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检索条件"任意字段=12th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2023"

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Artificial Intelligence applications and Innovations 1st ed. 2016

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丛书名： IFIP Advances in Information and Communication Technology

2016年

作者： Lazaros Iliadis Ilias Maglogiannis

ISBN: (数字)9783319449449

ISBN: (纸本)9783319449432

this book constitutes the refereed proceedings of the 12th IFIP WG 12.5 international conference on Artificial Intelligence applications and Innovations, AIAI 2016, and three parallel workshops, held in thessaloniki, Greece, in September 2016. the workshops are the third Workshop on New methods and Tools for Big Data, MT4BD 2016, the 5th Mining Humanistic Data Workshop, MHDW 2016, and the First Workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE 2016. the 30 revised full papers and 8 short papers presented at the main conference were carefully reviewed and selected from 65 submissions. the 17 revised full papers and 7 short papers presented at the 3 parallel workshops were selected from 33 submissions. the papers cover a broad range of topics such as artificial neural networks, classification, clustering, control systems - robotics, data mining, engineering application of AI, environmental applications of AI, feature reduction, filtering, financial-economics modeling, fuzzy logic, genetic algorithms, hybrid systems, image and video processing, medical AI applications, multi-agent systems, ontology, optimization, pattern recognition, support vector machines, text mining, and Web-social media data AI modeling.

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An empirical comparison of in-learning and post-learning optimization schemes for tuning the Support Vector Machines in cost-sensitive applications

An empirical comparison of in-learning and post-learning opt...

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12th international conference on Image Analysis and Processing

作者： Tortorella, F Univ Cassino DAEIMI I-03043 Cassino Italy

ISBN: (纸本)0769519482

Support Vector Machines (SVMs) are currently one of the classification systems most used in pattern recognition and data mining because of their accuracy and generalization capability. However, when dealing with very complex classification tasks where different errors bring different penalties, one should take into account the overall classification cost produced by the classifier more than its accuracy. It is thus necessary to provide some methods for tuning the SVM on the costs of the particular application. Depending on the characteristics of the cost matrix, this can be done during or after the learning phase of the classifier. In this paper we introduce two optimization schemes based on the two possible approach and compare their performance on various data sets and kernels. the first experimental results show that both the proposed schemes are suitable for tuning SVMs in cost-sensitive applications.

关键词： Support vector machines

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Face recognition System Invariant to Plastic Surgery

Face Recognition System Invariant to Plastic Surgery

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12th international conference on Intelligent Systems Design and applications (ISDA)

作者： Lakshmiprabha, N. S. Majumder, S. EADS Innovat Works Bangalore Karnataka India Cent Mech Engn Res Inst Surface Robot Lab Durgapur India

ISBN: (纸本)9781467351171;9781467351188

Facial plastic surgery changes facial features to large extend and thus creating a major problem to face recognition system. this paper proposes a new face recognition system using novel shape local binary texture (SLBT) feature from face images cascaded with periocular feature for plastic surgery invariant face recognition. In-spite of many uniqueness and advantages, the existing feature extraction methods are capable of extracting either shape or texture feature. A method which can extract both shape and texture feature is more attractive. the proposed SLBT can extract global shape, local shape and texture information from a face image by extracting local binary pattern (LBP) instead of direct intensity values from shape free patch of active appearance model (AAM). the experiments conducted using MUCT and plastic surgery face database shows that the SLBT feature performs better than AAM and LBP features. Further increase in recognition rate is achieved by cascading SLBT features from face with LBP features from periocular regions. the result from surgical and non-surgical face database shows that the proposed face recognition system can easily tackle illumination, pose, expression, occlusion and plastic surgery variations in face images.

关键词： Face recognition Plastic surgery Shape local binary texture Active appearance model Local binary pattern and Periocular biometrics

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On Efficient Computation of Tensor Subspace Kernels for Multi-dimensional Data 12

On Efficient Computation of Tensor Subspace Kernels for Mult...

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12th international Joint conference on Computer Vision, Imaging and Computer Graphics theory and applications (VISIGRAPP)

作者： Cyganek, Boguslaw Wozniak, Michal AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland Wroclaw Univ Sci & Technol Wroclaw Poland

ISBN: (纸本)9789897582264

In pattern classification problems kernel based methods and multi-dimensional methods have shown many advantages. However, since the well-known kernel functions are defined over one-dimensional vector spaces, it is not straightforward to join these two domains. Nevertheless, there are attempts to develop kernel functions which can directly operate with multi-dimensional patterns, such as the recently proposed kernels operating on the Grassmannian manifolds. these are based on the concept of the principal angles between the orthogonal spaces rather than simple distances between vectors. An example is the chordal kernel operating on the subspaces obtained after tensor unfolding. However, a real problem with these methods are their high computational demands. In this paper we address the problem of efficient implementation of the chordal kernel for operation with tensors in classification tasks of real computer vision problems. the paper extends our previous works in this field. the proposed method was tested in the problems of object recognition in computer vision. the experiments show good accuracy and accelerated performance.

