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6th international conference on soft computing Models in Industrial and Environmental Applications

作者： Chowdhury, Md Foezur Rahman Selouani, Sid-Ahmed O'Shaughnessy, Douglas Univ Quebec INRS EMT Montreal PQ H3C 3P8 Canada Univ Moncton Moncton NB Canada

ISBN: (纸本)9783642196430

In this paper, we developed soft computing models for on-line automatic speech recognition (ASR) based on Bayesian on-line inference techniques. Bayesian on-line inference for change point detection (BOCPD) is tested for on-line environmental learning using highly non-stationary noisy speech samples from the Aurora2 speech database. Significant improvement in predicting and adapting to new acoustic conditions is obtained for highly non-stationary noises. The simulation results show that the Bayesian on-line inference-based soft computing approach would be one of the possible solutions to on-line ASR for real-time applications.

关键词： Bayesian networks

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Gaussian mixture pdf in one-class classification:: computing and utilizing confidence values

Gaussian mixture pdf in one-class classification:: computing...

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18th international conference on pattern recognition (ICPR 2006)

作者： Ilonen, J. Paalanen, P. Kamarainen, J. -K. Kalviainen, H. Lappeenranta Univ Technol Dept Informat Technol POB 20 FI-53851 Lappeenranta Finland

ISBN: (纸本)0769525210

In this study a confidence measure for probability density functions (pdfs) is presented. The measure can be used in one-class classification to select a pdf threshold for class inclusion. In addition, confidence information can be used to verify correctness of a decision in a multi-class case where for example the Bayesian decision rule reveals which class is the most probable. Additionally, using confidence values - which represent in which quantile of the probability mass a pdf value resides ([0, 1]) - is often straightforward compared to using arbitrarily scaled pdf values. As the main contributions, use of confidence information in classification is described and a method for confidence estimation is presented.

关键词： pattern recognition

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Multi-class Classification Using an Improved Multiobjective Simultaneous Learning Framework

Multi-class Classification Using an Improved Multiobjective ...

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international conference on soft computing for Problem Solving (SocProS 2011)

作者： Bharill, Neha Tiwari, Aruna SGSITS Dept Comp Engn Indore India

ISBN: (纸本)9788132204909

In this paper, Multi-class classification using an Improved Multiobjective Simultaneous learning framework (MCIMSDC) is proposed. This learning algorithm is used to solve any multiclass classification problem. It is based on the framework proposed by Cai, Chen and Zhang [1] in 2010. In [1], the multiple objective functions are utilized to simultaneously optimize the clustering and classification learning by employing Bayesian theory. In [1], the selection of learning parameter i.e., clusters membership degree u(j)(x(i)) is initially chosen at random due to which the number of iteration and training time achieve while obtaining the stable cluster center is comparatively higher, but here in the proposed methodology, the value of clusters membership degree u(j)(x(i)) is calculated on the basis of randomly initialized cluster centers. Thus, these cluster centers are updated and corresponding u(j)(x(i)) is calculated iteratively. Experimental results show that, this method improve the performance by significantly reducing the number of iterations and training time required to obtain the cluster center. The same is being verified with six benchmark datasets.

关键词： pattern recognition Clustering learning Classification learning Bayesian theory Multiobjective optimization

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Research on pattern recognition Based on the Universal Electronic Tongue System

Research on Pattern Recognition Based on the Universal Elect...

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6th international conference on Wireless Communications, Networking and Mobile computing (WICOM)

作者： Zhou, Jianhui Lei, Wang Teng, Jionghua Tao, Zhou Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (纸本)9781424437092

Chemical taste is indispensable information in food testing. The technical of electronic tongue system is one of research directions to identify different chemical tastes. This paper focuses on the pattern recognition method based on learning vector quantization (LVQ) neural network. The electronic tongue system designed could identify all the samples of beer, fruit juice and milk successfully in the experiments. The result shows that LVQ neural network is applicable in the pattern recognition of electronic tongue system and can also be used on condition that information is gathered by multisensors array. The pattern recognition methods of the universal electronic tongue are proposed in this paper. The effective universal electronic tongue has much advantage over others such as simple methods of pattern recognition and classification, easy training approaches and wider application fields.

