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COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infective disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives with emergency use authorisation vaccines are being done across many countries, however, their long term efficacy and side-effects study are yet to be done. The research community is analysing the situation by collecting the datasets from various sources. Healthcare professionals must thoroughly analyse the situation, devise preventive measures for this pandemic, and even develop possible drug combinations. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses on the application of state-of-the-art methods in this combat against COVID-19. The application of Artificial intelligence (AI), and AI-driven tools are emerging as effective tools, especially with X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions etc. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e. >95%, as reported in various studies. AI-driven tools are being used in COVID diagnostic, therapeutics, trend prediction, drug design and prevention to help fight against this pandemic. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in this battle against the COVID-19 pandemic. The extensive literature is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 Prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing.
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Throughout the Covid-19 pandemic, a significant amount of effort had been put into developing techniques that predict the number of infections under various assumptions about the public policy and non-pharmaceutical interventions. While both the available data and the sophistication of the AI models and available computing power exceed what was available in previous years, the overall success of prediction approaches was very limited. In this paper, we start from prediction algorithms proposed for XPrize Pandemic Response Challenge and consider several directions that might allow their improvement. Then, we investigate their performance over medium-term predictions extending over several months. We find that while augmenting the algorithms with additional information about the culture of the modeled region, incorporating traditional compartmental models and up-to-date deep learning architectures can improve the performance for short term predictions, the accuracy of medium-term predictions is still very low and a significant amount of future research is needed to make such models a reliable component of a public policy toolbox. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This paper examines the relations among AI service attributes, brand image, brand familiarity and customer equity. The proposed relationships were tested by structural equation modeling of survey data of 210 usable responses in China. Test results indicate that problem-solving ability, accuracy, and customization of AI service have significant positive effects on brand image;the three constructs of customer equity (value equity, brand equity, and relationship equity) are all positively and strongly affected by brand image. Moreover, brand familiarity moderates the effect of customization, interaction, and problem-solving ability on brand image. By bringing together AI literature and consumer behavior literature, this research sheds light on the effectiveness of AI service in enhancing brand image and customer equity. This has important theoretical value in enriching the streams of AI and brand research. Additionally, this research integrates the AI technology within a corporate brand management strategy and offers practitioners and marketers in post-COVID era with a model with which they can find new ways to meet consumer demands and improve brand image via AI technology. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.
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This editorial presents the recent advances and challenges of deep learning. We reviewed four main challenges: heterogeneity, copious size, reproducibility crisis, and explainability. Finally, we present the prospect of deep learning in industrial applications. © 2023, Bentham Science Publishers. All rights reserved.
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Over the past twelve years, cloud systems have significantly changed business processes in all areas of business. Companies are using cloud services as a key factor in completing their digital transformation, and the COVID pandemic has further accelerated this task. The cloud is emerging as a top management agenda item as companies move from a separate approach to a more holistic, end-to-end digital transformation driven by the cloud. Cloud services save businesses time and money by increasing productivity, improving collaboration, and driving innovation. Now—during the coronavirus crisis, more than ever, cloud services are vital to help companies re-discover, reinvent and overcome uncertainty. Cloud services range from data storage to functional software, including accounting software, customer service tools, and remote desktop hosting. These services can be divided into three groups: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). This article is devoted to an analytical overview of modern digital cloud services, and the services they provide that can be applied in all areas of business. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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If understanding sentiments is already a difficult task in human-human communication, this becomes extremely challenging when a human-computer interaction happens, as for instance in chatbot conversations. In this work, a machine learning neural network-based Speech Emotion Recognition system is presented to perform emotion detection in a chatbot virtual assistant whose task was to perform contact tracing during the COVID-19 pandemic. The system was tested on a novel dataset of audio samples, provided by the company Blu Pantheon, which developed virtual agents capable of autonomously performing contacts tracing for individuals positive to COVID-19. The dataset provided was unlabelled for the emotions associated to the conversations. Therefore, the work was structured using a sort of transfer learning strategy. First, the model is trained using the labelled and publicly available Italian-language dataset EMOVO Corpus. The accuracy achieved in testing phase reached 92%. To the best of their knowledge, thiswork represents the first example in the context of chatbot speech emotion recognition for contact tracing, shedding lights towards the importance of the use of such techniques in virtual assistants and chatbot conversational contexts for psychological human status assessment. The code of this work was publicly released at: https://github.com/fp1acm8/SER. © 2023 The Authors. Cognitive Computation and Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Shenzhen University.
