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1.
39th National Radio Science Conference, NRSC 2022 ; 2022-November:241-253, 2022.
Article in English | Scopus | ID: covidwho-2192044

ABSTRACT

COVID-19 is a fatal disease that threatens the people's health worldwide in the last few years. Although the testing techniques for COVID-19 had become more widespread, they still lack the speed and accuracy of disease pattern detection. Thanks to Artificial Intelligence (AI) as it can accelerate the detection process by deep learning techniques that can be used to achieve high performance in COVID-19 identification. Many types of Convolutional Neural Networks (CNN) as the most image classification deep learning techniques are used for automatically diagnosing this disease using X-ray or Computerized Tomography (CT-scan) medical images. The individual CNN types can obtain good results with a specific type of images like X-ray or CT-scan images in a certain dataset but, it could not give the same quality for other types of images or datasets. Through this paper, multiple standards model and custom CNN model have been merged using ensemble method to enhance the overall performance, while the accuracy of each model is a parameter in majority voting. Consequently, the proposed method will started with an initial simple classifier to classify between X-ray image and CT-image then followed by the ensemble model, and lasted by the decision making algorithm. Using different image types like X-ray and CT-scan images from different dataset sources enhance the overall performance as will be cleared in our results. The proposed model has three main parts: Multimodal imaging data, Multi-model based CNN structure, and decision-making diffusion based on the Multi-model output part. The main objective of using multiple models or multiple algorithms in detecting COVID-19 is to decrease the error percentage and increase the validation accuracy. Testing and validation results assure that the performance of the proposed method for COVID-19 chest X-rays and CT-scan images outperforms the individual and classical CNN learners' design. © 2022 IEEE.

2.
2022 IEEE International Ultrasonics Symposium, IUS 2022 ; 2022-October, 2022.
Article in English | Scopus | ID: covidwho-2191976

ABSTRACT

Lung ultrasound has become one of the most promising medical techniques for the diagnosis and monitoring of pneumonia, which is one of the main complication of SARS-CoV-2 infection. Despite this, the lack of trained personnel in lung echography has restricted its use worldwide. Computer aided diagnosis could help reducing the learning curve for less experienced technicians and, therefore, extending the use of lung ultrasound more quickly, while reducing the exam duration. This work explores the feasibility of real-time image processing algorithms for automatic calculation of the lung score. A clinical trial with 30 patients was completed following the same protocol of acquiring saving 3 seconds videos of different thorax zones. Those videos were evaluated by an experienced physician and by a custom developed algorithm for detecting A-lines, B-lines, and consolidations. The concordance between both findings were 88% for B-lines, 93.4% for consolidations and 70.2% for A-lines, reducing the acquisition time using the ULTRACOV prototype [1] by more than half compared to a conventional scanner. The good agreement of the results proves the feasibility of implementing real-time algorithms for aided diagnosis in lung ultrasound equipment. © 2022 IEEE.

3.
Ieee Canadian Journal of Electrical and Computer Engineering ; 45(4):436-441, 2022.
Article in English | Web of Science | ID: covidwho-2191893

ABSTRACT

Smart health is a relatively new paradigm where information and communication technology is utilized to improve health care and medical services. In this article, we provide a literature-based overview of smart health systems, their components, architecture, technologies, benefits, applications, challenges, and opportunities. In addition, we discuss the potential benefits of big data, data analytics, artificial intelligence (AI), and machine learning (ML) in smart health systems. Moreover, we discuss the challenges as well as the open research issues that need further investigation to facilitate the implementation of smart health systems.

4.
3rd International Conference on Innovations in Communication Computing and Sciences, ICCS 2021 ; 2576, 2022.
Article in English | Scopus | ID: covidwho-2186579

ABSTRACT

COVID-19 is a coronavirus that causes sickness in the human respiratory system. It is the most recent virus that is wreaking havoc on the entire world. It spreads mainly through contact with an infected person. There are some vaccinations available to prevent this condition now. The flu causes symptoms such as fever, coughing, and breathing difficulties in humans. COVID-19: Classification of X-Ray Images This paper suggests using a Deep Convolution Neural Network-based Transfer Learning methodology. Deep CNN learns picture patterns and classifies X-RAY pictures using transfer learning technology. A dataset is created using publicly available photos of COVID-19 X-Ray. All images have been resized and rotated by 2 to 20 degrees. The file contains 6677 COVID-19 pictures and 5753 stock pictures. DCNN predictability is 99.64 percent on a training set, while on a test set, it is 99.79 percent. After the transfer of learning, predictive accuracy on the training set is 99.19 percent, while predictive accuracy on the test set is 99.31 percent. © 2022 Author(s).

