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5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Ma, Chun Jie Ding, Zheng Sheng Xian Univ Sci & Technol Coll Sci Xian 710600 Peoples R China

ISBN: (纸本)9781665423144

Aiming at the shortcoming that the efficiency of k-nearest neighbor algorithm (kNN) greatly decreases with the increase of the number of samples, a two-screen k-nearest neighbor algorithm is proposed. Its purpose is to increase the calculation speed of the algorithm while ensuring that the accuracy rate does not decrease, so that the improved algorithm can handle big data problems. Firstly, large-scale spectral clustering based on landmark representation (LSC algorithm) is used to cluster the data to find the cluster closest to the point to be measured;secondly, triangle inequality is used to process the data set close to the point to be measured and far from the nearest cluster center. The data after these two screenings is then used as the final training sample of the k-nearest neighbor algorithm. It can improve the diversity and effectiveness of data on the basis of reducing the number of training samples. Finally, it was verified on a public data set and compared with the traditional k nearest neighbor algorithm. The results show that the speed of the algorithm has been improved, the calculation amount has been reduced by 49%, and the accuracy has been maintained at about 95%.

关键词： k-nearest neighbor algorithm LSC algorithm triangle inequality Two screening classification

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dForml(kNN)-PseAAC: Detecting formylation sites from protein sequences using k-nearest neighbor algorithm via Chou's 5-step rule and pseudo components

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JOURNAL OF THEORETICAL BIOLOGY 2019年 470卷 43-49页

作者： Ning, Qiao Ma, Zhiqiang Zhao, Xiaowei Northeast Normal Univ Informat Sci & Technol Changchun 130117 Jilin Peoples R China

Formylation is a type of post-translational modification that can occur on lysine sites, which plays an irreplaceable role in organism. To better understand the mechanism, it is necessary to identify formylation sites in proteins accurately. Computational method is popular because of its more convenience and higher speed than traditional experimental methods. However, no computational method has been proposed for prediction of lysine formylation. In this study, we developed a predictor named LFPred to identify lysine formylation sites using sequence features (including amino acid composition (AAC), binary profile features (BPF), and amino acid index (AAI)) combined k-nearest neighbor algorithm as classifier. We chose discrete window instead of continuous window according to information entropy. Besides, we took measure to select more reliable negative samples and address the severe imbalance between positive samples and negative samples. Finally, the performance of LFPred is measured with a specificity of 79.9% and a sensibility of 81.4% using jackknife test method, which indicated that our method can be a useful tool for prediction of lysine formylation sites. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Formylation Sequence feature k-nearest neighbor algorithm Discrete window Information entropy

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Design and Implementation of Handwritten Digit Recognition Based on k-nearest neighbor algorithm

Design and Implementation of Handwritten Digit Recognition B...

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作者： Ying Wang Qingyun Liu Yaqi Sun Feng Zhang Yining Zhu College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application

In this paper, a handwritten digit recognition system based on knearest neighbor algorithm is designed and implemented. By analyzing the application principle and steps of k-nearest neighbor algorithm in machine learning classifier, we design handwritten digit recognition in Python *** order to improve the accuracy and accuracy of handwritten character recognition, the method of smooth denoising and character segmentation methods are used to preprocess the data, extract the structure and statistical features. The experiments had shown that the accuracy of the algorithm for handwritten character recognition can reach over 97%.

关键词： k-nearest neighbor algorithm Handwritten digit recognition Image preprocessing

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A novel single-ended fault location scheme for parallel transmission lines using k-nearest neighbor algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 69卷 41-53页

作者： Swetapadma, Aleena Yadav, Anamika KIIT Univ Sch Comp Engn Bhubaneswar Odisha India NIT Raipur Dept Elect Engn Chhattisgarh India

This paper proposes k-nearest neighbour (k-NN)-based method for fault location estimation of all types of fault in parallel lines using one-terminal measurement. Discrete Fourier Transform (DFT) is used for pre-processing the signals and then the standard deviation of one cycle of pre-fault and one cycle of post-fault samples are used as inputs to k-NN algorithm. The results obtained under various fault conditions demonstrate the high accuracy of the proposed scheme to estimate the fault location. The accuracy of the k-NN-based fault location scheme is not affected by alteration in fault type including inter-circuit faults, fault location, fault inception angle, fault resistance, and pre-fault load angle.

