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The study aims to evaluate the impact of the experience of using cloud computing on the development of accounting education in the Gulf Cooperation Council countries in light of the Corona pandemic. To achieve this goal, the researchers relied on reviewing previous literature and conducting interviews with a number of accounting professors and students in universities in the Gulf Cooperation Council countries in order to develop a proposed framework for developing accounting education programs using both traditional education and cloud-based education. During the Corona pandemic, educational institutions in the Gulf Cooperation Council countries relied on the interactive learning management system such as Microsoft Teams, Zoom, and Modal. to support the e-learning process, because these systems enable them to interact with their students and meet their needs, which made studying easier. These systems help Students acquire the skill of self-learning, organizing and managing time. In addition, improving the efficiency of the lecturer in managing his time, due to the decrease in the weekly time needed for lecture and preparation, and helping to social distance between students and the lecturer. On the other hand, students suffered from not accepting e-learning due to the difficulty of understanding lectures, and the lack of skills and experience of some professors and students in the field of e-learning due to the familiarity with traditional education. In addition, the professors suffered from the difficulty of evaluating students under this system. Through personal interviews with a number of accounting professors and students in universities in the Gulf Cooperation Council countries, the researchers found that the previous obstacles were at a great level at the beginning of the application of e-learning through cloud computing applications, but the level of these obstacles has decreased over time as professors and students gradually acquired teaching skills at the same time, many technical problems were solved. Despite some advantages achieved through the transition to accounting education based on cloud computing, the Gulf Cooperation Council countries decided to return completely to traditional education. Therefore, the GCC countries should draw on the advantages that have been achieved from e-learning with a return to traditional education and study the possibility of adopting hybrid accounting education. Therefore, the researchers tried to propose a framework for the development of hybrid accounting education programs, based on several basic components that represent the elements of the educational process represented in the material and technological capabilities, the preparation and preparation of human elements (professor, student, technicians and administrators) and the teaching process (commitment to international accounting education standards, the development of accounting curricula and educational aids, And the use of different teaching styles, and the development of methods of evaluating students). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Due to improvements in information and communication technology and growth of sensor technologies, Internet of Things is now widely used in medical field for optimal resource management and ubiquitous sensing. In hospitals, many IoT devices are linked together via gateways. Importance of gateways in modernization of hospitals cannot be overstated, but their centralized nature exposes them to a variety of security threats, including integrity, certification, and availability. Block chain technology for level monitoring in oxygen cylinders is a scattered record containing the data related to oxygen levels in the cylinder, patient's name, patient's ID number, patient's medical history, and all connected information carried out and distributed among the hospitals (nodes) present in the locality (network). Designing an oxygen level monitoring technique in an oxygen cylinder used as the support system for COVID-19-affected patients is a challenging task. Monitoring the level of oxygen in the cylinders is very important because they are used for saving the lives of the patients suffering from COVID-19. Not only the COVID-19 patients are dependent on this system, but this system will also be helpful for other patients who require oxygen support. The present scenario many COVID-19 hospitalized patients rely upon oxygen supply through oxygen cylinders and manual monitoring of oxygen levels in these cylinders has become a challenging task for the healthcare professionals due to overcrowding. If this level monitoring of oxygen cylinders are automated and developed as a mobile App, it would be of great use to the medical field, saving the lives of the patients who are left unmonitored during this pandemic. This proposal is entitled to develop a system to measure oxygen level using a smartphone App which will send instantaneous values about the level of the oxygen inside the cylinder. Pressure sensors and load cell are fitted to the oxygen cylinders, which will measure the oxygen content inside the cylinder in terms of the pressure and weight. The pressure sensors and load cells are connected to the Arduino board and are programmed to display the actual level of oxygen inside the cylinder in terms of numerical values. A beep sound is generated as an indicator to caution the nurses and attendants of the patients regarding the level of the oxygen inside the cylinder when it is only 15% of the total oxygen level in the cylinder in correlation to the pressure and weight. The signal with respect to the level corresponding to the measured pressure and weight of the cylinder is further transmitted to the monitoring station through Global System for Mobile communication (GSM). Graphical display is used at monitoring end to indicate the level of oxygen inside all oxygen cylinders to facilitate actions like 100% full, 80% full, 60% full, 40% full, 20% full which states that either the oxygen cylinder is in good condition, or requires a replacement of empty cylinders with filled ones in correlation to the pressure and weight being sensed by the sensors. The levels of the oxygen monitored inside the cylinder and other related data can also be stored on a cloud storage which will facilitate the retrieval of the status at any point of time, as when required by the physicians and nurses. These results reported, are valued in monitoring the level of the oxygen cylinder remotely connected to the patients, affected by COVID-19, using a smartphone App. This mobile phone App is an effective tool for investigating the oxygen cylinder level used as a life-support system for COVID-19-affected patients. A virtual model of the partial system is developed using TINKER CAD simulation package. In real time, the sensor data analysis with cloud computing will be deployed to detect and track the level of the oxygen cylinders. © 2023 Elsevier Inc. All rights reserved.
