| 8261. |
- Qu, Zhiguo, et al.
(författare)
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QB-IMD : A secure medical data processing system with privacy protection based on quantum blockchain for IoMT
- 2024
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Ingår i: IEEE Internet of Things Journal. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 11:1, s. 40-49
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Tidskriftsartikel (refereegranskat)abstract
- Security and privacy are issues that cannot be ignored when collecting and processing medical data in the Internet of Medical Things (IoMT). Blockchain technology is a decentralized ledger system that has diverse application scenarios in the medical field. Blockchain technology relies on traditional cryptography to ensure data integrity and verifiability, but the creation of quantum computing has made it possible to break traditional encryption and signature methods. Therefore, quantum blockchain can provide a higher level of security for handling medical data. This paper innovatively designs a new medical data processing system based on quantum blockchain (QB-IMD). In QB-IMD, a quantum blockchain structure and a novel electronic medical record algorithm (QEMR) are proposed to ensure that the processed data is legitimate and tamper-proof. QEMR combines quantum signature and quantum identity authentication to avoid the potential security risks of digital signatures. In addition, through delegated computing by quantum cloud, medical diagnostic data can be computed without leaking to quantum cloud servers, thus protecting user privacy. Through mathematical proof, theoretical analysis and simulation, it is demonstrated that our scheme can resist six attacks and is feasible to protect user privacy. © IEEE
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| 8262. |
- Qu, Zhiguo, et al.
(författare)
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QEPP : A Quantum Efficient Privacy Protection Protocol in 6G-Quantum Internet of Vehicles
- 2024
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Ingår i: IEEE Transactions on Intelligent Vehicles. - Piscataway, NJ : IEEE. - 2379-8858 .- 2379-8904. ; 9:1, s. 905-916
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Tidskriftsartikel (refereegranskat)abstract
- The increasing popularity of 6G communication within the Internet of Vehicles (IoV) ecosystem is expected to induce a surge in both user numbers and data volumes. This expansion will cause substantial challenges in ensuring network security and privacy protection, as well as in addressing the associated issue of inadequate cloud computing resources. In this article, we propose a Quantum Efficient Privacy Protection (QEPP) protocol that leverages reversible information hiding in quantum point clouds. This protocol utilizes quantum communication technology in edge-to-cloud communication of the IoV to transmit sensitive information embedded in quantum state data, thereby ensuring privacy protection. It employs quantum error-correction coding and efficient coding techniques to extract information and recover the carriers. In addition, the protocol utilizes an improved quantum Grover algorithm in the cloud to accelerate the processing speed of quantum data. By addressing security vulnerabilities and improving cloud-computing capabilities, the QEPP can effectively accommodate critical requirements, including precision, timeliness, and robust privacy protection. © IEEE
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| 8263. |
- Qu, Zhiguo, et al.
(författare)
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QFSM : A Novel Quantum Federated Learning Algorithm for Speech Emotion Recognition With Minimal Gated Unit in 5G IoV
- 2024
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Ingår i: IEEE Transactions on Intelligent Vehicles. - Piscataway, NJ : IEEE. - 2379-8858 .- 2379-8904.
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Tidskriftsartikel (refereegranskat)abstract
- The technology of speech emotion recognition (SER) has been widely applied in the field of human-computer interaction within the Internet of Vehicles (IoV). The incorporation of emerging technologies such as artificial intelligence and big data has accelerated the advancement of SER technology. However, this reveals challenges such as limited computational resources, data processing inefficiency, and security and privacy concerns. In recent years, quantum machine learning has been applied to the field of intelligent transportation, which has demonstrated its various advantages, including high prediction accuracy, robust noise resistance, and strong security. This study first integrates quantum federated learning (QFL) into 5G IoV using a quantum minimal gated unit (QMGU) recurrent neural network for local training. Then, it proposes a novel quantum federated learning algorithm, QFSM, to further enhance computational efficiency and privacy protection. Experimental results demonstrate that compared to existing algorithms using quantum long short-term memory network or quantum gated recurrent unit models, the QFSM algorithm has a higher recognition accuracy and faster training convergence rate. It also performs better in terms of privacy protection and noise robustness, enhancing its applicability and practicality. © IEEE
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| 8264. |
- Qu, Zhiguo, et al.
