检索结果-内蒙古大学图书馆

Application of an intelligent English text classification model with improved knn algorithm in the context of big data in libraries

引用

SYSTEMS AND SOFT COMPUTING 2025年 7卷

作者： Xu, Qinwen Henan Med Coll Publ Fdn Dept Zhengzhou 451191 Peoples R China

In the era of big data, libraries manage huge electronic text resources, of which English text resources are particularly critical for academic research, student learning, and professional knowledge acquisition. This paper aims to improve the K-nearest neighbor algorithm and design an intelligent classification model to improve the efficiency and quality of library services. An improved method based on in-class K-means clustering and class mean distance is used to characterize and extract text information with a vector space model. The results showed that the improved K-nearest neighbor algorithm achieved significant improvement in the precision, recall, and F1 values, reaching 90.50 %, 89.95 %, and 89.37 %, respectively. The classification time was significantly reduced to 1034.57 s. In addition, the improved algorithm had a classification accuracy of 94 %, surpassing other popular text classification algorithms. The research successfully realizes the efficient classification of text. The research results not only improve the classification efficiency of library English text resources but also provide strong support for readers to quickly obtain the required information, which has important application value and wide application prospects.

关键词： K -means clustering knn algorithm Category average distance Intelligent classification of English texts Big data analysis

来源：评论

学校读者我要写书评

暂无评论

A Rapid Forecast Method for the Process of Flash Flood Based on Hydrodynamic Model and knn algorithm

引用

WATER RESOURCES MANAGEMENT 2024年第6期38卷 1903-1919页

作者： Zhou, Nie Hou, Jingming Chen, Hua Chen, Guangzhao Liu, Bingyi Wuhan Univ State Key Lab Water Resources & Hydropower Engn Sc Wuhan Peoples R China Xian Univ Technol State Key Lab Base Ecohydraul Engn Arid Area 5 Jinhua Rd Xian 710048 Peoples R China

Using hydrodynamic models to carry out early warning and flash floods forecasting is an essential measure for loss reduction. Nevertheless, many current hydrodynamic models lack the necessary forecasting timeliness. To address this limitation, a method combining a hydrodynamic model with the K nearest neighbours (knn) algorithm is proposed to facilitate the rapid prediction of flash flood processes. With the rainfall sequence as the input data and the simulation results of the hydrodynamic model as the target data, the rapid forecast of water depth, water velocity and discharge are achieved. Then the Baogai Temple basin is utilized as a case study, and the rapid forecast model (RFM) is established and subjected to verification for reliability and timeliness. The results demonstrate that the established model exhibits remarkable accuracy, with 99% of the test data effectively limiting the error of accumulated inundation extent within 20%. Furthermore, the Nash-Sutcliffe efficiency (NSE) for cross-sectional discharge achieves a value of 0.98. In 75% of rainfall scenarios, both the maximum average water depth and velocity errors for the cross-sections are effectively confined to 7.5% and 10%, respectively. The model also boasts a substantial improvement in computational efficiency, enabling it to complete the prediction of the flooding process for the next 10 h within 25s. This enhancement offers valuable lead time for emergency decision-making and highlights its extensive application potential in managing flash floods.

关键词： Rapid forecast Flash flood knn algorithm Hydrodynamic model

来源：评论

学校读者我要写书评

暂无评论

Monitoring, profiling and classification of urban environmental noise using sound characteristics and the knn algorithm

引用

ENERGY REPORTS 2020年 6卷 223-230页

作者： Tsalera, Eleni Papadakis, Andreas Samarakou, Maria Univ West Attica Dept Informat & Comp Engn Egaleo 12243 Greece Sch Pedag & Technol Educ Dept Elect & Elect Engn Educators Athens 14122 Greece

