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A Novel Image Segmentation Algorithm Based on Harmony Fuzzy Search Algorithm

A Novel Image Segmentation Algorithm Based on Harmony Fuzzy ...

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international conference of soft computing and pattern recognition (socpar)

作者： Osama Moh'd Alia Rajeswari Mandava Dhanesh Ramachandram Mohd Ezane Aziz CVRG School of Computer Sciences Universiti Sains Islam Malaysia Penang Malaysia Department of Radiology Health Campus Universiti Sains Islam Malaysia Kelantan Malaysia

Image segmentation is considered as one of the crucial steps in image analysis process and it is the most challenging task. Image segmentation can be modeled as a clustering problem. Therefore, clustering algorithms have been applied successfully in image segmentation problems. Fuzzy c-mean (FCM) algorithm is considered as one of the most popular clustering algorithm. Even that, FCM can generate a local optimal solution. In this paper we propose a novel harmony fuzzy image segmentation algorithm (HFISA) which is based on harmony search (HS) algorithm. A model of HS which uses fuzzy memberships of image pixels to a predefined number of clusters as decision variables, rather than centroids of clusters, is implemented to achieve better image segmentation results and at the same time, avoid local optima problem. The proposed algorithm is applied onto six different types of images. The experiment results show the efficiency of the proposed algorithm compared to the fuzzy c-means algorithm.

关键词： Image segmentation Clustering algorithms Partitioning algorithms Pixel pattern recognition Radiology Image color analysis Image motion analysis Image texture analysis Digital images

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Rule Modeling Engine for Optimizing Complex Event Processing patterns

Rule Modeling Engine for Optimizing Complex Event Processing...

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international conference of soft computing and pattern recognition (socpar)

作者： Babak Behravesh Siti Mariyam Shamsuddin Anazida Zainal Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia Skudai Malaysia

In Complex Event Processing (CEP), we deal with how to search through a sequence of incoming events to find a specified and desired pattern. CEP has a broad use in today enterprise. It can act on sent and/or received events. The result can generate other events that can be used in different layers of an enterprise system. Growing number of areas dealing with arisen events like Business Activity Monitoring (BAM), Fraud detection and intrusion detection makes CEP a hot topic for *** efficient high-performance patterns is the issue which has been addressed in this paper. The pattern can be made from any query given by user. The user defined query is CQL (Continuous Query Language)which is relevant for time series data. NFA (Nondeterministic Finite Automaton) is used for modeling patterns although it has some defects which are *** focus of this paper is on developing a rule modeling engine and taking into account the role of historical data to make efficient patterns. We developed some algorithms for each component of proposed model. The results are optimized patterns produced based on historical data and queries given by user. Finally we show that these techniques can be efficient when we deal with high volume event-base data.

关键词： Engines Automata Data mining Association rules Computer science Event detection Transaction databases Itemsets pattern recognition Information systems

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Experimental Investigation of PSO Based Web User Session Clustering

Experimental Investigation of PSO Based Web User Session Clu...

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international conference of soft computing and pattern recognition (socpar)

作者： Haiyan Lu Thi Thanh Sang Nguyen Decision Systems & e-Service Intelligence Laboratory Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology Sydney University of Technology Sydney Australia Decision Systems & e-Service Intelligence Laboratory Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology-Sydney Sydney Australia

Web user session clustering is very important in web usage mining for web personalization. This paper proposes a particle swarm optimization (PSO) based sequence clustering approach and presents an experimentally investigation of the PSO based sequence clustering methods, which use three original PSO variants and their corresponding variants of a hybrid PSO with real value mutation. The investigation was conducted in 45 test cases using five web user session datasets extracted from a real world web site. The experimental results of these methods are compared with the results obtained from the traditional k-means clustering method. Some interesting observations have been made. In the most of test cases under consideration, the PSO and PSO-RVM methods have better performance than the k-means method. Furthermore, the PSO-RVM methods show better performance than the corresponding PSO methods in the cases in which the similarity measure function is more complex.

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

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Navigation and Browsing of Image Databases

Navigation and Browsing of Image Databases

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international conference of soft computing and pattern recognition (socpar)

作者： William Plant Gerald Schaefer School of Engineering and Applied Science Aston University UK Department of Computer Science Loughborough University UK

Image collections are growing at an exponential rate and solutions to manage vast databases of images are hence highly sought after. Content-based image retrieval techniques have shown great potential, yet commonly employed approaches like query-by-example are only of limited usefulness. An interesting alternative is provided by systems that allow visual exploration of an image dataset through a browsing interface. In these methods the complete database, or parts thereof, is visualised through application of dimensionality reduction techniques, clustered visualisations or display of a graph structure. Once visualised, it should then be possible to browse through the collection in an interactive, intuitive and efficient way. In this paper we present various browsing techniques that can be employed for this purpose. Browsing can be achieved in several ways. We can distinguish between horizontal browsing which works on images of the visualisation plane, and includes operations such as panning, zooming, magnification and scaling, and vertical browsing which allows navigation to a different level of a hierarchically organised visualisation. Furthermore, browsing can also be accomplished by taking into account time stamp information, hence enabling temporal browsing. We conclude, highlighting the need for objective evaluation and benchmarking of browsing system and see one of the next research challenges in the development of effective image browsing tools for mobile devices.

