ABSTRACT
The healthcare industry and the battle against the COVID‐19 pandemic are two areas where blockchain technology might be useful. In this study, blockchain's significance is examined. Blockchain technology and related procedures will be used in future healthcare systems for collecting sensor data, automated patient monitoring, and safe data storage. Because it can store a large amount of data in a dispersed and secure way and provide access whenever and wherever it is needed, this technology greatly simplifies the process of carrying out activities. The advantages of quantum computing, such as the speed with which patients can be found and monitored, may be fully used with the help of quantum blockchain. Quantum blockchain is an additional resource that may be used to safeguard the veracity, integrity, and availability of stored information. Combining quantum computing with blockchain technology may allow faster and more secure medical information processing. In this research, the authors examine the potential uses of blockchain and quantum technology in the healthcare industry. Quantum technologies, blockchain‐based technologies, and other cutting‐edge ICTs (such as ratification intelligence, machine learning, drones, and so on) were investigated and contrasted in this article. [ FROM AUTHOR] Copyright of Security & Privacy is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Involvement of multiple stakeholders in healthcare industry, even the simple healthcare problems become complex due to classical approach to treatment. In the Covid-19 era where quick and accurate solutions in healthcare are needed along with quick collaboration of stakeholders such as patients, insurance agents, healthcare providers and medicine supplier etc., a classical computing approach is not enough. Therefore, this study aims to identify the role of quantum computing in disrupting the healthcare sector with the lens of organizational information processing theory (OIPT), creating a more sustainable (less strained) healthcare system. A semi-structured interview approach is adopted to gauge the expectations of professionals from healthcare industry regarding quantum computing. A structured approach of coding, using open, axial and selective approach is adopted to map the themes under quantum computing for healthcare industry. The findings indicate the potential applications of quantum computing for pharmaceutical, hospital, health insurance organizations along with patients to have precise and quick solutions to the problems, where greater accuracy and speed can be achieved. Existing research focuses on the technological background of quantum computing, whereas this study makes an effort to mark the beginning of quantum computing research with respect to organizational management theory. © 2022
ABSTRACT
Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification (QIDEXAI-CDC) model for HIoT systems. The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems. The QIDEXAI-CDC model primarily uses bilateral filtering (BF) as a preprocessing tool to eradicate the noise. In addition, RetinaNet is applied for the generation of useful feature vectors from radiological images. For COVID-19 detection and classification, quantum-inspired differential evolution (QIDE) with kernel extreme learning machine (KELM) model is utilized. The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model. In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model, a wide range of simulations was carried out. Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches. © 2023 Authors. All rights reserved.
ABSTRACT
This Work-In-Progress paper describes a program in quantum machine learning launched in the academic year of 2021-22. The program engaged undergraduate students from STEM areas with faculty and industry mentors. Because of the COVID-19 conditions, this undergraduate engagement was offered in a virtual format. In 2022, some face-to-face meetings with presentations were also held. The program included: a) training in machine learning with quantum simulators, b) weekly presentations, and c) semester end presentations. The assessment of the program included surveys, interviews, and presentation observations. Challenges and opportunities from virtual engagement were also part of the assessment. © 2022 IEEE.
ABSTRACT
Accessible rapid COVID-19 testing continues to be necessary and several studies involving deep neural network (DNN) methods for detection have been published. As part of a sponsored NSF I/UCRC project, our team explored the use of deep learning algorithms for recognizing COVID-19 related cough audio signatures. More specifically, we have worked with several DNN algorithms and cough audio databases and reported results with the VGG-13 architecture. In this paper, we report a study on the use of quantum neural networks for audio signature detection and classification. A hybrid quantum neural network (QNN) model for COVID-19 cough classification is developed. The design of the QNN simulation architecture is described and results are given with and without quantum noise. Comparative results between classical and quantum neural network methods for COVID-19 audio detection are also presented. © 2022 IEEE.
