| 1. |
- Alam, Mahbub Ul, et al.
(författare)
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COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning
- 2023
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Ingår i: 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). ; , s. 646-653
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Konferensbidrag (refereegranskat)abstract
- COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive, and can be maintained remotely. This work explores the COVID-19 detection issue using the thermal image and associated tabular medical data obtained from a publicly available dataset. We incorporate a multi-modal machine learning approach where we investigate the different combinations of medical and data type modalities to get an improved result. We use different machine learning and deep learning methods, namely random forests, Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN). Overall multi-modal results outperform any single modalities, and it is observed that the thermal image is a crucial factor in achieving it. XGBoost provided the best result with the area under the receiver operating characteristic curve (AUROC) score of 0.91 and the area under the precision-recall curve (AUPRC) score of 0.81. We also report the average of leave-one-positive-instance-out cross- validation evaluation scores. This average score is consistent with the test evaluation score for random forests and XGBoost methods. Our results suggest that utilizing thermal image with associated tabular medical data could be a viable option to detect COVID-19, and it should be explored further to create and test a real-time, secure, private, and remote COVID-19 detection application in the future.
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| 2. |
- Alam, Mahbub Ul, et al.
(författare)
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FedSepsis : A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices
- 2023
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 23:2
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Tidskriftsartikel (refereegranskat)abstract
- The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision–recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision–recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further.
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| 3. |
- Chen, Ruoyu, et al.
(författare)
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Attribute-based Encryption with Flexible Revocation for IoV
- 2023
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Ingår i: Procedia Computer Science. - : Elsevier. ; , s. 131-138
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Konferensbidrag (refereegranskat)abstract
- Attribute-based encryption (ABE) has been used to provide data confidentiality and fine-grained access control in the Internet of Vehicles (IoV). However, the attributes of vehicles in IoV might change frequently due to the movements of vehicles. Thus, the invalid attributes need to be revoked in time and efficiently to ensure the security of the system. In this paper, we propose a data-sharing scheme based on ABE for IoV. By using a binary tree and attribute version keys, flexible revocation can be achieved for IoV. Moreover, the ciphertext can be stored on clouds, and the distribution and revocation of attribute keys can be realized by distributed attribute authorities. We performed the security analysis and proved the security of the proposed scheme. The results showed that the proposed scheme has lower average computing overhead in terms of attribute revocations compared with other schemes based on ABE, and can satisfy the performance requirement of data sharing for IoV.
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| 4. |
- Firouzi, Ramin, et al.
(författare)
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5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems
- 2023
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.
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| 5. |
- Li, Yuhong, et al.
(författare)
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Secure Data Sharing in Internet of Vehicles based on Blockchain and Attribute-based Encryption
- 2023
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Ingår i: 2023 IEEE International Conference on Smart Internet of Things (SmartIoT). - : IEEE conference proceedings. - 9798350316575 ; , s. 56-63
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Sharing data among vehicles is one of the most important ways to provide safety-related and value-added services to connected vehicles. Nevertheless, access to the shared data must be controlled to prevent the exposure of users' privacy and data leakage or corruption. Attribute-based encryption (ABE) can provide data confidentiality and fine-grained access control. However, the complex and dynamic driving environment of vehicles may cause the attributes of vehicles to change frequently, and thus put a huge burden on the attribute management of the system or degrade the security of the system. In this paper, we propose a secure data sharing method by using ABE and blockchain for Internet of Vehicles. By using ABE, the data owner can stipulate the policy of the data access control based on the attributes of vehicles. The trusted authority is replaced by blockchain, which reduces the burden and solved the problem of single point failure of the trusted authority and increases the transparency of the whole system. An adaptive attribute revocation method is used to balance the revocation time and system cost. Moreover, the shared data are stored in a distributed Inter-Planetary File System (IPFS) to improve the efficiency and security of the data sharing system. The test results show that the proposed method can well satisfy the performance requirement of secure data sharing for Internet of Vehicles.
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| 6. |
- Rahmani Chianeh, Rahim, et al.
(författare)
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Cognitive Controller for 6G-Enabled Edge Autonomic
- 2023
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Ingår i: Procedia Computer Science. - 1877-0509. ; 220, s. 71-77
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Tidskriftsartikel (refereegranskat)abstract
- This article proposes a new Artificial Intelligent (AI) and Machine Learning (ML) based framework for 6G-enabled Intelligent edge computing. The framework will be equipped with multiple cognitive controllers to harmoniously control various aspects in distributed intelligence toward edge nodes collaboration. Autonomic cognitive controller for edge computing is a popular computing paradigm where the distributed metadata processing and edge intelligence are performed at edge node in 5G/6G network for management, connectivity and interoperability. Some of studies focused on edge management improvement such as reduce the response time and bandwidth costs. However, the previous approaches are inadequate to support autonomously management for large-scale deployment for connectivity for dynamic and reliable communication. We propose a cognitive controller for edge autonomy and collaboration application development. Finally, we discuss challenges and open issues toward cognitive controller and distributed edge intelligence.
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| 7. |
- Sadique, Kazi Masum, et al.
(författare)
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DIdM-EIoTD : Distributed Identity Management for Edge Internet of Things (IoT) Devices
- 2023
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Ingår i: Sensors. - 1424-8220. ; 23:8
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Tidskriftsartikel (refereegranskat)abstract
- The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices’ heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible. As a result, identity management for IoT devices is still an open issue. Distributed Ledger Technology (DLT) and blockchain-based security solutions are becoming popular in different application domains. This paper presents a novel DLT-based distributed identity management architecture for edge IoT devices. The model can be adapted with any IoT solution for secure and trustworthy communication between devices. We have comprehensively reviewed popular consensus mechanisms used in DLT implementations and their connection to IoT research, specifically identity management for Edge IoT devices. Our proposed location-based identity management model is generic, distributed, and decentralized. The proposed model is verified using the Scyther formal verification tool for security performance measurement. SPIN model checker is employed for different state verification of our proposed model. The open-source simulation tool FobSim is used for fog and edge/user layer DTL deployment performance analysis. The results and discussion section represents how our proposed decentralized identity management solution should enhance user data privacy and secure and trustworthy communication in IoT.
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