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Principle and design of a dynamic neural network for efficient and accurate recognition of a time-varying object based on its static patterns and its dynamic pattern-variations

Principle and design of a dynamic neural network for efficie...

引用

conference on Image Processing - Algorithms and Systems VII

作者： Hu, Chialun John Univ Colorado Coll Engn Boulder CO 80309 USA

ISBN: (纸本)9780819474957

Based on our research in the last 17 years (with 68 papers published) on the subject of artificial neural network studied from the point of view of N-dimension geometry, a novel neural network system, the dynamic neural network, is proposed here for detecting an unknown moving (or time-varying) object such that the object will not only be detected by its static images, but also by the way it moves if this object follows a constant moving pattern. The system is designed to identify the unknown object by comparing a few time-separated snapshots of the object to a few standard moving objects learned or memorized in the system. The identification is determined by a user entered accuracy control. It could be very accurate, yet still be quite robust and quite fast in identification (e. g., identification in real-time) because of the simplicity of the algorithm. It is different from most other neural network systems because it employs the ND geometrical concept.

关键词： Neodymium Neural networks Analog electronics Dynamical systems Signal processing Artificial neural networks computing systems Image processing Optical filters System identification

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Toward Computational Modeling of the Consumer Based on a Large-Scale Dataset Observed in a Real Service

Toward Computational Modeling of the Consumer Based on a Lar...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Tsukasa Ishigaki Yoichi Motomura Center for Service Research National Institute for Advanced Industrial Science and Technology Tokyo Japan

In service industries, productivity growth requires matching the level of demand of the consumer and the level of service of the provider. This matching requires the service provider to have knowledge of consumer-related factors, such as the satisfaction level or the concept of value of the consumer. An intelligent model of the consumer is needed in order to estimate such factors because these factors cannot be observed directly by the service provider. However, obtaining knowledge of such factors in real services using conventional consumer behavior theory is difficult because the models are not designed for practical application, but rather are intended to provide a comprehensive and elaborative understanding of consumer behaviors. In addition, most conventional models are qualitative, and so cannot provide quantitative information for decision making by the providers. The present paper describes a method for computational modeling of the consumer by understanding the behavior based on large-scale datasets observed in real services. It is difficult to model the behavior or decision making process of the customer, which involves nonlinear or non-Gaussian variables, using conventional statistical modeling techniques, which assume linear or Gaussian models. We use a Bayesian network method, which can handle nonlinear and non-Gaussian variables as conditional probabilities. The models are constructed based on large-scale datasets observed in real services and present some practical applications of the models to retail and content providing services. The proposed method is efficient for many other services that use a variety of large-scale datasets.

关键词： Computational modeling Large-scale systems Consumer behavior Predictive models Bayesian methods Computer industry Productivity Decision making pattern recognition pattern matching

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Situation-Awareness and Sensor Stream Mining for Sustainable Human Life

Situation-Awareness and Sensor Stream Mining for Sustainable...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Zahoor ur Rehman Muhammad Shahbaz Muhammad Shaheen Aziz Guergachi Department of CS & E University of Engineering and Technology Lahore Pakistan Ryerson University Toronto ONT Canada Information Technology Management Ryerson University Toronto Canada

Criminal activities cause a huge amount of loss both financially and in terms of human lives. Because of these acts, business and social sectors are struggling. This paper illustrates the development of an online sensor stream mining system that is able to analyze the situational behavior of all of the persons in specific areas and in turn propose real-time alert systems to take countermeasures. This system gathers different information from heterogeneous sensors, fuse that information, and generate real-time alerts to minimize the likelihood of disaster. These alerts and alarms assist security personnel in making appropriate decisions in real-time scenarios. The novelty of this approach comprises context-awareness with online diagnoses to take countermeasures in real-time to reduce the loss of lives, and damage to societies and economies. This technique makes the sensor stream mining process more dependable and increases the reliability of the overall system. To fulfill the objectives of this research, we incorporate lightweight online mining algorithms to extract useful but hidden information from the data gathered. Contextual information such as a person's pattern of movement, current location, personal profile, and area of residence are exploited to detect anomalous behaviors. The major goal of this research is to detect those persons performing malicious activities and in turn minimize society's exposure to risks and vulnerabilities.