关键词： Navigation Image databases Visualization Image retrieval Visual databases Information retrieval Content based retrieval Principal component analysis pattern recognition Data engineering

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Improved Intrusion Detection System Using Fuzzy Logic for Detecting Anamoly and Misuse Type of Attacks

Improved Intrusion Detection System Using Fuzzy Logic for De...

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international conference of soft computing and pattern recognition (socpar)

作者： Bharanidharan Shanmugam Norbik Bashah Idris Centre for Advanced Software Engineering UTM International Campus Kuala Lumpur Malaysia

Currently available intrusion detection systems focus mainly on determining uncharacteristic system events in distributed networks using signature based approach. Due to its limitation of finding novel attacks, we propose a hybrid model based on improved fuzzy and data mining techniques, which can detect both misuse and anomaly attacks. The aim of our research is to reduce the amount of data retained for processing i.e., attribute selection process and also to improve the detection rate of the existing IDS using data mining technique. We then use improved Kuok fuzzy data mining algorithm, which in turn a modified version of APRIORI algorithm, for implementing fuzzy rules, which allows us to construct if-then rules that reflect common ways of describing security attacks. We applied fuzzy inference engine using mamdani inference mechanism with three variable inputs for faster decision making. The proposed model has been tested and benchmarked against DARPA 1999 data set for its efficiency and also tested against the ¿live¿ networking environment inside the campus and the results has been discussed.

关键词： Intrusion detection Fuzzy logic Data mining software engineering Turing machines Inference algorithms Data security Testing Information security Artificial intelligence

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Text-Based Protein Structure Modeling for Structure Comparison

Text-Based Protein Structure Modeling for Structure Comparis...

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international conference of soft computing and pattern recognition (socpar)

作者： Jafar Razmara Safaai B. Deris Sepideh Parvizpour Faculty of Computer Science and Information Systems Universiti Teknologi Malaysia Johor Bahru Malaysia Faculty of Bioscience and Bioengineering Universiti Teknologi Malaysia Johor Bahru Malaysia

Protein structure comparison in three dimensions is a vital step in structural biology in order to predict and analyze a new unknown protein function. Several methods have been explored over the past decade. However, none of them completely solves the problem. We introduce a novel method for protein structure modeling in textual sequence in order to structural comparison of proteins based on language modeling techniques. The roots of the comparison method are inspired from computational linguistics and the related techniques for quantifying and comparing strings of characters. In this way, the protein structure is represented in three sequences of characters and then n-gram modeling technique is applied to capture the contents regularities. In the sequel, these regularities are contrasted by cross-entropy concept and structural similarity between two proteins is measured. To find an overlap between two protein structures in 3D-space, a superposition task is also applied. In order to confirm the validity of the method, some experiments were performed using a collection of the protein data sets. The results represent the usefulness and applicability of the new approach and motivate further studies on development of tools based on computational linguistics methods.

关键词： Proteins Sequences Computer science Information systems Computational linguistics Amino acids Algorithm design and analysis Natural languages pattern recognition Predictive models

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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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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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A Novel Fuzzy Histogram Based Estimation of Distribution Algorithm for Global Numerical Optimization

A Novel Fuzzy Histogram Based Estimation of Distribution Alg...

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international conference of soft computing and pattern recognition (socpar)

作者： Weili Liu Jing-hui Zhong Wei-Gang Wu Jing Xiao Jun Zhang Department of Computer Science Sun Yat-Sen University Guangzhou China

Applying estimation of distribution algorithms (EDAs) to solve continuous problems is a significant and challenging task in the field of evolutionary computation. So far, various continuous EDAs have been developed based on different probability models. Initially, the EDAs based on a single Gaussian probability model are widely used but they have trouble in solving multimodal problems. Later EDAs based on a mixture model and on a clustering technique are then introduced to conquer such drawback. However, they are either time consuming or need prior knowledge of the problems. Recently, the histogram has begun to be used in continuous EDAs, but the histogram based EDAs (HEDAs) usually need too much time and space to gain a highly accurate solution. On the basis of pioneering contributions, this paper proposes a fuzzy histogram based EDA (FHEDA) for continuous optimization. In the FHEDA, the estimated range of the fuzzy histogram is adjusted adaptively by the current promising solutions, which leads the algorithm to search good solutions efficiently. A mutation mechanism is also introduced in the sampling operation to avoid being trapped in local optima. The performance of the proposed FHEDA is evaluated by testing seven benchmark functions with different characteristics. Two Gaussian based EDAs and the sur-shr-HEDA are studied for comparison. The results show that among all experimental algorithms, the FHEDA can give comparatively satisfying performance on unimodal and multimodal functions.

关键词： Histograms Electronic design automation and methodology Evolutionary computation Genetic mutations Distributed computing Sampling methods pattern recognition Benchmark testing Genetic algorithms Clustering algorithms

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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...

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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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