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Modifying Genetic algorithm with Species and Sexual Selection by using k-means algorithm

Modifying Genetic Algorithm with Species and Sexual Selectio...

引用

IEEE International Advance Computing Conference

作者： Patel, Rahila Raghuwanshi, M. M. Jaiswal, Anil N. GHRCE CSE M Tech Sem 4 Nagpur Ms India GHRCE Nagpur Ms India NYSS Coll Engn & Res Nagpur Maharashtra India

ISBN: (纸本)9781424429271

Presently, the optimization concept plays an important role in the problems related to engineering management and commerce etc. Recent trends in optimization, points towards the genetic algorithm and evolutionary approaches. Different genetic algorithms are proposed, designed and implemented for the single objective as well as for the multiobjective problems. GAS3[2006](Genetic algorithm with Species and Sexual Selection) proposed by *** and *** is a distributed Quasi steady state real-coded genetic algorithm. In this work, we have modified GAS3 algorithm. We introduce a reclustering module in GAS3 after simple distance based parameter less clustering (species formation). GAS3kM (Modifying Genetic algorithm with Species and Sexual Selection by using k-means algorithm) uses k-means clustering algorithm for reclustering. Experimental results show that GAS3kM has outperformed GAS3 algorithm when tested on unimodal and multimodal test functions.

关键词： Genetic algorithm clustering algorithms optimization evolutionary algorithm k-means clustering algorithm Pattern recognition

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S-DDoS: Apache spark based real-time DDoS detection system

S-DDoS: Apache spark based real-time DDoS detection system

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5th International Symposium on Intelligent Systems Technologies and Applications (ISTA) / International Conference on Applied Soft Computing and Communication Networks (ACN)

作者： Patil, Nilesh Vishwasrao krishna, C. Rama kumar, krishan Panjab Univ Natl Inst Tech Teachers Training & Res NITTTR Dept Comp Sci & Engn Chandigarh India Panjab Univ Univ Inst Engn & Technol UIET Dept Informat Technol Chandigarh India

A Distributed Denial of Service (DDoS) attack is the biggest threat to Internet-based applications and consumes victim service by sending a massive amount of attack traffic. In the literature, numerous approaches are available to protect the victim from the DDoS attacks. However, the attack incidents are increasing year by year. Further, several issues exist in the traditional framework based detection system such as itself becoming a victim, slow detection, no real-time response, etc. Therefore, the traditional framework based system is not capable of processing live traffic in the big data environment. This paper proposes a novel Spark streaming-based distributed and real-time DDoS detection system called S-DDoS. The proposed S-DDoS system employs the k-means clustering algorithm to recognize the DDoS attack traffic in real-time. The proposed detection model designed on the Apache Hadoop framework using highly scalable H2O sparkling water. The detection model deployed on the Spark framework to classify live traffic flows. The results show that the proposed S-DDoS detection system efficiently detects the DDoS attack from network traffic flows with higher detection accuracy (98%).

关键词： Distributed denial of service (DDoS) k-means clustering algorithm big data entropy network security apache spark

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An Improved Slope One algorithm for Collaborative Filtering

An Improved Slope One Algorithm for Collaborative Filtering

引用

9th International Conference on Natural Computation (ICNC)

作者： Li, Jiyun Feng, Pengcheng Lv, Juntao Donghua Univ Coll Comp Sci & Technol Shanghai Peoples R China MVS R&D Ctr Shanghai Peoples R China

ISBN: (纸本)9781467347143

The slope one scheme is a rating-based recommendation algorithm, and it is simple, efficient, easy to implement. However, the slope one scheme suffers from both data sparsity and new item problems, which seriously affect the performance of recommender systems. To solve these problems, we propose an improved slope one algorithm for collaborative filtering. In our algorithm, the new item problem is dealed with by introducing content similarity computation into the slope one scheme. According to the idea of item-based collaborative filtering algorithms, the target user's rating to the target item can be predicted based on the ratings that the target user has rated and the content similarities of items. And clustering algorithm is adopted to tackle the problem of data sparsity. By merging the set of items into several clusters based on the item rating data, the target user's rating to the target item can be predicted based on which cluster the target item belongs to. The final rating of the target user to the target item is the linear combination of the above two ratings. Experiments on the Movielens dataset show that our approach outperforms other three slope one algorithms and two traditional collaborative filtering algorithms on the prediction performance.

