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共 264 条记录，以下是51-60 订阅

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soft Feature Based Personal recognition 8th

Soft Feature Based Personal Recognition

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8th international conference on soft computing and pattern recognition (socpar) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Kulkarni, Sujata Raut, Ranjana Bhosle, Udhav RSTM Univ Nagpur Yeshwantrao Chavan Coll Engn Nagpur 441110 Maharashtra India SGM Univ Amaravati Elect Dept Amaravati 441110 India Univ Mumbai Rajiv Gandhi Inst Technol Bombay Maharashtra India

ISBN: (纸本)9783319606187;9783319606170

Biometric based personal recognition has become the integral part for administrate and operational aspects for private and government sector. This paper proposed the soft feature based personal recognition by integration of unique and non-unique or soft features to improve the performance of biometrics recognition system. The proposed biometrics recognition system uses unique features of back surface of finger called finger knuckle (FK) and complementary features known as soft features such as age and gender of each individual. Further author presents combination Walsh wavelet Transform based finger knuckle features and soft features at score level using Bayes formula. The algorithm is tested on own FK database and results demonstrated that performance of primary FK recognition system has improved by integrating soft features of same traits. Error Equal Rate (EER) of integrated biometrics is 6.5% which is less as compared to primary.

关键词： Primary biometrics Unique feature soft feature Walsh wavelet transform Bayes formula

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Review of Artificial Immune System in Web Personalization

Review of Artificial Immune System in Web Personalization

引用

international conference of soft computing and pattern recognition

作者： Rastegari, Hamid Shamsuddin, Siti Mariyam Univ Technol Malaysia Fac Comp Sci & Informat Syst Skudai Malaysia

ISBN: (纸本)9781424453306

This paper is a review on knowledge discovery in the field of web mining for the benefit of research on the personalization of web-based information services. The essence of personalization is the adaptability of information systems to the needs of their users. This issue is becoming increasingly important on the Web, as non-expert users are overcame by the quantity of information available online. This article investigates the application of artificial immune systems (AIS) to knowledge discovery as a web personalization tool. AIS are thought to confer the adaptability and learning required for this task.

关键词： Web Personalization Artificial Immune System Web Usage Mining pattern Discovery Clonal Selection

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Applicability of soft computing and Optimization Algorithms in software Testing and Metrics - A Brief Review 8th

Applicability of Soft Computing and Optimization Algorithms ...

引用

8th international conference on soft computing and pattern recognition (socpar) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Sharma, Deepak Chandra, Pravin Guru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol Delhi 110078 India

ISBN: (纸本)9783319606187;9783319606170

In spite of many years of work by scientists and specialists on various software qualities, testing stays one of the most broadly honed and concentrated on methodologies for evaluating and improving software quality. Our objective, in this paper, is to present how optimization techniques provide solutions to different and difficult issues in different areas of software engineering. Optimization algorithms are mathematical procedures, which intends to best optimal results for the defect, fault, failure to accomplish tractability, strength, and low arrangement cost. In this paper, a comprehensive overview of software testing and metrics based on soft computing and optimization techniques is presented. In this survey, we try to explain some major problems like defect prediction, software fault prediction and their solutions by soft computing and optimization algorithms. The paper presents an overview of the usage of Mathematical optimization Algorithms and soft computing approaches.

关键词： Optimization algorithms soft computing software testing Metrics software fault prediction

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Hiding data in audio using modified CPT scheme

Hiding data in audio using modified CPT scheme

引用

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

作者： Dieu, Huynh Ba Huy, Nguyen Xuan International School Duy Tan University Danang Viet Nam Institute of Information Technology Vietnamese Academy of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9781479934003

This paper presents a method to hide information in audio based on CPT scheme. The proposed method is done by modifying CPT scheme used for hiding data in binary image. We do not use two matrices as key for embedding and extracting process to reduce the processing time. To ensure the security, we use Arnold transform to scramble the secret message. Experimental results show that the proposed method is inaudible and suitable for hiding data in audio. © 2013 IEEE.

关键词： Arnold transform audio steganography CPT scheme LSB perceptiveness

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A more powerful Random Neural Network model in supervised learning applications

A more powerful Random Neural Network model in supervised le...

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international conference of soft computing and pattern recognition (socpar)

作者： Basterrech, Sebastian Rubino, Gerardo VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic INRIA Rennes France

ISBN: (纸本)9781479934003

Since the early 1990s, Random Neural Networks (RNNs) have gained importance in the Neural Networks and Queueing Networks communities. RNNs are inspired by biological neural networks and they are also an extension of open Jackson's networks in Queueing Theory. In 1993, a learning algorithm of gradient type was introduced in order to use RNNs in supervised learning tasks. This method considers only the weight connections among the neurons as adjustable parameters. All other parameters are deemed fixed during the training process. The RNN model has been successfully utilized in several types of applications such as: supervised learning problems, pattern recognition, optimization, image processing, associative memory. In this contribution we present a modification of the classic model obtained by extending the set of adjustable parameters. The modification increases the potential of the RNN model in supervised learning tasks keeping the same network topology and the same time complexity of the algorithm. We describe the new equations implementing a gradient descent learning technique for the model.

关键词： Random Neural Network Supervised Learning pattern recognition Numerical Optimization Gradient Descent

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Integrated Feature Selection and Parameter Optimization for Evolving Spiking Neural Networks using Quantum Inspired Particle Swarm Optimization

Integrated Feature Selection and Parameter Optimization for ...

