Deep Learning Radial Basis Function Neural Networks Based Automatic Detection of Diabetic Retinopathy

James, F Scholl; Priya, M Deva

doi:10.2139/ssrn.3531619

Cited by 2 publications

(2 citation statements)

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“…Equation Formula (12) indicates that the occupied channel bandwidth cannot exceed the total channel bandwidth. Equation Equation (13) indicates that the signal-to-noise ratio of a node must be higher than the threshold so as to ensure the transmission rate.…”

Section: System Model and Optimization Modelmentioning

confidence: 99%

“…Machine learning uses algorithms to analyze and learn from data to make decisions and predictions about real-world events. Among them, deep learning is the most popular machine learning method at present, which has been well applied in automatic detection [13,14], case recognition [15][16][17], environmental monitoring [18], and epidemic prediction [19], etc. In terms of channel allocation, with the rapid growth of network size and data volume, deep learning can significantly improve the processing speed for a large number of nodes [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Channel Allocation for Age of Information Optimization in Internet of Medical Things

Wei

Zhang

Wang

2021

Wireless Communications and Mobile Computing

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Along with the development of realtime applications, the freshness of information becomes significant because the overdue information is worthless and useless and even harmful to the right judgement of system. Therefore, The Age of Information (AoI) used for marking the freshness of information is proposed. In Internet of Medical Things (IoMT), which is derived from the requirement of Internet of Thins (IoT) in medicine, high freshness of medical information should be guaranteed. In this paper, we introduce the AoI of medical information when allocating channels for users in IoMT. Due to the advantages of Deep Q-learning Network (DQN) applied in resource management in wireless network, we propose a novel DQN-based Channel Allocation (DQCA) algorithm to provide the strategy for channel allocation under the optimization of the system cost considering the AoI and energy consumption of coordinator nodes. Unlike the traditional centralized channel allocation methods, the DQCA algorithm is distributed as each user performs the DQN process separately. The simulation results show that our proposed DQCA algorithm is superior to Greedy algorithm and Q-learning algorithm in terms of the average AoI, average energy consumption and system cost.

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Section: System Model and Optimization Modelmentioning

confidence: 99%