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The development of financial technology has promoted the innovation and digital transformation of commercial banks. Through digital transformation, commercial banks can improve bank efficiency and operational capabilities. Through empirical analysis, this study explored the relationship between digital bank transformation and commercial bank operating capabilities and how COVID-19, bank categories, and enterprise life cycles affect the relationship between digital bank transformation and commercial bank operating capabilities. This study selected data from China's commercial banks from 2011 to 2021 and used the regression method of fixed effects to conduct an empirical analysis. The research results show that the digital transformation of banks has improved the operational capabilities of commercial banks. Further analysis showed that the emergence of COVID-19 has negatively affected their relationship. At the same time, compared with rural commercial banks and commercial banks in the recession and phase-out periods, non-rural commercial banks and commercial banks in the growth and maturity stages play a more vital moderating role in the impact of the digital transformation of banks on the financial performance of commercial banks. The main research object of this study is Chinese commercial banks, and this study examines the results of banks' digital transformation and enriches the research on digital transformation. At the same time, this study is helpful to investors who like investment banks and has good practical significance.
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With the COVID-19 pandemic, people's real-life interactions diminished, and the game-based metaverse platforms such as Minecraft and Roblox are on the rise. The main users of these platforms are teenagers, they generate content in a virtual environment, which can significantly increase the activity of the platform. However, the experience of User-Generated Content in the metaverse is not very good. So what kind of support do users need to improve the efficiency of generating content in the metaverse? To investigate teenage users' preferences and expectations of it, this paper interviewed 72 teenagers aged 12-22 who are familiar with the metaverse game, and distilled 4 suggestions that can help promote metaverse users to generate content. © 2023 IEEE.
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Affective computing (AC) has been regarded as a relevant approach to identifying online learners' mental states and predicting their learning performance. Previous research mainly used one single-source data set, typically learners' facial expression, to compute learners' affection. However, a single facial expression may represent different affections in various head poses. This study proposed a dual-source data approach to solve the problem. Facial expression and head pose are two typical data sources that can be captured from online learning videos. The current study collected a dual-source data set of facial expressions and head poses from an online learning class in a middle school. A deep learning neural network using AlexNet with an attention mechanism was developed to verify the syncretic effect on affective computing of the proposed dual-source fusion strategy. The results show that the dual-source fusion approach significantly outperforms the single-source approach based on the AC recognition accuracy between the two approaches (dual-source approach using Attention-AlexNet model 80.96%;single-source approach, facial expression 76.65% and head pose 64.34%). This study contributes to the theoretical construction of the dual-source data fusion approach, and the empirical validation of the effect of the Attention-AlexNet neural network approach on affective computing in online learning contexts.
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Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews' classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users' sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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Artificial Intelligence is categorized into the domain of computer science focused on creating intelligent machines that function like humans. Artificial Intelligence supports institutions including Islamic Financial Services in learning, making decision, and providing useful predictive analytics. The progress and promise that artificial intelligence has made and presented in finance have so far been remarkable, allowing for cheaper, faster, closer, more accessible, more lucrative, and more efficient finance especially during the pandemic covid-19 when people are required to stay at home yet still doing a banking transaction. Despite the incredible progress and promise made possible by advances in financial artificial intelligence, it nevertheless presents some serious perils and limitations. Three categories of risks and limitations involve the rise of virtual threats and cyber conflicts in the financial system, society behavioural changes, and legal amendments that cannot respond to technological developments, especially in developing countries. The main objective of this article is to evaluate the operations of the potential risks that may arise in the use of Artificial Intelligence in Islamic finance services, especially dealing with the legal arrangement that is supposed to be in line with business development. Indonesia is a country that adheres to civil law system, in which every legal arrangement is supposed to be based on written law. The lack of this legal system is where the speed of legal changes cannot keep up with the pace of technological development, which is present as a hinder to the development of Artificial Intelligence in the financial system. This article concludes that Artificial Intelligence will have a huge impact in the future on the Islamic Finance industry, but in Indonesian context, it still needs various efforts to reduce the potential risk that eventually has a big impact on the progress of Islamic banks. © 2022, University of Muhammadiyah Malang. All rights reserved.
