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

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

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

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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

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

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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

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Method of Physiological State Abnormality Discrimination Based on Improved knn algorithm 10

Method of Physiological State Abnormality Discrimination Bas...

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10th International Conference on Big Data and Information Analytics

作者： Miao Yongfei Li Xiaolong Wu Shikai Zhao Xiaoqing Yang Yufeng Yu Yue Air Force Commun NCO Acad Dept Ground Air Nav Dalian Peoples R China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638;9798350354621

Physiological state abnormality due to genetic diseases, excessive exercise, etc. is becoming a fatal killer endangering people's life and safety because of its hidden characteristics. K-Nearest Neighbor(knn) algorithm is widely used in various fields due to its simple implementation, but when the sample capacity is too large or the feature attributes are too many, the classification efficiency decreases significantly. This paper proposed an improved knn(Iknn) algorithm based on clustering by hierarchically clustering the data in data pre-processing, which reduced the search space of the algorithm and effectively improved the search efficiency. When the improved knn algorithm was applied in the physiological state abnormality discrimination field, which better improved the efficiency and accuracy of physiological abnormality discrimination. Results show that this could provide an effective guarantee for the early discovery of physiological parameter abnormality symptom, the timely adoption of dispositive measures, and the maintenance of people's life safety.

关键词： Physiological Abnormality Classification algorithm Hierarchical Clustering knn algorithm

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Classroom Attendance Monitoring using Haar Cascade and knn algorithm 2

Classroom Attendance Monitoring using Haar Cascade and KNN A...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Vara Prasad, K. Sai Kumar, D. Hemanth Mohith, T. Sameer, Md. Lohith, K. Dept. of Electronics and Communication Engineering Vijayawada India Dept. of Electronics and Communication Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9798350327533

Attendance management within educational settings is undeniably vital, but the conventional manual method poses several issues, including time consumption and vulnerability to proxy attendance. To address these challenges, we have introduced an innovative face recognition system that outperforms previous technologies like iris recognition and traditional biometrics. By employing the Haar Cascade Classifier for precise face detection and the K-Nearest Neighbors (knn) algorithm for face recognition, this system offers a robust solution. It maintains a dynamic dataset to store student information, ensuring adaptability as the student body evolves. Once a student's face is recognized, their attendance data is effortlessly recorded in a digital format, typically stored in a spreadsheet, there by eliminating manual record-keeping and enhancing overall classroom management efficiency. This automated approach not only saves valuable instructional time but also greatly reduces the potential for attendance inaccuracies, making it an invaluable asset for educational institutions. © 2024 IEEE.

关键词： database Face detection Face recognition Haar cascade knn algorithm

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Visual Power Distribution Room Fault Detection System Based on knn algorithm And Unity3D Engine 6

Visual Power Distribution Room Fault Detection System Based ...

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6th International Conference on HVDC, HVDC 2024

作者： Li, Xingyu Qu, Chengxin Zhang, Menghua Cheng, Xingong Huang, Weijie University of Jinan School of Electrical Engineering Jinan China

ISBN: (纸本)9798350387759

In this study, a visual power distribution room fault detection system based on knn algorithm and Unity3D engine is proposed. The simulation system is designed to provide a realistic, interactive environment for simulating and analyzing the operation of the distribution room. With the power of the Unity engine, users can visualize the various components of the power distribution room, simulate the circuitry, and interact with a variety of devices and parameters. The system provides functions such as equipment failure simulation, load prediction, anomaly detection, and energy efficiency optimization. With this simulation platform, users can gain insight into how the distribution room operates, test different scenarios, and improve the decision-making process. Overall, the switchroom simulation system based on Unity and knn algorithms provides a valuable tool in the field of electrical fault detection. © 2024 IEEE.

