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Zhao, S., Jiang, A. & Ding, J. (2025). MoCoDiff: Momentum context diffusion model for low-dose CT denoising. Digital signal processing (Print), 156, Article ID 104868.
Open this publication in new window or tab >>MoCoDiff: Momentum context diffusion model for low-dose CT denoising
2025 (English)In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 156, article id 104868Article in journal (Refereed) Published
Abstract [en]

Low-Dose Computed Tomography (LDCT) has gradually replaced Normal-Dose Computed Tomography (NDCT) due to its lower radiation exposure. However, the reduction in radiation dose has led to increased noise and artifacts in LDCT images. To date, many methods for LDCT denoising have emerged, but they often struggle to balance denoising performance with reconstruction efficiency. This paper presents a novel Momentum Context Diffusion model for low-dose CT denoising, termed MoCoDiff. First, MoCoDiff employs a Mean-Preserving Stochastic Degradation (MPSD) operator to gradually degrade NDCT to LDCT, effectively simulating the physical process of CT degradation and greatly reducing sampling steps. Furthermore, the stochastic nature of the MPSD operator enhances the diversity of samples in the training space and calibrates the deviation between network inputs and time-step embedded features. Second, we propose a Momentum Context (MoCo) strategy. This strategy uses the most recent sampling result from each step to update the context information, thereby narrowing the noise level gap between the sampling results and the context data. This approach helps to better guide the next sampling step. Finally, to prevent issues such as over-smoothing of image edges that can arise from using the mean square error loss function, we develop a dual-domain loss function that operates in both the image and wavelet domains. This approach leverages wavelet domain information to encourage the model to preserve structural details in the images more effectively. Extensive experimental results show that our MoCoDiff model outperforms competing methods in both denoising and generalization performance, while also ensuring fast training and inference. © 2024 Elsevier Inc.

Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Denoising, Diffusion model, Low-dose CT, Momentum context, Stochastic degradation operator, Computerized tomography, Image denoising, Image sampling, Mean square error, De-noising, Dose computed tomographies, Low dose, Sampling results, Sampling steps, Stochastic degradation, Stochastic models
National Category
Computer graphics and computer vision Medical Imaging
Identifiers
urn:nbn:se:bth-27176 (URN)10.1016/j.dsp.2024.104868 (DOI)001360503500001 ()2-s2.0-85209128081 (Scopus ID)
Available from: 2024-11-29 Created: 2024-11-29 Last updated: 2025-02-09Bibliographically approved
Zhang, F., Yang, P., Li, R., Li, S., Ding, J., Xu, J. & Ning, H. (2024). A multi-strategy ontology mapping method based on cost-sensitive SVM. Journal of Cloud Computing: Advances, Systems and Applications, 13(1), Article ID 144.
Open this publication in new window or tab >>A multi-strategy ontology mapping method based on cost-sensitive SVM
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2024 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 13, no 1, article id 144Article in journal (Refereed) Published
Abstract [en]

As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
Ontology mapping, Embedding vector, Similarity computation, Support vector machine
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-27001 (URN)10.1186/s13677-024-00708-7 (DOI)001321783000001 ()2-s2.0-85205668137 (Scopus ID)
Available from: 2024-10-14 Created: 2024-10-14 Last updated: 2024-10-18Bibliographically approved
Sarwatt, D. S., Kulwa, F., Ding, J. & Ning, H. (2024). Adapting Image Classification Adversarial Detection Methods for Traffic Sign Classification in Autonomous Vehicles: A Comparative Study. IEEE transactions on intelligent transportation systems (Print), 25(11), 19046-19061
Open this publication in new window or tab >>Adapting Image Classification Adversarial Detection Methods for Traffic Sign Classification in Autonomous Vehicles: A Comparative Study
2024 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 25, no 11, p. 19046-19061Article in journal (Refereed) Published
Abstract [en]