ABSTRACT
In recent years, researchers have oriented their studies towards new technologies based on quantum physics that should allow the resolution of complex problems currently considered to be intractable. This new research area is called Quantum Computing. What makes Quantum Computing so attractive is the particular way with which quantum technology operates and the great potential it can offer to solve real-world problems. This work focuses on solving combinatorial optimization problems, specifically assignment problems, by exploiting this novel computational approach. A case-study, denoted as the Seating Arrangement Optimization problem, is considered. It is modeled through the Quadratic Unconstrained Binary Optimization (QUBO) paradigm and solved through two tools made available by the D-Wave Systems company, QBSolv and a quantum-classical hybrid system. The obtained experimental results are compared in terms of solution quality and computational efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Quantum computation, particularly in the field of machine learning, is a rapidly growing technology. Major advantage of Quantum computing is its speed to perform calculations. This paper proposes a novel model architecture for feature extraction. It extracts the features from a colored spectrogram as an extension to the already existing Quanvolutional Neural Network which works on only grayscale images or 2-dimensional representation of spectrograms. The proposed model architecture works on all the three layers of an image (RGB) and uses random quantum circuits to extract features from them and distribute them into several output images and out of them one is selected which contains the most important and pertinent features from the original image helping the training of the CNN model used ahead. COVID-19 use case is used for performance evaluation. Normally the testing methods used to detect virus are expensive, examples include RT-PCR test, CT scan images. These methods require a medical professional to conduct the test while being in the proximity of the patient. Also, the testing kits once used cannot be used again. One of the most evident changes in a Covid 19 patient is the change in his/her coughing and breathing pattern. This work analyzed the spectrograms of the audio samples of coughing and breathing patterns of Covid 19 patients using the proposed model architecture and provided subsequent results. Finally to generalize our model’s applicability, the model is also run-on Alzheimer disease dataset and corresponding results are provided. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning has become quite a challenging field in the case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models and quantum circuits using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.
ABSTRACT
This work in progress paper describes our efforts and challenges in delivering undergraduate and graduate courses during COVID-19 conditions. More specifically, we focus on the adaptation and delivery of digital signal analysis laboratories for all the remote learners during the pandemic conditions. Methods for online labs and workforce training have been developed and deployed on a virtual basis. These labs and simulation environments have been deployed in signals and systems and DSP classes as well as in workforce development programs such as the REU and RET. The assessment of these efforts included evaluation forms and interviews. Challenges and opportunities from virtual delivery of content and labs were also part of the assessment.
ABSTRACT
We present an educational path for teacher professional development whose primary purpose is to enhance physics teachers’ knowledge and awareness of topics related to quantum computation and quantum information, and of their relevance for technological advancement. Besides their objective importance, also stressed by several authors and projects, the choice of topics not traditionally covered in the final year physics curricula also arises from the concrete possibility of developing a multidisciplinary path, able to represent under a unified perspective several subjects treated in secondary school physics and mathematics. The project is realized in the context of the Italian PLS (Plan for Science Degrees) and the education section of the Quantum Flagship. Due to the limitations related to the COVID-19 pandemic it was entirely delivered in the form of synchronous distance learning and was attended by around 30 teachers. Asynchronous discussion was performed using both generally available tools (Google drive, forms etc.) and a dedicated online forum set up on the servers of the University of Pavia. We discuss the structure of the educational path and the results of the first part of the course whose purpose was describing the transition from classical to quantum computation. In general, from both the written pre-questionnaire and the mid-course interviews, strong appreciation and fascination emerge for the cultural significance of the introduced topics and connections.
