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2014 International Conference on Soft Computing & Machine Intelligence (ISCMI 2014)

作者： Verma, Ankit Vig, Lovekesh Jawaharlal Nehru Univ Sch Computat & Integrat Sci New Delhi 110067 India

ISBN: (纸本)9781479942312

The human visual cortex is extremely adept at distinguishing between male and female faces, or performing "Gender Classification". While the subject of face detection and recognition has received a lot of focus, research into the features or cognitive processes that are useful for identifying gender have received relatively little attention. Researchers have attempted to extract hand crafted features like wavelet coefficients, histograms etc. on the basis of which to generate a model to classify the male and female faces. However, these models tend to compress the image into a vector and disregard the two dimensional spatial correlations between the pixels in an image. Additionally, these features have to hand crafted and may or may not be ideal for the classification at hand. Ideally, the system should be able to generate specific features from the input face image which would help in classification of male faces from female faces. In this paper and a Deep Convolution Neural Network (CNN) model is presented for gender classification. The features generated by the CNN appear to agree with known results from the cognitive science community indicating that these models may be closer to biological neuronal processes governing gender classification. The classification results are compared with different regularization techniques and other standard classifiers, and the CNN models yield higher accuracy than both svms and random forest classifiers.

关键词： convolutional neural network backpropagation algorithm gender classification horizontal flipping L2 Regularization

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Neurofuzzy Inference System for Diagnosis of Malaria

Neurofuzzy Inference System for Diagnosis of Malaria

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International Conference on Innovative Applications of Computational Intelligence on Power Energy and Controls with their Impact on Humanity (CIPECH)

作者： Rastogi, Aayush Gupta, Neeraj Kumar Tyagi, Praveen Kumar KIET Dept Elect & Elect Engn Ghaziabad India

ISBN: (纸本)9781479958726

In this paper, a structure of adaptive system is proposed with the help of Neurofuzzy System (NFS) for diagnosis of Malaria. Investigation of malaria using Neurofuzzy system has been used for decision making ability based on predefined rules and learning by the backpropagation algorithm. Mapping Network in backpropagation algorithm is applied to minimize the errors in the output. Investigation of malaria by the proposed system is illustrated and good performance is achieved with maximum instant error of 0.06144.

关键词： Neurofuzzy inference system backpropagation algorithm Malaria

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Neural Network Based Electronic Nose for Discrimination of Fish Sauces

Neural Network Based Electronic Nose for Discrimination of F...

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11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology

作者： Nimsuk, Nitikarn Thammasat Univ Fac Engn Dept Elect & Comp Engn Pathum Thani Thailand

ISBN: (纸本)9781479929931

Fish sauce is one of the signature condiments in various cuisines in many countries. In this study, fish sauces are successfully discriminated depending on their quality indicated by the level of Total Nitrogen (TN) content. We introduce an electronic nose (E-nose) technology to measure the odor of fish sauce. Feature extraction methods are also performed in order to obtain information relevant to the odor discrimination task. Four features based on both steady-state and transient responses are extracted from each response signal obtained from the E-nose. The principal component analysis (PCA) is also introduced to reduce the dimensionality of feature vector for avoiding the curse of dimensionality. The backpropagation (BP) learning algorithm is then used for the discrimination. Using this method, we achieve high discrimination performance to the fish sauces.

关键词： electronic nose neural network fish sauce principal component analysis backpropagation algorithm

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Implementation of Multimodal Biometric Authentication Using Soft Computing Techniques

Implementation of Multimodal Biometric Authentication Using ...

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International Conference on Information Communication and Embedded Systems (ICICES)

作者： Gunasekaran, K. Mahalakshmi, P. Pavendar Bharathidasan Coll Engg & Tech Dept CSE Tiruchirappalli Tamil Nadu India

ISBN: (纸本)9781479938346

Identity Management refers to a challenge of providing authorized users with secure and easy access to information and services for personal identification system. The objective is to implement a secure identification system for determining an individual's identity. Conventional methods for personal identification include keys, tokens, access cards, PINs and passwords which leads to drawbacks such as stealing, duplicating, cracking and sharing. In order to overcome the drawbacks, biometrics based identification is implemented. Nevertheless, unimodal biometrics is suffered due to noise, intra class variations, spoof attacks, non-universality. To avoid these attacks, multimodal biometrics which is the combination of more modalities is adapted. Fingerprint and Palmprint identification are the emerging and leading technologies used as a source of authentication. Neural Networks one of the Soft Computing techniques has been proposed for feature extraction and minutiae point detection. Euclidean distance values are used for minutiae matching. The proposed method achieves excellent identification rate and are more secure in noisy environment.

关键词： Biometrics 2D-Gabor Filter K-Nearest Neighborhood algorithm(KNN) Artificial Neural Networks(ANN) Feed Forward Neural Networks(FNN) MultilayerPerceptron(MLP) backpropagation algorithm Euclidean distance

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backpropagation Learning Method with Interval Type-2 Fuzzy Weights in Neural Networks

Backpropagation Learning Method with Interval Type-2 Fuzzy W...

