Application of Internet of Things and Naive Bayes in Public Health Environmental Management of Government Institutions in China

Zhang, Zhipeng; Zhang, Shuxiang

doi:10.1155/2021/9171756

Cited by 7 publications

(7 citation statements)

References 39 publications

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“…Firstly, the data cleaning work is carried out on the original input data, mainly to judge the noise data, identify the outliers, and correct the inconsistency of some data. e noise data and outliers are identified through manual screening after being identified by professional software [11]. e original data table is imported into the "national physique monitoring data screening software" for monitoring index deviation detection and logical judgment.…”

Section: National Physique Data Collection and Managementmentioning

confidence: 99%

Feature Extraction of National Physical Fitness Data Based on Data Mining

Huang¹,

Li²,

Ren³

2022

Mathematical Problems in Engineering

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In order to better understand and optimize the national physical fitness, this paper puts forward the national physical fitness data change feature extraction method based on data mining, uses the decision tree and association rule data mining algorithm to collect the national physical fitness data in recent years, constructs the database to realize the effective data management, and uses the data mining algorithm to construct the physical fitness change feature evaluation index. Finally, through experiments, it is confirmed that the national physique data change feature extraction method based on data mining has high effectiveness in the process of practical application. It can better understand the national physique change trend and put forward targeted suggestions for national physique health optimization.

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Section: National Physique Data Collection and Managementmentioning

confidence: 99%

Feature Extraction of National Physical Fitness Data Based on Data Mining

Huang¹,

Li²,

Ren³

2022

Mathematical Problems in Engineering

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“…Using the same dataset NSL-KDD, the detection accuracy of seven traditional machine learning algorithms [20][21][22][23][24][25][26][27][28][29][30][31][32][33] such as decision tree, Naive Bayes, Naive Bayes tree, random tree, random forest, support vector machine, multilayer perceptron, and the detection accuracy of the FCRNN-IDS model in the case of 2-class (normal, abnormal) and 5-class (normal, probe, Dod, R2L and U2R) were studied and compared.…”

Section: Comparative Experiments 1: Comparison With Traditional Machi...mentioning

confidence: 99%

An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network

2022

Scientific Programming

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Now, the use of deep learning technology to solve the problems of the low multiclassification task detection accuracy and complex feature engineering existing in traditional intrusion detection technology has become a research hotspot. In all kinds of deep learning, recurrent neural networks (RNN) are very important. The RNN processes 41 feature attributes and maps them to a 122-dimensional high-dimensional feature space. To detect multiclassification tasks, this study proposes an intrusion detection method based on fully connected recurrent neural networks and compares its performance with previous machine learning methods on benchmark datasets. The research results show that the intrusion detection system (IDS) model based on fully connected recurrent neural network is very suitable for classification of intrusion detection. Classification methods, especially in multiclassification tasks, have high detection accuracy, significantly improve the detection performance of detection attacks and DoS attacks, and it provides a new research direction for the future attempts of intrusion detection methods for industrial control systems.

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“…KNN is widely used in the IoT environment because of its computational efficiency and complexity [28]. The Gaussian Naive Bayes (GNB) algorithm is based on assumptions made by conditional independence and helps in analyzing data collected by the IoT system [29]. The major innovation and main goal of this work is to predict early faults in any IoT device in a hospital environment.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

Uppal

Gupta

Juneja³

et al. 2022

Sustainability

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The amount of data captured is expanding day by day which leads to the need for a monitoring system that helps in decision making. Current technologies such as cloud, machine learning (ML) and Internet of Things (IoT) provide a better solution for monitoring automation systems efficiently. In this paper, a prediction model that monitors real-time data of sensor nodes in a clinical environment using a machine learning algorithm is proposed. An IoT-based smart hospital environment has been developed that controls and monitors appliances over the Internet using different sensors such as current sensors, a temperature and humidity sensor, air quality sensor, ultrasonic sensor and flame sensor. The IoT-generated sensor data have three important characteristics, namely, real-time, structured and enormous amount. The main purpose of this research is to predict early faults in an IoT environment in order to ensure the integrity, accuracy, reliability and fidelity of IoT-enabled devices. The proposed fault prediction model was evaluated via decision tree, K-nearest neighbor, Gaussian naive Bayes and random forest techniques, but random forest showed the best accuracy over others on the provided dataset. The results proved that the ML techniques applied over IoT-based sensors are well efficient to monitor this hospital automation process, and random forest was considered the best with the highest accuracy of 94.25%. The proposed model could be helpful for the user to make a decision regarding the recommended solution and control unanticipated losses generated due to faults during the automation process.

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Application of Internet of Things and Naive Bayes in Public Health Environmental Management of Government Institutions in China

Cited by 7 publications

References 39 publications

Feature Extraction of National Physical Fitness Data Based on Data Mining

Feature Extraction of National Physical Fitness Data Based on Data Mining

An Intrusion Detection Method Based on Fully Connected Recurrent Neural Network

Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

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