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Due to the complexity of transactions and the availability of Big Data, many banks and financial institutions are reviewing their business models. Various tasks get involved in determining the credit worthiness like working with spreadsheets, manually gathering data from customers and corporations, etc. In this research paper, we aim to automate and analyze the credit ratings of the Information and technology industry in India. Various Deep-Learning models are incorporated to predict the credit rankings from highest to lowest separately for each company to find the best fit Margin, inventory valuation, etc., are the parameters that contribute to the credit rating predictions. The data collected for the study spans between the years FY-2015 to FY-2020. As per the research been carried out with efficiencies of different Deep Learning models been tested and compared, MLP gained the highest efficiency for predicting the same. This research contributes to identifying how we can predict the ratings for several IT companies in India based on their Financial risk, Business risk, Industrial risk, and Macroeconomic environment using various neural network models for better accuracy. Also it helps us understand the significance of Artificial Neural Networks in credit rating predictions using unstructured and real time Financial data consisting the influence of COVID-19 in Indian IT industry.
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Digital transformation (DX) was proposed by Stolterman, who contended that the development of information technology could provide us with a good life. Recently, medical DX has progressed rapidly based on the key trends of big data, artificial intelligence (AI), cloud technology, and next-generation communication. Emerging digital technologies are expected to change the face of otolaryngologic medical care. We summarized recent developments in medical DX in the field of otorhinolaryngology, especially over the last 2-3 years. Big data includes information about insurance receipts, electronic medical records, patients, registry, and health records. Medical AI programs provide automatic interpretations of endoscopic images, CT images, voice data, etc., to assist in the diagnosis of diseases by machine learning of the inputs gathered from a large amount of data. Telemedicine using the next-generation communication technology 5G is found to be useful to provide consultation for patients with upper respiratory symptoms in this COVID-19 pandemic era. Mobile health includes wearable devices and applications on the smartphone. Medical DX is expected to present better medicine for prevention, diagnosis, treatment, and welfare of patients in the field of otorhinolaryngology. © 2023 Society of Practical Otolaryngology. All rights reserved.
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An editorial is presented on International social work in the new era. Topics include number of infected cases being decreasing, the loss of life being reducing, and social and economic activities again started resuming;and globalization moving towards de-globalization and various supply chains such as human talents, financial capital, ideas, and information.
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Even before the current (2020/2021) pandemic began, Conversational User Interfaces (CUI) had been seen as a valuable way to ease the burden on medical staff in many countries. In times of restricted direct contact with people, the need for online or virtual tools to connect patients with physicians has become even more visible. In addi-tion, these restrictions hamper the training conditions for prospective doctors. This paper describes the design and implementation of a CUI covering patients' minor complaints of the Ear, Nose, and Throat (ENT), which can be correlated with infection by COVID-19. The purpose of this study is to provide pilot test results for an online anamnesis and diagnosis tool supporting the cooperative work of specialists and non-specialists at their workplaces. We have designed and created the cooperative online anamnesis and diagnosis system (COLDS) using 1) a knowledge-based system for the anamnesis mainly of complaints related to ENT including the eyes, 2) a knowledge base of disorders regard-ing ENT and eyes, and 3) a user interface that assists patients as well as cooperative processes involving non-specialists and specialists. COLDS is part of a clinical decision support system. The system has been evaluated in a two-tier pilot test process set in a real-life environment: Tier 1 was concerned with the usability of the system;whereas Tier 2 involved medical specialists to evaluate the outcome and recommendations created by the system based on an adapted Objective Structured Clinical Examination (OSCE) framework. Medical interns and doctors evaluated the system with a five-point Likert scale and the results show that 4.38 for the ease of system and 4.51 for overall satisfac-tion with the system at the confidence interval 95%. © 2023 ICIC International.
