Computational Intelligence Against Covid-19 for Diagnosis Using Image Processing Techniques in Healthcare Sector

Coronavirus 2019 (COVID-19) medical images detection and classification are used in artificial intelligence (AI) techniques. Few months back, from the observation it is witnessed that there is a rapid increase in using AI techniques for diagnosing COVID-19 with chest computed tomography (CT) images. AI more accurately detects COVID-19;moreover efficiently differentiates this from other lung infection and pneumonia. AI is very useful and has been broadly accepted in medical applications as its accuracy and prediction rates are high. This paper is developed and aims to fight against corona through AI using computational intelligence in detecting and classifying COVID-19 using Densnet-121 architecture on chest CT images from a global diverse multi-institution dataset. Furthermore, data from clinics and images from medical applications improve the performance of the proposed approach and provide better response with practical applications. Classification performance was evaluated by confusion matrices followed by overall accuracy, precision, recall and specificity for precisely classifying COVID-19 against any condition. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Scale Up Multilingualism in Health Emergency Learning: Developing an Automated Transcription and Translation Tool.

World Health Organization's (WHO) emergency learning platform OpenWHO provided by Hasso Plattner Institut (HPI) delivered online learning in real-time and in multiple languages during the COVID-19 pandemic. The challenge was to move from manual transcription and translation to automated to increase the speed and quantity of materials and languages available. TransPipe tool was introduced to facilitate this task. We describe the TransPipe development, analyze its functioning and report key results achieved. TransPipe successfully connects existing services and provides a suitable workflow to create and maintain video subtitles in different languages. By the end of 2022, the tool transcribed nearly 4,700 minutes of video content and translated 1,050,700 characters of video subtitles. Automated transcription and translation have enormous potential as a public health learning tool, allowing the near-simultaneous availability of video subtitles on OpenWHO in many languages, thus improving the usability of the learning materials in multiple languages for wider audiences.

The Coronavirus Pandemic and the Transformation of Telehealth in the Context of Artificial Intelligence

The pandemic is seriously affecting individuals' wellbeing, occupations, economies, and practices. This pandemic has shaken the world dramatically and framed a moment to think about the future, incorporating our relationship with nature. Since the COVID-19 pandemic started, it's been relied upon to drive remarkable development in telehealth, especially for demonstrative patients, to stay at home and talk with specialists through virtual stations, helping with diminishing the spread of the disease to mass and the clinical staff on the ground zero. The novel coronavirus epidemic has changed our way of living, society, and human services framework. This study proposed the application of artificial intelligence to make its classification. The outcomes of the proposed systems are equated with pre-existing algorithms to highlight the benefits of test time minimization and classification error. Furthermore, this study tries to analyse corona time series data on the level of classification and found that the decision tree algorithm gives the best accuracy of approx. 100% with zero error and zero standard deviation with 7098 milliseconds. © 2022 IEEE.

30 years of artificial intelligence (AI) research relating to the hospitality and tourism industry

PurposeThis study aims to review the progress of research on artificial intelligence (AI) relating to the hospitality and tourism industry, focusing on the content, focal points, key terms and trends of AI research.Design/methodology/approachA total of 491 referred papers are selected from the Web of Science core collection database. These papers, published in the past 30 years (1991–2021), are analyzed by using Gephi and VOSviewer software.FindingsAI research shows a growing trend since 1991, and the number of publications and citations increased significantly since 2018, indicating that AI became a focus for researchers. AI studies are grouped into four clusters, namely, AI technology, technology acceptance, customers' perception and future trends. The research focus changed from AI technology in the early stage to customers' attitudes toward and willingness to accept AI.Research limitations/implicationsThe findings contribute to advance knowledge development, identify research gaps and shed light on future research. The results offer practical enlightenment for governments, tourism destinations and hospitality organization.Practical implicationsThe results offer practical enlightenment for governments, tourism destinations and hospitality organization.Originality/valueThis study is the initial attempt to provide a systematic review of AI research relating to the tourism and hospitality fields.