关键词： Humans Design optimization Printing Integer linear programming Constraint optimization Testing Containers pattern recognition Computer applications Laboratories

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Computer Analysis of Images and patterns - 13th international conference, CAIP 2009, Proceedings

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13th international conference on Computer Analysis of Images and patterns, CAIP 2009

ISBN: (纸本)3642037666

The proceedings contain 149 papers. The topics discussed include: head pose estimation by a stepwise nonlinear regression;model-based illumination correction for face images in uncontrolled scenarios;a new gabor phase difference pattern for face and ear recognition;second-level partition for estimating FAR confidence intervals in biometric systems;smooth multi-manifold embedding for robust identity-independent head pose estimation;human age estimation by metric learning for regression problems;face detection using GPU-based convolutional neural networks;Gaussian weak classifiers based on haar-like features with four rectangles for real-time face detection;model based analysis of face images for facial feature extraction;regression based non-frontal face synthesis for improved identity verification;differential feature analysis for palmprint authentication;calibration of radially symmetric distortion by fitting principal component;and calibration of rotating sensors.

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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...

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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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Feature Extraction for Traditional Malay Musical Instruments Classification System

Feature Extraction for Traditional Malay Musical Instruments...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Norhalina Senan Rosziati Ibrahim Nazri Mohd Nawi Musa Mohd Mokji Faculty of Information Technology and Multimedia Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Malaysia Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia

Automatic musical instrument classification system deals with a large number of sound database and various types of features schemes. With the lack of data pre-processing, it might become invaluable asset that can impact the whole classification tasks. In handling an effective classification system, finding the best data sets with the best features schemes often a vital step in the data representation and feature extraction process. Thus, this study is conducted in order to investigate the impact of several factors that might affecting the classification accuracy such as audio length, segmented frame size and data sets size (for training and testing) towards traditional Malay musical instruments sounds classification system. The perception-based and MFCC features schemes with total of 37 features was utilized in this study. Meanwhile, multi-layered perceptrons classifier is employed to evaluate the modified data sets and extracted features schemes in terms of their classification performance. Results show that the highest accuracy of 99.57% was obtained from the best data sets with the combination of full features. It is also revealed that the identified factors had a significant role to the performance of classification accuracy. Hence, this study suggest that further feature analysis research is necessary for better solution in traditional Malay musical instruments sounds classification system problem.

关键词： Feature extraction Instruments Data mining Mel frequency cepstral coefficient Multilayer perceptrons Multimedia databases Testing Cepstral analysis Performance analysis pattern recognition

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Performance Comparison of 2D-Discrete Cosine Transform and 2D-Discrete Wavelet Transform for Neural Network-Based Face Detection

Performance Comparison of 2D-Discrete Cosine Transform and 2...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Moeen Tayyab Zafar M. F. Syed Sohaib Ali International Islamic University Islamabad Pakistan International Islamic University Islamabad

Human brain can detect faces from the images constructed in their eyes. The face detection is a computerize method of locating the face in the digital image. It is an important challenge to locate faces from uncontrolled and indistinguishable background of the digital image. This paper presents human face detection from the colored images. Skin color segmentation is used for localizations of skin colored components in the digital image. The features are extracted by using 2D-Discrete Cosine Transform (DCT2) and 2D-Discrete Wavelet Transform (DWT2). The Back Propagation neural Network (BPN) is used for training and testing purposes. In this research, three sets of images: 50, 100 and 180 have been used for experimentation. About 60 % of the images were used in training phase and rests were used for testing purpose. This paper presents the performance comparison of DCT2 and DWT2 used for feature extraction. The best detection rate of 84.03 % with the false positive rate of 5.05% was obtained using DCT2 as compare to DWT2 which provided detection rate of 83.61 % with the false positive rate of 8.43%.

