检索结果-内蒙古大学图书馆

Systems and Soft Computing 2025年

作者： Changjun Wang Fengxia You Yu Wang Library University of Sanya Sanya 572022 China

With the development of the digital age, library services are facing the challenge of fast and accurate text classification. The traditional K-nearest neighbor algorithm is limited by low classification efficiency and high computational complexity when processing big data. This makes it difficult to meet the dual requirements of real-time processing and high-precision classification of large amounts of literature in the digital transformation of libraries. To address this issue, this study proposes an improved K-nearest neighbor algorithm (CCVknn), which optimizes the algorithm structure to significantly improve processing speed while ensuring classification accuracy. First, this method constructs a text intelligent classification model and uses K-means to create a multi-level clustering center system within the category. This transforms global search into local computation. To improve processing speed and meet the requirements of intelligent text classification, a K-nearest neighbor text intelligent classification algorithm on the ground of clustering and center vectors is further proposed. The experimental results showed that the K-nearest neighbor text intelligent classification algorithm on the ground of clustering and center vectors improved the accuracy of sports, tourism, and education categories by 2.9%, 2.4%, and 2.35%, respectively. Meanwhile, it saved 200 s in classification time compared to the density-based K-nearest neighbor training data reduction algorithm, and 560 s compared to the traditional K-Nearest Neighbor. These findings indicate that the K-nearest neighbor text intelligent classification algorithm on the ground of clustering and center vectors can improve efficiency while maintaining high accuracy. This indicates that the algorithm is suitable for processing large-scale text data in library services, effectively improving classification speed and accuracy. This study provides an efficient text classification solution for the digital transfor

关键词： knn algorithm Intelligent text classification Library K-means CCVknn

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Evaluation of image processing technique in identifying rice blast disease in field conditions based on knn algorithm improvement by K-means

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FOOD SCIENCE & NUTRITION 2019年第12期7卷 3922-3930页

作者： Larijani, Mohammad Reza Asli-Ardeh, Ezzatollah Askari Kozegar, Ehsan Loni, Reyhaneh Univ Mohaghegh Ardabili Dept Biosyst Engn Ardebil Iran Univ Guilan Dept Comp Sci & Engn Guilan Iran Tarbiat Modares Univ Dept Biosyst Engn Tehran Iran

Nowadays, rice farming is affected by various diseases that are economically significant and worthy of attention. One of these diseases is blast. Rice blast is one of the most important limiting factors in rice yield. The purpose of this study is the timely and rapid diagnosis of rice blast based on the image processing technique in field conditions. To do so, color images were prepared using image processing technique and improved knn algorithm by K-means was used to classify the images in Lab color space to detect disease spots on rice leaves. Squared classification was based on Euclidean distance, and the Otsu method was used to perform an automatic threshold histogram of images based on shape or to reduce the gray level in binary images. Finally, to determine the efficiency of the designed algorithm, sensitivity, specificity, and overall accuracy were examined. The classification results showed that the sensitivity and specificity of the designed algorithm were 92% and 91.7%, respectively, in the determination of the number of disease spots, and 96% and 95.65% in determining the quality of disease spots. The overall accuracy of the designed algorithm was 94%. Generally, the results obtained showed that the above method has a great potential for timely diagnosis of rice blast.

关键词： blast disease image processing K-means algorithm knn algorithm rice

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Research on Cache Timing Template Attack Based on knn algorithm

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电脑学刊 2020年第1期31卷 184-191页

作者： Cai-Sen Chen Zhi-Wei Cheng Yu-Bo Wang An Wang Xiang-liang Ma

For the Cache timing template attack, a large amount of timing information needs to be collected during attacking other processes. For the processing of high-noise data, the attack efficiency is low and the success rate is not high. It is proposed to establish a template matrix with the Cache hit rate feature and use the knn algorithm. Analyze and judge. First, normalize the collected Cache timing data, and then use the hit rate to create a template for attack. Experiment results show that the accuracy of attack using knn algorithm is higher than that of traditional mean square error method, and can reach about 90%.