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The concept of Artificial Intelligence (AI), born as the possibility of simulating the human brain's learning capabilities, quickly evolves into one of the educational technology concepts that provide tools for students to better themselves in a plethora of areas. Unlike the previous educational technology iterations, which are limited to instrumental use for providing platforms to build learning applications, AI has proposed a unique education laboratory by enabling students to explore an instrument that functions as a dynamic system of computational concepts. However, the extent of the implications of AI adaptation in modern education is yet to be explored. Motivated to fill the literature gap and to consider the emerging significance of AI in education, this paper aims to analyze the possible intertwined relationship between students' intrinsic motivation for learning Artificial Intelligence during the COVID-19 pandemic;the relationship between students' computational thinking and understanding of AI concepts;and the underlying dynamic relation, if existing, between AI and computational thinking building efforts. To investigate the mentioned relationships, the present empirical study employs mediation analysis based upon collected 137 survey data from Universidad Politécnica de Madrid students in the Institute for Educational Science and the School of Naval Architecture and Marine Engineering during the first quarter of 2022. Findings show that intrinsic motivation mediates the relationship between perceived Artificial Intelligence learning and computational thinking. Also, the research indicates that intrinsic motivation has a significant relationship with computational thinking and perceived Artificial Intelligence learning. © 2023
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Purpose: In this paper, the author has tried to outline the main ideas in connection with what the author conceives to be the university of the future, a university that should not only educate people within the university system but also prepare them to fill specific job positions at both local and global levels, apart from necessarily providing them with the critical thinking and competences in autonomous learning that will make them flexible and capable of adapting to the job market and to a fast-changing world in general. Design/methodology/approach: The author has revised some of the major issues that are going to determine the direction of the university of the future, i.e. the employment opportunities of tomorrow;the role of new technologies, especially the impact of artificial intelligence (AI);quality in higher education;and internationalization. Findings: The author has also pointed out the importance of the technologies and the great role they indisputably play in present and future education at all levels, a fact that has been particularly and hugely enhanced and promoted by the COVID-19 pandemic situation, thereby facilitating and fostering distance learning. This is very much connected to the application of AI to higher education, another unavoidable issue of utmost importance for the university of the future. While these technological advances present a challenge to universities, which must determine which are necessary and desirable and how to implement them, it is, ultimately, our responsibility to use them, in an ethical way, to the benefit of our students. The university of the future also has to be of high quality, and this involves carrying out important and decisive action having to do with matters of inclusion, hiring policies and the expansion of international opportunities for all parties involved. Originality/value: This paper outlines the main ideas in connection with what the author conceives to be the university of the future, a university that should not only educate people within the university system but also prepare them to fill specific job positions at both local and global levels, apart from necessarily providing them with the critical thinking and competences in autonomous learning that will make them flexible and capable of adapting to the job market and to a fast-changing world in general. Moreover, the role of new technologies (especially the impact of AI), quality and internationalization are also discussed as relevant factors in this view of the university of the future. © 2022, Emerald Publishing Limited.
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Background: Heart failure remains a considerable burden to healthcare in Asia. Early intervention, mainly using echocardiography, to assess cardiac function is crucial. However, due to limited resources and time, the procedure has become more challenging during the COVID-19 pandemic. On the other hand, studies have shown that artificial intelligence (AI) is highly potential in complementing the work of clinicians to diagnose heart failure accurately and rapidly. Methods: We systematically searched Europe PMC, ProQuest, Science Direct, PubMed, and IEEE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and our inclusion and exclusion criteria. The 14 selected works of literature were then assessed for their quality and risk of bias using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies). Results: A total of 2105 studies were retrieved, and 14 were included in the analysis. Five studies posed risks of bias. Nearly all studies included datasets in the form of 3D (three dimensional) or 2D (two dimensional) images, along with apical four-chamber (A4C) and apical two-chamber (A2C) being the most common echocardiography views used. The machine learning algorithm for each study differs, with the convolutional neural network as the most common method used. The accuracy varies from 57% to 99.3%. Conclusions: To conclude, current evidence suggests that the application of AI leads to a better and faster diagnosis of left heart failure through echocardiography. However, the presence of clinicians is still irreplaceable during diagnostic processes and overall clinical care;thus, AI only serves as complementary assistance for clinicians.
