-19 is taken from Data.gov for experimentation. The experimental results divulge an increase in deduplication ratio, less time consumed and a gain in the storage space used.
ABSTRACT
Today, the entire world is suffering and fear with the epidemic of coronavirus. The statistics of coronavirus as on current data, 213 countries affected by this pandemic, more than 1.1 cr people, suffering from this killer virus, and about 6L fatality are recording. This virus is spreading speedily, and the patients are mainly suffering from breathing. The patient having previous health issues will get more possibility of this disease. In this work, try to evaluate the COVID 19 patient x-ray images by using DL (deep learning) techniques developed on the grouping of a recurrent neural network (RNN) and a correlational network to identify COVID-19 automatically is used in the prediction of high-risk outbreaks to learn on the prognostic code sequences of patients. By use of RNNs helps the model to assess difference in patient status in terms of time and thereby improve predictive precision. A correlational neural network is to identify the salient features for CORONAVIRUS, and these features are feed into a Probabilistic neural network (PNN) for better corona diagnosis. The experimental result gives improved accuracy for analyzing coronavirus disease. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Coronavirus is one of the world’s most critical issues, till date. Comprehension of causative variables such as mellitus, heart-related issues, asthma, blood pressure, etc., including the intrinsic transmission mechanisms of the disease, COVID 19 and its eradication are important for neurological investigation. Hence, the advance of appropriate modeling approaches and methods applied to current corona information on the pervasiveness of the pandemic and other serious illness aspects, is taking consideration. The prevalence of COVID 19 in India has reached epidemic proportions, and this disease is becoming a significantly increasing case in India. In this work, polynomial regression analysis methods employ to to forecast the number of COVID 19 corona patients. In this, we described a decision tree, polynomial and random forest classification of disease in COVID 19 incidences modelling and forecasting in India and a predicted prevalence of high level of confidence.