Autonomous UAV with obstacle management using AIoT: a case study on healthcare application

With the simultaneously connected 26.66 billion devices worldwide, the Internet of Things (IoT) is becoming a vast field of research and helping hand to every individual. However, when IoT and Artificial Intelligence (AI) and machine learning (ML) consolidate, it results in smart applications and future revolutions that are known as Artificial Intelligent of Things (AIoT). Similarly, the unmanned aerial vehicle (UAV) domain is also developing daily, helping many unrest people in the healthcare industry. One step towards developing the healthcare industry is the use of UAV devices like drones embedded with AIoT to work autonomously in the healthcare industry. This can help the healthcare industry in many ways. This chapter proposes an algorithm to recast these UAV drones to autonomous UAV drones and use them as intelligent or smart for various healthcare purposes like COVID-19. The proposed autonomous UAV drone uses Raspberry Pi 3, a Hubney, and a bearing formula to automatically determine the direction of the UAV movement, making it work without any controller. Also, the comparative study presented in this chapter highlighted the benefits of this proposed algorithm with others present in the literature.

The use of teledentistry and Artificial Intelligence (AI) in dental and oral health services for the elderly in the era of the covid-19 pandemic: A systematic review

Objective: This paper aims to determine the use of teledentistry and AI in the elderly to maintain the health of their oral cavity and teeth during the COVID-19 pandemic. Methods: Technology in dentistry today is developing very rapidly, improving the quality of dental and oral health services. During the pandemic, the elderly has concerns about Covid-19 contamination when they have to see a dentist. Their situation has led to discussions and efforts to use teledentistry and Artificial intelligence to facilitate services and care for the elderly during the pandemic. Results: Teledentistry is used as a medium for consultation, diagnosis, referral system, treatment, and follow-up. While Artificial Intelligence (AI) has been used in diagnostic, patient data management, restoration, and CAD/CAM-based denture manufacture, detecting periodontal disease, and dental radiology. Conclusion: Teledentistry and AI can be a promising alternative in dental and oral health services to reduce anxiety and fear of contamination with Covid-19. The technologies make it easier for health workers, especially dentists, to maintain and improve the quality of life of the elderly during the pandemic. © 2022 JDMFS. Published by Faculty of Dentistry, Hasanuddin University. All rights reserved.

Successful Role of Data Science In Managing Covid-19 Battle

The corona pandemic's wild and unchecked spread over more than a few months around the world is a worldwide problem. To solve this worldwide issue, information technology innovation is employed along with medicine, biotechnology, and medical equipment. The fight against COVID-19 is greatly aided by Machine-Learning (ML), Artificial-Intelligence (AI), and data science (DS). By utilising such technologies, there is a good chance that the pandemic may be stopped, and that life can return to normal, as it did before the pandemic. In this essay, many technologies are analysed in relation to various situations, including social exclusion and prevention, confinement and isolation, corona virus testing and detection, management of the hospital, patient care, and therapy. This study provides transparent planning, technological techniques, digital procedures, together with the most recent smart technology in a number of disciplines, to battle the severity of the coronavirus. © 2023 IEEE.

The shift in research trends related to artificial intelligence in library repositories during the coronavirus pandemic

Purpose: This study sought to analyze the correlation between artificial intelligence (AI) and libraries and examine whether there were any shifts in research trends related to these two topics during the coronavirus pandemic. Design/methodology/approach: The study gathered secondary data from the Scopus website using the keywords "AI,” "library” and "repository,” from 1993 to 2022. Data were re-analyzed using the bibliometric software VOSviewer to examine the trending country's keyword relations and appearance and Biblioshiny to study the publication metadata. Findings: Index keywords, such as "human,” "deep learning,” "machine learning,” "surveys” and "open-source software,” became popular during 2020, being closely related to digital libraries. Additionally, the annual scientific production of papers increased significantly in 2021. Words related to data mining also had the most significant growth from 2019 to 2022 because of the importance of data mining for library services during the pandemic. Practical implications: This study provides insight for librarians for the implementation of AI to support repositories during the pandemic. Librarians can learn how to maximize the AI-based repository services in academic libraries during the pandemic. Furthermore, academic libraries can create policies for repository services using AI. Social implications: This study can lead researchers, academicians and practitioners in conducting research on AI in library repositories. Originality/value: As research on AI and digital repositories remains limited, the study identifies themes and highlights the knowledge gap existing in the field. © 2023, Emerald Publishing Limited.