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COVID-19 pandemic is behind the implementation of the "AI recruitment system.” The number of companies trying to introduce AI recruitment systems is increasing because the non-face-to-face method is recommended due to the COVID-19 pandemic and the management change of the organization comes with the development of IT technology. Behind the positive evaluation that the development of AI technology improves the efficiency of work, the demand for fair and transparent recruitment procedures has been increasing as controversy over fairness and objectivity has increased due to various hiring irregularities. This study aimed to approach in a more systematic and scientific way to maximize the effect of recruiting talent. In the previous study, voice and video were identified based on ML. In situations where the problem of truth and falsehood is raised, this study conducted EEG-based biological experimental studies with a deep learning method to explore more objectively. Also, the experimental design applied biological experiments between brain activity patterns and brain regions as signals from EEG-based 14 channels to explore the truth/false authenticity of the experimenters. As a result of the experiment, the best performance and effect were shown in the CNN model with an accuracy of 91% truth and 89% false among the comparative analysis of Decision Tree, Random Forest, and CNN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The world have experienced a severe human-health crisis as a result of the emergence of a novel coronavirus (COVID-19), which was declared a global pandemic by WHO. As close human-to-human contact can spread the COVID-19 causing virus, keeping social distance is now an absolute necessity as a preventative measure. At a time of global pandemic, there is a huge need to treat patients with little patient–doctor interaction by using robots. Robots can be characterized as machines that can execute a wide range of tasks with greater autonomy and degree of freedom (DoF) than humans, making it difficult to identify them from other machines. A wide range of equipment, sensors, and information and communication technology (ICT) are now part of the healthcare system, which has become increasingly complicated. Protecting front-line personnel from virus exposure is the primary goal of using robots in health care. The aim of this study is to emphasize the evolving importance of robotics applications in health care and related fields. This paper examines in depth the design and operation of a wide range of healthcare robots in use around the world. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Sensitivity Analysis is a method to determine how possible changes or errors in parameter values affect model outputs. This study evaluates the AHP based decision support system used for supplier selection in a glove manufacturing industry by performing a sensitivity analysis. Uniqueness of this study is that it deals with the sensitivity analysis of criteria used for supplier selection during COVID pandemic. The pandemic has altered the weightages of factors considered for supplier selection in a normal times. Expert Choice software is used to analyze the sensitivity of these parameters. The study facilitates the decision maker to understand and experiment on the effect of criterion weights on ranking of the suppliers. This makes the decision maker confident about the decisions in both favorable and unfavorable conditions. © 2022
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As the global economy grapples with the advent of novel coronavirus and its variants, the aftermath has left all industries with ongoing uncertainties and incalculable loss of life and livelihood in most countries worldwide. In such unpredictable situations, the insurance industry and governments worldwide have become the prominent source of optimism to sail through the situation. This applies to the insurance industry globally, which is currently in the grip of fear due to the COVID-19 outbreak and anticipating significant economic slowdown and hardship because insurance rides on the back of other Industries. Therefore, to overcome a few of the tenacious roadblocks due to the COVID outbreak, Insurers will be forced to reassess all aspects of their business life cycle and take necessary steps to continue operations with minimum disruption. Precisely, the impact of COVID on General Insurers and Life and Health Insurers varied depending on the lines of business, product lines, and a bouquet of benefits offered by the insurers. The pandemic has taken a hit on new gross written premiums on specific lines of business, such as medical, travel, commercial, and business insurance. Few lines of business such as motor and home have remained muted during the COVID timeframe. However, the claims volumes for personal insurance (e.g., motor) have significantly decreased due to the lockdown and travel restriction;the industry has witnessed the highest claims volumes in life and health compared to the past several decades. They say, "As every dark cloud has a silver lining,” it has given an opportunity to many insurers to develop new products (e.g., Pay Mile Auto insurance) and push toward greater productivity, i.e., digital capability across product range which will result in an elevated position to understand and address to the customer and