Autonomous driving systems critically depend on accurately classifying traffic signs, a task that is jeopardized by adversarial attacks. This paper focuses on the relatively unexplored domain of Traffic Sign Classification (TSC) in the context of detecting adversarial attacks. We conduct a rigorous evaluation of six prominent adversarial example detection methods, each representing a distinct detection category and well-regarded within the research community. Our methodology involves adapting these techniques, originally developed for general image classification (IC), to the unique challenges posed by traffic sign images, characterized by their complexity due to factors like varying environmental conditions and a large number of classes. Our study reveals insights into their effectiveness, with the Natural Scene Statistics (NSS) method outperforming others with 83.42%, 86.42%, and 99.96% detection rates; 0.08%, 0.05%, and 0.04%, false positive rates; and 0.02sec, 0.01sec, and 0.02sec detection times for Chinese, Belgium and German traffic sign datasets, respectively. NSS's superiority is crucial for autonomous vehicles. Our study also sheds light on the often-neglected aspect of detection time in IC, which plays a vital role in ensuring operational efficiency and safety for autonomous vehicles. Our research highlights the need for customized defense strategies tailored to the TSC domain, considering our evaluation's findings. By identifying promising techniques for detecting adversarial attacks in TSC, we contribute to enhancing the safety and robustness of autonomous driving systems. This study fills a critical knowledge gap, providing valuable insights into understanding and defending against adversarial attacks, specifically in the TSC context.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Perturbation methods, Safety, Autonomous vehicles, Real-time systems, Image classification, Complexity theory, Task analysis, Adversarial attacks, detection methods, traffic sign classification
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-26845 (URN)10.1109/TITS.2024.3435715 (DOI)001288416200001 ()2-s2.0-85208689719 (Scopus ID)
Available from: 2024-08-27 Created: 2024-08-27 Last updated: 2024-11-22Bibliographically approved
Zhang, Z., Ning, H., Farha, F., Ding, J. & Choo, K.-K. R. (2024). Artificial intelligence in physiological characteristics recognition for internet of things authentication. Digital Communications and Networks, 10(3), 740-755
Open this publication in new window or tab >>Artificial intelligence in physiological characteristics recognition for internet of things authentication
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2024 (English)In: Digital Communications and Networks, ISSN 2468-5925, E-ISSN 2352-8648, Vol. 10, no 3, p. 740-755Article, review/survey (Refereed) Published
Abstract [en]

Effective user authentication is key to ensuring equipment security, data privacy, and personalized services in Internet of Things (IoT) systems. However, conventional mode-based authentication methods (e.g., passwords and smart cards) may be vulnerable to a broad range of attacks (e.g., eavesdropping and side-channel attacks). Hence, there have been attempts to design biometric-based authentication solutions, which rely on physiological and behavioral characteristics. Behavioral characteristics need continuous monitoring and specific environmental settings, which can be challenging to implement in practice. However, we can also leverage Artificial Intelligence (AI) in the extraction and classification of physiological characteristics from IoT devices processing to facilitate authentication. Thus, we review the literature on the use of AI in physiological characteristics recognition published after 2015. We use the three-layer architecture of the IoT (i.e., sensing layer, feature layer, and algorithm layer) to guide the discussion of existing approaches and their limitations. We also identify a number of future research opportunities, which will hopefully guide the design of next generation solutions. © 2022 Chongqing University of Posts and Telecommunications

Place, publisher, year, edition, pages
KeAi Communications Co., 2024
Keywords
Artificial intelligence, Biological-driven authentication, Internet of things, Physiological characteristics recognition, Data privacy, Physiology, Side channel attack, Smart cards, Authentication methods, Behavioral characteristics, Characteristic recognition, Mode-based, Personalized service, Physiological characteristic recognition, Physiological characteristics, Security datum, User authentication, Authentication
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-26748 (URN)10.1016/j.dcan.2022.10.006 (DOI)001273473200001 ()2-s2.0-85196808067 (Scopus ID)
Available from: 2024-08-05 Created: 2024-08-05 Last updated: 2024-08-13Bibliographically approved
Lifelo, Z., Ding, J., Ning, H., Qurat-Ul-Ain, . & Dhelim, S. (2024). Artificial Intelligence-Enabled Metaverse for Sustainable Smart Cities: Technologies, Applications, Challenges, and Future Directions. Electronics, 13(24), Article ID 4874.
Open this publication in new window or tab >>Artificial Intelligence-Enabled Metaverse for Sustainable Smart Cities: Technologies, Applications, Challenges, and Future Directions
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2024 (English)In: Electronics, E-ISSN 2079-9292, Vol. 13, no 24, article id 4874Article, review/survey (Refereed) Published
Abstract [en]

Rapid urbanisation has intensified the need for sustainable solutions to address challenges in urban infrastructure, climate change, and resource constraints. This study reveals that Artificial Intelligence (AI)-enabled metaverse offers transformative potential for developing sustainable smart cities. AI techniques, such as machine learning, deep learning, generative AI (GAI), and large language models (LLMs), enhance the metaverse’s capabilities in data analysis, urban decision making, and personalised user experiences. The study further examines how these advanced AI models facilitate key metaverse technologies such as big data analytics, natural language processing (NLP), computer vision, digital twins, Internet of Things (IoT), Edge AI, and 5G/6G networks. Applications across various smart city domains—environment, mobility, energy, health, governance, and economy, and real-world use cases of virtual cities like Singapore, Seoul, and Lisbon are presented, demonstrating AI’s effectiveness in the metaverse for smart cities. However, AI-enabled metaverse in smart cities presents challenges related to data acquisition and management, privacy, security, interoperability, scalability, and ethical considerations. These challenges’ societal and technological implications are discussed, highlighting the need for robust data governance frameworks and AI ethics guidelines. Future directions emphasise advancing AI model architectures and algorithms, enhancing privacy and security measures, promoting ethical AI practices, addressing performance measures, and fostering stakeholder collaboration. By addressing these challenges, the full potential of AI-enabled metaverse can be harnessed to enhance sustainability, adaptability, and livability in smart cities. 