Environmental noise is a key factor affecting the quality of life in modern societies as they influence an extended set of human activities. Unwanted sounds, typically characterized as noise, can be of many types and vary in their impact and the ways to be confronted on behalf of competent public authorities. In this work, we describe environmental noise in a qualitative manner using sound-specific features from the time and spectral domains. These features consist of 8 temporal (including RMS, standard deviation, Zero Crossing Rate), 11 spectral (including spectral envelope slope, skewness, spectrum mass center, peak amplitude crest, spread and skewness) and 4 perceptual (including Mel Frequency Cepstral Coefficients) descriptors. Based upon a set of 8 discriminant types of unwanted sounds, typically met in urban environments (car horn, children playing, dog barking, drilling, engine idling, jack hammer, siren and street music), we specify a methodology of matching environmental noise into these categories. Using training and test data from the UrbanSound8K public dataset, we use the K-Nearest Neighbors (knn) algorithm for classification. The algorithm has been configured to allow from 1 to 3 neighbors, while three distance metrics (Euclidean, Chebyshev and cosine) have been employed to create 9 models that achieve performance between 70% and 85%. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Urban noise Sound classification Supervised machine learning knn algorithm

来源：评论

学校读者我要写书评

暂无评论

Application Research of knn algorithm Based on Clustering in Big Data Talent Demand Information Classification

引用

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第6期34卷

作者： Xiao, Qingtao Zhong, Xin Zhong, Chenghua Army Mil Univ Vocat Educ Ctr Chongqing Peoples R China Chongqing Technol & Business Univ Mental Hlth Educ & Counseling Ctr Chongqing Peoples R China Chongqing Technol & Business Univ Coll Environm & Resources Chongqing Peoples R China

With the growth of massive data in the current mobile Internet, network recruitment is gradually growing into a new recruitment channel. How to effectively mine available information in the massive network recruitment data has become the technical bottleneck of current education and social supply and demand development. The renewal of talent demand information is carried out every day, which produces a large amount of text data. How to manage these talents' demand information reasonably becomes more and more important. Artificial classification is time-consuming and laborious, which is unrealistic naturally. Therefore, using automatic text categorization technology to classify and manage this information becomes particularly important. To break through the bottleneck of this technology, a heuristic knn text categorization algorithm based on ABC (artificial bee colony) is proposed to adjust the weight of features, and the similarity between test observation and training observation is measured by using the method of fuzzy distance measurement. Firstly, the recruitment information is segmented and feature selection and noise data elimination are carried out by using term frequency-inverse document frequency (TF-IDF) algorithm and AP (affinity propagation) clustering algorithm. Finally, the text information is classified by using knn algorithm combined with heuristic search and fuzzy distance measurement. The experimental results show that this method effectively solves the problem of poor stability and low classification accuracy of traditional knn algorithm in text categorization method for talent demand.

关键词： Clustering knn algorithm artificial intelligence information classification

来源：评论

学校读者我要写书评

暂无评论

Development of computer aided classroom teaching system based on machine learning prediction and artificial intelligence knn algorithm

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1879-1890页

作者： Quan, Yu Lingnan Normal Univ Zhanjian 524048 Guangdong Peoples R China

With the continuous development of science and technology, computer-aided teaching has become a common mode of school teaching. From the current situation, it can be seen that the current computer-aided teaching mostly replaces the traditional teaching mode with multimedia, and does not play the role of functional teaching, and teachers cannot effectively grasp the students' psychological thoughts in teaching. Based on this, this study combines machine learning prediction and artificial intelligence knn algorithm to actual teaching. Moreover, this study collects video and instructional images for student feature behavior recognition, and distinguishes individual features from group feature recognition, and can detect student expression recognition in detail. In addition, this study designed a case study to analyze the performance of the algorithm. From the experimental results, it can be seen that the proposed algorithm has certain effects and can be used as an algorithm to assist the teaching process and can provide theoretical reference for subsequent related research.

