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A Hybrid Approach for Segmenting and Validating T1-Weighted Normal Brain MR Images by Employing ACM and ANN

A Hybrid Approach for Segmenting and Validating T1-Weighted ...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： M. Masroor Ahmed Dzulkifli Bin Mohamad Mohammad S. Khalil Faculty of Computer Science & Information System Universiti Teknologi Malaysia Skudai Skudai Malaysia

This study focuses on segmentation and validation of brain MR images. Artificial neural network (ANN) has been applied to obtain the targeted segments from these images. In preprocessing step for avoiding the chances of misclassification during training of ANN, the unwanted skull tissues were removed by employing active contour modeling (ACM). The removal of these tissues leaves an image containing various regions of interest. For training ANN these distinctive regions of interest were clustered into their respective regions by employing KMeans algorithm. Then a neural net work is trained on this classified data which eventually facilitated in obtaining the desired segments. The boundaries of these segments were detected and the pixels constituting these boundaries were counted. For validating the segments produced by ANN, ground truth segments were taken under consideration. The boundaries of these ground truth segments were also detected and pixels forming the edges of these segments were counted. Finally a comparison was drawn between the pixel counts of ANN produced segments and ground truth segments. On the basis of this comparison, accuracy of ANN is calculated.

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

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Effects of the Use of Multiple Fuzzy Partitions on the Search Ability of Multiobjective Fuzzy Genetics-Based Machine Learning

Effects of the Use of Multiple Fuzzy Partitions on the Searc...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Yusuke Nojima Yusuke Nakashima Hisao Ishibuchi Department of Computer Science and Intelligent Systems Graduate School of Engineering Osaka Prefecture University Sakai Osaka Japan

An important issue in the design of fuzzy rule-based systems is to find a good accuracy-complexity tradeoff. While simple fuzzy systems with high interpretability are usually not accurate, complicated fuzzy systems with high accuracy are usually not interpretable. Recently evolutionary multiobjective optimization (EMO) algorithms have been used to search for simple and accurate fuzzy systems. The main advantage of EMO-based approaches over single-objective techniques is that a number of alternative fuzzy systems with different accuracy-complexity tradeoffs can be obtained by their single run. We have already proposed a multiobjective fuzzy genetics-based machine learning (GBML) algorithm for pattern classification problems. In our GBML algorithm, multiple fuzzy partitions with different granularities are simultaneously used. This is because we usually do not know an appropriate fuzzy partition for each input variable. However, the use of multiple fuzzy partitions significantly increases the size of the search space. In this paper, we examine the effect of the use of multiple fuzzy partitions on the search ability of our multiobjective fuzzy GBML algorithms through computational experiments.

关键词： Machine learning Fuzzy systems Machine learning algorithms Partitioning algorithms Input variables Knowledge based systems Genetic algorithms Classification algorithms pattern classification Algorithm design and analysis

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Cellular learning automata with external input and its applications in pattern recognition

Cellular learning automata with external input and its appli...

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international conference on soft computing, computing with Words and Perceptions in System Analysis, Decision and Control, ICSCCW

作者： Meysam Ahangaran Hamid Beigy Computer Engineering Department Sharif University of Technology Tehran Iran

Cellular learning automata (CLA) which has been introduced recently, is a combination of cellular automata (CA) and learning automata (LA). A CLA is a CA in which a LA is assigned to its every cell. The LA residing in each cell determines the state of the cell on basis of its action probability vector. Like CA, there is a local rule that CLA operates under it. In this paper we introduce a new model of CLA in which each cell gets an external input vector from the environment in addition to reinforcement signal, so this model can work in non-stationary environments. Then two applications of the new model on image segmentation and clustering are given, and the results show that the proposed algorithm outperforms the similar algorithms.