关键词： fault detection system knn algorithm machine learning power distribution room unity3D engine

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A Study of Biological Sequence Classification Based on Local Globally Weighted knn algorithm

A Study of Biological Sequence Classification Based on Local...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Lin, Yang Zhou, Xiao'An Chen, Yuyuan College of Electronics and Information Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350309614

Classification and identification is of great significance in biological sequence analysis research, which can help to solve the problems of functional identification, medical diagnosis, epidemic monitoring and so on. At present, there are mainly machine learning methods and non-machine learning methods to classify DNA sequences, and both methods have their own merits when the data volume is small, but with the explosive growth of genomic data, the advantages of machine learning methods become more and more obvious. Meanwhile, compared with other machine learning methods, there are problems such as complex training models, overfitting, and error amplification when dealing with sequence classification problems. To address these problems, the LGWknn algorithm based on the interaction of local weights and global weights is introduced, which experimentally confirms the feasibility of sequence classification as well as the advantages over traditional knn algorithms and weighting algorithms, achieving a classification accuracy of 97.71%. © 2023 IEEE.

关键词： knn algorithm LGWknn algorithm machine learning sequence classification weighting algorithm

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Cluster Analysis of E-Commerce User Consumption Data based on knn algorithm

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Procedia Computer Science 2024年 247卷 1120-1127页

作者： Wenjun Zhang Liaoning Normal University Dalian 116000 Liaoning China

By analyzing and processing user consumption behavior data on agricultural e-commerce platforms, selecting the appropriate algorithm - knn algorithm, conducting cluster analysis on these data, analyzing the consumption habits of each user, and then classifying these users based on this result to understand and master different product attributes and user characteristics. Finally, predicting the market value of each product inside can provide more accurate marketing decision support for agricultural e-commerce platforms and promote their development. This study shows that the application of knn algorithm in precision marketing of agricultural product e-commerce plays an important role and can provide important reference for the formulation of marketing strategies.

关键词： Data Mining knn algorithm Cluster Analysis

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Quantitative Timing Strategy Model Based on Improved knn algorithm 12

Quantitative Timing Strategy Model Based on Improved KNN Alg...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Lou, Bingjie Shaoa, Bo Ni, Chenchen Dong, Yixuan Yue, Shuning Zhu, Ming Zhejiang International Studies University 299 Liuhe Road Xihu District Zhejiang Province Hang Zhou310000 China

In this paper, given the mindset of data analysis, we adopt the knn algorithm based on the data-driven principle to research and judge the stock price trend. We use a weighted modified Euclidean distance algorithm to measure the distance between each feature value and the target to be measured, calculate the neighborhood K values using cross-validation, and train the *** decided to use a combination of indicators, KDJ and RSI to establish a timing strategy based on the principle of "volume and price matching", and set a stop-loss point to control the risk in order to maximize the stock return and minimize the risk. © 2022 The Authors. Published by Elsevier B.V.

关键词： knn algorithm Machine Learning Multivariate statistical analysis principal component analysis model SPSS

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Using knn algorithm for classification of Distribution transformers Health index 4

Using kNN Algorithm for classification of Distribution trans...

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4th International Conference on Innovative Computing (ICIC)

作者： Javid, Jahanzaib Mughal, Muhammad Ali Karim, Mustansir HITEC Univ Taxila Dept Elect Engn Taxila Pakistan

ISBN: (纸本)9781665400916

Distribution transformers are significant components in a grid utility system. Therefore, knowing the health status of a transformer becomes vital for the safe operation of a grid. Health Index (HI) defined the health status based on laboratory tests and interpreted by the standards specified by IEEE, IEC. The obtained HI number represents whether a transformer is healthy and able to operate further or unhealthy and needs a replacement. In this paper, the k Nearest Neighbors (knn) algorithm is employed to predict the health status based on the laboratory test data. A total of six tests performed on a class of distribution transformers are used as predictors. The knn algorithm has proved to be a good classifier for the transformer health status prediction problem by achieving good accuracy.

关键词： Distribution transformers knn algorithm Health index machine learning

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