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A New SAFR Tool for Face recognition Using EGV-LBP-CMI-LDA Wrapped with Secured DWT Based Steganography

A New SAFR Tool for Face Recognition Using EGV-LBP-CMI-LDA W...

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3rd IEEE international Advance Computing conference (IACC)

作者： Jhodge, Shami Chiddarwar, Girija Shinde, Gitanjali Sinhgad Coll Management Dept MCA Pune Maharashtra India Sinhgad Coll Engn Dept Comp Engn Pune Maharashtra India SKN Coll Engn Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9781467345286

Face recognition technique nowadays is emerging as the most significant and challenging aspects in terms of security for identification of images in various fields viz. banking, police records, biometric etc. other than an individual's thumb and documented identification proofs. Till date for efficient net banking to be initiated, one has to provide the appropriate user name and password for purpose of authentication. This project introduces a vehicle to take a step forward in easy and more reliable authentication of an individual by providing Face Image along with User Name and Password to the system. In this an individual's face is identified by biometric authentication support with which, only a person whose account is, can access it. However while transferring this sensitive data of user image, from client machine to bank server it has to be protected from hackers and intruders from manhandling it, hence it is transferred using covert communication called Wavelet Decomposition based steganography. As face images are affected by different expressions, poses, occlusions, illuminations and aging over a period of time and it differs from the same person than those from different ones is the main difficult task in face recognition. Whenever image information is jointly co-ordinated in three aspects viz. image space, scale and orientation domains they carry much higher clues than seen in each domain individually. In the proposed method combination of Local Binary pattern (LBP) and Gabor features are used to increase the face recognition performance significantly to compare individual's face presentations.

关键词： DWT (Discrete Wavelet Transformation) E-GV-LBP (Effective Gabor Volume Linear Binary pattern) CMI (Conditional Mutual Information)

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learning model rules from high-speed data streams 3

Learning model rules from high-speed data streams

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3rd Workshop on Ubiquitous data mining, UDM 2013 - Co-located with the 23rd international Joint conference on Artificial Intelligence, IJCAI 2013

作者： Almeida, Ezilda Ferreira, Carlos Gama, João LIAAD INESC Porto L.A. Portugal

Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule in AMRules uses a Page-Hinkley test to detect changes in the process generating data and react to changes by pruning the rule set. In the experimental section we report the results of AMRules on benchmark regression problems, and compare the performance of our algorithm with other streaming regression algorithms. © 2013 IJCAI.

关键词： Regression analysis

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Evaluating the Effects of Textual Features on Authorship Attribution Accuracy

Evaluating the Effects of Textual Features on Authorship Att...

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3rd international conference on Computer and Knowledge Engineering (ICCKE)

作者： Ramezani, Reza Sheydaei, Navid Kahani, Mohsen Ferdowsi Univ Mashhad Dept Comp Engn DDEmS Lab Mashhad Iran Isfahan Univ Technol DM Lab Esfahan Iran

ISBN: (纸本)9781479920938

Authorship attribution (AA) or author identification refers to the problem of identifying the author of an unseen text. From the machine learning point of view, AA can be viewed as a multiclass, single-label text-categorization task. This task is based on this assumption that the author of an unseen text can be discriminated by comparing some textual features extracted from that unseen text with those of texts with known authors. In this paper the effects of 29 different textual features on the accuracy of author identification on Persian corpora in 30 different scenarios are evaluated. Several classification algorithms have been used on corpora with 2, 5, to, 20 and 40 different authors and a comparison is performed. The evaluation results show that the information about the used words and verbs are the most reliable criteria for AA tasks and also NLP based features are more reliable than BOW based features.

关键词： Authorship Attribution Author Identification Textual Features Persian Corpus data mining Classification

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A Push Strategy for delivering of learning Objects using meta data based association analysis (FP-Tree)

A Push Strategy for delivering of Learning Objects using met...

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3rd international conference on Computer Communication and Informatics (ICCCI)

作者： Sabitha, A. Sai Mehrotra, Deepti Amity Univ Dept CS ASET Noida Uttar Pradesh India ASCS Noida Uttar Pradesh India

ISBN: (纸本)9781467329071;9781467329064

learning Materials are structured as learning Objects and are available in learning Object Repository(LOR) which are used in various courses of an Elearning environment. learning Management System aggregates these objects found in LOR, provides an infrastructure and platform through which learning content is delivered and managed. Adaptation, personalization, usage statistics are some of the LMS functionality. But due to the exponential availability of learning Objects, it leads to increase in difficulty to find the right resource to the user based on the context of learning or his/her preferences. When we search through keywords it results in huge quantity of information being displayed. In this paper we are considering the Search patterns of the users stored in search logs and based on it association rules are generated using Frequent pattern Tree. We can generate a list of Frequent learning objects using frequent item set mining approach FP-Tree, so that a reduced, appropriate and relevant objects can be delivered to the users.