Artificial Intelligence in COVID-19 Management: A Systematic Review

With the development of modern technologies in the field of healthcare, the use of Artificial Intelligence (AI) in disease management is increasing. AI methods may assist healthcare providers in the COVID-19 era. The current study aimed to observe the efficacy and importance of AI for managing the COVID-19 pandemic. An organized search was conducted, utilizing PubMed, Web of Science, Scopus, Embase, and Cochrane up to September 2022. Studies were considered qualified for inclusion if they met the inclusion criterion. We conducted review according to the Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) guidelines. There were 52 documents that met the eligibility criteria to be included in the review. The most common item using AI during the COVID-19 era was predictive models to foretell pneumonia and mortality risks in people with COVID-19 based on medical and experimental parameters. COVID-19 mortality was related to being male and elderly based on the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) logistic regression analysis of demographics, clinical data, and laboratory tests of hospitalized COVID-19 patients. AI can predict, diagnose and model COVID-19 by using techniques such as support vector machines, decision trees, and neural networks. It is suggested that future research should deal with the design and development of AI-based tools for the management of chronic diseases such as COVID-19. © 2023 Samaneh Mohammadi, SeyedAhmad SeyedAlinaghi, Mohammad Heydari, Zahra Pashaei, Pegah Mirzapour, Amirali Karimi, Amir Masoud Afsahi, Peyman Mirghaderi, Parsa Mohammadi, Ghazal Arjmand, Yasna Soleimani, Ayein Azarnoush, Hengameh Mojdeganlou, Mohsen Dashti, Hadiseh Azadi Cheshmekabodi, Sanaz Varshochi, Mohammad Mehrtak, Ahmadreza Shamsabadi, Esmaeil Mehraeen, and Daniel Hackett. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Development of hybrid technology using Machine Learning and Block chain Technology to prevent from COVID 19 through the proper information gathering

Blockchain and artificial intelligence (AI) have shown promise in combating the Covid epidemic. Blockchain in particular may aid in early detection to fight pandemics. The methods established for infection prevention include the use of face masks, public isolation within a 6 metre radius, regular check-ups, and two doses of vaccinations.This system has features for detecting masks, people, temperature, information tracking, in-person interactions, and a person's medical history. Diseases might be monitored and their spread contained with the advancement of technology and the rise in smartphone use. Because additional economic sectors are opening up and because Covid is still spreading widely, adhering to the guidelines is more important than ever for avoiding infection. © 2023 IEEE.

COVID-19 Vaccine Hesitancy: Analysing Twitter to Identify Barriers to Vaccination in a Low Uptake Region of the UK.

To facilitate effective targeted COVID-19 vaccination strategies, it is important to understand reasons for vaccine hesitancy where uptake is low. Artificial intelligence (AI) techniques offer an opportunity for real-time analysis of public attitudes, sentiments, and key discussion topics from sources of soft-intelligence, including social media data. In this work, we explore the value of soft-intelligence, leveraged using AI, as an evidence source to support public health research. As a case study, we deployed a natural language processing (NLP) platform to rapidly identify and analyse key barriers to vaccine uptake from a collection of geo-located tweets from London, UK. We developed a search strategy to capture COVID-19 vaccine related tweets, identifying 91,473 tweets between 30 November 2020 and 15 August 2021. The platform's algorithm clustered tweets according to their topic and sentiment, from which we extracted 913 tweets from the top 12 negative sentiment topic clusters. These tweets were extracted for further qualitative analysis. We identified safety concerns; mistrust of government and pharmaceutical companies; and accessibility issues as key barriers limiting vaccine uptake. Our analysis also revealed widespread sharing of vaccine misinformation amongst Twitter users. This study further demonstrates that there is promising utility for using off-the-shelf NLP tools to leverage insights from social media data to support public health research. Future work to examine where this type of work might be integrated as part of a mixed-methods research approach to support local and national decision making is suggested.