关键词： LVQ neural network pattern recognition universal electronic tongue system taste recognition

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Information Flow Clustering via Similarity of a Propagation Tree 7

Information Flow Clustering via Similarity of a Propagation ...

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Joint 7th international conference on soft computing and Intelligent Systems (SCIS) and 15th international Symposium on Advanced Intelligent Systems (ISIS)

作者： Terunuma, Naoki Yaguchi, Yuichi Watanobe, Yutaka Oka, Ryuichi Univ Aizu Aizu Wakamatsu Fukushima 9658580 Japan

ISBN: (纸本)9781479959556

Social network services (SNSs) serve numerous users with large amounts of information of different kinds. On an SNS, information will propagate on a user network, which is represented as a complex network in general but can be reformed as a tree by using the direction of propagation and allowing duplication. Our goal in this study was to show the propagation of a particular kind of information on an SNS, as well as the clustering of a similar propagation scheme for each user. For this goal, we used elastic tree pattern matching to calculate the similarity of two tree structures. A set of users are propagated from source to destination in the same or similar way, and these users are given information from a similar source. We also aimed to find the high-influence person who is at the start of the same or similar propagation, which will indicate that she/he is the moderator of a topic. We used tumblr data for the experiment. Findings indicated that the similar part of each information propagation tree on tumblr was too small for the clustering propagation pattern.

关键词： network theory (graphs) pattern clustering pattern matching social networking (online) trees (mathematics) SNS clustering propagation pattern complex network elastic tree pattern matching information flow clustering information propagation tree propagation tree similarity social network services tree structures Blogs Complex networks Equations Heuristic algorithms Indexes Mathematical model Social network services

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Discrimination and Detection of Face and Non-face Using Multilayer Feedforward Perceptron

Discrimination and Detection of Face and Non-face Using Mult...

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international conference On soft computing Systems (ICSCS)

作者： Gautam, K. S. Kumar, T. Senthil Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Coimbatore 641042 Tamil Nadu India

ISBN: (纸本)9788132226710;9788132226697

The paper proposes a face detection system that locates and extracts faces from the background using the multilayer feedforward perceptron. Facial features are extracted from the local image using filters. In this approach, feature vector from Gabor filter acts as an input for the multilayer feedforward perceptron. The points holding high information on face image are used for extraction of feature vectors. Since Gabor filter extracts features from varying scales and orientations, the feature points are extracted with high accuracy. Experimental results show the multilayer feedforward perceptron discriminates and detects faces from non-face patterns irrespective of the illumination changes.

关键词： Face detection Multilayer feedforward perceptron pattern recognition Computer vision Gabor filter

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soft-computing methods for robust authentication using soft-biometric data

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NEURAL computing & APPLICATIONS 2011年第6期20卷 865-877页

作者： Malcangi, Mario Univ Milan Dipartimento Informat & Comunicaz I-20135 Milan Italy

Biometrics is the measurement of person's physiological or behavioral characteristics. It enables authentication of a person's identity using such measurements. Biometric-based authentication is thus becoming increasingly important in computer-based applications because the amount of sensitive data stored in such systems is growing. Particularly challenging is the implementation of biometric-based authentication in embedded computer system applications, because the resources of such systems are scarce. Reliability and performance are two primary requirements to be satisfied in embedded system applications. Single-mode and hard-feature-based biometrics do not offer enough reliability and performance to satisfy such requirements. Multimode biometrics is a primary level of improvement. soft-biometric features can thus be considered along with hard-biometric features to further improve performance. A combination of soft-computing methods and soft-biometric data can yield more improvements in authentication performance by limiting requirements for memory and processing power. The multi-biometric approach also increases system reliability, since most embedded systems can capture more than one physiological or behavioral characteristic. A multi-biometric platform that combines voiceprint and fingerprint authentication was developed as a reference model to demonstrate the potential of soft-computing methods and soft-biometric data. Hard-computing pattern-matching algorithms were applied to match hard-biometric features. Artificial neural network (ANN) processing was applied to match soft-biometric features. Both hard-computing and soft-computing matching results are inferred by a fuzzy logic engine to perform smart authentication using a decision-fusion paradigm. The embedded implementation was based on a single-chip, floating-point, digital signal processor (DSP) to demonstrate the practical embeddability of such an approach and the improved performance that can be attaine