关键词： learning Objects Frequent patternTree Apriori FP Growth Recommender Systems

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Network traffic anomaly detection based on growing hierarchical SOM

Network traffic anomaly detection based on growing hierarchi...

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43rd Annual IEEE/IFIP international conference on Dependable Systems and Networks (DSN)

作者： Huang, Shin-Ying Huang, Yen-Nun Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan

ISBN: (纸本)9781479901814;9781467364713

Network anomaly detection aims to detect patterns in a given network traffic data that do not conform to an established normal behavior. Distinguishing different anomaly patterns from large amount of data can be a challenge, let alone visualizing them in a comparative perspective. Recently, the unsupervised learning method such as the K-means [3], self-organizing map (SOM) [2], and growing hierarchical selforganizing map (GHSOM) [1] have been shown to be able to facilitate network anomaly detection [4][5]. However, there is no study addressing both mining and detecting task. This study leverages the advantage of GHSOM to analyze the network traffic data and visualize the distribution of attack patterns with hierarchical relationship. In the mining stage, the geometric distances between each pattern and its descriptive information are revealed in the topological space. The density and the sample size of each node can help to detect anomalous network traffic. In the detecting stage, this study extends the traditional GHSOM and uses the support vector machine (SVM) [6] to classify network traffic data into the predefined categories. The proposed approach achieves (1) help understand the behaviors of anomalous network traffic data (2) provide effective classification rule to facilitate network anomaly detection and (3) accumulate network anomaly detection knowledge for both mining and detecting purpose. The public dataset and the private dataset are used to evaluate the proposed approach. The expected result is to confirm that the proposed approach can help understand network traffic data, and the detecting mechanism is effective for identifying anomalous behavior.

关键词： Network anomaly detection Neural networks data Clustering data Classification Visualization

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A Neural Network Model for Online Multi-Task Multi-Label pattern recognition

A Neural Network Model for Online Multi-Task Multi-Label Pat...

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23rd international conference on Artificial Neural Networks (ICANN)

作者： Higuchi, Daisuke Ozawa, Seiichi Kobe Univ Grad Sch Engn Nada Ku Kobe Hyogo 6578501 Japan

ISBN: (纸本)9783642407277;9783642407284

This paper presents a new sequential multi-task learning model with the following functions: one-pass incremental learning, task allocation, knowledge transfer, task consolidation, learning of multi-label data, and active learning. This model learns multi-label data with incomplete task information incrementally. When no task information is given, class labels are allocated to appropriate tasks based on prediction errors;thus, the task allocation sometimes fails especially at the early stage. To recover from the misallocation, the proposed model has a backup mechanism called task consolidation, which can modify the task allocation not only based on prediction errors but also based on task labels in training data (if given) and a heuristics on multi-label data. The experimental results demonstrate that the proposed model has good performance in both classification and task categorization.

关键词： multi-task learning incremental learning neural networks multi-label recognition

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Using LS-SVM pattern recognizer to detect change-point in ARMA process

Using LS-SVM pattern recognizer to detect change-point in AR...

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3rd international conference on Frontiers of Manufacturing and Design Science (ICFMD 2012)

作者： Cheng Zhiqiang North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450011 Henan Peoples R China

ISBN: (纸本)9783037855782

Based on LS-SVM pattern recognizer, this paper develops an intelligent method for solving the problem of change-point detection, and the proposed model is applied to detect change-point of process mean-shift in auto-correlated time series process. In this research, LS-SVM algorithm and moving window method are used to detect the location of the mean shift signal, the LS-SVM pattern recognizer is designed and the performance of the recognizer is evaluated in terms of Accuracy Rate. Results of simulation experiment show that the proposed intelligent model is an effective method to detect change-point in ARMA data series.

关键词： change-points detection least squares support vector machine pattern recognition ARMA process

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Emerging Intelligent Computing Technology and Applications: 9th international conference, ICIC 2013, Nanning, China, July 28-31, 2013. Proceedings 1

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丛书名： Communications in Computer and Information Science 375

2013年

作者： Zhifeng Hao, Jinlong Huang, Ruichu Cai, Wen Wen (auth.), De-Shuang Huang, Phalguni Gupta, Ling Wang, Michael Gromiha (eds.)