The Influence of Emerging Technologies on Distance Education

Recently, during the COVID-19 pandemic, distance education became mainstream. Many students were not prepared for this situation—they lacked equipment or were not even connected to the Internet. Schools and government institutions had to react quickly to allow students to learn remotely. They had to provide students with equipment (e.g., computers, tablets, and goggles) but also provide them with access to the Internet and other necessary tools. On the other hand, teachers were trying to adopt new technologies in the teaching process to enable more interactivity, mitigate feelings of isolation and disconnection, and enhance student engagement. New technologies, including Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Extended Reality (XR, so-called Metaverse), Big Data, Blockchain, and Free Space Optics (FSO) changed learning, teaching, and assessing. Despite that, some tools were implemented fast, and the COVID-19 pandemic was the trigger for this process;most of these technologies will be used further, even in classroom teaching in both schools and universities. This paper presents a concise review of the emerging technologies applied in distance education. The main emphasis was placed on their influence on the efficiency of the learning process and their psychological impact on users. It turned out that both students and teachers were satisfied with remote learning, while in the case of undergraduate children and high-school students, parents very often expressed their dissatisfaction. The limitation of the availability of remote learning is related to access to stable Internet and computer equipment, which turned out to be a rarity. In the current social context, the obtained results provided valuable insights into factors affecting the acceptance and emerging technologies applied in distance education. Finally, this paper suggests a research direction for the development of effective remote learning techniques. © 2023 by the authors.

Machine learning approach for predicting inhalation injury in patients with burns.

BACKGROUND: The coronavirus disease pandemic has had a tangible impact on bronchoscopy for burn inpatients due to isolation and triage measures. We utilised the machine-learning approach to identify risk factors for predicting mild and severe inhalation injury and whether patients with burns experienced inhalation injury. We also examined the ability of two dichotomous models to predict clinical outcomes including mortality, pneumonia, and duration of hospitalisation. METHODS: A retrospective 14-year single-centre dataset of 341 intubated patients with burns with suspected inhalation injury was established. The medical data on day one of admission and bronchoscopy-diagnosed inhalation injury grade were compiled using a gradient boosting-based machine-learning algorithm to create two prediction models: model 1, mild vs. severe inhalation injury; and model 2, no inhalation injury vs. inhalation injury. RESULTS: The area under the curve (AUC) for model 1 was 0·883, indicating excellent discrimination. The AUC for model 2 was 0·862, indicating acceptable discrimination. In model 1, the incidence of pneumonia (P < 0·001) and mortality rate (P < 0·001), but not duration of hospitalisation (P = 0·1052), were significantly higher in patients with severe inhalation injury. In model 2, the incidence of pneumonia (P < 0·001), mortality (P < 0·001), and duration of hospitalisation (P = 0·021) were significantly higher in patients with inhalation injury. CONCLUSIONS: We developed the first machine-learning tool for differentiating between mild and severe inhalation injury, and the absence/presence of inhalation injury in patients with burns, which is helpful when bronchoscopy is not available immediately. The dichotomous classification predicted by both models was associated with the clinical outcomes.

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

An Intensive Reviews on AI and its Impact on E-commerce

We now understand the value of practicing social distance thanks to COVID-19. The only way to meet our basic necessities in the year 2020 due to a sudden global lockdown was through e-commerce websites and online purchasing, and since technology has advanced, having a website online is now essential. All of these items, including meals, groceries, and our go-to clothing, are now available online. During the shutdown, it was seen that the firms with no social media presence faced significant losses. On the other hand, those who had already developed a web presence noticed a sharp increase in their overall sales. This research explores how recent developments in AI and ML have increased sales across a range of industries. After making a lot of observations and analyzing the consumer behavior patterns that affect sales, the ML model eventually contributes to the creation of an algorithm that is an effective recommendation system. This study also covers how transactions can be safeguarded and authenticated with the help of blockchain technology and cyber security, which has helped e-commerce businesses thrive by winning over customers' trust. © 2022 IEEE.