关键词： collaborative filtering k-means clustering algorithm slope one scheme item's content similarity

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Cluster Analysis Based on Opinion Mining 8

Cluster Analysis Based on Opinion Mining

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8th International Conference on Ubi-Media Computing (UMEDIA)

作者： Wang, Xiaoye Chai, Xiaorui Hsu, Ching-Hsien Xiao, Yingyuan Li, Yukun Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin 300191 Peoples R China Tianjin Univ Technol Minist Educ Key Lab Comp Vis & Syst Tianjin 300191 Peoples R China Chung Hua Univ Dept Comp Sci & Informat Engn Hsinchu Taiwan

ISBN: (纸本)9781467382700

Mining based on opinions can extract useful information from users' comments. After doing cluster and analysis on the information, users can get a detailed understanding of the commodity, then determine to buy the commodity or not. In this paper, firstly, we extract evaluation objects and evaluation words, then cluster the evaluation objects. Next based on SO-PMI algorithm, judge the polarity of evaluation words and determine their polarity intensity values, then use k-means clustering algorithm to cluster the evaluation words. Last, for every kind of target evaluation object, make a count on the proportion of each kind evaluation word, and show the result to users in an intuitive way. This paper uses noun phrase pattern to match comments to extract evaluation objects and put forward the thematic words extraction algorithm. On judging the evaluation words' polarity, this paper establishes an emotional seed dictionary for each target object. The method of establishing dictionary for every attribute can eliminate the influence that less-correlation evaluation words have on the polarity judgment.

关键词： Opinion mining polarity judgment k-means clustering algorithm SO-PMI algorithm thematic words extraction algorithm

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Intelligent Site Detection Based on Improved YOLO algorithm

Intelligent Site Detection Based on Improved YOLO Algorithm

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International Conference on Big Data Engineering and Education (BDEE)

作者： Sun, Shidan Zhao, Shijia Zheng, Jiachun Jimei Univ Xiamen 361021 Peoples R China

ISBN: (纸本)9781665439572

An improved YOLOv4 algorithm is proposed to improve the detection accuracy of helmets and reflective clothing for safety problems on construction sites that require simultaneous automatic detection of helmets and reflective clothing. Firstly, the clustering algorithm in YOLOv4 network is optimized, and weighted kernel k-means clustering algorithm is used to analyze the dataset, so as to select the anchor box more suitable for small targets and improve the average accuracy and speed of detection;secondly, the Darknet feature network layer inside the YOLO network is optimized, and the feature maps extracted by quadruple downsampling are doubly upsampled, then convolved and fused with the doubled downsampling, and transported to the subsequent network together with quadruple downsampling, eightfold downsampling, and sixteenfold downsampling to achieve the reduction of the probability of missing detection of small targets. Experimental results show that, the average detection accuracy of the improved algorithm is improved by 8.4% when the helmets and reflective clothing are worn at the same time.

关键词： YOLO v4 algorithm safety helmet wearing test k-means clustering algorithm reflective clothing wear detection

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A Method for Coherency Identification Based on Singular Value Decomposition

A Method for Coherency Identification Based on Singular Valu...

引用

IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Zhu, Qiaomu Chen, Jinfu Duan, Xianzhong Sun, Xin Li, Yinhong Shi, Dongyuan Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9781509041688

A novel method to identify coherent generator groups based on singular value decomposition (SVD) in power system is presented in this paper. Via the SVD algorithm, the high-dimensional real-time rotor-angle data of generators is projected into low-dimensional subspace, and the key characteristic coefficients indicating the information of coherency is obtained. Then, the coherency identification is realized by running kmeans clustering algorithm on the coefficients. The dimensionality reduction for clustering algorithm by SVD speeds up the coherency identification. Using the real-time rotor-angle data, the proposed method has the potential to be applied to measured data to implement coherency identification. The effectiveness of this method is demonstrated by the simulation results on IEEE 39-bus system.

关键词： coherency identification k-means clustering algorithm real-time rotor angles singular value decomposition (SVD)

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Impact of typical demand day selection on CCHP operational optimization

Impact of typical demand day selection on CCHP operational o...