关键词： Discrete Fourier transform Fault location k-nearest neighbor algorithm Parallel transmission lines

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Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on k-nearest neighbor algorithm

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Transactions of Tianjin University 2018年第3期24卷 282-289页

作者： Denghua Zhong Rongxiang Du Bo Cui Binping Wu Tao Guan State Key Laboratory of Hydraulic Engineering Simulation and Safety School of Civil EngineeringTianjin University

During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the k-nearest neighbor algorithm. Taking the LHk core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface.

关键词： Core rockfill dam Dam storehouse surface construction Spreading thickness k-nearest neighbor algorithm Real-time monitor

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A k-nearest neighbor algorithm Based on Homomorphic Encryption

A k-Nearest Neighbor Algorithm Based on Homomorphic Encrypti...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Zhenzhou Guo Shan Wang Weifeng Jin Changqing Gong Na Lin School of Computer Science Shenyang Aerospace University Shenyang China

ISBN: (纸本)9781728152103

Protection of privacy has become an essential problem in cloud platform security. In many cases, data is shared with the third party for the analysis purpose. However, the sharing of data for analysis is not safe. Fully homomorphic encryption (FHE) is very promising to deal with ciphertext without decryption, FHE has become one of the key technologies to improve the security of user sensitive information. In this paper, we solve the problem of privacy preserving k-nearest neighbor classification (k-NN), which forms the basis of many data analysis applications. We propose a scheme Fk-NN, which is based on homomorphic encryption and numerical comparator. In our scheme, the homomorphic subtraction operation is designed and implemented firstly. Then, the cloud calculates the nearest neighbors of a given data point while the data point as well as the data points in the training set are in encrypted form. We can obtain classification results which are in encrypted form. The correctness of the scheme has been shown over cardiac disease dataset. The results show the efficiency of the proposed scheme satisfy the requirements of the system and accurately classify data which is in encrypted form.

关键词： cloud computing cryptography data analysis data privacy learning (artificial intelligence) pattern classification Data privacy Homomorphic encryption Pattern recognition Data analysis cryptography Cloud Computing nearest neighbor algorithm nearest neighbor k-nearest neighbor algorithm Statistical analysis Ciphertext Privacy key technology Third party

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An Optimized k-nearest neighbor algorithm for Extending Wireless Sensor Network Lifetime 3rd

An Optimized K-Nearest Neighbor Algorithm for Extending Wire...

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3rd International Conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： Ahmed, Mohammed M. Taha, Ayman Hassanien, Aboul Ella Hassanien, Ehab Minia Univ Fac Comp & Informat Al Minya Egypt Cairo Univ Fac Comp & Informat Giza Egypt Sci Res Grp Egypt SRGE Giza Egypt

ISBN: (纸本)9783319746906;9783319746890

This paper presents an optimized k-nearest neighbors (kNNs) classification algorithm using the metaheuristic whale optimization to searches for sink node in wireless sensor networks. Sink node aggregate data from all sensor nodes and reducing the energy consumption network to prolong network lifetime. To reach aforementioned, a fitness function has formulated to choose the best location of sink node with high residual neighbor's sensor nodes energy to leads to maximizing the network lifetime. Eventually, the experimental results have been conducted whereas sensor nodes are propagated in a random location within the desired network area. The system has 11% improvement on the network's energy consumption that increases the lifetime of the network.

关键词： k-nearest neighbor algorithm Energy-efficient Classification Wireless sensor networks Swarm optimization

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Solving vehicle routing problem by using improved k-nearest neighbor algorithm for best solution

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JOURNAL OF COMPUTATIONAL SCIENCE 2017年第Jul.期21卷 232-240页

作者： Mohammed, Mazin Abed Abd Ghani, Mohd khanapi Hamed, Raed Ibraheem Mostafa, Salama A. Ibrahim, Dheyaa Ahmed Jameel, Humam khaled Alallah, Ahmed Hamed Univ Tekn Malaysia Melaka Fac Informat & Commun Technol Biomed Comp & Engn Technol BIOCORE Appl Res Grp Durian Tunggal Malaysia Univ Anbar Univ Headquarter Planning & Follow Up Dept Anbar Iraq Univ Human Dev Coll Sci & Technol Dept IT Sulaymaniyah Krg Iraq Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Johor Baharu Malaysia Univ Anbar Coll Comp Sci & Informat Technol Informat Syst Dept Anbar Iraq