ABSTRACT
The purpose of this study was in the research of prospects for simultaneous use of 6G generation cellular communications for the purposes of automatization of cost accounting of the activity of enterprises of various branches and cybersecurity of accounting information. The theoretical and methodological aspects of the use of 6G cellular network technologies for accounting and cybersecurity purposes have been studied on the basis of general research methods - institutional and innovative;economic and mathematical methods of analysis using Excel spreadsheets were used to predict the pace of implementation of cellular communication of new generations;to determine perspective areas of use of 6G technology - methods of bibliographic and comparative analysis using the information resource "ResearchGate". The methods of permanent collection and transmission of accounting data about the production process and the procedure for monitoring the stay of employees or outsiders at the workplace using production equipment connected to the 6G cellular network has been developed. The procedure for combining the functional abilities of Global Positioning System (GPS) and cellular positioning (mobile subscribers)for accounting of transport costs and control over the movement and economic use of vehicles has been proposed.The procedure for combining unmanned aerial vehicles in a cluster on the basis of 6G communication with the purpose of aerovisual surveillance of agricultural and construction activities for automated accounting of production costs and prevention of unauthorized getting into an enterprise ofpersons (drones). The methods for determining the cost of rental space from the lessor based on counting the popularity among visitors and identifying offenders (thieves of information and material resources) through automated monitoring of the location of 6G cellular subscribers. The practical implementation of the developments presented in the article on the use of 6G cellular technologies will contribute to reliable costing and accounting of production costs of production, agricultural, construction, trade activities in combination with effective cyber protection of enterprises in preventing and detecting violators of information and territorial security. Further research is needed on the methods of management of business entities on the basis of accounting information obtained with the use of 6G cellular network technology.
ABSTRACT
COVID-19 is an airborne virus that can be spread directly or indirectly from one person to another. Spreading the virus strongly depends on the location and time and hence, a Spatio-temporal event. Moreover, traffic congestion will increase the spread of the virus not only because of the vicinity but also because of increased temperature and humidity in these spaces for a short or long time. This paper introduces a vehicle routing optimization model to reduce COVID-19 exposure risk during a city journey by solving it as a quadratic unconstrained binary optimization problem on a quantum annealing computer. Indeed, the objective of the COVID-19 prevention optimization problem is to minimize the risk of exposure for a given set of road users between origins and destinations. Microsoft Taxi data from the city of Beijing have been used to simulate road users’ movement. The problem has been run onto three different solvers. One of the solvers is executed on classical computers, and two other solvers are executed on hybrid quantum solvers. Hybrid solvers return the solution within less than 0.03 seconds on quantum processing unit time. However, the results will be returned at least 5 seconds after the execution in the classical solver. It is worth mentioning that, as there is no direct access to the quantum computers, it is hard to compare the results on the same scale as the queries will go on a queue in D-wave quantum computers. Applying the proposed model on the trajectory data shows a better distribution of the vehicles on the road network.
ABSTRACT
Involvement of multiple stakeholders in healthcare industry, even the simple healthcare problems become complex due to classical approach to treatment. In the Covid-19 era where quick and accurate solutions in healthcare are needed along with quick collaboration of stakeholders such as patients, insurance agents, healthcare providers and medicine supplier etc., a classical computing approach is not enough. Therefore, this study aims to identify the role of quantum computing in disrupting the healthcare sector with the lens of organizational information processing theory (OIPT), creating a more sustainable (less strained) healthcare system. A semi-structured interview approach is adopted to gauge the expectations of professionals from healthcare industry regarding quantum computing. A structured approach of coding, using open, axial and selective approach is adopted to map the themes under quantum computing for healthcare industry. The findings indicate the potential applications of quantum computing for pharmaceutical, hospital, health insurance organizations along with patients to have precise and quick solutions to the problems, where greater accuracy and speed can be achieved. Existing research focuses on the technological background of quantum computing, whereas this study makes an effort to mark the beginning of quantum computing research with respect to organizational management theory.
ABSTRACT
The Energy and Water Development and Related Agencies appropriations bill provides funding for civil works projects of the U.S. Army Corps of Engineers (USACE);the Department of the Interiors Bureau of Reclamation (Reclamation) and Central Utah Project (CUP);the Department of Energy (DOE);the Nuclear Regulatory Commission (NRC);the Appalachian Regional Commission (ARC);and several other independent agencies. DOE typically accounts for about 80% of the bills funding.