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Objectives: COVID 19 and increasing unmet needs of health technology had accelerated an adoption of digital health globally and the major categories are mobile-health, health information technology, telemedicine. Digital health interventions have various benefit on clinical efficacy, quality of care and reducing healthcare costs. The objective of the study is to identify new reimbursement policy trend of digital health medical devices in South Korea. Method(s): Official announcements published in national bodies and supplementary secondary research were used to capture policies, frameworks and currently approved products since 2019. Result(s): With policy development, several digital health devices and AI software have been introduced as non-reimbursement by utilizing new Health Technology Assessment (nHTA) pathway including grace period of nHTA and innovative medical devices integrated assessment pathway. AI based cardiac arrest risk management software (DeepCARS) and electroceutical device for major depressive disorders (MINDD STIM) have been approved as non-reimbursement use for about 3 years. Two digital therapeutics for insomnia and AI software for diagnosis of cerebral infarction were approved as the first innovative medical devices under new integrated assessment system, and they could be treated in the market. In addition, there is remote patient monitoring (RPM) reimbursement service fee. Continuous glucose monitoring devices have been reimbursed for type 1 diabetes patients by the National Health Insurance Service (NHIS) since January 2019. Homecare RPM service for peritoneal dialysis patients with cloud platform (Sharesource) has been reimbursed since December 2019, and long-term continuous ECG monitoring service fee for wearable ECG monitoring devices (ATpatch, MEMO) became reimbursement since January 2022. Conclusion(s): Although Korean government has been developed guidelines for digital health actively, only few products had been reimbursed. To introduce new technologies for improved patient centric treatment, novel value-based assessment and new pricing guideline of digital health medical devices are quite required.Copyright © 2023
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The multi-generational workforce presents challenges for organizations, as the needs and expectations of employees vary greatly between different age groups. To address this, organizations need to adapt their development and learning principles to better suit the changing workforce. The DDMT Teaching Model of Tsing Hua STEAM School, which integrates design thinking methodology, aims to address this challenge. DDMT stands for Discover, Define, Model & Modeling, and Transfer. The main aim of this study is to identify the organization development practices (OD) and gaps through interdisciplinary models such as DDMT and design thinking. In collaboration with a healthcare nursing home service provider, a proof of concept using the DDMT-DT model was conducted to understand the challenges in employment and retention of support employees between nursing homes under the healthcare organization. The paper highlights the rapid change in human experiences and mindsets in the work culture and the need for a design curriculum that is more relevant to the current and future workforce. The DDMT-DT approach can help organizations address these challenges by providing a framework for HR personnel to design training curricula that are more effective in addressing the issues of hiring and employee retention. By applying the DDMT-DT model, HR personnel can better understand the needs and motivations of the workforce and design training programs that are more relevant to their needs. The proof-of-concept research pilot project conducted with the healthcare nursing home service provider demonstrated the effectiveness of the DDMT-DT model in addressing the issues of hiring and employee retention. The project provides a valuable case study for other organizations looking to implement the DDMT-DT model in their HR practices. Overall, the paper highlights the importance of adapting HR practices to better suit the changing workforce. The DDMT-DT model provides a useful framework for organizations looking to improve their HR practices and better address the needs of their workforce.
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Artificial Intelligence (AI) is contributing to the campaign against the Coronavirus Disease 2019 (COVID-19). Since 2019, more and more AI frameworks and applications in COVID-19 have been proposed, and the recent research has shown that AI is a promising technology because AI can achieve a higher degree of scalability, a more comprehensive and identification of patterns in the vast amount of unstructured and noisy data, accelerated processing power, and strategies to outperform traditional methods in many specific tasks. In this chapter, we focus on the specific AI applications in the clinical immunology/immunoinformatics for COVID-19. More precisely, on one hand, we discuss the application of deep learning in designing SARS-CoV-2 vaccines, and, on the other hand, we discuss the development of a machine learning framework for investigating the SARS-CoV-2 mutations that can help us better respond to the future mutant viruses, including designing more robust vaccines based on such AI approaches. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Diabetes mellitus is prevalent worldwide and affects 1 in 10 adults. Despite the successful development of glucose-lowering drugs, such as glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 inhibitors recently, the proportion of patients achieving satisfactory glucose control has not risen as expected. The heterogeneity of diabetes determines that a one-size-fits-all strategy is not suitable for people with diabetes. Diabetes is undoubtedly more heterogeneous than the conventional subclassification, such as type 1, type 2, monogenic and gestational diabetes. The recent progress in genetics and epigenetics of diabetes has gradually unveiled the mechanisms underlying the heterogeneity of diabetes, and cluster analysis has shown promising results in the substratification of type 2 diabetes, which accounts for 95% of diabetic patients. More recently, the rapid development of sophisticated glucose monitoring and artificial intelligence technologies further enabled comprehensive consideration of the complex individual genetic and clinical information and might ultimately realize a precision diagnosis and treatment in diabetics.