(författare)
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QMFND : A quantum multimodal fusion-based fake news detection model for social media
- 2024
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Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 104
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Tidskriftsartikel (refereegranskat)abstract
- Fake news is frequently disseminated through social media, which significantly impacts public perception and individual decision-making. Accurate identification of fake news on social media is usually time-consuming, laborious, and difficult. Although the leveraging of machine learning technologies can facilitate automated authenticity checks, the time-sensitive and voluminous nature of the data brings considerable challenge for fake news detection. To address this issue, this paper proposes a quantum multimodal fusion-based model for fake news detection (QMFND). QMFND integrates the extracted images and textual features, and passes them through a proposed quantum convolutional neural network (QCNN) to obtain discriminative results. By testing QMFND on two social media datasets, Gossip and Politifact, it is proved that its detection performance is equal to or even surpasses that of classical models. The effects of various parameters are further investigated. The QCNN not only has good expressibility and entangling capability but also has good robustness against quantum noise. The code is available at © 2023 Elsevier B.V.
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| 8265. |
- Qu, Zhiguo, et al.
(författare)
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QNMF : A quantum neural network based multimodal fusion system for intelligent diagnosis
- 2023
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Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 100
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Tidskriftsartikel (refereegranskat)abstract
- The Internet of Medical Things (IoMT) has emerged as a significant research area in the medical field, enabling the transmission of various types of data to the cloud for analysis and diagnosis. Fusing data from multiple modalities can enhance accuracy but requires substantial computing power. Theoretically, quantum computers can rapidly process large volumes of high-dimensional medical data. Despite accelerated developments in quantum computing, research on quantum machine learning (QML) for multimodal data processing remains limited. Considering these factors, this paper presents a quantum neural network-based multimodal fusion system for intelligent diagnosis (QNMF) that can process multimodal medical data transmitted by IoMT devices, fuse data from different modalities, and improve the performance of intelligent diagnosis. This system employs a quantum convolutional neural network (QCNN) to efficiently extract features from medical images. These QCNN-based features are then fused with other modality features (such as blood test results or breast cell slices), and used to train an effective variational quantum classifier (VQC) for intelligent diagnosis. The experimental results demonstrate that a QCNN can effectively extract image data features. Furthermore, QNMF achieved an accuracy of 97.07% and 97.61% on breast cancer diagnosis and Covid-19 diagnosis experiments, respectively. In addition, the QNMF exhibits strong quantum noise robustness. © 2023 Elsevier B.V.
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| 8266. |
- Qu, Zhiguo, et al.
(författare)
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Quantum conditional generative adversarial network based on patch method for abnormal electrocardiogram generation
- 2023
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Ingår i: Computers in Biology and Medicine. - Oxford : Elsevier. - 0010-4825 .- 1879-0534. ; 166, s. 1-13
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Tidskriftsartikel (refereegranskat)abstract
- To address the scarcity and class imbalance of abnormal electrocardiogram (ECG) databases, which are crucial in AI-driven diagnostic tools for potential cardiovascular disease detection, this study proposes a novel quantum conditional generative adversarial algorithm (QCGAN-ECG) for generating abnormal ECG signals. The QCGAN-ECG constructs a quantum generator based on patch method. In this method, each sub-generator generates distinct features of abnormal heartbeats in different segments. This patch-based generative algorithm conserves quantum resources and makes QCGAN-ECG practical for near-term quantum devices. Additionally, QCGAN-ECG introduces quantum registers as control conditions. It encodes information about the types and probability distributions of abnormal heartbeats into quantum registers, rendering the entire generative process controllable. Simulation experiments on Pennylane demonstrated that the QCGAN-ECG could generate completely abnormal heartbeats with an average accuracy of 88.8%. Moreover, the QCGAN-ECG can accurately fit the probability distribution of various abnormal ECG data. In the anti-noise experiments, the QCGAN-ECG showcased outstanding robustness across various levels of quantum noise interference. These results demonstrate the effectiveness and potential applicability of the QCGAN-ECG for generating abnormal ECG signals, which will further promote the development of AI-driven cardiac disease diagnosis systems. The source code is available at github.com/VanSWK/QCGAN_ECG. © 2023 The Author(s)
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| 8267. |
- Qu, Zhiguo, et al.