关键词： Wavelet transforms Neural networks Face detection Digital images Discrete wavelet transforms Biological neural networks Humans Skin Feature extraction Testing

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A Hybrid of Rule and Frame Based Approach in Solving Hajj Complex Problems

A Hybrid of Rule and Frame Based Approach in Solving Hajj Co...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Halimah Mohamed Shahida Sulaiman Maziani Sabudin Information Technology Session Fisheries Research Institute Batu Maung Penang Malaysia School of Computer Sciences Universiti Sains Islam Malaysia Penang Malaysia

Hajj is the fifth commandment in Islamic religion that is compulsory to all able bodied Muslims to perform at least once in their whole life. Hajj pilgrims must be well prepared before going to Mecca. They may face complex problems when preparing for and performing Hajj. In such situations they require experts' guidance. Accessing information from explicit and tacit knowledge from Hajj experts has some limitations in the aspects of availability, time and cost. Thus, it motivates us to propose a hybrid of rule and frame based knowledge representation approach to eliminate current drawbacks on using both methods separately. The main objective is to solve complex and layered problems with regards to Hajj rituals. The proposed Hajj knowledge base applies the hybrid approach due to several factors such as more structured, support large number of conditions and rules, support complex tasks, more flexible and easy to use. We implement the approach in a prototype. We conducted both lab experiment for comparative study and usability study to evaluate the effectiveness of the proposed approach. The result shows that the hybrid approach never fails to solve problems given since this approach predetermine the problems and solutions before transforming the knowledge into the Hajj knowledge base. The knowledge becomes cases to be used in future reasoning. In addition, the approach reduces the time to solve a problem as compared to searching static information via the Internet.

关键词： Knowledge representation Books Knowledge based systems Internet Problem-solving Humans Knowledge engineering pattern recognition Information technology Aquaculture

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Video shot segmentation using graph-based dominant-set clustering 09

Video shot segmentation using graph-based dominant-set clust...

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1st international conference on Internet Multimedia computing and Service, ICIMCS 2009

作者： Li, Li Zeng, Xianglin Li, Xi Hu, Weiming Zhu, Pengfei National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781605588407

Video shot segmentation is a solid foundation for automatic video content analysis, for most content based video retrieval tasks require accurate segmentation of video boundaries. In recent years, using the tools of data mining and machine learning to detect shot boundaries has become more and more popular. In this paper, we propose an effective video segmentation approach based on a dominant-set clustering algorithm. The algorithm can not only automatically determine the number of video shots, but also obtain accurate shot boundaries with low computation complexity. Experimental results have demonstrated the effectiveness of the proposed shot segmentation approach. Copyright 2009 ACM.

关键词： Clustering algorithms

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Application of nonlinear classifiers with the principal component analysis in fMRI language activation pattern recognition in a multisite study for pediatric epilepsy

Application of nonlinear classifiers with the principal comp...

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2009 international conference on Image Processing, Computer Vision, and pattern recognition, IPCV 2009

作者： You, X. Guillen, M. Gaillard, W.D. Adjouadi, M. College of Engineering and Computing Florida International University Miami FL United States Department of Neuroscience Children's National Medical Center Washington DC United States

ISBN: (纸本)9781601321190

This paper describes a unique classification approach using nonlinear decision functions (NDF) as means to automatically classify language related functional Magnetic Resonance Imaging (fMRI) brain activation maps into typical and atypical categories involving subjects from a multisite consortium for pediatric epilepsy research. The classification results obtained are compared to the well-established method of support vector machines (SVM), The NDF are evaluated under different degrees of complexity, and assuming any number of dimensions in the decision space, where the leading eigenvectors of the principal component analysis (PCA) are considered. Results reveal high classification accuracy and sensitivity on the testing data in both methods. However, NDF were found to have the best performance with a complexity degree of 7 in a 4D feature space, yielding the following results: 96% accuracy, 97% sensitivity, 95% specificity, and 95% precision. Distinct activation patterns are observed when applying the NDF-based classifier on 122 real datasets.

关键词： Support vector machines

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