5.
Telematics and Informatics Reports ; 9:100041, 2023.
Article in English | ScienceDirect | ID: covidwho-2183757

ABSTRACT

Background The implementation of artificial intelligence technology in health care improves disease prediction, classification, and diagnosis, benefiting both patients and healthcare providers. a surge in artificial intelligence popularity, owing largely to an enormous increase in computational capabilities and an even greater increase in data generation. The study's purpose was to conduct a bibliometric analysis on healthcare-related artificial intelligence research from the years 2000 to 2021. Methods The Scopus dataset has been used to find and retrieve all existing and referenced healthcare-related artificial intelligence research published in English. Based on bibliometric indicators, the rate of publication growth, the subject area, and the top active countries, institutions, journals, and funding sponsors were analyzed. Results The search identified non-duplicated 5,019 papers. During the years 2000 to 2009, there were fewer publications, but they increased in the subsequent years. Moreover, research released after 2012 constitutes 88.88% of the total publications. Overall, 96.85% of the included studies have been published in 9 countries. About 41.84% of the studies included were from the US. The technology keywords that appeared most were "Machine Learning”, "Electronic health records”, and "Natural language processing”. Furthermore, Covid-19, Diabetes, Mental Health, Asthma, Dementia, and Cancer are some of the disease-related keywords that appeared frequently in healthcare-related artificial intelligence research. Conclusions The study carried out a thorough bibliometric study on healthcare-related artificial intelligence research, which will help researchers, legislators, and practitioners understand the field's growth and the prerequisites for responsible use of artificial intelligence technology within the healthcare system.

6.
Front Artif Intell ; 5:1031450, 2022.
Article in English | PubMed | ID: covidwho-2199577

ABSTRACT

INTRODUCTION: Artificial intelligence in the educational domain has many uses;however, using AI specifically to enhance education and teaching in a K-12 environment poses the most significant challenges to its use. Beyond usage and application, the quality of the education is made even more arduous due to the dynamics of teaching primary and secondary school children, whose needs far exceed mere fact recollection. Utilizing prior research using AI in education and online education in the K-12 space, we explore some of the hurdles that AI applications face in K-12 teaching and provide core attributes for a "Turing Teacher," i.e., an AI powered technology for learning, specifically targeting the K-12 space. METHODS: Using a survey, which included qualitative responses during the implementation of online learning during the Covid Pandemic, we analyze the results using univariate and multivariate tests and analyzed the qualitative responses to create core attributes needed for AI powered teaching technology. RESULTS: The results present the challenges faced by any technology in an education setting and show that AI technology must help overcome negative feelings about technology in education. Further, the core attributes identified in the research must be addressed from the three stakeholder perspectives of teachers, parents and students. DISCUSSION: We present our findings and lay the groundwork for future research in the area of AI powered education. The Turing Teacher must be able to adapt and collaborate with real teachers and address the varying needs of students. In addition, we explore the use of AI technology as a means to close the digital divide in traditionally disadvantaged communities.

7.
NeuroQuantology ; 20(20):1566-1576, 2022.
Article in English | EMBASE | ID: covidwho-2206899

ABSTRACT

The major goal of this study is to investigate how business intelligence is used to develop business operations in SMEs, as well as the elements that influence business intelligence adoption. Following the sample verification procedure, 232 samples were collected. The SEM software was used to process all of the data acquired in the research investigation. The study's findings show that TOE has a significant effect on SMEs' adoption of business intelligence solutions. According to the study's results, the researcher believes that leaders and decision-makers in firms would use business intelligence systems to define all activities, responsibilities, and work procedures in order to increase organizational ambidexterity and performance. Copyright © 2022, Anka Publishers. All rights reserved.