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
adaptive urban systems, artificial intelligence, digital twins, generative AI, large language models, metaverse, smart cities, sustainable cities, urban planning, urban transformation
National Category
Computer Systems Computer Engineering
Identifiers
urn:nbn:se:bth-27371 (URN)10.3390/electronics13244874 (DOI)001386778700001 ()2-s2.0-85213202921 (Scopus ID)
Available from: 2025-01-10 Created: 2025-01-10 Last updated: 2025-01-10Bibliographically approved
Qammar, A., Naouri, A., Ding, J. & Ning, H. (2024). Blockchain-based optimized edge node selection and privacy preserved framework for federated learning. Cluster Computing, 27(3), 3203-3218
Open this publication in new window or tab >>Blockchain-based optimized edge node selection and privacy preserved framework for federated learning
2024 (English)In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543, Vol. 27, no 3, p. 3203-3218Article in journal (Refereed) Published
Abstract [en]

Federated learning is a distributed paradigm that trained large-scale neural network models with the participation of multiple edge nodes and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client–server architecture, which leads to the single-point-of-failure (SPoF) attack, and the random selection of edge devices for model training compromised the accuracy of the model. Furthermore, adversaries try to initiate inference attack i.e., attack on privacy leads to gradient leakage attack. Hence, we proposed a blockchain-based optimized edge node selection and privacy-preserved framework to address the aforementioned issues. We have designed three kinds of smart contracts (1) registration of edge nodes (2) forward bidding to select optimized edge devices for FL model training, and (3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with the Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with other state-of-the-art studies. Consequently, we have achieved a higher accuracy and privacy-preserved FL framework with a decentralized nature. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
Blockchain, Edge-node/device, Federated learning, Privacy, Security, Smart contracts, Cryptography, Data privacy, Learning systems, Block-chain, Edge nodes, Local model, Model training, Model updates, Node selection, Smart contract
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-25450 (URN)10.1007/s10586-023-04145-0 (DOI)001196291000001 ()2-s2.0-85172016469 (Scopus ID)
Available from: 2023-10-10 Created: 2023-10-10 Last updated: 2024-06-12Bibliographically approved
Wassie, G., Ding, J. & Wondie, Y. (2024). Detecting and Predicting Models for QoS Optimization in SDN. Journal of Computer Networks and Communications, 2024, Article ID 3073388.
Open this publication in new window or tab >>Detecting and Predicting Models for QoS Optimization in SDN
2024 (English)In: Journal of Computer Networks and Communications, ISSN 2090-7141, E-ISSN 2090-715X, Vol. 2024, article id 3073388Article in journal (Refereed) Published
Abstract [en]

Recently, deep learning algorithms and software-defined networking technologies enabled traffic management to be more controllable in IP networking and mobile Internet to yield quality services to subscribers. Quality of service (QoS) needs more effort to optimize QoS performance. More specifically, elephant flow management is a critical task that needs further research since its heavy hit behavior leads to high CPU utilization, packet drops, high latency, packet overflow, and network congestion problems. For this purpose, we focused on elephant flow management since elephant flows are big flows that hinder good service delivery (QoS) on demand. Hence, elephant flow detection and early prediction techniques optimize QoS. In this regard, we employed DNN and CNN deep learning models to detect elephant flows, and the random forest model predicts elephant flows in the SDN. As a result of our experiments, the findings reveal that deep learning algorithms within the Ryu controller significantly outperform in detecting and predicting performance in order to yield good network throughput. This solution proves to be significant for QoS optimization in data centers.