ABSTRACT
Despite the great efforts to find an effective way for coronavirus disease 2019 (COVID-19) prediction, the virus nature and mutation represent a critical challenge to diagnose the covered cases. However, developing a model to predict COVID-19 via chest X-ray images with accurate performance is necessary to help in early diagnosis. In this paper, a hybrid quantum-classical convolutional neural network (HQ-CNN) model using random quantum circuits as a base to detect COVID-19 patients with chest X-ray images is presented. A collection of 5445 chest X-ray images, including 1350 COVID-19, 1350 normal, 1345 viral pneumonia, and 1400 bacterial pneumonia images, were used to evaluate the HQ-CNN. The proposed HQ-CNN model has achieved higher performance with an accuracy of 98.6% and a recall of 99% on the first experiment (COVID-19 and normal cases). Besides, it obtained an accuracy of 98.2% and a recall of 99.5% on the second experiment (COVID-19 and viral pneumonia cases). Also, it obtained 98% and 98.8% for accuracy and recall, respectively, on the third dataset (COVID-19 and bacterial pneumonia cases). Lastly, it achieved accuracy and recall of 88.2% and 88.6%, respectively, on the multiclass dataset cases. Moreover, the HQ-CNN model is assessed with the statistical analysis (i.e. Cohen's Kappa and Matthew correlation coefficients). The experimental results revealed that the proposed HQ-CNN model is able to predict the positive COVID-19 cases.
ABSTRACT
ACM has named a 14-person team from Chinese institutions as recipients of the 2021 ACM Gordon Bell Prize for their project, Closing the "Quantum Supremacy" Gap: Achieving Real-Time Simulation of a Random Quantum Circuit Using a New Sunway Supercomputer. The ACM Gordon Bell Prize tracks the progress of parallel computing and rewards innovation in applying high-performance computing to challenges in science, engineering, and large-scale data analytics. In addition, ACM awarded the 2021 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research to a six-member team from Japan for their project Digital transformation of droplet/ aerosol infection risk assessment realized on "Fugaku" for the fight against COVID-19.
ABSTRACT
Healthcare cyber-physical system significantly facilitates healthcare services and patient treatment effectiveness by analyzing patients’ health information data conveniently. Nevertheless, it also develops the threats to the confidentiality of health information, patients’ privacy, and decidability of medical disputes. And, with the advances of quantum computing technology, most existing anonymous authentication schemes are becoming a growing threat to traditional cryptosystems. To address these problems, for healthcare cyber-physical systems, we propose a new lattice-based self-enhancement authorized accessible privacy authentication scheme by using a strong designated verifier double-authentication-preventing signature technique, called SEAPA. The SEAPA achieves three security and privacy requirements including unforgeability, anonymity for patients’ information, and self-enhancement for patients themselves. A detailed security proof shows our proposal achieves those required security goals. Finally, our construction is demonstrated by parameter analysis and performance evaluation to have reasonable efficiency.
ABSTRACT
This paper prosed a novel 6G QoS over the future 6G wireless architecture to offer excellent Quality of Service (QoS) for the next generation of digital TV beyond 2030. During the last 20 years, the way society used to watch and consume TV and Cinema has changed radically. The creation of the Over The Top content platforms based on Cloud Services followed by its commercial video consumption model, offering flexibility for subscribers such as n Video on Demand. Besides the new business model created, the network infrastructure and wireless technologies also permitted the streaming of high-quality TV and film formats such as High Definition, followed by the latest widespread TV standardization Ultra-High- Definition TV. Mobile Broadband services onset the possibility for consumers to watch TV or Video content anywhere at any time. However, the network infrastructure needs continuous improvement, primarily when crises, like the coronavirus disease (COVID-19) and the worldwide pandemic, creates immense network traffic congestions. The outcome of that congestion was the decrease of QoS for such multimedia services, impacting the user's experience. More power-hungry video applications are commencing to test the networks' resilience and future roadmap of 5G and Beyond 5G (B5G). For this, 6G architecture planning must be focused on offering the ultimate QoS for prosumers beyond 2030.