8.
Journal of Pharmaceutical Negative Results ; 13:5946-5951, 2022.
Article in English | EMBASE | ID: covidwho-2206748

ABSTRACT

"NECESSITY IS THE MOTHER OF INVENTION'' The emergency of protecting people from COVID-19 pandemic accelerated Technology development and Applications in various field. Technology became a part and parcel in one's life. Information technology played a vital role in diagnosing, medicating, communicating, and scaling down COVID-19. COVID -19 pandemic became uphill battle for the scholars, scientists, researchers of Information technology, medicine, health care system, education, economy of every country. The immediate necessity for new applications and technology in grappling pandemic situation instituted sophisticated software, medical devices, and applications. COVID- 19 made up people mind for future pandemic. This article further discusses about how Technology Development and Applications became a solution to COVID-19 pandemic. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

9.
Computing and Informatics ; 41(4):1114-1135, 2022.
Article in English | Web of Science | ID: covidwho-2205794

ABSTRACT

The COVID-19 influenza became a curse on the world. It has been around for two years, so no one needs to make a big introduction of it. It has became a significant challenge around the world. Owing to this, we made dy-namic networks using an amalgamating of fuzzy logic and neural networks for the prediction of sufferers of COVID-19. These hybrid networks serve for the assess-ment of the COVID-19 victims and usefully serve for the assessment of the medical resources needed for future victims. This manuscript proposed Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction model for COVID-19 predic-tion in Andhra Pradesh, India. We gathered data on positive COVID-19 sufferers in Andhra Pradesh for this purpose. The data can be separated into three categories: training set, testing set and checking set. We have utilized Root Mean Square Devi-ation (RMSD) for prediction precision. If the prediction model has a lower RMSD value, it is regarded as the best forecast. In this study, we concluded that the 3 Tri-angular MFns for each input were excellent with the extreme precision for all of the districts based on our expertise. In the end, we deployed seven SANFIS replicas in Andhra Pradesh, but we discovered that SANFIS6 and SANFIS7 provided excellent COVID-19 prediction results. These findings will assist the government, healthcare agencies, and medical organizations in planning for future COVID-19 victims' med-ical requirements. These sorts of Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction models based on Artificial Intelligence (AI) will be beneficial in overcoming the COVID-19.

10.
International Journal on Recent and Innovation Trends in Computing and Communication ; 10(11):171-180, 2022.
Article in English | Scopus | ID: covidwho-2204437

ABSTRACT

To stop the COVID-19 epidemic from spreading among their populations, several countries have implemented lockdowns. Students are being forced to stay at home during these lockdowns, which is causing them to use mobile phones, social media, and other digital technologies more frequently than ever. Their poor utilization of these digital tools may be detrimental to their emotional and mental health. In this study, we implement an Artificial Intelligence (AI) approach named Hierarchy-based K-Means Clustering (HKMC) algorithm to group individuals with comparable Twitter consumption habits to detect addictive Twitter activity during the epidemic. The effectiveness of the suggested HKMC is evaluated in terms of accuracy, precision, recall, and f1-score in respect to the association between students' mental health and mobile phone dependency. Additionally, this study offers a comparative examination of both the suggested and existing procedures. © 2022 The authors.

11.
Agathos-an International Review of the Humanities and Social Sciences ; 13(2):177-195, 2022.
Article in English | Web of Science | ID: covidwho-2147743

ABSTRACT

The Covid-19 viral disease has spread worldwide very rapidly and was declared as a pandemic by the World Health Organization. The unexpected rapid increase in the number of infected people has resulted in adverse impact on all spheres of life, and countries have turned to various practices in combating the pandemic. Smart city applications and artificial intelligence technologies were among the first tools that various countries utilized to get quick results in their fight against Covid-19. The aim of this study is to reveal the importance of smart city applications and artificial intelligence in combating Covid-19 by presenting China, US, France, Italy, and Turkey as examples. These countries were comparatively analyzed in terms of their policies, smart city applications and artificial intelligence technologies they have been utilizing to contain and cope with Covid-19. Our findings indicate that these countries mostly benefited from smart city applications, broadband technologies, 5G technology, mobile applications, health code, radiation management, early diagnosis kit and face recognition technologies in coping with the pandemic.

12.
Annals of Translational Medicine ; 0(0):0-0, 2022.
Article in English | Web of Science | ID: covidwho-2145934

ABSTRACT

Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years;it is used mostly for diagnosis;COVID-19 is already in the top-3 of diseases studied using AI;China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research;Stanford University is the world's leading university in AI research;and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

13.
Artificial Intelligence and Computational Dynamics for Biomedical Research ; 8:117-143, 2022.
Article in English | Scopus | ID: covidwho-2140786

