Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks

Mohanty, Sachi Nandan; Lydia, E. Laxmi; Elhoseny, Mohamed; Otaibi, Majid M. Gethami Al; Shankar, K.

doi:10.1016/j.phycom.2020.101097

Cited by 104 publications

(39 citation statements)

References 8 publications

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“…e process of data mining starts from receiving and inputting the original data [9], screening important data items, reducing dimension and concentrating data set, noise reduction, and standardizing data and other preprocessing steps, and then carries out multidimensional analysis, pattern recognition, model evaluation, difference significance analysis, and other work on the data to complete the transmission process of the original data from data to information, and then to knowledge [10,11]. Because the value of y can only be 0 and 1, the loss function is constructed as follows:…”

Section: Electronic Medical Record Data Drive and Prediction Of Gestamentioning

confidence: 99%

Prediction Method of Gestational Diabetes Based on Electronic Medical Record Data

Liu

Sun

2021

Journal of Healthcare Engineering

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At present, the secondary application of electronic medical records is focused on auxiliary medical diagnosis to improve the accuracy of clinical diagnosis. The main research in this article is the prediction method of gestational diabetes based on electronic medical record data. In the original data, the ID number of the medical examiner did not match the medical examination record. In order to ensure the accuracy of the data, this part of the record was removed. First, the preparation stage before building the model is to determine the baseline accuracy of the original data, test the effectiveness of the machine learning algorithm, and then balance the target data set to solve the bias caused by the imbalance between data classes and the illusion of excessive model prediction results. Then, the disease prediction model is constructed by dividing the data set, selecting parameters and algorithms, and visualizing the model. Finally, the effect of predictive model construction is comprehensively judged based on multiple evaluation indicators and control experimental models. In this paper, the RF model can be used to rank the importance of the feature importance of the output feature on the importance of the classification result of the input feature. In order to test the accuracy of regression prediction, the experiment uses absolute mean error and root mean square error to evaluate the accuracy of fasting blood glucose prediction. A logistic regression model is constructed through the training set, and the test set data are brought into the prediction model for prediction. Experimental data show that when the features filtered by WBFS are used, the accuracy, F1 value, and AUC value of logistic regression are 0.809, 0.881, and 0.825, respectively, which is an increase of about 12% compared with when the feature is not used. The results show that the electronic medical record data drive can effectively improve the accuracy of predicting gestational diabetes.

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Section: Electronic Medical Record Data Drive and Prediction Of Gestamentioning

confidence: 99%

Prediction Method of Gestational Diabetes Based on Electronic Medical Record Data

Liu

Sun

2021

Journal of Healthcare Engineering

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“…A relatively simple method is the distribution variance of images. The distribution variance of the image can be calculated by formula (11).…”

Section: Identity Card Character Feature Extractionmentioning

confidence: 99%

“…Some of these devices are carried by users in wearable form, while others are fixed in the home environment. Although functions are different, they are generally considered as sensor devices to collect physiological data or environmental data of users [11]. After the completion of the acquisition, these devices will send data to the host in the home health network, and the host will gather data and analyze the [12].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Home Health System Based on ID Card Identification and Multidevice Access

Gao

2021

Wireless Communications and Mobile Computing

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With the development of society and the gradual arrival of an ageing society, various diseases threaten human health. If you use the software that comes with your medical device to understand and analyze your health, not only the user is in trouble but also a variety of health data can not be linked, giving users a more three-dimensional health analysis. In this case, this article will design and implement a mobile client for a home health system. The user’s information is hosted by the remote data center. It is necessary to collect the user’s information to facilitate management and analysis of health conditions. The manual input method is easy to input errors due to the long address and ID number, so the system will collect user information based on ID card identification. Train ID information to achieve accurate identification. For the multidevice access function, the key parts of the IEEE 11073 standard are used for communication. The mobile phone client that implements the home health system runs on the Android platform. According to the user information management, the user physiological data management, the user physiological data collection, and the user information synchronization are divided into four modules. The ID card identification module is called in the user management module, and the IEEE11073 communication plug-in is called by JNI in the health data measurement module. The health data can be presented intuitively to the user, or the information can be synchronized to the remote data center for further analysis.

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“…Besides, it employs NN in detection and is suitable for financial data like stock markets. Since the Support Vector Machine (SVM) method is accurate, it is capable of minimizing the over-fitting issue and many developers follow quantitative analysis [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

et al. 2020

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The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.

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Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks

Cited by 104 publications

References 8 publications

Prediction Method of Gestational Diabetes Based on Electronic Medical Record Data

Prediction Method of Gestational Diabetes Based on Electronic Medical Record Data

Design and Implementation of Home Health System Based on ID Card Identification and Multidevice Access

An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

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