ISBN: (数字)9783642396786

ISBN: (纸本)9783642396779;9783642396786

This book constitutes the refereed proceedings of the 9th international conference on Intelligent Computing, ICIC 2013, held in Nanning, China, in July 2013. The 192 revised full papers presented in the three volumes LNCS 7995, LNAI 7996, and CCIS 375 were carefully reviewed and selected from 561 submissions. The papers in this volume (CCIS 375) are organized in topical sections on Neural Networks; Systems Biology and Computational Biology; Computational Genomics and Proteomics; Knowledge Discovery and data mining; Evolutionary learning and Genetic Algorithms; machine learning Theory and Methods; Biomedical Informatics Theory and Methods; Particle Swarm Optimization and Niche Technology; Unsupervised and Reinforcement learning; Intelligent Computing in Bioinformatics; Intelligent Computing in Finance/Banking; Intelligent Computing in Petri Nets/Transportation Systems; Intelligent Computing in Signal Processing; Intelligent Computing in pattern recognition; Intelligent Computing in Image Processing; Intelligent Computing in Robotics; Intelligent Computing in Computer Vision; Special Session on Biometrics System and Security for Intelligent Computing; Special Session on Bio-inspired Computing and Applications; Computer Human Interaction using Multiple Visual Cues and Intelligent Computing; Special Session on Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.

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Face recognition based on Opposition Particle Swarm Optimization and Support Vector machine

Face Recognition based on Opposition Particle Swarm Optimiza...

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3rd IEEE international conference on Signal and Image Processing Applications (ICSIPA)

作者： Hasan, Mohammed Abdullah, Siti Norul Huda Sheikh Othman, Zulaiha Ali Univ Kebangsaan Malaysia Fac Informat Sci & Technol Pattern Recognit Res Grp Bandar Baru Bangi 43600 Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Data Min & Optimizat Grp Bandar Baru Bangi 43600 Malaysia

ISBN: (纸本)9781479902699;9781479902675

one of the most recently developed face recognition technique has utilized PSO-SVM, this method lacks in the initial phase of the PSO technique. That is in PSO;initially the populations are generated in random manner. Due to this random process, the population results may also be in random. Thus, it is not certain that this method will produce precise result. Hence to avoid this drawback, a modified face recognition method is proposed in this paper. Here, a new face recognition method based on Opposition based PSO with SVM (OPSO-SVM) is introduced. To accomplish the face recognition with our proposed OPSO-SVM, initially feature extraction process is carried out on the image database. In the feature extraction process, the efficient features are extracted and then given to the SVM training and testing process. In OPSO, the populations are generated in two ways: one is random population as same as the normal PSO technique and the other is opposition population, which is based on the random population values. The optimized parameters in SVM by OPSO efficiently perform the face recognition process. Two human face databases FERET and YALE are utilized to analyze the performance of our proposed OPSO-SVM technique and also this OPSO-SVM is compared with PSO-SVM and standard SVM techniques.

关键词： face recognition feature extraction particle swarm optimisation random processes support vector machines

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An Interactive Rough Set Attribute Reduction Using Great Deluge Algorithm

An Interactive Rough Set Attribute Reduction Using Great Del...

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3rd international Visual Informatics conference (IVIC)

作者： Jaddi, Najmeh Sadat Abdullah, Salwani Univ Kebangsaan Malaysia Fac Informat Sci & Technol Data Min & Optimizat Res Grp DMO Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia

ISBN: (纸本)9783319029573;9783319029580

Dimensionality reduction from an information system is a problem of eliminating unimportant attributes from the original set of attributes while avoiding loss of information in data mining process. In this process, a subset of attributes that is highly correlated with decision attributes is selected. In this paper, performance of the great deluge algorithm for rough set attribute reduction is investigated by comparing the method with other available approaches in the literature in terms of cardinality of obtained reducts (subsets), time required to obtain reducts, number of calculating dependency degree functions, number of rules generated by reducts, and the accuracy of the classification. An interactive interface is initially developed that user can easily select the parameters for reduction. This user interface is developed toward visual data mining. The carried out model has been tested on the standard datasets available in the UCI machine learning repository. Experimental results show the effectiveness of the method especially with relation to the time and accuracy of the classification using generated rules. The method outperformed other approaches in M-of-N, Exactly, and LED datasets with achieving 100% accuracy.

关键词： interactive data mining great deluge algorithm rough set theory attribute reduction classification

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