ABSTRACT

In today's era, the healthcare domain is highly influenced by the widespread applications of big data analytics and artificial intelligence (AI). These technologies are being used for high-level molecular research, drug development and predictive analysis of various diseases. Currently, big data and AI are being used in several aspects against corona virus disease 2019 (COVID-19) pandemic. Literature as well as the genomic studies available for various strains of severe acute respiratory syndrome coronavirus 2, suggest how the concepts of big data and AI are being implemented to understand COVID-19 better which can assist with its antiviral drug development as well as vaccine production. These concepts of big data and AI have effectively helped with the contact tracing, epidemiology, molecular studies, medical diagnosis and treatment of COVID-19 that can help against future pandemics. Similarly, with this computational approach, various disease patterns are being recognized in different types of cancer. AI plays a major role in biomarker identification for disease progression to recognize novel hallmarks of cancer as well as in the predictive analysis of the disease. This allows an early-stage diagnosis of cancer which leads to a better prognosis. With an interdisciplinary collaboration of big data and predictive analytics, patient risk can be analyzed and estimated concerning the new treatment options available for cancer. Such high-throughput technologies have improvised the diagnosis and treatment options available for various diseases. With the advancements in science and technology and the introduction of computational models, public health and medicine have been completely revolutionized for the betterment of society. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

14.
Asia Pacific Journal of Tourism Research ; 27(9):954-966, 2022.
Article in English | Web of Science | ID: covidwho-2134278

ABSTRACT

The COVID-19 pandemic created a strong urgency for the application of artificial intelligence (AI) in the tourism and hospitality industry. This paper was set to develop a scale of AI needs in health tourism. A total of 556 valid data were collected, and both exploratory and confirmatory factor analyses were employed to analyze the data. Six constructs containing 18 items were identified, and the reliability and validity were examined to reach satisfactory levels. The measurement scale developed may serve as a foundation for future research, and shed light on tourism managers, marketers, AI designers and policymakers.

15.
Jindal Journal of Business Research ; 11(2):205-217, 2022.
Article in English | ProQuest Central | ID: covidwho-2118737

ABSTRACT

This research article determines the most adopted and used technology by common public and end consumers during the COVID-19 pandemic and tries to determine the technologies that will continue to see increased adoption rate beyond the COVID-19 pandemic. It examines examples of how digital technologies and services have been employed in daily lives of individuals and enterprises/businesses during the COVID-19 crisis. We have used responses to the survey built using Google Forms as the method for data collection. The study undertaken was exploratory in nature to identify the most adopted and used technology among common public during the COVID-19 pandemic. We used descriptive statistics, R-test, R-squared test, and ANOVA for the study. The results show that video-based collaboration technology-related services had the highest adoption and usage among respondents, followed by process automation, mobile/web-based services and microservices, and artificial intelligence (AI) and analytics. As an implication of the study, businesses can focus upon technologies such as video-based collaboration technology, process automation, mobile/web-based services and microservices, and AI and analytics—in that order—to roll out new services and models while dealing with their end customers and prospects, during and beyond the COVID-19 pandemic.

16.
IEEE Access ; 10: 87168-87181, 2022.
Article in English | MEDLINE | ID: covidwho-2097585

ABSTRACT

To date, the novel Coronavirus (SARS-CoV-2) has infected millions and has caused the deaths of thousands of people around the world. At the moment, five antibodies, two from China, two from the U.S., and one from the UK, have already been widely utilized and numerous vaccines are under the trail process. In order to reach herd immunity, around 70% of the population would need to be inoculated. It may take several years to hinder the spread of SARS-CoV-2. Governments and concerned authorities have taken stringent measurements such as enforcing partial, complete, or smart lockdowns, building temporary medical facilities, advocating social distancing, and mandating masks in public as well as setting up awareness campaigns. Furthermore, there have been massive efforts in various research areas and a wide variety of tools, technologies and techniques have been explored and developed to combat the war against this pandemic. Interestingly, machine learning (ML) algorithms and internet of Things (IoTs) technology are the pioneers in this race. Up till now, several real-time and intelligent IoT-based COVID-19 diagnosing, and monitoring systems have been proposed to tackle the pandemic. In this article we have analyzed a wide range of IoTs technologies which can be used in diagnosing and monitoring the infected individuals and hotspot areas. Furthermore, we identify the challenges and also provide our vision about the future research on COVID-19.