(författare)
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Quantum detectable Byzantine agreement for distributed data trust management in blockchain
- 2023
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Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 637
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Tidskriftsartikel (refereegranskat)abstract
- No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockchain is proposed, considering the threat posed by the growth of quantum information technology on the traditional blockchain. Relying on the nonlocality of the GHZ state, the proposed protocol detects the honesty of nodes by allocating the entanglement resources between different nodes. The GHZ state is notably simpler to prepare than other multi-particle entangled states, thus reducing preparation consumption and increasing practicality. When the number of network nodes increases, the proposed protocol provides better scalability and stronger practicability than the current quantum DBA. In addition, the proposed protocol has the optimal fault-tolerant found and does not rely on any other presumptions. A consensus can be reached even when there are n−2 traitors. The performance analysis confirms viability and effectiveness through exemplification. The security analysis also demonstrates that the quantum DBA protocol is unconditionally secure, effectively ensuring the security of data and realizing data consistency in the quantum blockchain. © 2023 The Authors
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| 8268. |
- Qu, Zhiguo, et al.
(författare)
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Quantum Fuzzy Federated Learning for Privacy Protection in Intelligent Information Processing
- 2024
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Ingår i: IEEE transactions on fuzzy systems. - Piscataway, NJ : IEEE. - 1063-6706 .- 1941-0034.
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of the intelligent information processing era, more and more private sensitive data are being collected and analyzed for intelligent decision making tasks. Such information processing also brings many challenges with existing privacy protection algorithms. On the one hand, the algorithms based on data encryption compromise the integrity of the original data or incur high computational and communication costs to some extent. On the other hand, algorithms based on distributed learning require frequent sharing of parameters between different computing nodes, which poses risks of leaking local model information and reducing global learning efficiency. To mitigate the impact of these issues, a Quantum Fuzzy Federated Learning (QFFL) algorithm is proposed. In the QFFL algorithm, a Quantum Fuzzy Neural Network (QFNN) is designed at the local computing nodes, which enhances data generalization while preserving data integrity. In global model, QFFL makes predictions through the Quantum Federated Inference (QFI). QFI leads to a general framework for quantum federated learning on non-IID data with oneshot communication complexity, achieving privacy protection of local data and accelerating the global learning efficiency of the algorithm. The experiments are conducted on the COVID19 and MNIST datasets, and the results indicate that QFFL demonstrates superior performance compared to the baselines, manifesting in faster training efficiency, higher accuracy, and enhanced security. In addition, based on the fidelity experiments and related analysis under four common quantum noise channels, the results demonstrated that it has good robustness against quantum noises, proving its applicability and practicality. Our code is available at https://github.com/LASTsue/QFFL. © IEEE
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| 8269. |
- Qvarfordt, Maria, 1982-, et al.
(författare)
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Factors influencing physical activity in patients with early rheumatoid arthritis : A mixed-methods study.
