Fluid antenna multiple access (FAMA) exploits spatial opportunities in wireless channels through port switching to overcome multiuser interference, achieving better performance than traditional fixed MIMO systems. Int...
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The rising use of IoT devices in Public Health informationsystems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersec...
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ISBN:
(数字)9798331536336
ISBN:
(纸本)9798331536343
The rising use of IoT devices in Public Health informationsystems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats.
Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inher...
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The Ethereum blockchain and its ERC20 token standard have revolutionized the landscape of digital assets and decentralized applications. ERC20 tokens developed on the Ethereum blockchain have gained significant attent...
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A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large ex...
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The sixth-generation (6G) network is expected to achieve global coverage based on the space-air-ground integrated network, and the latest satellite network will play an important role in it. The introduction of inter-...
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Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve training efficiency, end-to-end meth...
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This paper mainly studies the parking lot counting system, which is a modern and new system integrating detection and conversion technology, computer technology, information processing, digital technology and other te...
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Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts. However, the current research on video attention generally foc...
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This paper reports a bionic silk-based neural probe implantation method which inspired by the anatomy of the mouth parts of the tea wing bug. Remarkably, compared to the latest polymer-coated reinforcement, the implan...
This paper reports a bionic silk-based neural probe implantation method which inspired by the anatomy of the mouth parts of the tea wing bug. Remarkably, compared to the latest polymer-coated reinforcement, the implanted footprint area was reduced by 2.17 times [1] to only 3450 μm 2 . In terms of structural design, we incorporated a Tshaped structure, effectively doubling the yield strength within the same area; Furthermore, through the utilization of riboflavin-ultraviolet crosslinking, we significantly enhanced the Young's modulus of the protein reinforcement layer to approximately ~2.77 GPa, representing an impressive 20~30 times increase compared to conventional polymer like PEG. In addition, the protein was regulated to dissolve after 120 s, which ensured the strength of probe during the deep brain implantation and eliminated the presence of foreign bodies in tissues after the implantation, which further reduced inflammation. Moreover, the riboflavin-ultraviolet cross-linked protein matrix offers distinct advantages with controllable degradation. It retains the ability to carry active factors and drugs, enabling concurrent disease treatment while collecting neural signals.
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