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It is important to evaluate medical imaging artificial intelligence (AI) models for possible implicit discrimination (ability to distinguish between subgroups not related to the specific clinical task of the AI model) and disparate impact (difference in outcome rate between subgroups). We studied potential implicit discrimination and disparate impact of a published deep learning/AI model for the prediction of ICU admission for COVID-19 within 24 hours of imaging. The IRB-approved, HIPAA-compliant dataset contained 8,357 chest radiography exams from February 2020-January 2022 (12% ICU admission within 24 hours) and was separated by patient into training, validation, and test sets (64%, 16%, 20% split). The AI output was evaluated in two demographic categories: sex assigned at birth (subgroups male and female) and self-reported race (subgroups Black/African-American and White). We failed to show statistical evidence that the model could implicitly discriminate between members of subgroups categorized by race based on prediction scores (area under the receiver operating characteristic curve, AUC: median [95% confidence interval, CI]: 0.53 [0.48, 0.57]) but there was some marginal evidence of implicit discrimination between members of subgroups categorized by sex (AUC: 0.54 [0.51, 0.57]). No statistical evidence for disparate impact (DI) was observed between the race subgroups (i.e. the 95% CI of the ratio of the favorable outcome rate between two subgroups included one) for the example operating point of the maximized Youden index but some evidence of disparate impact to the male subgroup based on sex was observed. These results help develop evaluation of implicit discrimination and disparate impact of AI models in the context of decision thresholds © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.
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Past research has discovered that the shape design and interaction process design of AI robots, as well as the users' constant features, are the major factors that affect users' willingness to interact with AI robots. Currently, AI robots that play a vital part in the daily activities of our society are becoming increasingly prevalent, thus things about AI robots have gone from mythic to prosaic. But when and where people are more likely to adopt AI robots remains an important research topic. With the development of online technology and the long-term impact of COVID-19, there has been a recent trend in the lower frequency of socializing. To investigate whether a state of low socializing frequency is a robotic moment and whether it affects people's willingness to interact with AI robots, we conducted two-wave questionnaire surveys to collect data from 300 participants from 23 provinces in China. The results showed that the frequency of socializing had a significant negative correlation with the willingness to interact with the AI robots via the mediation role of social compensation. Furthermore, the relationship between social compensation and willingness to interact with the AI robots was demonstrated to be stronger, when participants had a lower anthropomorphic tendency. These findings have theoretical implications for the human-computer interaction literature and managerial implications for the robotics industry. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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Remote healthcare is a well-accepted telemedicine service that renders efficient and reliable healthcare to patients suffering from chronic diseases, neurological disorders, diabetes, osteoporosis, sensory organs, and other ailments. Artificial intelligence, wireless communication, sensors, organic polymers, and wearables enable affordable, non-invasive healthcare to patients in all age groups. Telehealth services and telemedicine are beneficial to people residing in remote locations or patients with limited mobility, rehabilitation treatment, and post-operative recovery. Remote healthcare applications and services proved to be significant during the COVID-19 pandemic for both patients and doctors. This study presents a detailed study of the use of artificial intelligence and the internet of things in applications of remote healthcare in many domains of health, along with recent patents. This research also presents network diagrams of documents from the Scopus database using the tool VOSViewer. The paper highlights gap which can be undertaken by future researchers. © 2023 IEEE.
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The Intensive Care Unit (ICU) is a paradigmatic example of the potential reach of data-centred knowledge discovery. This is because the contemporary ICU heavily depends on medical devices for patient monitoring through electronic data acquisition. This poses a unique opportunity for multivariate data analysis to support evidence-based medicine (EBM), particularly in the form of Artificial Intelligence (AI) approaches. The COVID-19 pandemic has tested the limits of critical care management, often overwhelming ICUs. In this brief chapter, we sketch the role of AI, especially in the form of Machine Learning (ML), at the ICU and discuss what can it offer to address COVID-19 disruption in this environment. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Given the time criticality of finding treatments for the novel COVID-19 pandemic disease, drug repurposing has proved to be a vital strategy as the first response while de novo drug and vaccine developments are underway. Furthermore, Artificial Intelligence (AI) has also accelerated drug development in general. Key desirable features of AI that support a rapid and sustained response along the pandemic timeline include technical flexibility and efficiency (i.e. speed, resource-efficiency, algorithm adaptability), and clinical applicability and acceptability (i.e. scientific rigor, physiological applicability and practical implementation of proposed drugs). This chapter reviews a selection of AI-based applications used in drug development targeting COVID-19, including IDentif.AI-a small data platform for a rapid identification of optimal drug combinations, to illustrate the potential of AI in drug repurposing. The benefits and limitations of using Real-World Data are also discussed. The response to the COVID-19 pandemic has offered multiple learnings which highlight the need to strengthen both short- and long-term strategies in developing AI technologies, scientific and regulatory frameworks as well as worldwide collaborations to enable effective preparedness for future epidemic and pandemic risks. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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PurposeThe COVID-19 pandemic has impacted 222 countries across the globe, with millions of people losing their lives. The threat from the virus may be assessed from the fact that most countries across the world have been forced to order partial or complete shutdown of their economies for a period of time to contain the spread of the virus. The fallout of this action manifested in loss of livelihood, migration of the labor force and severe impact on mental health due to the long duration of confinement to homes or residences.Design/methodology/approachThe current study identifies the focus areas of the research conducted on the COVID-19 pandemic. s of papers on the subject were collated from the SCOPUS database for the period December 2019 to June 2020. The collected sample data (after preprocessing) was analyzed using Topic Modeling with Latent Dirichlet Allocation.FindingsBased on the research papers published within the mentioned timeframe, the study identifies the 10 most prominent topics that formed the area of interest for the COVID-19 pandemic research.Originality/valueWhile similar studies exist, no other work has used topic modeling to comprehensively analyze the COVID-19 literature by considering diverse fields and domains.