关键词： Cache timing attack Cache timing information knn algorithm template attack

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A Bluetooth Location Method Based on knn algorithm 15

A Bluetooth Location Method Based on kNN Algorithm

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15th International Computer Engineering Conference (ICENCO)

作者： Wang, Songmei Ma, Ruili Li, Yanshuang Wang, Qingyu Beijing Gas Grp Beijing Peoples R China

ISBN: (数字)9781728151465

ISBN: (纸本)9781728151465

The Bluetooth sensor networks provides us with a very feasible solution for obtaining indoor locations. We can use the Bluetooth sensor to obtain the indoor position by establishing fingerprint database. In the process of positioning by fingerprint database, the matching positioning method is an important research directions of fingerprint database positioning technology. In this work, we proposed a knn algorithm based Bluetooth fingerprint library location method. The knn algorithm processes the matching of measured RSSI value and the closest value in the fingerprint library. Experiment result show that it is very simple and intuitive to obtain the positioning position.

关键词： Indoor location Bluetooth fingerprint library knn algorithm

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News Readers' Sentiment Analysis Based on Fused-knn algorithm 4

News Readers' Sentiment Analysis Based on Fused-KNN Algorith...

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4th IEEE International Conference on Computational Intelligence and Applications (ICCIA)

作者： Jiang, C. S. Li, Y. Q. Li, L. Liu, A. H. Liu, C. Wuhan Univ Technol Coll Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781728121284

News readers' sentiment analysis means speculating emotions that readers may express after reading the news according to the comments. When traditional knn algorithm is applied to the emotional prediction task, there are some deficiencies in definiting distance and judging category. These issues ultimately lead to poor evaluation of the classification's results. Therefore, this paper proposed the Fused-knn algorithm which adopted the improved neighbor sample selection strategy and the improved category judgment strategy. Firstly, with considering the specific application area of news readers' sentiment analysis, emotional amount is structured combining the Emotion Word Ontology of Dalian University of Technology and semantic rules. Then, four attributes are proposed which include emotional amount and the number of commendatory terms, neutral terms and derogatory terms. Distance between samples is measured by the attribute-correlated distance. Based on this, the k nearest neighbors are selected. Meanwhile, emotional polarity is predicted by the sum of neighbor samples' polarity influence factors, instead of the most neighbor samples' category in traditional method. The experiment results show that the algorithm achieves superior performance and can better complete the sentiment classification task for news comment samples.

关键词： knn algorithm emotion word ontology semantic rules attribute-correlated distance news readers' emotion

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Research on knn algorithm in Malicious PDF Files Classification under Adversarial Environment 19

Research on KNN Algorithm in Malicious PDF Files Classificat...

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4th International Conference on Big Data and Computing (ICBDC)

作者： Li, Kunming Gu, Yijun Zhang, Peijing An, Wang Li, Wenzheng Peoples Publ Secur Univ China Coll Informat Technol & Network Secur Beijing 100038 Peoples R China China Univ Min &Technol Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Peoples Publ Secur Univ China Network Informat Ctr Beijing 100038 Peoples R China

ISBN: (纸本)9781450362788

Traditional machine learning classifiers are usually based on the same distribution of training and testing sets, and only pay attention to the accuracy of the classifier, when attackers change the data distribution, the usability of the model is reduced. A method on how to improve the robustness of the knn classifier is proposed. Firstly, the gradient descent attack method is used to attack the knn algorithm. Secondly, add the adversarial samples generated by the gradient descent attack to the training set to train a new knn classifier. Finally, compare the robustness of the improved classifier and the initial classifier by simulating different attack strengths. The experimental results show that adding the adversarial samples to the knn classifier can effectively improve the performance of the classifier against the evasion attacks.

关键词： knn algorithm Adversarial machine learning Evasion attacks Robustness

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Design and Research of Gait Recognition Method of Upper Knee Prosthesis Based on knn algorithm 2019

Design and Research of Gait Recognition Method of Upper Knee...