- 2019
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Ingår i: SAGE Open Medicine. - Thousand Oaks, CA : Sage Publications. - 2050-3121. ; 7, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The goal of this study was to provide a greater understanding of physical activity in patients with early rheumatoid arthritis. The aim was twofold: first to explore if physical activity was associated with factors in the clinical picture of rheumatoid arthritis in this patient group, and second, to explore factors influencing physical activity in patients with early rheumatoid arthritis.Methods: A total of 66 patients with early rheumatoid arthritis were included in the study. A sequential explanatory mixed-methods design was used, where quantitative data from a questionnaire were analysed with Mann-Whitney, post hoc Kruskal-Wallis and χ2 test in order to detect differences between groups, and find possible associations between physical activity and independent variables, such as disease activity, health-related quality of life and physical function. Qualitative data were collected in a follow-up questionnaire with open-ended questions that focused on factors influencing physical activity.Results: Associations between physical activity, disease activity and health-related quality of life were seen in patients with early rheumatoid arthritis together with strong negative correlations between physical activity and physical function. Patients on sick leave showed the strongest associations between disease-related variables and lower levels of physical activity. The findings from the qualitative analysis showed that physical limitations, awareness as a motivational factor and external environment factors influenced physical activity in patients with early rheumatoid arthritis.Conclusion: The results showed a complex underlying motive where physical, psychological and environmental factors influenced the physical activity in patients with early rheumatoid arthritis. In order to provide more effective health interventions, it is important to consider the complex nature of practicing physical activity, where a person-centred approach should be considered. Factors such as physical limitations, economic aspects and time for practicing physical activity should be included in the person-centred approach.
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| 8270. |
- Qvarfordt, Maria, 1982-, et al.
(författare)
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Patients’ experiences of reasons to being physically active in early rheumatoid arthritis – a mixed methods study
- 2019
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Ingår i: Annals of the Rheumatic Diseases. - London, UK : BMJ Publishing Group Ltd. - 0003-4967 .- 1468-2060. ; 78:Suppl 2, s. 1454-1455
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Tidskriftsartikel (refereegranskat)abstract
- Background: The importance of physical activity in rheumatoid arthritis (RA) is well known and patients are informed about the importance of being physically active. Despite this knowledge there is a lack of compliance to this advice. Studies comparing physical activity in different groups of patients with RA and reasons influencing physical activity are needed. Objectives: The objectives were to compare physical activity (PA) in workers, retired and patients with sick-leave with early RA and further to explore reasons to being physically active in these patients. Methods: A total of 66 patients with early RA were included in the study. A sequential explanatory mixed methods design was used. The groups were compared with clinical data as: disease activity (DAS28); pain (VAS 0-100, best to worst); health-related quality of life (EQ5D, -0.594-1 worse to best) and a physical function (HAQ, 0-3 best to worst). ESR and CRP. Patients were dichotomized as being active on recommended levels of PA (MVPArec; physically active on a moderate level ≥150min/week (MPA) or on an intense level ≥75min/week (VPA)) or not (sedentary). The patients were grouped on self-reported working ability; workers, patients with sick-leave and retired patients. Qualitative data was collected by a questionnaire with open-ended questions about reasons influencing PA. The qualitative data was analysed with a manifest qualitative content analysis to gain a greater understanding of patients’ experiences of PA in early RA. Results: There were no significant differences between the groups in disease activity, physical function, swollen joints, health-related quality of life or inflammatory parameters (ESR, CRP). Patients on sick-leave had more tender joints median (min-max) 9 (2-18) vs. 4 (0-20) and 3 (0-10), p=0.013. Workers reported higher intensity of pain, though not significant. Retired patients fulfilled MVPA criteria to a higher rate (86%) than workers (42%) or patients with sick-leave (40%), p=0.010. The qualitative content analysis resulted in three categories. Reasons to being physically active in patients with early RA were; limitations (pain, physical function, stiffness, limited strength and fatigue), awareness as motivation (fear of movement and health benefits) and external environment (weather, transports to activity, economy and time, especially for workers). Conclusion: Knowledge of reasons to being physically active in patients with RA is important to facilitate and support the patients. Joint pain seems to be an issue for patients with sick-leave. This could be associated to fear of movement and in this aspect these patients need to be supported. Time could be a limiting issue for working patients, which need to be highlighted and solved for these patients. © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.
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