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Sustainable agriculture is the backbone of food security systems and a driver of human well-being in global economic development (Sustainable Development Goal SDG 3). With the increase in world population and the effects of climate change due to the industrialization of economies, food security systems are under pressure to sustain communities. This situation calls for the implementation of innovative solutions to increase and sustain efficacy from farm to table. Agricultural social networks (ASNs) are central in agriculture value chain (AVC) management and sustainability and consist of a complex network inclusive of interdependent actors such as farmers, distributors, processors, and retailers. Hence, social network structures (SNSs) and practices are a means to contextualize user scenarios in agricultural value chain digitalization and digital solutions development. Therefore, this research aimed to unearth the roles of agricultural social networks in AVC digitalization, enabling an inclusive digital economy. We conducted automated literature content analysis followed by the application of case studies to develop a conceptual framework for the digitalization of the AVC toward an inclusive digital economy. Furthermore, we propose a transdisciplinary framework that guides the digitalization systematization of the AVC, while articulating resilience principles that aim to attain sustainability. The outcomes of this study offer software developers, agricultural stakeholders, and policymakers a platform to gain an understanding of technological infrastructure capabilities toward sustaining communities through digitalized AVCs.
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According to research, for example, the bio/pharmaceutical manufacturing market should witness compound annual growth in the region of 11% between 2022 and 2027 (2), thanks in part to advancing manufacturing technologies. "Most recently, biopharmaceutical manufacturing has been impacted by pressures on supply chain," specifies Antonio Crincoli, vice president of Engineering, Pharma and Consumer Health, Catalent. [...]there is a focus on quality management systems that ensure data integrity and governance, and that digitization occurs with appropriate validation, also, where necessary, that there is segregation of operating systems to eliminate risk of corruption." Antonio Crincoli, Catalent "PAT and an emphasis on process understanding have been embraced by the majority of pharmaceutical manufacturers, and there are several case studies where both artificial intelligence (Al) and machine learning (ML) have led to improved quality or increased yield, even in good manufacturing practice (GMP) facilities," adds Byrd.
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In today's computer systems, the mouse is an essential input device. Touch interfaces are high-contact planes that we use on a regular basis and frequently throughout the period. As a result, the input device gets infested with bacteria and pathogens. Despite the fact that wireless mouse have eliminated the bunch of tangled wires, there is still a desire to tap the gadget. In light of the epidemic, this proposed method employs a outlying webcam or an in-built image sensor to capture arm gestures and identify fingertip detection, allowing users to execute standard mouse activities such as left click, scrolling and other mouse activities. The algorithm is trained using machine learning with the use of image sensor and the fingers are identified efficiently. As a result, this reliance on corporeal devices to manage the computational system cancels out the requirement of man-machine interface. Thus the suggested approach will prevent the proliferation of Covid-19. © 2023 IEEE.
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The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.
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Goliath dynamic The CRO space has seen a flurry of merger and acquisition activity in recent years. 2016 saw the merger of IMS Health with Quintiles, resulting in the birth of IQVIA. "In a commoditized service area, scale is important, because it gives you a depth of business development and sales coverage in the market," Getz explains. John Kreger, an equity research analyst with William Blair in Chicago, IL, says the pharma industry's source of innovation has aggressively shifted from large pharmaceutical companies to small biotech startups. Coldwell says most CROs will forge partnership agreements with DCT software developers, but three or four of the world's largest CROs are building DCT technology in-house.