关键词： Learning automata pattern recognition Clustering algorithms Image segmentation Mathematical model Lattices Biological system modeling Stochastic processes Application software Neurons

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Advances in Computational Science and Engineering 2009

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丛书名： Communications in Computer and Information Science

2009年

作者： Laurence Tianruo Yang Jong Hyuk Park Thanos Vasilakos Yan Zhang Damien Sauveron Xingang Wang Young-Sik Jeong

ISBN: (数字)9783642102387

ISBN: (纸本)9783642102370

This volume constitutes the selected and extended papers of workshops and symposia of the international conference, on Advances in Computational Science and Engineering, FGCN2008, held in Sanya, Hainan Island, China in December 2008. The volume contains the workshops on peer-to-peer computing for information search (P2PSearch 2008), on wireless ad hoc, mesh and sensor networks (WAMSNet-08), on sensor networks and applications (SNA 2008), on wireless communications for intelligent transportation systems (WITS-08), on network assurance (NA 2008) , on communication & network technology management 2008 (CNM 2008), on forensics for future generation communication environments (F2GC-08), as well as on silver technology for aging society (STAS2008), together with the symposia on the 3rd international symposium on smart home (SH'08), on database theory and application (DTA-08), on u- and e- service, science and technology (UNESST-08), on control and automation (CA-08) , on grid and distributed computing (GDC-08), as well as on signal processing, image processing and pattern recognition (SIP 2008).

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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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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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An Efficient Prediction Model for Diabetic Database Using soft computing Techniques 1

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12th international conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing

作者： Bhat, Veena H. Rao, Prasanth G. Shenoy, P. Deepa Venugopal, K. R. Patnaik, L. M. Bangalore Univ Univ Visvesvaraya Coll Engn Bangalore 560056 Karnataka India Deemed Univ Def Inst Adv Technol Pune Maharashtra India

ISBN: (数字)9783642106460

ISBN: (纸本)9783642106453

Organizations aim at harnessing predictive insights, using the vast real-time data stores that they have accumulated through the years, using data mining techniques. Health sector, has an extremely large source of digital data - patient-health related data-store, which can be effectively used for predictive analytics. This data, may consists of missing, incorrect and sometimes incomplete values sets that can have a detrimental effect on the decisions that are outcomes of data analytics. Using the PIMA Indians Diabetes dataset, we have proposed an efficient imputation method using a hybrid combination of CART and Genetic Algorithm, as a preprocessing step. The classical neural network model is used for prediction, on the preprocessed dataset. The accuracy achieved by the proposed model far exceeds the existing models, mainly because of the soft computing preprocessing adopted. This approach is simple, easy to understand and implement and practical in its approach.

关键词： Predictive analytics

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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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A New Gabor Phase Difference pattern for Face and Ear recognition

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13th international conference on Computer Analysis of Images and patterns

作者： Guo, Yimo Zhao, Guoying Chen, Jie Pietikainen, Matti Xu, Zhengguang Univ Oulu Elect & Informat Engn Dept Machine Vis Grp POB 4500 Oulu 90014 Finland Univ Sci & Technol Beijing Sch Informat Engn Beijing 100083 Peoples R China

ISBN: (纸本)9783642037665

A new local feature based image representation method is proposed. It is derived from the local Gabor phase difference pattern (LGPDP). This method represents images by exploiting relationships of Gabor phase between pixel and its neighbors. There are two main contributions: 1) a novel phase difference measure is defined;2) new encoding rules to mirror Gabor phase difference information are designed. Because of them, this method describes Gabor phase difference more precisely than the conventional LGPDP. Moreover, it could discard useless information and redundancy produced near quadrant boundary, which commonly exist in LGPDP. It is shown that the proposed method brings higher discriminative ability to Gabor phase based pattern. Experiments are Conducted on the FRGC version 2.0 and USTB Ear Database to evaluate its validity and generalizability. The proposed method is also compared with several state-of-the-art approaches. It is observed that our method achieves the highest recognition rates among them.

关键词： Image representation

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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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