Place, publisher, year, edition, pages
John Wiley & Sons, 2024
Keywords
deep learning, elephant flows, QoS, SDN
National Category
Computer Sciences Communication Systems
Identifiers
urn:nbn:se:bth-27152 (URN)10.1155/2024/3073388 (DOI)001350799600001 ()
Available from: 2024-11-25 Created: 2024-11-25 Last updated: 2024-11-25Bibliographically approved
Lin, Y., Chen, L., Ali, A., Nugent, C., Cleland, I., Li, R., . . . Ning, H. (2024). Human digital twin: a survey. Journal of Cloud Computing: Advances, Systems and Applications, 13(1), Article ID 131.
Open this publication in new window or tab >>Human digital twin: a survey
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2024 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 13, no 1, article id 131Article, review/survey (Refereed) Published
Abstract [en]

The concept of the Human Digital Twin (HDT) has recently emerged as a new research area within the domain of digital twin technology. HDT refers to the replica of a physical-world human in the digital world. Currently, research on HDT is still in its early stages, with a lack of comprehensive and in-depth analysis from the perspectives of universal frameworks, core technologies, and applications. Therefore, this paper conducts an extensive literature review on HDT research, analyzing the underlying technologies and establishing typical frameworks in which the core HDT functions or components are organized. Based on the findings from the aforementioned work, the paper proposes a generic architecture for the HDT system and describes the core function blocks and corresponding technologies. Subsequently, the paper presents the state of the art of HDT technologies and their applications in the healthcare, industry, and daily life domains. Finally, the paper discusses various issues related to the development of HDT and points out the trends and challenges of future HDT research and development.

Place, publisher, year, edition, pages
Springer, 2024
Keywords
Human digital twin, Human modeling technology, Generic architecture, Digital twin
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:bth-26844 (URN)10.1186/s13677-024-00691-z (DOI)001291233300002 ()2-s2.0-85201372363 (Scopus ID)
Available from: 2024-08-27 Created: 2024-08-27 Last updated: 2024-08-30Bibliographically approved
Jiang, Y., Wang, W., Ding, J., Lu, X. & Jing, Y. (2024). Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber–Physical Production Systems. Future Internet, 16(4), Article ID 134.
Open this publication in new window or tab >>Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber–Physical Production Systems
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2024 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 16, no 4, article id 134Article in journal (Refereed) Published
Abstract [en]

The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology. © 2024 by the authors.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
asset visibility, cybersecurity, cyber–physical system (CPS), dependence analysis, digital twin (DT), manufacturing system, mitigation prioritization, Network security, Visibility, Cybe-physical systems, Cyber physicals, Cyber security, Cyber-physical systems, Cybe–physical system, Digital twin, Prioritization
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-26191 (URN)10.3390/fi16040134 (DOI)001210241000001 ()2-s2.0-85191387617 (Scopus ID)
Funder
EU, Horizon 2020Knowledge Foundation
Available from: 2024-05-15 Created: 2024-05-15 Last updated: 2024-05-24Bibliographically approved
Sarwatt, D. S., Lin, Y., Ding, J., Sun, Y. & Ning, H. (2024). Metaverse for Intelligent Transportation Systems (ITS): A Comprehensive Review of Technologies, Applications, Implications, Challenges and Future Directions. IEEE transactions on intelligent transportation systems (Print), 25(7), 6290-6308
Open this publication in new window or tab >>Metaverse for Intelligent Transportation Systems (ITS): A Comprehensive Review of Technologies, Applications, Implications, Challenges and Future Directions
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2024 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 25, no 7, p. 6290-6308Article, review/survey (Refereed) Published
Abstract [en]

Intelligent transportation systems (ITS) have made significant advancements in enhancing transportation safety, reliability, and efficiency. However, challenges persist in security, privacy, data management, and integration. Metaverse, an emerging technology enabling immersive and simulated experiences, presents promising solutions to overcome these challenges. By establishing secure communication channels, facilitating virtual simulations for safe testing and training, and enabling centralized data management with real-time analytics, metaverse offers a transformative approach to address these challenges. While metaverse has found extensive applications across industries, its potential in transportation remains largely untapped. This comprehensive review delves into the integration of the metaverse in ITS, exploring key technologies like virtual reality, digital twin, blockchain, and artificial intelligence, and their specific applications in the context of ITS. Real-world case studies, research projects, and initiatives are compiled to showcase the metaverse’s potential for ITS. It also examines the societal, economic, and technological implications of metaverse integration in ITS and highlights the associated integration challenges. Lastly, future research directions are identified to unlock the metaverse’s full potential in enhancing transportation systems. IEEE

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Digital twins, Fundamental metaverse technologies, Industries, intelligent transportation system, Metaverse, metaverse integration, Training, Transportation, Virtual reality, X reality, E-learning, Information management, Integration, Intelligent systems, Intelligent vehicle highway systems, Virtual addresses, Fundamental metaverse technology, Intelligent transportation systems, Metaverses, Review of technologies, Technology application
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-25941 (URN)10.1109/TITS.2023.3347280 (DOI)001166580300001 ()2-s2.0-85182923382 (Scopus ID)
Available from: 2024-02-02 Created: 2024-02-02 Last updated: 2024-08-05Bibliographically approved
Projects
CPS-based resilience for critical infrastructure protection [2019-05020_Vinnova]; University of Skövde
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8927-0968

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