Improved KNN Algorithm Based on Preprocessing of Center in Smart Cities

Wang, Haiyan; Xu, Peidi; Zhao, Jinghua

doi:10.1155/2021/5524388

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…Literature [19] analyzes that the KNN algorithm in data preprocessing may produce certain errors. The use of the PK-means algorithm to improve the traditional KNN algorithm which can improve the improved algorithm in the classification of the accuracy and time.…”

Section: ) Application Of Knn Algorithmmentioning

confidence: 99%

Research on the Application of KNN Algorithm Incorporating Gaussian Functions in Precision Marketing Classification of E-commerce Platforms

Wang

2023

Applied Mathematics and Nonlinear Sciences

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The technology can fully explore the user’s consumption behavior habits and help the e-commerce platform formulate more precise marketing strategies in a targeted manner. This paper firstly analyzes the optimization of marketing strategy based on the 3R marketing theory, gives the design process of the precise marketing strategy of an e-commerce platform, and analyzes the personalized service based on consumer classification. Secondly, for the shortcomings of the KNN algorithm in the process of accurate classification, the Gaussian function is introduced to weight the optimization of the algorithm, which further realizes the construction of the G-KNN algorithm. Finally, the testing and application analysis of the algorithm model was carried out using the actual user consumption data of the e-commerce platform. The results show that the classification accuracy of the G-KNN algorithm has been maintained at about 95% when the K value exceeds 800, and the F1 composite value of this paper’s algorithm fluctuates around 56% when the K value exceeds 1000. On the e-commerce platform, except for the electrical appliances category classification test, the fit and accuracy of other categories basically match. Using the KNN algorithm incorporating the Gaussian function can effectively realize the accurate classification of user characteristics on the e-commerce platform and provide data support for the e-commerce platform to formulate accurate marketing strategies based on consumer preferences.

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Section: ) Application Of Knn Algorithmmentioning

confidence: 99%

Research on the Application of KNN Algorithm Incorporating Gaussian Functions in Precision Marketing Classification of E-commerce Platforms

Wang

2023

Applied Mathematics and Nonlinear Sciences

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“…Another effective algorithm, in terms of reducing noise and maintaining the informativeness of the original image, is K-Nearest Neighbors [11]. This algorithm is conceptually similar to the above NLM, but, as indicated in studies, with lower computational complexity [12].…”

Section: Analysis Of Literature Sourcesmentioning

confidence: 99%

Comparison of the Efficiency of Parallel Algorithms KNN and NLM Based on CUDA for Large Image Processing

Mochurad¹,

Bliakhar²

2022

Computer Modeling and Intelligent Systems

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“…An accuracy rate of 92% is reached in [46] by using Random Forest algorithm. k-NN algorithm is a widely used non-parametric classification method [47]. It is employed in the categorization and regression of data.…”

Section: A Attacker Modelmentioning

confidence: 99%

Ensuring IoT Privacy using Padding Strategies against Machine Learning Approaches

Ergun,

Can

2022

IJMSIT

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The widespread usage of Internet of Things (IoT) devices is increasing by the recent advances in embedded systems, cloud computing, artificial intelligence, and wireless communications. Besides, a huge amount of data is transmitted between IoT devices over insecure networks. The transferred data can be sensitive and confidential. On the other hand, these transmitted data may not appear to be sensitive or confidential data. However, machine learning techniques are used on these non-confidential data (such as packet length) to obtain data such as the type of the IoT device. An observer can monitor traffic to infer sensitive data by using machine learning techniques to analyze the generated encrypted traffic. For this purpose, padding can be added to the packets to ensure traffic privacy. This paper presents privacy problems that are caused by the traffic generated during the communication of IoT devices. Also, security and privacy measures that should be taken against the related privacy problems are explained. For this purpose, the current studies are examined by considering the attacker and the defender models.

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Improved KNN Algorithm Based on Preprocessing of Center in Smart Cities

Cited by 9 publications

References 9 publications

Research on the Application of KNN Algorithm Incorporating Gaussian Functions in Precision Marketing Classification of E-commerce Platforms

Research on the Application of KNN Algorithm Incorporating Gaussian Functions in Precision Marketing Classification of E-commerce Platforms

Comparison of the Efficiency of Parallel Algorithms KNN and NLM Based on CUDA for Large Image Processing

Ensuring IoT Privacy using Padding Strategies against Machine Learning Approaches

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