17.
1st International Conference on Advances in Computational Science and Engineering, ICACSE 2020 ; 2519, 2022.
Article in English | Scopus | ID: covidwho-2096921

ABSTRACT

The recent outbreak of COVID-19 across the globe has been a challenge not just to the health & well-being of people. However, it has also led to a downturn in the economy throughout the world. The purpose of this study is to thoroughly investigate the possible use of emerging technologies such as robotic process automation (RPA) and artificial intelligence (AI) in order to find ways to tackle corona virus, its impact on the economy, and the people. The other technology involved is RPA which is a type of process that can easily and efficiently make it convenient for any machine to handle a repetitive task. When these two technologies are coupled, they can be used in a wide range of applications. This paper explains how RPA and AI can separately, used in various industries and sectors to tackle and effectively reduce the corona virus pandemic's negative impact. © 2022 Author(s).

18.
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice ; 42(9):2426-2446, 2022.
Article in Chinese | Scopus | ID: covidwho-2090893

ABSTRACT

Because of double pressures from “twelve consecutive declines” in monthly sales year-on-year and the novel coronavirus outbreak, purchase subsidy decrease for new energy vehicle had to be postponed from the end of 2020 to 2022. However, carmakers concentrating on intelligentization were outstanding and became the center of attention. Therefore, when purchase subsidy decrease should apply for and whether intelligentization can hedge the negative effect should be answered immediately. This paper builds a computable general equilibrium (CGE) model by adding productive intelligentization factors to production function and using procedure intelligentization factors to utility function, using different macro-closure to simulate the period of insufficient supply or demand and Chinese statistical data in 2018 to calibrate parameters, to analyze the suitable situation for purchase subsidy decrease and the difference and function border of hedging between productive intelligentization and using procedure intelligentization. The results show that purchase subsidy decrease for new energy vehicle should apply for the period of insufficient supply, because it will promote the low correlation industries and decrease the income inequality, while using procedure intelligentization developed moderately can hedge the negative effects to social farewell and new energy industry caused by purchase subsidy decrease and will not over hedge the above positive effects at the same time. But in the period of insufficient demand, the above positive effects will be altered to negative and hardly be hedged by intelligentization. Therefore, the policy decision of purchase subsidy decrease for new energy vehicle should depend on the degree of economic recovery from the novel coronavirus outbreak. If economic recovery is rapid, we can develop using procedure intelligentization moderately, namely, self driving, to hedge the subsidy decrease. Otherwise, we should postpone the subsidy decrease. © 2022 Systems Engineering Society of China. All rights reserved.

19.
Sensors (Basel) ; 22(19)2022 Oct 05.
Article in English | MEDLINE | ID: covidwho-2066354

ABSTRACT

Modern life quality is strongly supported by the advances made in biosensors, which has been attributed to their crucial and viable contribution in point-of-care (POC) technology developments. POC devices are exploited for the fast tracing of disease progression, rapid analysis of water, and food quality assessment. Blood glucose meters, home pregnancy strips, and COVID-19 rapid tests all represent common examples of successful biosensors. Biosensors can provide great specificity due to the incorporation of selective bio-recognition elements and portability at significantly reduced costs. Electrochemical biosensor platforms are one of the most advantageous of these platforms because they offer many merits, such as being cheap, selective, specific, rapid, and portable. Furthermore, they can be incorporated into smartphones and various analytical approaches in order to increase their sensitivity and many other properties. As a very broad and interdisciplinary area of research and development, biosensors include all disciplines and backgrounds from materials science, chemistry, physics, medicine, microbiology/biology, and engineering. Accordingly, in this state-of-the-art article, historical background alongside the long journey of biosensing construction and development, starting from the Clark oxygen electrode until reaching highly advanced wearable stretchable biosensing devices, are discussed. Consequently, selected examples among the miscellaneous applications of nanobiosensors (such as microbial detection, cancer diagnosis, toxicity analysis, food quality-control assurance, point of care, and health prognosis) are described. Eventually, future perspectives for intelligent biosensor commercialization and exploitation in real-life that is going to be supported by machine learning and artificial intelligence (AI) are stated.


Subject(s)
Biosensing Techniques , COVID-19 , Artificial Intelligence , Blood Glucose , COVID-19/diagnosis , Electrochemical Techniques , Humans , Oxygen , Water
20.
Phytomedicine ; 107: 154481, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2061761

ABSTRACT

BACKGROUND: Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs. PURPOSE: This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development. STUDY DESIGN: We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research. METHODS: The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM. RESULT: Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery. CONCLUSIONS: The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.


Subject(s)
COVID-19 , Medicine, Chinese Traditional , Artificial Intelligence , COVID-19/drug therapy , Drug Design , Humans , Ligands , Pharmaceutical Preparations
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