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4th International Conference on Robotics, Control and Automation (ICRCA)

作者： Hu, Yue Li, Xisheng Liu, Qing Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China New TER BeiJing Co Ltd Beijing Peoples R China

ISBN: (纸本)9781450371834

Due to the influence of diseases, accidents and other factors, the number of thigh amputations is increasing year by year, and the demand for intelligent and diversified artificial limbs is also increasing. Aiming at the poor accuracy of gait recognition of prosthesis, this paper takes the upper part of the knee of healthy people as the main research object, collects acceleration signal and gyroscope signal, and performs wavelet packet denoising on them. knn algorithm was used to construct the classification model for the collected examples to be classified. Three typical gait, stationary, flat walking and stair climbing were selected, and k nearest neighbor samples of unknown samples were studied to predict the category of unknown samples, namely the gait of healthy people. It provides an accurate gait recognition condition for the research of dynamic prosthesis.

关键词： Gait recognition knn algorithm Wavelet packet denoising

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A Weighted knn algorithm Based on Entropy Method 1st

A Weighted KNN Algorithm Based on Entropy Method

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1st International Conference on Intelligent Manufacturing and Internet of Things (IMIOT) / 5th International Conference on Computing for Sustainable Energy and Environment (ICSEE)

作者： Zhang, Hui Hou, Kaihu Zhou, Zhou Kunming Univ Sci & Technol Kunming 650000 Yunnan Peoples R China

ISBN: (纸本)9789811323843;9789811323836

Aiming at the problem that the classification accuracy of K-nearest neighbor algorithm is not high, this paper proposes a K-nearest neighbor algorithm that uses the weighted entropy method of Extreme value (EEM-knn algorithm). The entropy method assigns weight to the sample's feature index, and then introduces the weight of the feature index when calculating the distance between the query sample vector and the training sample vector. The four groups of classification data sets are used as test samples to test the effectiveness of the improved knn algorithm, it also compares the difference between the improved algorithm and the traditional algorithm under different K values. algorithms are implemented and tested on the Jupyter Notebook interactive platform. The improved knn algorithm is verified by experiments, and the classification accuracy is improved.

关键词： knn algorithm Distance metric Entropy method Weighting

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Spectrum Sensing Based on knn algorithm for 230 MHz Power Private Networks 12

Spectrum Sensing Based on kNN Algorithm for 230 MHz Power Pr...

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12th International Symposium on Antennas, Propagation and Electromagnetic Theory (ISAPE)

作者： An, Chunyan Zhang, Donglei Yuan, Chenglong Li, Liang Zhao, Xiongwen Geng, Suiyan Zheng, Weijun Shao, WeiPing Global Energy Interconnect Res Inst Co Ltd Beijing Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China State Grid Jiaxing Power Supply Co Jiaxing Peoples R China State Grid Zhejiang Elect Power Ltd Co Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538673027

In this work, a spectrum sensing method based on K-nearest neighbors (knn) algorithm is proposed. The energy and cyclic spectrum parameters of primary signal are extracted and system performance is analyzed. Results show that the detection probability of the proposed knn method has better performance than support vector machine (SVM), energy detection (ED), and cyclostationary eigenvalue detection (CD) methods. False alarm rate of the knn algorithm is much lower comparing with other detection methods. The proposed method reduces the complexity of cyclostationary signal detection as the knn algorithm is easy to implement based on distance division, not on category. The provided results are useful for design of 230 MHz power private networks.

关键词： spectrum sensing knn algorithm detection probability false alarm rate power private network

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A Design Method of Performance Test Case Based on knn algorithm

A Design Method of Performance Test Case Based on kNN Algori...

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作者： Ji-jun ZENG Fa-mao MEI De-xin HUANG Information Center Guangdong Power Grid Co.Ltd CSG-key Laboratory of Information Technology Testing

This paper based on existing historical test case set to combined with the knn algorithm to train the classification model. The model is used to assist the design of the software performance test to obtain the optimal... 详细信息

关键词： knn algorithm Performance Test Test Case

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