• Title/Summary/Keyword: Feature Pattern

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Sequential Pattern Mining for Intrusion Detection System with Feature Selection on Big Data

  • Fidalcastro, A;Baburaj, E
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.10
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    • pp.5023-5038
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    • 2017
  • Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

A study on local facial features using LDP (LDP를 이용한 지역적 얼굴 특징 표현 방법에 관한 연구)

  • Cho, Young Tak;Jung, Woong Kyung;Ahn, Yong Hak;Chae, Ok Sam
    • Convergence Security Journal
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    • v.14 no.5
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    • pp.49-56
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    • 2014
  • In this paper, we proposed a method for representing local facial features based on LDP (Local Directional Pattern). To represent both PFF (Permanent Facial Features) and TFF (Transient Facial Features) effectively, the proposed method configure local facial feature vectors based on overlapped blocks for each facial feature in the forms of various size and shape. There are three advantages - it take advantages of geometric feature based method; it shows robustness about detection error using movement characteristics of each facial feature; and it shows reduced sampling error because maintain spatial information caused by block size variability. Proposed method shows better classification accuracy and reduced amount of calculation than existing methods.

Speaker and Context Independent Emotion Recognition System using Gaussian Mixture Model (GMM을 이용한 화자 및 문장 독립적 감정 인식 시스템 구현)

  • 강면구;김원구
    • Proceedings of the IEEK Conference
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    • 2003.07e
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    • pp.2463-2466
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    • 2003
  • This paper studied the pattern recognition algorithm and feature parameters for emotion recognition. In this paper, KNN algorithm was used as the pattern matching technique for comparison, and also VQ and GMM were used lot speaker and context independent recognition. The speech parameters used as the feature are pitch, energy, MFCC and their first and second derivatives. Experimental results showed that emotion recognizer using MFCC and their derivatives as a feature showed better performance than that using the Pitch and energy Parameters. For pattern recognition algorithm, GMM based emotion recognizer was superior to KNN and VQ based recognizer

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Pattern recognition of SMD IC using wavelet transform and neural network (웨이브렛 변환과 신경회로망을 이용한 SMD IC 패턴인식)

  • 이명길;이준신
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.34S no.7
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    • pp.102-111
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    • 1997
  • In this paper, a patern recognition method of surface mount device(SMD) IC using wavelet transform and neural network is proposed. We chose the feature parameter according to the characteristics of coefficient matrix which is obtained from four level discrete wavelet transform (DWT). These feature parameters are normalized and then used for the input vector of neural network which is capable of adapting the surroundings such as variation of illumination, arrangement of objects and translation. Experimental results show that when the same form of feature pattern, as is used for learning, is put into neural network and gained 100% rate ofrecognition irrespective of SMD IC kinds, location and variation of illumination. In the case of unused feature pattern for learning, the recognition rate is 85.9% under the similar surroundings, where as an average recognition rate is 96.87% for the case of reregulated value of illumination. Proosed method is relatively simple compared with the traditional space domain method in extracting the feature parameter and is also well suited for recognizing the pattern's class, position and existence. It can also shorten the processing tiem better than method extracting feature parameter with the use of discrete cosine transform(DCT) and adapt the surroundings such as variation of illumination, the arrangement and the translation of SMD IC.

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Ultrasonic Signal Analysis with DSP for the Pattern Recognition of Welding Flaws

  • Kim, Jae-Yeol;Cho, Gyu-Jae;Kim, Chang-Hyun
    • International Journal of Precision Engineering and Manufacturing
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    • v.1 no.1
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    • pp.106-110
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    • 2000
  • The researches classifying the artificial flaws in welding parts are performed using the pattern recognition technology. For this purpose the signal pattern recognition package including user defined function is developed and the total procedure is made up the digital signal processing, feature extraction, feature selection, classfier design. Specially it is composed with and discussed using the ststistical classfier such as the linear discriminant function classfier, the empirical Bayesian classfier.