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The Corona Virus Disease 2019 (COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches, thereby highlighting a scenario where artificial intelligence (AI) merges with traditional Chinese medicine (TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors, nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors' authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users' lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.Copyright © 2022 Digital Chinese Medicine
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In this article, we present a [Formula Omitted]-learning-enabled safe navigation system—S-Nav—that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.
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Healthcare is a critical field of research and equally important for all nations. Providing secure healthcare facilities to citizens is the primary concern of each nation. However, people living in remote areas do not get timely and sufficient healthcare facilities, even in developed countries. During the recent COVID-19 pandemic, many fatalities occurred due to the inaccessibility of healthcare facilities on time. Therefore, there is a need to propose a solution that may help citizens living in remote areas with proper and secure healthcare facilities without moving to other places. The revolution in ICT technologies, especially IoT, 5G, and cloud computing, has made access to healthcare facilities easy and approachable. There is a need to benefit from these technologies so that everyone can get secure healthcare facilities from anywhere. This research proposes a framework that will ensure 24/7 accessibility of healthcare facilities by anyone from anywhere, especially in rural areas with fewer healthcare facilities. In the proposed approach, the patients will receive doorstep treatment from the remote doctor in rural areas or the nearby local clinic. Healthcare resources (doctor, treatment, patient counseling, diagnosis, etc.) will be shared remotely with people far from these facilities. The proposed approach is tested using mathematical modeling and a case study, and the findings confirm that the proposed approach helps improve healthcare facilities for remote patients.
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Since massive numbers of images are now being communicated from, and stored in different cloud systems, faster retrieval has become extremely important. This is more relevant, especially after COVID-19 in bandwidth-constrained environments. However, to the best of our knowledge, a coherent solution to overcome this problem is yet to be investigated in the literature. In this article, by customizing the Progressive JPEG method, we propose a new Scan Script to ensure Faster Image Retrieval. Furthermore, we also propose a new lossy PJPEG architecture to reduce the file size as a solution to overcome our Scan Script's drawback. In order to achieve an orchestration between them, we improve the scanning of Progressive JPEG's picture payloads to ensure Faster Image Retrieval using the change in bit pixels of distinct Luma and Chroma components ([Formula Omitted], [Formula Omitted], and [Formula Omitted]). The orchestration improves user experience even in bandwidth-constrained cases. We evaluate our proposed orchestration in a real-world setting across two continents encompassing a private cloud. Compared to existing alternatives, our proposed orchestration can improve user waiting time by up to 54% and decrease image size by up to 27%. Our proposed work is tested in cutting-edge cloud apps, ensuring up to 69% quicker loading time.
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The world invasion of dangerous virus diseases such as Covid 19, in the last few years, force people to wear masks as precaution. Although this prudence reduces the risk of infection and viruses' spread, it adds difficulty to distinguishing or identifying a person. This paper proposes a method to analyze images of masked persons for classifying their gender, in addition to identifying the colors of their skin and their eyes. We apply residual learning using the convolutional neural network (CNN) based on the visible part of the face. Cloud computing resources have been used as a convenient environment of substantial computing ability. Also, new database of RGB face images was created for testing. Experiments have been operated on the constructed database beside other datasets of facial images after cropping. The proposed model gives 96% gender classification accuracy and 100% skin/eye color identification. © 2023, Ismail Saritas. All rights reserved.
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This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management.