引用

international conference of soft computing and pattern recognition

作者： Hamed, Haza Nuzly Abdull Kasabov, Nikola Shamsuddin, Siti Mariyam Auckland Univ Technol KEDRI Auckland New Zealand Univ Teknol Malaysia Soft Computing Res Grp Johor Baharu Johor Malaysia

ISBN: (纸本)9781424453306

This paper proposes a novel method for optimizing features and parameters in the Evolving Spiking Neural Network (ESNN) using Quantum-inspired Particle Swarm Optimization (QiPSO). This study reveals the interesting concept of QiPSO in which information is represented as binary structures. The mechanism simultaneously optimizes the ESNN parameters and relevant features using wrapper approach. A synthetic dataset is used to evaluate the performance of the proposed method. The results show that QiPSO yields promising outcomes in obtaining the best combination of ESNN parameters as well as in identifying the most relevant features.

关键词： Evolving Spiking Neural Network Particle Swarm Quantum computing Feature Optimization Parameter Optimization

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Deep Learning Approach for recognition of Handwritten Kannada Numerals 8th

Deep Learning Approach for Recognition of Handwritten Kannad...

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8th international conference on soft computing and pattern recognition (socpar) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Ganesh, Anirudh Jadhav, Ashwin R. Pragadeesh, K. A. Cibi Vellore Inst Univ Vellore Tamil Nadu India

ISBN: (纸本)9783319606187;9783319606170

We come across a large volume of handwritten texts in our daily lives and handwritten character recognition has long been an important area of research in pattern recognition. The complexity of the task varies among different languages and it so happens largely due to the similarity between characters, distinct shapes and number of characters which are all language-specific properties. There have been numerous works on character recognition of English alphabets and with laudable success, but regional languages have not been dealt with very frequently and with similar accuracies. In this paper, we explored the performance of Convolutional Neural Networks, and Deep Belief Networks in the classification of Handwritten Kannada numerals, and conclusively compared the results obtained. The proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages such as similarity between characters and minute nuances that differentiate them. We can further extend this to all the Kannada characters.

关键词： Deep Belief Networks Convolutional Neural Networks LeNet pattern recognition Restricted boltzmann machine Deep learning

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Hybrid soft computing Methods for Prediction of Oil Prices 6

Hybrid Soft Computing Methods for Prediction of Oil Prices

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6th international conference on soft computing and pattern recognition (socpar)

作者： Gabralla, Lubna A. Abraham, Ajith Sudan Univ Sci & Technol Fac Comp Sci & Informat Technol Khartoum Sudan Machine Intelligence Res Labs Auburn WA USA VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic

ISBN: (纸本)9781479959341

This paper aims to provide combination of multiple prediction models using different strategies including ensemble selection, voting, stacking and multi-schemes to design a model capable of predicting oil prices accurately. Daily data from 1999 to 2012 with 14 variables were used, which were further divided into 10 sub-datasets according to various attribute selection methods. Four groups of training and testing were examined. Experimental results conclude that performance of the combination model works better than author's previous work and ensemble selection outperforms other combination methods.

关键词： ensemble selection vote stacking multi-scheme prediction oil prices

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socpar 2009 - soft computing and pattern recognition

SoCPaR 2009 - Soft Computing and Pattern Recognition

引用

international conference on soft computing and pattern recognition, socpar 2009

ISBN: (纸本)9780769538792

The proceedings contain 137 papers. The topics discussed include: a new clustering method based on weighted kernel K-means for non-linear data;a review of recent alignment-free clustering algorithms in expressed sequence tag;league championship algorithm: a new algorithm for numerical function optimization;an improved discrete particle swarm optimization in evacuation planning;PSO-based optimization of state feedback tracking controller for a flexible link manipulator;a novel fuzzy histogram based estimation of distribution algorithm for global numerical optimization;rule modeling engine for optimizing complex event processing patterns;correlation research of association rules and application in the data about coronary heart disease;linear antenna array synthesis with invasive weed optimization algorithm;robust type-2 fuzzy control of an automatic guided vehicle for wall-following;and a modified differential evolution algorithm and its application to engineering problems.

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Edge direction matrixes-based local binary patterns descriptor for invariant pattern recognition

Edge direction matrixes-based local binary patterns descript...

引用

international conference of soft computing and pattern recognition (socpar)

作者： Talab, Mohammed Ahmed Abdullah, Siti Norul Huda Sheikh Razalan, Mohammad Hakim Assiddiq Univ Kebangsaan Malaysia Fac Technol & Informat Sci Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia

ISBN: (纸本)9781479934003

Invariant descriptor for shape and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from shape images via human knowledge and works. The descriptors need to have strong Local Binary pattern (LBP) in order to encode the information distinguishing them. Local Binary pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. It is needed as the edge direction matrices (EDMS) only apply global invariant descriptor which employs first and secondary order relationships. The main objective of this paper is the need of improved recognition capabilities which achieved by the combining LBP and EDMS. Working together, these two descriptors will add advantages to the program and enable the researcher to investigate the weaknesses of each one. Two classifiers are used: multi-layer neural network and random forest. The techniques used in this paper are compared with Gray-Level Co-occurrence matrices (GLCM-EDMS) and Scale Invariant Feature Transform (SIFT) by using two benchmark dataset: MPEG-7 CE-Shape-1 for shape and Arabic calligraphy for texture. The experiments have shown the superiority of the introduced descriptor over the GLCMEDMS and the SIFT.

关键词： Feature Extraction Local Binary patterns(LBP) Edge Direction Matrixes(EDMS) Classification

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