A Data Classification Algorithm of Internet of Things Based on Neural Network

Li, Zhenjun

doi:10.3991/ijoe.v13i09.7587

Cited by 7 publications

(4 citation statements)

References 7 publications

(7 reference statements)

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“…Empirical data indicates that approximately 80% to 90% of artificial neural network models in practical applications rely on BP networks. These algorithms serve as the foundation for advanced network architectures, encapsulating the core capabilities of neural networks [10]. The merit of the method lies in its ability to classify heart rate data (X3) generated by the prototype, along with additional data input via an Android smartphone, such as age (X1) and activity (X2).…”

Section: Methodsmentioning

confidence: 99%

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Suryani,

Faizal

2024

Int. J. Onl. Eng.

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Real-time heart rate monitoring and early detection of heart abnormalities are vital to determine heart health before it worsens. To achieve this goal, this project uses the backpropagation neural network (BPNN) method including its capability to classify heartbeats into normal or abnormal by inputting heartbeat values in BPM units derived from prototypes utilizing sensors like Sensor Easy Pulse and NodeMCU, along with considerations of age and sports activity. All data from sensors will be stored in Firebase. Then Firebase will connect to Android, and the normal and abnormal heart classification results will be displayed on the Android system. Simulation results successfully examined 40 people as a sample and provided information from real-time heart rate monitoring, age, and sports activity as input. This research seeks to contribute to improving health services at various public health service centers and independently in detecting heart health early.

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Section: Methodsmentioning

confidence: 99%

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Suryani,

Faizal

2024

Int. J. Onl. Eng.

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“…Neural Networks have many names including Artificial Neural Networks (ANN), Artificial Neural Networks (ANN), Simulated Neural Networks (SNN), Neural Networks (NN) [13]. Neural Network is a network system whose network structure is like the human brain and is a category of Soft Computing science that adopts the capabilities of the human brain which is usually implemented using electronic components [14] [15] [16]. One of the most popular Neural Network classification methods is the Backpropagation algorithm.…”

Section: Neural Networkmentioning

confidence: 99%

Classification and Risk-Mapping of River Water Quality in Surabaya with Semantic Visualitzation

Miko¹,

Harsono²,

Barakbah³

2019

emitter

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River water pollution is one of the environmental problems that occur in Surabaya. The amount of industrial waste and household waste makes Surabaya River water easily polluted every day, besides that there are also many people who are not aware about the quality of river water in Surabaya. In this paper, we present a new system to classify water quality of river in surabaya. The system involve a semantic visualization of risk-mapping for the river, so that the people of Surabaya are easier to get information about the quality of Surabaya River water. In this paper, we measured the water quality of Surabaya River using Horiba sensor measuring instruments using 5 parameters, namely temperature, PH, DO, Turbidity, TDS. These five parameters are input variables for calculating water quality with the methods applied in this research. We use the Storet Method to determine the quality of Surabaya River water. The results of the Storet Method explained that there were 0.03% of the data on lightly polluted water quality and there were 37.41% of the data being moderately polluted and there were 59.29% of the data heavily polluted. The results of the calculation using the Storet method concluded that the condition of Surabaya River water quality was not good. We also apply the rule of the Storet Method to the Neural Network by using Surabaya River water quality data as learning data and gave performance 70.02% accuracy.

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“…For example, the sensor network of the perception layer is connected to the Internet, and the key authentication center can interact with the sensor network to realize sensing in the network security management and authen-ticate device nodes [2]. Li (2017) proposed that the distributed management model, which considers the heterogeneous network as the center, was easier to implement in the Internet and mobile communication network. The sensing layer of the perception layer is the key to solving the problem of key management and authentication because of its limited resources [3].…”

Section: State Of the Artmentioning

confidence: 99%

“…Li (2017) proposed that the distributed management model, which considers the heterogeneous network as the center, was easier to implement in the Internet and mobile communication network. The sensing layer of the perception layer is the key to solving the problem of key management and authentication because of its limited resources [3].…”

Section: State Of the Artmentioning

confidence: 99%

Security of the Internet of Things Based on Game Algorithm

Yue-e

2018

Int. J. Onl. Eng.

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To explore the security mechanism of the Internet of Things (IoT) perception environment, we perform a security research on the IoT on the basis of game algorithm. The dynamic game method of node cooperation is used in the experiments. Firstly, multiple report nodes are merged into a game party, and the dynamic game for two parties is established with the detection node. In the environment where the malicious nodes are dominant, the detection nodes collaborate, and the state of the unknown nodes is conjectured by the reputation value of the reporting nodes. The high trust reference report is used for the modification and reduction the weight of malicious nodes in the overall report, for node merging, and finally for bias equilibrium. The results show that cooperative game can significantly improve the success rate of incident monitoring and reduce the number of forged reports.

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A Data Classification Algorithm of Internet of Things Based on Neural Network

Cited by 7 publications

References 7 publications

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Prototype Realtime Detection Of Abnormal Heart Beat Using Multiple Back Propagation Neural Network (BPNN)

Classification and Risk-Mapping of River Water Quality in Surabaya with Semantic Visualitzation

Security of the Internet of Things Based on Game Algorithm

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