2020
DOI: 10.1109/access.2020.2967075
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Medical Multimedia Big Data Analysis Modeling Based on DBN Algorithm

Abstract: With the development of medical multimedia analysis methods based on DBN, DBN models have gained the ability to surpass medical experts in the evaluation of multimedia in some clinical examinations. Firstly, based on the existing architecture of the Internet of Things, combined with the actual characteristics of the hospital, the medical multimedia data is accessed from the IoT support platform. Secondly, the medical multimedia data modeling and classification method based on DBN is studied and analyzed. Three… Show more

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Cited by 24 publications
(7 citation statements)
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“…e sampling carrier frequency time width of FBG sensor network information transmission risk is 1.25 ms, the normalized initial frequency of risk prediction is f 11 � 0.05, f 12 � 0.15, the number of sampling points of risk information data is N � 120, and the variation range of interference signal-to-noise ratio is −12∼12 dB. According to the above simulation environment and parameter settings, other algorithms and this algorithm are selected to collect the risk data of wireless sensor networks, respectively, and take the collected data as the test object to simulate the dynamic prediction of information transmission risk of high reliability fiber Bragg grating sensor networks [24,25], and the comparison of time cost of information transmission risk prediction of high reliability fiber Bragg grating sensor network is shown in Figure 3.…”
Section: Experimental Analysismentioning
confidence: 99%
“…e sampling carrier frequency time width of FBG sensor network information transmission risk is 1.25 ms, the normalized initial frequency of risk prediction is f 11 � 0.05, f 12 � 0.15, the number of sampling points of risk information data is N � 120, and the variation range of interference signal-to-noise ratio is −12∼12 dB. According to the above simulation environment and parameter settings, other algorithms and this algorithm are selected to collect the risk data of wireless sensor networks, respectively, and take the collected data as the test object to simulate the dynamic prediction of information transmission risk of high reliability fiber Bragg grating sensor networks [24,25], and the comparison of time cost of information transmission risk prediction of high reliability fiber Bragg grating sensor network is shown in Figure 3.…”
Section: Experimental Analysismentioning
confidence: 99%
“…In light of the aforementioned, the danger information from wireless sensor networks is used in this method as well as other algorithms, which are selected for the simulation environment and parameter settings. high-performance fiber the dynamic prediction of information transmission risk was modelled using Bragg grating sensor networks [23,24] (Figure3). High-reliability fib grating sensor networks' information transmission time and risk assessment are also assessed.…”
Section: Resultsmentioning
confidence: 99%
“…DBN is a generative model. By training the weights between its neurons, the entire neural network will generate training data based on the maximum probability [22]. The model is based on the restricted Boltzmann machine (RBM) and a multihidden layer neural network which is composed of multiple RBM stacks.…”
Section: Fault Location Methodsmentioning
confidence: 99%