Context: Vehicle routing problem (VRP) is one of the many difficult issues that have no perfect solutions yet. Many researchers over the last few decades have established numerous researches and used many methods with different techniques to handle it. But, for all research, finding the lowest cost is very complex. However, they have managed to come up with approximate solutions that differ in efficiencies depending on the search space. Problem: In this study the problem is as follows: have a number of vehicles which are used for transporting applications to instance place. Each vehicle starts from a main location at different times every day. The vehicle picks up applications from start locations to the instance place in many different routes and return back to the start location in at specific times every day, starting from early morning until the end of official working hours, on the following conditions: (1) Every location will be visited once in each route, and (2) The capacity of each vehicle is enough for all applications included in each route. Objectives: Our paper attempt to find an optimal route result for VRP by using k-nearest neighbor algorithm (kNNA). To achieve an optimal solution for VRP with the accompanying targets: (1) To reduce the distance and the time for all paths this leads to speedy the transportation of customers to their locations, (2) To implement the capacitated vehicle routing problem (CVRP) model for optimizing the solutions. Approach: The approach has been presented based on two phases: firstly, the algorithms have been adapted to solve the research problem, where its procedure is different than the common algorithm. The structure of the algorithm is designed so that the program does not require a large database to store the population, which speeds up the implementation of the program execution to obtain the solution;secondly, the algorithm has proven its success in solving the problem and finds a shortest route. For the purpose of

关键词： k-nearest neighbor algorithm Vehicle routing problem Capacitated vehicle routing problem

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Post-fault Transient Stability Assessment Based on k-nearest neighbor algorithm with Mahalanobis Distance

Post-fault Transient Stability Assessment Based on <i>k</i>-...

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International Conference on Power System Technology (POWERCON)

作者： Fan, Hang Chen, Ying Huang, Shaowei Zhang, Xuemin Guan, Huizhe Shi, Dongyu Tsinghua Univ State Key Lab Power Syst Dept Elect Engn Beijing 100084 Peoples R China CEPRI Beijing Peoples R China

ISBN: (纸本)9781538664612

To enhance the security of power system, fast and accurate transient stability assessment methods are highly in demand. Many works have been done to explore the stability classification by searching and matching based on the post-fault PMU measurement data. Generally, the Euclidean distance is adopted to evaluate the similarity of different time series data. However, the correlation of the sample attributes is ignored in the classifier based on Euclidean distance. Besides, due to the imbalance feature of the transient stability assessment, costs of false classifications are unequal. For example, classifying the stable samples into unstable ones introduces less operational risks than classifying unstable samples into the stable. To handle above technique challenges, this paper proposes a k-nearest neighbor algorithm using Mahalanobis distance for transient stability assessments, in which the correlation of critical time series data is carefully considered. To strengthen the cost-sensitive feature of the proposed classifier, training processes are optimized regarding an area under the curve index. Finally, a case study is presented where the IEEE 10-machine 39-bus system is used for tests. Results of classifiers with different distance are compared. Test results validate the efficacy of the proposed k-nearest neighbor algorithm using Mahalanobis distance. Besides, it is also demonstrated that using Mahalanobis distance, the stability assessment can be accurate and robust even only based on a few measured states in form of time series data.

关键词： Power system stability assessment Class imbalance Mahalanobis distance criterion k-nearest neighbor algorithm

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Text Sentiment Analysis of Garbage Incineration “Nimby” Event Based on k-nearest neighbor algorithm

Text Sentiment Analysis of Garbage Incineration “Nimby” Ev...

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第15届创新与管理国际学术会议（ICIM2018）

作者： Huang Pan Yang Qing School of Management Wuhan University of Technology

In order to identify the emotional tendency of garbage incineration "Nimby" events, a k-nearest neighbor algorithm is proposed to analyze the events by analyzing the structure of the text and the characteristics of emotional expression. The text of garbage incineration "Nimby" case is divided into partial emotions and overall emotions. Through the construction of the model, the overall sentiment of the entire event text is calculated and the construction of a garbage incineration "Nimby" case library is facilitated.

关键词： Garbage incineration Nimby k-nearest neighbor algorithm Sentiment analysis

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