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2013 international conference on soft computing and pattern recognition, socpar 2013

作者： Hang, See Pheng Shamsuddin, Siti Mariyam Ralescu, Anca Soft Computing Research Group Universiti Teknologi Malaysia Johor Malaysia School of Computing Sciences and Informatics University of Cincinnati CincinnatiOH United States

ISBN: (纸本)9781479934003

We proposed a pixel-based machine learning algorithm in the training of artificial immune recognition system (AIRS) to detect lung lesions in two-dimensional computed tomography (CT) scans. AIRS is an immune based algorithm which inspired by several biological mechanisms in mammalian immune system such as mutation, clonal expansion and immune memory generation. The proposed framework implements the concept of pixel machine learning (PML) where no segmentation and features calculation are required in the pre-processing of pixels. Hounsfield (HU) values in the selected region of interest (ROI) in CT scan are used directly to form a large number of learning sub-regions for massive training process. By using raw data in training, the loss of pixel information during detection of abnormality on medical images can be avoided. There are two versions of the AIRS (AIRS1 and AIRS2) algorithms are involved in the experiments of comparing their performance in the classification of medical images. The main advantage of these AIRS algorithms is to remove surplus training data while remain only relevant features in the processing of large amount of data training. The validation of results based on visualization validation and quantitative comparison using Kullback Leibler Divergence (KLD) are introduced. In this research, the massive training AIRS (MTAIRS) algorithms have generated promising results in visualization for lesions enhancement and detection in CT scans. © 2013 IEEE.

关键词： Pixels

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Face sketch recognition using local invariant features 7

Face sketch recognition using local invariant features

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7th international conference of soft computing and pattern recognition, socpar 2015

作者： Tharwat, Alaa Mahdi, Hani Hennawy, Adel El Hassanien, Aboul Ella Faculty of Engineering Suez Canal University Ismailia Egypt Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computers and Information Cairo University Cairo Egypt Faculty of Computers and Information Beni Suef University Egypt Egypt

ISBN: (纸本)9781467393607

Face sketch recognition is one of the recent biometrics, which is used to identify criminals. In this paper, a proposed model is used to identify face sketch images based on local invariant features. In this model, two local invariant feature extraction methods, namely, Scale Invariant Feature Transform (SIFT) and Local Binary patterns (LBP) are used to extract local features from photos and sketches. Minimum distance and Support Vector Machine (SVM) classifiers are used to match the features of an unknown sketch with photos. Due to high dimensional features, Direct Linear Discriminant Analysis (Direct-LDA) is used. CHUK face sketch database images is used in our experiments. The experimental results show that SIFT method is robust and it extracts discriminative features than LBP. Moreover, different parameters of SIFT and LBP are discussed and tuned to extract robust and discriminative features. © 2015 IEEE.

关键词： Local binary pattern

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Massive Training in Artificial Immune recognition Algorithm for Enhancement of Lung CT Scans

Massive Training in Artificial Immune Recognition Algorithm ...

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international conference of soft computing and pattern recognition (socpar)

作者： See Pheng Hang Shamsuddin, Siti Mariyam Ralescu, Anca Univ Teknol Malaysia Soft Comp Res Grp Johor Baharu Malaysia Univ Cincinnati Sch Comp Sci & Informat Cincinnati OH USA

ISBN: (纸本)9781479934003

关键词： massive pixel machine training: artificial immune recognition system lung CT scans

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Image Based Analysis of Tomato Leaves to Determine the Number of Petioles

Image Based Analysis of Tomato Leaves to Determine the Numbe...