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Applied Energy Symposium and Forum - Low-Carbon Cities and Urban Energy Systems (CUE)

作者： Gao, Yuan Liu, Qianying Wang, Shuxia Ruan, Yingjun Tongji Univ Mech & Energy Engn Sch 4800 Caoan Highway Shanghai 201804 Peoples R China

Due to high variability of energy demand in a whole year, optimizing the configuration and operation of a CCHP system will take very high and unfeasible computational time expenses. To overcome this problem, this paper presents a new and creative method to get few representative days that adequately preserve significant demand characteristics. Typical demand days are selected based on the use of k-means clustering algorithm and average method. A hypothetical CCHP system is optimized with mixed-integer linear programming algorithm (MILP) in order to confirm the selection of typical days in this paper. This paper also imitates traditional method. A case study of a Qingdao office building is discussed to demonstrate the proposed method. The results illustrate that the magnitude of Mean Absolute Percentage Errors (MAPE) between actual demand load and typical days load can affect actual operational effect. In conclusion, optimal number of typical days for actual CCHP operation can obtain very low MAPE and lowest annual total cost. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

关键词： CCHP system k-means clustering algorithm typical demand days MILP

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Multifactorial user models for personalized mobile-assisted language learning

Multifactorial user models for personalized mobile-assisted ...

引用

kES International Conference on Smart Digital Futures (SDF)

作者： Troussas, Christos Virvou, Maria Alepis, Efthimios Univ Piraeus Piraeus Greece

ISBN: (纸本)9781614994053;9781614994046

In this paper, we present our multilingual mobile-assisted language learning system which incorporates multifactorial clustering. This means that it provides machine learning techniques for clustering of multiple user characteristics. Multifactorial classification is conducted by the k-means clustering algorithm which takes as input seven important users' characteristics in order to initialize the process. The clustering is conducted by k-means clustering algorithm, which takes as input multiple user characteristics, in order to initialize the process. The aforementioned characteristics tend to influence the educational procedure. k-means algorithm creates clusters based on data from empirical studies. After determining in which cluster each new student belongs, the system can reason about this specific student, adapting its behavior to the user's needs. The resulting adaptation emerges from the analysis of each cluster's characteristics that derive from each cluster as a superset of already existing user models. The communication between the system and its potential users as students is accomplished through the use of web services.

关键词： k-means clustering algorithm mobile learning multifactorial clustering multilingual mobile assisted language learning user classification

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International Symposium on Communication and Information Technologies

作者： Leauhatong, Thurdsak Hamamoto, kazuhiko Atsuta, kiyoaki kondo, Shozo Tokai Univ Grad Sch Sci & Technol Hiratsuka Kanagawa 25912 Japan Tokai Univ Sch Informat & Telecommun engn Hiratsuka Kanagawa 25912 Japan

ISBN: (纸本)9781424423354

This paper proposes a new similarity measure for the content-based image retrieval (CBIR) systems. The similarity measure is based on the multidimensional generalization of the Wald-Wolfowitz (MWW) runs test and the k-means clustering algorithm. The performance comparisons between the proposed method and the current CBIR method based on MWW runs test were performed, and it can be seen that the proposed methods outperform the current method in the sense that the proposed method provides higher performance than the current method for the same computational time.

关键词： Color Histogram Content-Based Image Retrieval k-means clustering algorithm minimal spanning tree Wald-Wolfowitz runs test

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A Novel Method for Text and Non-Text Segmentation in Document Images

A Novel Method for Text and Non-Text Segmentation in Documen...

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2nd IEEE International Conference on Communications and Signal Processing (ICCSP)

作者： Deivalakshmi, S. Palanisamy, P. Vishwanathan, Gayatri Natl Inst Technol Elect & Commun Engn Dept Tiruchirappalli 620015 Tamil Nadu India

ISBN: (纸本)9781467348669;9781467348652

Segmentation of the contents of document images into text and non-text regions is an essential pre-processing step for applications such as document analysis and classification, as well as OCR. This paper presents a novel technique to segment the document image into text and non-text regions using a combination of Wavelet-based Gray Level Co-Occurrence Matrix (GLCM) features and k-means clustering. A comparison between the performances of different wavelets in document image segmentation is also performed and tabulated. The technique was tested on a number of scanned article images from the MediaTeam Document Database and results show a marked improvement over the already existing method based on GLCM features.

关键词： Discrete Wavelet Transform Document Image Segmentation GLCM Features k-means clustering algorithm

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