Wireless Channel Scene Recognition Method Based on an Autocorrelation Function and Deep Learning

Ning, Shuguang; He, Yigang; Liu, Yuan; Yuan, Hongxing; Wang, Shudong; Cheng, Tongtong; Sui, Yongbo

doi:10.1109/access.2020.3044167

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…is method has complex associations with medical inquiry information and infers hidden features [34,35]. It is possible to learn disease physiological detection information to improve the accuracy of disease diagnosis.…”

Section: Based On Rbm Model Testmentioning

confidence: 99%

The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method

Lü

Zhao

et al. 2022

International Transactions on Electrical Energy Systems

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In recent years, the clinical decision support system (CDSS) has been gradually improved, which effectively reduces the probability of doctors’ misdiagnosis or missed diagnosis. Therefore, the clinical decision support system has always been a research hotspot, deep learning and collaborative filtering technologies are developing rapidly, and more and more are applied to different fields. Based on the deep learning technology, this paper conducts in-depth research on the methods of assisted diagnosis of clinical diseases and prediction of clinical high-risk diseases in the field of CDSS. Aiming at the problem of clinical decision support system, this article analyzes the deep learning identification method in depth and is committed to applying machine deep learning to clinical decision-making, changing the lack of information and its challenges to clinical decision-making. Based on previous studies, two unsupervised learning methods based on machine learning are proposed, namely user collaborative filtering and RBM, to improve CDSS. The experimental results show that the overall performance of the RBM-based method is the best. When the missing degree of the two data sets is 30.6%, the classification accuracy rate is still more than 92.8%.

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Section: Based On Rbm Model Testmentioning

confidence: 99%

The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method

Lü

Zhao

et al. 2022

International Transactions on Electrical Energy Systems

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“…However, the experimental results showed that the input value of the restricted Boltzmann machine could only receive the approximate value to prevent over-fitting, and there was a certain deviation in the results. Ning et al ( 2020 ) introduced the CD-k (k-step contrastive divergence) algorithm to optimize the traditional DBN network, thereby effectively calculating the logarithmic gradient of the Boltzmann machine. Here, experimental results show that the classification accuracy of the proposed method is higher than that of several existing technologies.…”

Section: Discussionmentioning

confidence: 99%

Piano Education of Children Using Musical Instrument Recognition and Deep Learning Technologies Under the Educational Psychology

2021

Front. Psychol.

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The objective of the study was to enhance quality education in the traditional pre-school piano education. Deep Learning (DL) technology is applied to piano education of children to improve their interest in learning music. Firstly, the problems of the traditional piano education of children were analyzed with the teaching patterns discussed under educational psychology, and a targeted music education plan was established. Secondly, musical instrument recognition technology was introduced, and the musical instrument recognition model was implemented based on DL. Thirdly, the proposed model was applied to the piano education of children to guide the music learning of students and improve their interest in piano learning. The feature recognition and acquisition of the proposed model were improved. Finally, the different teaching patterns were comparatively analyzed through the Questionnaire Survey (QS). The experimental results showed that the instrument recognition accuracy of Hybrid Neural Network (HNN) is 97.2%, and with the increase of iterations, the recognition error rate of the model decreases and stabilizes. Therefore, the proposed HNN based on DL for musical instrument recognition can accurately identify musical features. The QS results showed that the introduction of musical instrument recognition technology in the piano education of children can improve their interest in piano learning. Therefore, the establishment of the piano education patterns based on the piano education model can improve the effectiveness of teaching piano to students. This research provides a reference for the intelligentization of children's piano education.

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“…In recent years, neural networks (NNs) have been widely used in a series of challenging fields such as image recognition [1], computer vision [2,3] and large-scale data processing [4,5]. Among them, deep learning (DL) has performed particularly well.…”

Section: Introductionmentioning

confidence: 99%

Greedy Broad Learning System

et al. 2021

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In order to overcome the extremely time-consuming drawback of deep learning (DL), broad learning system (BLS) was proposed as an alternative method. This model is simple, fast, and easy to update. To ensure the fitting and generalization ability of BLS, the hidden layer neurons are often set too many, in fact, a lot of neurons are not needed. Greedy BLS (GBLS) is proposed in this paper to deal with the redundancy of the hidden layer in BLS from another perspective. Different from BLS, the structure of GBLS can be seen as a combination of unsupervised multi-layer feature representation and supervised classification or regression. It trains with a greedy learning scheme, performs principal component analysis (PCA) on the previous hidden layer to form a set of compressed nodes, which are transformed into enhancement nodes and then activated by nonlinear functions. The new hidden layer is composed of all newly generated compressed nodes and enhancement nodes, and so on. The last hidden layer of the network contains the higher-order and abstract essential features of the original data, which is connected to the output layer. Each time a new layer is added to the model, and there is no need to retrain from the beginning, only the previous layer is trained. Experimental results demonstrate that the proposed GBLS model outperforms BLS both in classification and regression.

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Wireless Channel Scene Recognition Method Based on an Autocorrelation Function and Deep Learning

Cited by 7 publications

References 37 publications

The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method

The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method

Piano Education of Children Using Musical Instrument Recognition and Deep Learning Technologies Under the Educational Psychology

Greedy Broad Learning System

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