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The covid-19 pandemic causes a lot of changes in our lifestyles. Usage of information technology (IT) increased in all fields (government, business, industry, and academia). Despite its virtues, information technology has contributed to the repercussions. With advancements in the fields of information, communications, and technology, new types of IT risk have emerged. Because of the benefits of working remotely, cost minimization, and service availability, the usage of cloud computing services has expanded in all disciplines, particularly e-learning. There are big challenges facing cloud computing services like security and awareness of how to use these cloud services. Governance assesses performance and analyses adherence to agreed-upon goals and objectives. In this study, we will focus on using cloud computing in e-learning and the importance of governance while using cloud computing services in E-learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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PurposeThe mobile shadow information technology (IT) phenomenon is both completely misunderstood and negatively explored by those participating inside the organizational ecosystem. It represents all internet-based software, any other solutions for communications or employees' sharing without any formal authorization or approval from the IT department. Such behavior can lead to a security breach of the organization's data privacy, as these risks could disseminate it without the organization fully knowing. Recent research identifies that shadow IT is rarely covered from the knowledge sharing and knowledge protection (KP) perspective. This paper aims to provide an insight on how mobile shadow IT as a phenomenon could impact KP of an organization as a whole.Design/methodology/approachThis is an exploratory study based on a qualitative approach. The authors conducted interviews with 11 IT users to answer the main research question. The interview guidelines were divided into three parts: types of mobile shadow IT and occurrence;KP nature in the workplace;and mobile shadow IT impact on KP.FindingsThe research findings identified that most interviewees use mobile shadow IT without any notice or permission from their IT departments. This sharing also negatively impacts the KP in the organization. The most common mobile shadow IT applications are the clouded type like Dropbox, Google Drive and WhatsApp. Interviewees are using mobile shadow IT mainly because organizations do not provide suitable tools to communicate efficiently. The authors concluded that mobile shadow IT harms KP with no security and privacy on what is being shared because this process is unmonitored by the organization.Practical implicationsFor adequate knowledge and data protection, IT departments need to take more actions and efforts. This study can help IT decision-makers cope with the technology changes while understanding mobile shadow IT impacts. This study also offers insight regarding types of applications that can be used as an alternative tool for employees rather than using unauthorized applications. This research shows that medium-sized organizations are free to use these applications, which can cause damage to organizations.Originality/valueThis research is arguably among the first to explore the interviewees' perspectives on how mobile shadow IT impacts KP. This paper also provides theoretical and practical insights by identifying the three primary constructs and how mobile shadow IT usage can affect KP.
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Coronavirus (COVID-19) is a very contagious infection that has drawn the world's attention. Modeling such diseases can be extremely valuable in predicting their effects. Although classic statistical modeling may provide adequate models, it may also fail to understand the data's intricacy. An automatic COVID-19 detection system based on computed tomography (CT) scan or X-ray images is effective, but a robust system design is challenging. In this study, we propose an intelligent healthcare system that integrates IoT-cloud technologies. This architecture uses smart connectivity sensors and deep learning (DL) for intelligent decision-making from the perspective of the smart city. The intelligent system tracks the status of patients in real time and delivers reliable, timely, and high-quality healthcare facilities at a low cost. COVID-19 detection experiments are performed using DL to test the viability of the proposed system. We use a sensor for recording, transferring, and tracking healthcare data. CT scan images from patients are sent to the cloud by IoT sensors, where the cognitive module is stored. The system decides the patient status by examining the images of the CT scan. The DL cognitive module makes the real-time decision on the possible course of action. When information is conveyed to a cognitive module, we use a state-of-the-art classification algorithm based on DL, i.e., ResNet50, to detect and classify whether the patients are normal or infected by COVID-19. We validate the proposed system's robustness and effectiveness using two benchmark publicly available datasets (Covid-Chestxray dataset and Chex-Pert dataset). At first, a dataset of 6000 images is prepared from the above two datasets. The proposed system was trained on the collection of images from 80% of the datasets and tested with 20% of the data. Cross-validation is performed using a tenfold cross-validation technique for performance evaluation. The results indicate that the proposed system gives an accuracy of 98.6%, a sensitivity of 97.3%, a specificity of 98.2%, and an F1-score of 97.87%. Results clearly show that the accuracy, specificity, sensitivity, and F1-score of our proposed method are high. The comparison shows that the proposed system performs better than the existing state-of-the-art systems. The proposed system will be helpful in medical diagnosis research and healthcare systems. It will also support the medical experts for COVID-19 screening and lead to a precious second opinion.
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Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.
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Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.
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This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages. © 2023 IEEE.
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This study focusses on providing state-of-the-art infrastructure for data pipelines in e-Commerce sector, especially for online stores. With people going digital and also latest impact of Covid-19, daily e-Commerce companies are dealing with large amount of data (terabytes to petabytes). With growing Internet of Things, systems of computing devices which are interrelated. The inter-relation may be between mechanical and digital machines, objects or people. The interrelated objects will be provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Growth of big data poses several challenges and opportunities in every field of its usage. Realtime analysis of data and its inference gives a competitive edge over its partners in every business field especially in e-commerce. Recent advances in technology and tools have exposed new opportunities to get actionable insights from historical data like market data, customer demographics, along with real-time data. Advancement in distributed streaming technology makes it important to investigate existing streaming data pipeline capabilities in eCommerce sector with a focus on online stores. This study analyzes the published research works on streaming data pipelines in e-commerce sector also to facilitate e-commerce's variety of data streaming applications requirement. A state-of-the-art lambda architecture for streaming is proposed completely based on open-source technologies. Challenge in proprietary owned streaming platforms are vendor lock-in, limited ability to customize, cost, limited innovation & support. Proposed reference architecture will address many streaming use cases compared to its competitors, it has support of large open-source community in providing the inter-operability between streaming & related technologies like connectors, apart from providing better performance apart from other open-source based product advantages. © 2023 IEEE.