Exploring the Major Trends and Emerging Themes of Artificial Intelligence in the Scientific Leading Journals amidst the COVID-19 Era

Artificial intelligence (AI) has recently become the focus of academia and practitioners, reflecting the substantial evolution of scientific production in this area, particularly during the COVID-19 era. However, there is no known academic work exploring the major trends and the extant and emerging themes of scientific research production of AI leading journals. To this end, this study is to specify the research progress on AI among the top-tier journals by highlighting the development of its trends, topics, and key themes. This article employs an integrated bibliometric analysis using evaluative and relational metrics to analyze, map, and outline the key trends and themes of articles published in the leading AI academic journals, based on the latest CiteScore of Scopus-indexed journals between 2020 and 2021. The findings depict the major trends, conceptual and social structures, and key themes of AI leading journals' publications during the given period. This paper represents valuable implications for concerned scholars, research centers, higher education institutions, and various organizations within different domains. Limitations and directions for further research are outlined. © 2023 by the authors.

Computer Vision-Based IoT Architecture for Post COVID-19 Preventive Measures

The COVID-19 pandemic has wreaked havoc on people all across the world. Even though the number of verified COVID-19 cases is steadily decreasing, the danger persists. Only societal awareness and preventative measures can assist to minimize the number of impacted patients in the work environment. People often forget to wear masks before entering the work premises or are not careful enough to wear masks correctly. Keeping this in mind, this paper proposes an IoT-based architecture for taking all essential steps to combat the COVID-19 pandemic. The proposed low-cost architecture is divided into three components: one to detect face masks by using deep learning technologies, another to monitor contactless body temperature and the other to dispense disinfectants to the visitors. At first, we review all the existing state-of-the-art technologies, then we design and develop a working prototype. Here, we present our results with the accuracy of 97.43% using a deep Convolutional Neural Network (CNN) and 99.88% accuracy using MobileNetV2 deep learning architecture for automatic face mask detection. © 2023 by the authors.

X-Ray Image Classification for COVID-19 Using Transfer Learning

SARS-CoV-2, the cause of one of the significant pandemics in history, first appeared in Wuhan, China. It spreads rapidly, with symptoms like fever, cough, tiredness, and loss of taste or smell. We came up with many measures where the most effective was vaccines. Yet it's not enough against the rapidly appearing waves of SARS-CoV-2. A deep learning algorithm has proven efficient in detecting Covid-19 based on pneumonia and respiratory problems. These problems have been identified with the help of CT scans and X-ray images. It'll make it a lot easier to determine who's Infected and would save a lot of time and expenses overall would provide for extensive relief in the Covid-19 pandemic. This paper uses publically available COVID-19 X-Ray and CT Scan images to create a dataset. The Deep Learning based model is used to train and test the dataset. In the experiment, the overall accuracy is 98%, and in the testing process, the overall accuracy is 99%. © 2023 The authors and IOS Press.

Coronavirus disease (COVID-19) cases analysis using machine-learning applications.

Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.

Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification

In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack of diversity, which could lead to being trapped in local optimaand premature convergence. This study presents two stages for enhancing the fundamental BWO algorithm. The initial stage of BWO's Opposition-Based Learning (OBL), also known as OBWO, helps to expedite the search process and enhance the learning methodology to choose a better generation of candidate solutions for the fundamental BWO. The second step, referred to as OBWOD, combines the Dynamic Candidate Solution (DCS) and OBWO based on the k-Nearest Neighbor (kNN) classifier to boost variety and improve the consistency of the selected solution by giving potential candidates a chance to solve the given problem with a high fitness value. A comparison study with present optimization algorithms for single-objective bound-constraint optimization problems was conducted to evaluate the performance of the OBWOD algorithm on issues from the 2022 IEEE Congress on Evolutionary Computation (CEC'22) benchmark test suite with a range of dimension sizes. The results of the statistical significance test confirmed that the proposed algorithm is competitive with the optimization algorithms. In addition, the OBWOD algorithm surpassed the performance of seven other algorithms with an overall classification accuracy of 85.17% for classifying 10 medical datasets with different dimension sizes according to the performance evaluation matrix.