intermediary self-service (such as Portals) and implicit and explicit needs. Notably, the Insurance industry is likely to lean toward offering personalized yet custom-made products and services, which are sharply focused on preventative care and embracing digitalization across the value chain. Besides enabling scalability and connectivity, insurers are strategically focused on digitizing the core of the business and cloud implementation;automation across the insurance value chain is necessary to compete successfully with new innovative product development or inclusive business models. Around the globe, the insurance industry is continuously putting a deep focus on revitalizing the technology paradigm to grow and strive to achieve cost-effectiveness amid emerging markets, rapidly changing economic conditions and stiff competition from Insurtech. According to industry experts across geographies, growth may be a balanced blend of preventative and protective approaches, with a gamut of new and improved services and products, and insurers are deeply fostering redefining service-oriented strategies and innovative products. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The COVID-19 disease is a threat to public health around theworld. Early diagnosis and detection will be critical factors in preventing the spread of COVID-19. Computed tomography has a significant role in COVID-19 detection because it gives both fast and best results. Hence it is very significant to develop an accurate and rapid computer-assisted tool for helping clinical experts to identify COVID-19 patients from CT scan images. The project's main objective is to develop an artificial intelligence-assisted tool for predicting the severity of COVID-19 with the help of CT scan images. We introduce a new dataset that contains 47,144 CT scan images from 292 normal persons and 14,346 images from 92 patients with COVID-19 infections. In the first stage, the system runs our proposed image processing algorithm that analyses the view of the lung to discard those CT images inside the lung that are not properly visible. This action helps to reduce the processing time and false detection. Then those chosen images from the CT selection algorithm will be fed to the ResNet50V2 model, so the model becomes able to investigate different resolutions of the image and does not lose the data of small objects. Apart from 152 patients,47 patients have been detected with COVID-19, and 105 patients have been detected as Normal. It shows that the model obtained 97.89% correctness overall and 95.45% along with class with COVID-2019 sensitivity.
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Healthcare systems worldwide are confronted with numerous challenges such as an aging population, an increasing number of chronically ill patients, innovations as cost drivers and growing cost pressure. The COVID-19 pandemic causes additional burden for healthcare systems. In order to overcome these challenges, digital technologies are increasingly used. Especially the past decade witnessed a tremendous boom of artificial intelligence (AI) within the healthcare sector. AI has the potential to revolutionize healthcare and to mitigate the challenges healthcare systems are confronted with. The existing literature has frequently examined specific benefits of AI within the healthcare sector. However, there are still research gaps according to different application areas in healthcare. For this reason, an empirical study design has been conducted to investigate the potentials of AI in healthcare and to consequently identify its role. Based on a Systematic Literature Review (SLR), the following application areas for key determinants in healthcare have been identified: management tasks, medical diagnostics, medical treatment and drug discovery. By means of structural equation modeling (SEM), the study confirmed medical diagnostics and drug discovery as positive and significant influencing factors on the potential benefits of AI in healthcare. The other determinants didn't prove a significant influence. Based on the findings of the study, various recommendations have been derived to further exploit the potentials of AI in healthcare. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Situated at the intersection of technology and medicine, the Internet of Things (IoT) holds the promise of addressing some of healthcare's most pressing challenges, from medical error, to chronic drug shortages, to overburdened hospital systems, to dealing with the COVID-19 pandemic. However, despite considerable recent technological advances, the pace of successful implementation of promising IoT healthcare initiatives has been slow. To inspire more productive collaboration, we present here a simple—but surprisingly underrated—problem-oriented approach to developing healthcare technologies. To further assist in this effort, we reviewed the various commercial, regulatory, social/cultural, and technological factors in the development of the IoT. We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem. To this end, we explore the key enabling technologies that underpin the fog architecture, from the sensing layer all the way up to the cloud. It is our hope that ongoing advances in sensing, communications, cryptography, storage, machine learning, and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people. © 2022 Chongqing University of Posts and Telecommunications