Structured Medical Pathology Data Hiding Information Association Mining Algorithm Based on Optimized Convolutional Neural Network

Li, Xiaofeng; Wang, Yanwei; Liu, Gang

doi:10.1109/access.2019.2960456

Cited by 26 publications

(23 citation statements)

References 33 publications

(67 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It integrates functions with cloud-based SaaS applications, has a suitable information security mechanism, and provides security control in a private network or Internet environment and even personal real-time monitoring. Through location detection, alarm connection, task indication, project management, remote control, safety protection, remote maintenance, online upgrades, statistical reports, decision support, leadership, and other management service functions to achieve comprehensive management, control, and application of safety and environmental protection, we will use convolutional neural networks to improve IoT RFID wireless sensing technology and capabilities [ 3 ].…”

Section: Clinical Research Methods Based On the Internet Of Thingsmentioning

confidence: 99%

See 1 more Smart Citation

Clinical Study on Electronic Medical Neuroelectric Stimulation Based on the Internet of Things to Treat Epilepsy Patients with Anxiety and Depression

Zhang¹,

Wang²,

Wang³

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

With the continuous development and improvement of the level of medical technology in our country in recent years, the treatment of epilepsy has been constantly updated and developed. Nerve electrical stimulation is considered to be a very effective method for treating epilepsy with anxiety and depression. There are many traditional methods for the treatment of epilepsy. For example, vagus nerve stimulation (VNS) has been applied earlier, and the therapeutic effect has been confirmed, but it will cause serious complications and is easier to be uncomfortable; deep brain stimulation for epilepsy is still in the immature stage, and there is no final conclusion. Therefore, this article proposes a clinical study on the treatment of patients with epilepsy with anxiety and depression based on the electronic medical nerve stimulation of the Internet of Things. First of all, this article uses the literature method to study the causes of epilepsy and previous treatment methods. Then, we designed an experimental study of epilepsy with depression based on the Internet of Things electronic medical neuroelectric stimulation therapy and selected the core quality of life questionnaire, SDS, and SAS as observation indicators. Finally, the comparison of epilepsy symptoms and depression and anxiety between the control group and the observation group before and after treatment was analyzed. The results of the experiment showed that, among the 50 subjects in the study, the observation group that used electrical nerve stimulation therapy had 5 people who stopped seizures after treatment, accounting for 10%, while in the control group of traditional drug treatment methods, after treatment, only one person stopped the seizure, accounting for 2%. In addition, the SAS and SDS scores of the observation group were also lower than those of the control group. Therefore, the use of nerve electrical stimulation to treat epilepsy with anxiety and depression symptoms has better performance and can help patients recover as soon as possible.

show abstract

Section: Clinical Research Methods Based On the Internet Of Thingsmentioning

confidence: 99%

“…The results show that, compared with the control group, patients with epilepsy had higher levels of anxiety and depression symptoms. Patients also tended to use neurotic defense mechanisms, which may be related to poor emotional regulation [ 3 ]. Patients with epilepsy after cognitive stimulation had lower electrical skin activity.…”

Section: Introductionmentioning

confidence: 99%

Clinical Study on Electronic Medical Neuroelectric Stimulation Based on the Internet of Things to Treat Epilepsy Patients with Anxiety and Depression

Zhang¹,

Wang²,

Wang³

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…First, 93% of the subsequences decomposed by EEMD are used as the training group, and the remaining 7% of the data are used as the test group, and the training data are respectively normalized. Then, set the number of hidden layers of the model to 8, and set the input and output layers to 3 [38]. Finally, the number of training samples in each batch is set to 40, and the time step is set to 15.…”

Section: Data Trainingmentioning

confidence: 99%

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

2024

View full text Add to dashboard Cite

Yield prediction has always been the focus of stock investors' attention in stock investment. This research uses the virtual stock scene constructed by VR technology to explore the mathematical model of return rate prediction. First, neural nodes, activation functions and output functions are designed under the LSTM framework, and a recurrent neural network that can handle long-term sequences is constructed. Use VR technology to simulate stock data and extract key frames from the image sequence. Aiming at the timing of key curve nodes, this study uses a long and short-term memory neural network that is good at processing time series data to classify curve trends, and realizes the identification of abnormal fluctuations in virtual scenes of stock images. Then, according to the existing probability control asset allocation theory, the index of investment allocation is adjusted from the entire time series to a dynamic time series. According to the stock price decomposition of EEMD, the EEMD-LSTM model is constructed to predict the stock market price. Use EEMD to decompose the original sequence to obtain IMF and trend items containing various timescale characteristics. Then, the LSTM neural network is used to predict and analyze each rate of return, and the analysis software Python is used to process and count the predicted results. At the same time, retrospective restrictions are regarded as riskier assets. Finally, the investment is made under conditions that do not allow shorting in the VR simulation scenario. The accuracy of the calculation is more than 70% except for the Shanghai and Shenzhen 300 Index reaching 66%. The research results show that in the case of different stock price forecasts, the prediction ability of LSTM neural network has been greatly improved compared with that of support vector machine, which fully reflects the advantages of LSTM in time series prediction.

show abstract

“…In order to show the performance of the convolution neural network coordinate conversion algorithm studied in this paper [43], the traditional method, BP neural network and convolution neural network are used to transform GPS coordinates, and the results are shown in Table 3. According to Table 3, the accuracy of GPS coordinate transformation using convolutional neural network studied in this paper is up to 85%.…”

Section: Figure 9 Error Convergence Curvementioning

confidence: 99%

Retracted: Analysis and Research of Artificial Intelligence Algorithms in GPS Data

Luo²,

Pan

et al. 2024

IEEE Access

View full text Add to dashboard Cite

With the development of GPS technology, it has been gradually applied in engineering, science and technology and daily life. The requirement of positioning accuracy in these fields is also increasing. Therefore, this paper studies GPS coordinate conversion and positioning technology from the aspect of artificial intelligence algorithm to improve positioning accuracy. Aiming at the problem of coordinate transformation, a method of GPS coordinate transformation based on convolution neural network model is proposed. Firstly, the input GPS raw data is converted into unstructured images, then the model is used to learn the features of the data, and finally the converted coordinate data is output. Aiming at the problem that the traditional vehicle GPS navigation system is easy to be interfered by the external environment and is not conducive to the system tracking and positioning, AdaBoost algorithm is introduced into the navigation system to better solve the problem. GPS lock. The strong classifier can be obtained by iterative practice, and the filter value can be measured accurately during GPS interference. At the same time, the combination of Adaboost method and BP algorithm can help the filter process information and avoid information loss caused by GPS interference. In this way, not only the integrity of information can be guaranteed, but also the stability and accuracy of the system can be guaranteed. The results show that the coordinate transformation method based on convolution neural network has better conversion accuracy than the traditional method, and the navigation method based on Adaboost algorithm can improve the navigation accuracy and optimize the system performance.

show abstract

Structured Medical Pathology Data Hiding Information Association Mining Algorithm Based on Optimized Convolutional Neural Network

Cited by 26 publications

References 33 publications

Clinical Study on Electronic Medical Neuroelectric Stimulation Based on the Internet of Things to Treat Epilepsy Patients with Anxiety and Depression

Clinical Study on Electronic Medical Neuroelectric Stimulation Based on the Internet of Things to Treat Epilepsy Patients with Anxiety and Depression

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

Retracted: Analysis and Research of Artificial Intelligence Algorithms in GPS Data

Contact Info

Product

Resources

About