Collaborative APIs recommendation for Artificial Intelligence of Things with information fusion

Xu, Yueshen; Wu, Yinchen; Gao, Honghao; Song, Shaoxu; Yin, Yuyu; Xiao, Xichu

doi:10.1016/j.future.2021.07.004

Cited by 45 publications

(16 citation statements)

References 30 publications

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“…Information fusion technology is a new science and technology produced by interdisciplinary integration. With the vigorous development of computer technology, the research on information fusion technology has also been further developed [ 1 – 4 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Multimodal Fusion Technology in Image Analysis of Pretreatment Examination of Patients with Spinal Injury

Guo-cheng

Zhang

et al. 2022

Journal of Healthcare Engineering

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As one of the most common imaging screening techniques for spinal injuries, MRI is of great significance for the pretreatment examination of patients with spinal injuries. With rapid iterative update of imaging technology, imaging techniques such as diffusion weighted magnetic resonance imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy are frequently used in the clinical diagnosis of spinal injuries. Multimodal medical image fusion technology can obtain richer lesion information by combining medical images in multiple modalities. Aiming at the two modalities of DCE-MRI and DWI images under MRI images of spinal injuries, by fusing the image data under the two modalities, more abundant lesion information can be obtained to diagnose spinal injuries. The research content includes the following: (1) A registration study based on DCE-MRI and DWI image data. To improve registration accuracy, a registration method is used, and VGG-16 network structure is selected as the basic registration network structure. An iterative VGG-16 network framework is proposed to realize the registration of DWI and DCE-MRI images. The experimental results show that the iterative VGG-16 network structure is more suitable for the registration of DWI and DCE-MRI image data. (2) Based on the fusion research of DCE-MRI and DWI image data. For the registered DCE-MRI and DWI images, this paper uses a fusion method combining feature level and decision level to classify spine images. The simple classifier decision tree, SVM, and KNN were used to predict the damage diagnosis classification of DCE-MRI and DWI images, respectively. By comparing and analyzing the classification results of the experiments, the performance of multimodal image fusion in the auxiliary diagnosis of spinal injuries was evaluated.

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Section: Introductionmentioning

confidence: 99%

Application of Multimodal Fusion Technology in Image Analysis of Pretreatment Examination of Patients with Spinal Injury

Guo-cheng

Zhang

et al. 2022

Journal of Healthcare Engineering

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“…Meanwhile, the minimum distance movement path and the numbers of allocated time slices of the other meshes are updated. Although the payoff of time slice in the mesh may appear similar to the 'jitter' situation of mesh C rather than monotonously decreasing as the number increases, its overall trend inevitably decreases according to property of the payoff function (2). In order to avoid the influence of path update, the recall process always restarts from the recharged mesh with the lowest payoff in updated OCM if any recharged mesh is recalled.…”

Section: Time Slice Allocation Layer Solutionmentioning

confidence: 99%

“…The construction of Internet of Things (IoT) requires the support of sensing equipment, where stable power supply is requisite [1,2]. But given the extremely high wiring cost in some application scenarios, it is not suitable to provide main power for all sensors [3].…”

Section: Introductionmentioning

confidence: 99%

Payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger in IoT

Guo

et al. 2021

IET Communications

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In order to maximize the work efficiency of wireless mobile charger, a payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh structure and multi‐node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade‐off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.

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“…Among them, access control technology has become a hotspot of current research, but it mainly targets at the field of operating system, and there are not many researches in the information field, especially the research on the security and privacy protection of medical Big Data. Inspired by previous studies [4][5][6], this paper proposes a medical Big Data privacy protection model based on risk-adaptive access control. e main contributions are as follows:…”

Section: Introductionmentioning

confidence: 99%

Healthcare Big Data Privacy Protection Model Based on Risk-Adaptive Access Control

Jiang

Han

Shi

et al. 2022

Security and Communication Networks

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Edge computing is playing an increasingly important role in the field of health care. Edge computing provides high-quality personalized services to patients based on user and device data information. However, edge nodes will collect a large amount of sensitive patient information, and patients will also bear the risk of privacy disclosure while enjoying personalized services. How to reduce the risk of privacy disclosure while ensuring that patients enjoy personalized services brought by edge computing is the research content of this paper. In this paper, the work flow and management mode of Hospital Information System (HIS) are investigated on the spot, and the risk-adaptive access control model based on entropy is established. First, we use International Classification of Diseases, Tenth Revision (ICD-10) to mark the information resources accessed by users and use information entropy to measure the correlation “α” between medical information accessed by users and work tasks. Finally, we analyze the relationship between correlation “α” and risk through an example. The results show that users with high correlation α have low risk of access behavior, and users with low risk have high correlation α of access information resources and work goals. This discovery can help managers predict users’ access behavior in the Big Data environment, so as to dynamically formulate access control policies according to the actual access situation of users and then realize the privacy protection of medical big health data.

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Collaborative APIs recommendation for Artificial Intelligence of Things with information fusion

Cited by 45 publications

References 30 publications

Application of Multimodal Fusion Technology in Image Analysis of Pretreatment Examination of Patients with Spinal Injury

Application of Multimodal Fusion Technology in Image Analysis of Pretreatment Examination of Patients with Spinal Injury

Payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger in IoT

Healthcare Big Data Privacy Protection Model Based on Risk-Adaptive Access Control

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