关键词： Machine learning prediction artificial intelligence knn algorithm auxiliary teaching feature recognition

来源：评论

学校读者我要写书评

暂无评论

RETRACTED: Research on English grammar recognition system based on combination of genetic algorithm and knn algorithm (Retracted Article)

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期38卷 7291-7302页

作者： Wanni, Mo Guangxi Univ Nationalities Xiangsihu Coll Nanning Peoples R China

It is a very challenging task to develop a grammatical error correction model with many types of error correction and high accuracy of error correction for various English grammar mistakes. Based on Chinese English learners, this paper analyzes the grammatical mistakes that they often make, and then studies and implements an English grammar correction model with high precision and many types of error correction. Moreover, this study combines genetic algorithm and knn algorithm to construct a smart English grammar recognition system. At the same time, on the basis of summarizing the work of predecessors, this paper, based on advanced theoretical and technical knowledge, designs a grammatical error correction model based on rules and statistics. In addition, for the corresponding related word errors, this study proposes an error correction model of related word grammar. Finally, this study selects the features of artificially extracted English related words for error correction, and finally selects the optimal feature subset to improve the accuracy of model error correction.

关键词： Genetic algorithm knn algorithm English grammar feature recognition

来源：评论

学校读者我要写书评

暂无评论

Tumor growth prediction and classification based on the knn algorithm and discrete-time Markov chains (DTMC)

引用

NEURAL COMPUTING & APPLICATIONS 2023年第13期35卷 9739-9751页

作者： El Fatimi, Lahcen Boucheneb, Hanifa Polytech Sch Montreal Dept Comp Engn Montreal PQ H3T 1J4 Canada

In recent years, brain tumors have become one of the most common fatal diseases. Despite the existence of an important number of research studies on tumors, the proportion of research on predicting the growth of tumors remains insufficient due to the intricate nature of this research domain. Therefore, the presence of any application able to predict the growth of the tumor may have a role in eliminating the tumor by finding the appropriate treatment for it before it grows. This paper investigates tumor growth and presents a technique for tumor growth prediction based on the Discrete Time Markov Chain (DTMC) and K-Nearest Neighbor (knn) algorithms. The design and development of this technique consists of a proposition of a stochastic model of tumor progression. This is followed by an extension of the mode to several cases that allow the derivation of new cases based on the study of predictive probabilities. The aim of this paper is to develop a model based on the knn and DTMC algorithms that can classify tumors and predict the future state based on the current state of the tumor without the knowledge of the past state. In other words, all relevant information about the past and the present that would be useful in making predictions is available in the current state. In terms of performance evaluation metrics, the results show that the proposed method exceeds the existing methods with 97.65% accuracy, 71.65% specificity and 99.087% sensitivity.

关键词： Tumor growth prediction Probabilities DTMC knn algorithm

来源：评论

学校读者我要写书评

暂无评论

Anomaly detection of bridge health monitoring data based on knn algorithm

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5243-5252页

作者： Lei, Zhen Zhu, Liang Fang, Youliang Li, Xiaolei Liu, Beizhan Hebei Univ Hebei Prov Civil Engn Monitoring & Evaluat Techno Baoding 071002 Hebei Peoples R China Hebei Univ Sch Cyber Secur & Comp Sci Baoding 071002 Hebei Peoples R China Hebei Univ Coll Civil Engn & Architecture Baoding Hebei Peoples R China

Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on knn distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the knn method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.

关键词： Artificial intelligence bridge health monitoring data anomaly detection knn algorithm multivariate time series

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Realization of optical ADDER circuit using photonic structure and knn algorithm

引用

OPTIK 2020年 212卷 164675-164675页

作者： Nayak, SuryaKanta Panda, M. Palai, G. Utkal Univ Dept Comp Sci & Applicat Bhubaneswar India Gandhi Inst Technol Adv GITA Dept Elect & Commun Engn Bhubaneswar India

Nevertheless a meticulous research on photonic integrated circuit have been focused since a last decade, the present research brings a different type of approach to envisage optical ADDER circuit. Further the novelty of present research deals with the realization of photonic based ADDER circuit with the use of minimum number optical elements, where operational mechanism relies on the logic gate operation. Further plane wave explanation method is applied here to study the photonic band gap analysis to understand the logic gate operation, where the minimization of photonic structure can be made through knn algorithm. To sum up; the present paper claims that the optical ADDER circuit could be realized by using the minimum number of photonic structures.

关键词： Optical ADDER knn algorithm 2D photonic structure

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：