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The pandemic has forced industries to move immediately their critical workload to the cloud in order to ensure continuous functioning. As cloud computing expansions pace and organisations strive for methods to increase their network, agility and storage, edge computing has shown to be the best alternative. The healthcare business has a long history of collaborating with cutting-edge information technology, and the Internet of Things (IoT) is no exception. Researchers are still looking for substantial methods to collect, view, process, and analyze data that can signify a quantitative revolution in healthcare as devices become more convenient and smaller data become larger. To provide real-time analytics, healthcare organisations frequently deploy cloud technology as the storage layer between system and insight. Edge computing, also known as fog computing, allows computers to perform important analyses without having to go through the time-consuming cloud storage process [15, 16]. For this form of processing, speed is key, and it may be crucial in constructing a healthcare IoT that is useful for patient interaction, inpatient treatment, population health management and remote monitoring. We present a thorough overview to highlight the most recent trends in fog computing activities related to the IoT in healthcare. Other perspectives on the edge computing domain are also offered, such as styles of application support, techniques and resources [17]. Finally, necessity of edge computing in era of Covid-19 pandemic is addressed. © The Institution of Engineering and Technology 2022.
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An education sector across the globe is facing numerous challenges. It is being one of the most badly affected sectors due to Covid-19. This paper presents a perspective on applying Cloud Computing technologies in the field of education at several ion levels. In addition, it proposes Education and Learning as a Service model and Decision-Making Matrix for an Organization. © 2023 IEEE.
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Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData Catalystâ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.
Subject(s)
COVID-19 , Cloud Computing , Humans , Ecosystem , Reproducibility of Results , Lung , SoftwareABSTRACT
Cloud computing (CC) is a revolution that can provide information technology (IT) as a service. CC offers infrastructure, platform, and software services, as demand peaks and surges. This paper aims to investigate how prospective adopters behave when external factors such as "Coronavirus Pandemic- COVID-19” impact their technology adoption decision-making. The study also explores how a prospective adopter behaves i.e., if his/her intention to adopt any new innovation increases in presence of stronger disruptive factors (COVID-19). This research empirically examines if the intent to adopt secured (online) services impacts actual CC adoption (CCA) in pre-COVID-19 and during COVID-19 eras. It also provides an idea of how prospective adopters behave when they face disruptions caused by the pandemic situation, and how the holistic relation is reflected in terms of its influence on academic performance. This study has used Technology Acceptance Model (TAM) with sequential mediation effect of intent to adopt secured online services and CCA on Academic Performance (AP) using a sample of 867 students from 25 different Indian universities in Tier 1 and Tier 2 cities. Using AMOS, a structural equation modelling was conducted to test the research model. The results highlight that there is a significant difference between the influence of perceived usefulness (PU) as well as perceived ease of use (PEOU) on CCA due to COVID-19. The results also provide empirical evidence of gender moderating the relationship of PU as well as PEOU with CCA. This is the first study that provides comparative results from pre-COVID and post-COVID era, this work provides a reference point to practitioners and academicians, especially when evaluating factors before making a final decision regarding any emerging technology's adoption. © 2023 Elsevier Inc.
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This chapter attempts to provide a comprehensive overview of the ongoing technological disruption in the finance world. There is no denying that technology has already brought disruption of unprecedented scale and type in terms of bringing innovative solutions like never seen before in the financial sector. The disruptive innovation like P2P lending, Crowdfunding, Cryptocurrency, Regtech, Insurtech mobile payment, etc. has changed the way traditional financial institutions used to operate. Against such a backdrop, this chapter attempts to provide an overview of this disruption. The chapter also explores how these innovations have brought changes in the working cultures among financial institutions. The study suggests, based on the analysis of facts and figures that the disruptive technology has brought positive changes in the society in terms of delivering valuable stimulus and financial aid to the vulnerable and affected by the COVID-19 pandemic. The findings of the study further suggest that the Fintech disruption has been a blessing in disguise for the overall growth and development of the finance community. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.