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International Joint Conference on Neural Networks (IJCNN)

作者： Gaxiola, Fernando Melin, Patricia Valdez, Fevrier Castillo, Oscar Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781467361293;9781467361286

In this paper a neural network learning method with lower and upper type-2 fuzzy weight adjustment is proposed. The general mathematical analysis of the proposed learning method architecture and the adaptation of the interval type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that manage weight adaptation and especially type-2 fuzzy weights. In this paper the neural network architecture managing lower and upper type-2 fuzzy weights and the obtained lower and upper final results are presented. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system

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Neural Network with Lower and Upper Type-2 Fuzzy Weights using the backpropagation Learning Method

Neural Network with Lower and Upper Type-2 Fuzzy Weights usi...

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Joint World Congress of the International-Fuzzy-Systems-Association (IFSA) / Annual Meeting of the North-American-Fuzzy-Information-Processing-Society (NAFIPS)

作者： Gaxiola, Fernando Melin, Patricia Valdez, Fevrier Tijuana Inst Technol Tijuana Mexico

ISBN: (纸本)9781479903481

In this paper the lower and upper type-2 fuzzy weight adjustment applied in a neural network performing the learning method is proposed. The mathematical representation of the adaptation of the interval type-2 fuzzy weights and the proposed learning method architecture are presented. This research is based in the analysis of the recent methods that manage weight adaptation and implementing this analysis in the adaptation of these methods with type-2 fuzzy weights. In this paper, we work with type-2 fuzzy weights lower and upper in the neural network architecture and the lower and upper final results obtained are presented in the final. The proposed approach is applied to a case of Mackey-Glass time series prediction.

关键词： Neural Networks Type-2 Fuzzy Weights backpropagation algorithm Type-2 fuzzy system

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Structural damage detection of steel bridge girder using artificial neural networks and finite element models

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STEEL AND COMPOSITE STRUCTURES 2013年第4期14卷 367-377页

作者： Hakim, S. J. S. Razak, H. Abdul Univ Malaya Dept Civil Engn StrucHMRS Grp Kuala Lumpur 50603 Malaysia

Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

关键词： artificial neural networks (ANNs) finite element damage detection backpropagation algorithm natural frequency

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Generation of artificial neural networks models in anticancer study

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NEURAL COMPUTING & APPLICATIONS 2013年第3-4期23卷 577-582页

作者： Sousa, Ines J. Padron, Jose M. Fernandes, Miguel X. Univ Madeira Ctr Quim Madeira Ctr Competencia Ciencias Exatas & Engn P-9000390 Funchal Portugal Univ La Laguna BioLab Inst Univ Bioorgan Antonio Gonzalez San Cristobal la Laguna 38206 Spain

Artificial neural networks (ANNs) have several applications;one of them is the prediction of biological activity. Here, ANNs were applied to a set of 32 compounds with anticancer activity assayed experimentally against two cancer cell lines (A2780 and T-47D). Using training and test sets, the obtained correlation coefficients between experimental and calculated values of activity, for A2780, were 0.804 and 0.829, respectively, and for T-47D, we got 0.820 for the training set and 0.927 for the test set. Comparing multiple linear regression and ANN models, the latter were better suited in establishing relationships between compounds' structure and their anticancer activity.

关键词： backpropagation algorithm Correlation coefficients Heuristics Learning algorithms Machine learning Neural network models Nonlinear models Prediction methods Radial base function network

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A hybrid algorithm for artificial neural network training

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第1期26卷 293-301页

作者： Yaghini, Masoud Khoshraftar, Mohammad M. Fallahi, Mehdi Iran Univ Sci & Technol Sch Railway Engn Rail Transportat Dept Tehran Iran

Artificial neural network (ANN) training is one of the major challenges in using a prediction model based on ANN. Gradient based algorithms are the most frequent training algorithms with several drawbacks. The aim of this paper is to present a method for training ANN. The ability of metaheuristics and greedy gradient based algorithms are combined to obtain a hybrid improved opposition based particle swarm optimization and a back propagation algorithm with the momentum term. Opposition based learning and random perturbation help population diversification during the iteration. Use of time-varying parameter improves the search ability of standard PSO, and constriction factor guarantees particles convergence. Since several contingent local minima conditions may happen in the weight space, a new cross validation method is proposed to prevent overfitting. Effectiveness and efficiency of the proposed method are compared with several other famous ANN training algorithms on the various benchmark problems. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Artificial Neural Networks Hybrid training algorithm Particle swarm optimization backpropagation algorithm Cross validation Time-varying parameter

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A novel weight pruning method for MLP classifiers based on the MAXCORE principle

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NEURAL COMPUTING & APPLICATIONS 2013年第1期22卷 71-84页

作者： Medeiros, Claudio M. S. Barreto, Guilherme A. Fed Inst Ceara Dept Ind BR-60040531 Fortaleza Ceara Brazil Univ Fed Ceara Dept Teleinformat Engn Ctr Technol BR-60455970 Fortaleza Ceara Brazil

We introduce a novel weight pruning methodology for MLP classifiers that can be used for model and/or feature selection purposes. The main concept underlying the proposed method is the MAXCORE principle, which is based on the observation that relevant synaptic weights tend to generate higher correlations between error signals associated with the neurons of a given layer and the error signals propagated back to the previous layer. Nonrelevant (i.e. prunable) weights tend to generate smaller correlations. Using the MAXCORE as a guiding principle, we perform a cross-correlation analysis of the error signals at successive layers. Weights for which the cross-correlations are smaller than a user-defined error tolerance are gradually discarded. Computer simulations using synthetic and real-world data sets show that the proposed method performs consistently better than standard pruning techniques, with much lower computational costs.

关键词： MLP classifier backpropagation algorithm Weight pruning Feature selection

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