关键词： Artificial neural networks Fuzzy logic engine soft-biometric data Multi-biometrics Embedded personal authentication systems Digital signal processor

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An Interval-Valued Fuzzy soft Set Approach for Normal Parameter Reduction

An Interval-Valued Fuzzy Soft Set Approach for Normal Parame...

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13th international conference on Rough Sets, Fuzzy Sets and Granular computing (RSFDGrC) was Co-Organized with the 4th international conference on pattern recognition and Machine Intelligence (PReMI)

作者： Ma, Xiuqin Sulaiman, Norrozila Univ Malaysia Pahang Fac Comp Syst & Software Engn Gambang 26300 Kuantan Malaysia

ISBN: (纸本)9783642218804

soft set theory in combination with the interval-valued fuzzy set has been proposed as the concept of the interval-valued fuzzy soft set. However, up to the present, few documents have focused on parameter reduction of the interval-valued fuzzy soft sets. In this paper, we propose a definition of normal parameter reduction of interval-valued fuzzy soft sets, which considers the problems of sub-optimal choice and added parameters. Then, a heuristic algorithm of normal parameter reduction for interval-valued fuzzy soft sets is presented. Finally, an illustrative example is employed to show our contribution.

关键词： Fuzzy soft sets soft sets Interval-valued fuzzy soft sets Reduction Normal parameter reduction

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pattern learning for scheduling microservice workflow to cloud containers

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international JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第9期15卷 3701-3714页

作者： Li, Wenzheng Li, Xiaoping Chen, Long Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Jiangsu Peoples R China

patterns are crucial for efficiently scheduling microservice workflow applications to containers in cloud computing scenarios. However, it is challenging to learn patterns of microservice workflows because of their complex precedence constrained structures provided by users with more lightweighted, diversified, and personalized services. In this paper, we propose a graph neural network is designed to identify patterns within a set of microservice workflows by mining the common substructures of workflows. Based on the learned patterns, a pattern-based scheduling algorithm framework is developed for microservice workflows with soft deadline constraints to minimize the average tardiness. A sorting strategy is introduced based on urgency and pattern coverage rate. For simplification of the task sorting process, the pattern-based task sorting algorithm (PB-TS) is devised. Furthermore, a resource selection phase is incorporated to the pattern-based resource selection algorithm (PB-RS) to minimize the candidate resource space. Experimental results demonstrate the proposed method is much efficient as compared to three classical algorithms.

关键词： Service computing Scheduling optimization Microservice workflow Container cloud pattern recognition

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Survey on Frequent pattern Mining Algorithm for Kernel Trace 7

Survey on Frequent Pattern Mining Algorithm for Kernel Trace

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7th IEEE international Advance computing conference (IACC)

作者： Tate, Aniket Bewoor, Laxmi Vishwakarma Inst Informat Technol Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9781509015603

Kernel tracing facilitates to demonstrate various activities running inside the Operating System. Kernel tracing tools like LTT, LTTng, DTrace, FTrace provide details about processes and their resources but these tools lack to extract knowledge from it. pattern recognition is a major field of data mining and knowledge discovery. This paper presents a survey of widely used algorithms like Apriori, Tree-projection, FP-growth, Eclat for finding frequent pattern over the database. This paper presents a comparative study of frequent pattern mining algorithm and suggests that the FP-growth algorithm is suitable for finding patterns in kernel trace data.

关键词： Frequent pattern pattern recognition Apriori Tree-projection FP-growth RARM ASPMS Eclat LTTng kernel trace

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