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EXTRACTING INSIGHTS OF CLASSIFICATION FOR TURING PATTERN WITH FEATURE ENGINEERING

  • OH, SEOYOUNG;LEE, SEUNGGYU
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.24 no.3
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    • pp.321-330
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    • 2020
  • Data classification and clustering is one of the most common applications of the machine learning. In this paper, we aim to provide the insight of the classification for Turing pattern image, which has high nonlinearity, with feature engineering using the machine learning without a multi-layered algorithm. For a given image data X whose fixel values are defined in [-1, 1], X - X3 and ∇X would be more meaningful feature than X to represent the interface and bulk region for a complex pattern image data. Therefore, we use X - X3 and ∇X in the neural network and clustering algorithm to classification. The results validate the feasibility of the proposed approach.

Hybrid Feature Selection Using Genetic Algorithm and Information Theory

  • Cho, Jae Hoon;Lee, Dae-Jong;Park, Jin-Il;Chun, Myung-Geun
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.13 no.1
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    • pp.73-82
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    • 2013
  • In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.

Spectral Pattern Based Robust Speech Endpoint Detection in Noisy Environments (스펙트럼 패턴 기반의 잡음 환경에 강인한 음성의 끝점 검출 기법)

  • Park, Jin-Soo;Lee, Yoon-Jae;Lee, In-Ho;Ko, Han-Seok
    • Phonetics and Speech Sciences
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    • v.1 no.4
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    • pp.111-117
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    • 2009
  • In this paper, a new speech endpoint detector in noisy environment is proposed. According to the previous research, the energy feature in the speech region is easily distinguished from that in the speech absent region. In conventional method, the endpoint can be found by applying the edge detection filter that finds the abrupt changing point in feature domain. However, since the frame energy feature is unstable in noisy environment, the accurate edge detection is not possible. Therefore, in this paper, the novel feature extraction method based on spectrum envelop pattern is proposed. Then, the edge detection filter is applied to the proposed feature for detection of the endpoint. The experiments are performed in the car noise environment and a substantial improvement was obtained over the conventional method.

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Real-Time Locomotion Mode Recognition Employing Correlation Feature Analysis Using EMG Pattern

  • Kim, Deok-Hwan;Cho, Chi-Young;Ryu, Jaehwan
    • ETRI Journal
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    • v.36 no.1
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    • pp.99-105
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    • 2014
  • This paper presents a new locomotion mode recognition method based on a transformed correlation feature analysis using an electromyography (EMG) pattern. Each movement is recognized using six weighted subcorrelation filters, which are applied to the correlation feature analysis through the use of six time-domain features. The proposed method has a high recognition rate because it reflects the importance of the different features according to the movements and thereby enables one to recognize real-time EMG patterns, owing to the rapid execution of the correlation feature analysis. The experiment results show that the discriminating power of the proposed method is 85.89% (${\pm}2.5$) when walking on a level surface, 96.47% (${\pm}0.9$) when going up stairs, and 96.37% (${\pm}1.3$) when going down stairs for given normal movement data. This makes its accuracy and stability better than that found for the principal component analysis and linear discriminant analysis methods.

Pattern Recognition System Combining KNN rules and New Feature Weighting algorithm (KNN 규칙과 새로운 특징 가중치 알고리즘을 결합한 패턴 인식 시스템)

  • Lee Hee-Sung;Kim Euntai;Kim Dongyeon
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.42 no.4 s.304
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    • pp.43-50
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    • 2005
  • This paper proposes a new pattern recognition system combining the new adaptive feature weighting based on the genetic algorithm and the modified KNN(K Nearest-Neighbor) rules. The new feature weighting proposed herein avoids the overfitting and finds the Proper feature weighting value by determining the middle value of weights using GA. New GA operators are introduced to obtain the high performance of the system. Moreover, a class dependent feature weighting strategy is employed. Whilst the classical methods use the same feature space for all classes, the Proposed method uses a different feature space for each class. The KNN rule is modified to estimate the class of test pattern using adaptive feature space. Experiments were performed with the unconstrained handwritten numeral database of Concordia University in Canada to show the performance of the proposed method.