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international conference of soft computing and pattern recognition

作者： Chakraborty, A. K. Sanyal, P. Mukherjee, S. Chatterjee, S. Hazra, P. Bandyopadhyay, S. K. West Bengal Univ Technol Kolkata India B P Poddar Inst Technol Comp Sci & Engn Kolkata India BCKV Dept Vegs & Crops Nadia W Bengal India Univ Calcutta Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9781424453306

The tomato (Lycopersicon esculentum) is an herbaceous, usually sprawling plant which belong to Solanaceae or nightshade family. Genetic evidence shows that the progenitors of tomatoes were herbaceous green plants with small green *** are a great many (around 7500) tomato varieties grown for various purposes. Their identifications had been studied using various laboratory methods. The morphological and genetical characteristics were employed to classify different tomato cultivars. However, the presence of wide morphological varieties through evolution among various tomato cultivars made it more complex and difficult to classify them. Petioles plays a very crucial role in determining the characteristics of a tomato plant. The number of petioles present, their angle with the leaf stalk or their distance from the stalk represent genetical characteristics which differentiate various cultivars of tomato. This article proposed various methods to find the number of petioles present in a tomato leaf using an image analysis based approach.

关键词： pattern recognition Tomato Cultivars Petioles Image Processing

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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

作者： Behravesh, Babak Shamsuddin, Siti Mariyam Zainal, Anazida Univ Teknol Malaysia Fac Comp Sci & Informat Syst Skudai 81310 Malaysia

ISBN: (纸本)9781424453306

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 researchers. Generating 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 (Non-deterministic Finite Automaton) is used for modeling patterns although it has some defects which are addressed The 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： Data Mining Association Rules A-priori algorithm Event streams pattern matching Query optimization

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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...

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international conference of soft computing and pattern recognition

作者： Nojima, Yusuke Nakashima, Yusuke Ishibuchi, Hisao Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Naka Ku Osaka 5998531 Japan

ISBN: (纸本)9781424453306

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： Fuzzy genetics-based machine learning (GBML) Evolutionary multiobjective optimization (EMO) Fuzzy rules Fuzzy partitions pattern classification problems

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Welcome from the Program Chairs socpar 2009

SoCPaR 2009 - Soft Computing and Pattern Recognition

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socpar 2009 - soft computing and pattern recognition 2009年 xvii页

作者： Muda, Azah Kamilah Ishibuchi, Hisao

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Customized hidden layered ann based pattern recognition technique for differential protection of power transformer 11th

Customized hidden layered ann based pattern recognition tech...

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11th international conference on soft computing and pattern recognition, socpar 2019, and 11th World Congress on Nature and Biologically Inspired computing, NaBIC 2019

作者： Balaga, Harish Marrapu, Deepthi Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9783030493448

This article presents the use of Customized Multi-Layer ANN based pattern recognition Technique for the numerical differential protection of a power transformer. An efficient Resilient Back Propagation trained neural network model with customized parallel hidden layers is proposed for the said purpose. The task of the ANN is to discriminate among various operating conditions of the transformer and issue trip signal, only in the case of internal fault. The data base required for the training of algorithm is obtained by using MATLAB/SIMULINK environment. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Power transformers

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conference Committees

Conference Committees

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international conference of soft computing and pattern recognition (socpar)

Provides a listing of current committee members.

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Advances in neural networks research: An introduction

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NEURAL NETWORKS 2009年第5-6期22卷 489-490页

作者： Kozma, Robert Bressler, Steven Perlovsky, Leonid Venayagamoorthy, Ganesh Kumar Univ Memphis FedEx Inst Technol Memphis TN 38152 USA Florida Atlantic Univ Boca Raton FL 33431 USA Harvard Univ Cambridge MA 02138 USA Missouri Sci & Technol Univ Rolla MO USA

The present Special Issue "Advances in Neural Networks Research: IJCNN2009" provides a state-of-art overview of the field of neural networks. It includes 39 papers from selected areas of the 2009 international joint conference on Neural Networks (IJCNN2009). IJCNN2009 took place on June 14-19, 2009 in Atlanta, Georgia, USA, and it represents an exemplary collaboration between the international Neural Networks Society and the IEEE Computational Intelligence Society. Topics in this issue include neuroscience and cognitive science, computational intelligence and machine learning, hybrid techniques, nonlinear dynamics and chaos, various soft computing technologies, intelligent signal processing and pattern recognition, bioinformatics and biomedicine, and engineering applications. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Neuroscience Cognition Machine Learning Hybrid Systems soft computing Dynamic Systems Image Processing Bioinformatics Robotics Power Systems

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