关键词： Reproducing Kernel Hilbert Spaces Chordal Kernel Multi-dimensional patterns Tensors Grassmannian Manifolds pattern Classification

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Facial Family Similarity recognition Using Local Gabor Binary pattern Histogram Sequence

Facial Family Similarity Recognition Using Local Gabor Binar...

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12th international conference on Hybrid Intelligent Systems (HIS)

作者： Dehshibi, Mohammad Mahdi Shanbezadeh, Jamshid Alavi, Meysam IAU Dept Comp Engn Sci & Res Branch Tehran Iran Kharazmi Univ Dept Comp Engn Karaj Iran Univ Sci & Culture Dept Comp Engn Hamadan Iran

ISBN: (纸本)9781467351157;9781467351164

Facial image analysis is one of the areas that have been received considerable attention in recent decades. In addition to areas such as face recognition, gender classification, emotion recognition, and age estimation, there are new applications that have not been studied yet. Family similarity recognition is a new trend that has been studied in this paper for the first time. Local Gabor Binary pattern Histogram Sequence (LGBPHS) has led to many important advances in face recognition, including over looking generalizability and training issues. Given the current status of this study, two approaches were considered: (1) holistic approach and (2) component-based approach, which embodies the practical principles of theory. In order to model facial family manifold, LGBP is used both for the holistic view and components of the face. For recognition, histogram intersection is used to measure the similarity of different LGBPHSes and the nearest neighborhood is exploited for final clustering. In order to prove the efficiency of the proposed method three set of experiments are conducted in which both subjective and algorithmic issues are considered. It is observed in the course of experiments that the proposed method outperforms the subjective test up to 15%, and outperforms the observed state of the art face recognition methods up to 25.06%.

关键词： Facial Family Similarity recognition Nearest Neighborhood Clustering Facial Family Database LGBPHS

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Towards Better Ocular recognition for Secure Real-World applications 17

Towards Better Ocular Recognition for Secure Real-World Appl...

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17th IEEE international conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) / 12th IEEE international conference on Big Data Science and Engineering (IEEE BigDataSE)

作者： Parizi, Reza M. Dehghantanha, Ali Choo, Kim Kwang Raymond Kennesaw State Univ Dept Software Engn & Game Dev Marietta GA 30060 USA Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX USA

ISBN: (纸本)9781538643877

In the last decade, biometric has become one of the most common uses for verification and authentication of someone's personal identity. Biometrics are defined as the measurement and analysis of unique physical or behavioral characteristics. Fingerprint is undoubtedly the most well-known biometric, but in the recent years ocular recognition has taken a huge jump forward. Ocular recognition (OR) is one of the most reliable personal identification methods in biometrics that can have flexible applications as opposed to the iris recognition. the basic breakdown of the eye is the iris, cornea, retina, pupil, sclera, and conjunctiva. the iris is where eye is actually colored;this is determined by the amount and type of pigment in one's iris. Iris recognition is an accurate form of ocular recognition. It works by using biometric systems to apply pattern recognition to a person's eye since no two people have the same patterns in their eye. Ocular recognition has been used as an identification method on both Apple and Android devices without much popularity. the reason for this is mainly related to the issues with the underlying algorithm used that affects the accuracy and security measures. Many different algorithms have been proposed and tested;some are more accurate than others. Our goal is to investigate different technologies currently used to help combine the best parts of both (Iris and Retina) in order to propose a better system for future implementation. With the proper design, ocular recognition could be a great tool for user authentication in emerging Internet of things (IoT) and mobile markets.

关键词： Ocular recognition Iris recognition Biometrics authentication Secure applications Mobile security Digital verification

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ICCPR 2023 - Proceedings of the 2023 12th international conference on Computing and pattern recognition

ICCPR 2023 - Proceedings of the 2023 12th International Conf...

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12th international conference on Computing and pattern recognition, ICCPR 2023

ISBN: (纸本)9798400707988

the proceedings contain 85 papers. the topics discussed include: entity attribute alignment method based on attribute value distribution;solving the lunar lander problem with multiple uncertainties using a deep Q-learning based short-term memory agent;sentiment analysis of Weibo text using a deep combination model;an ontology-enhanced knowledge graph embedding method;Chinese event extraction algorithm of multi-information semantic enhancements;comparative analysis of classification methods for diagnosing myasthenia gravis based on lumbar electromyography;rare association rule mining based on reinforcement learning;research on identification and prediction of financial fraud of listed companies based on machine learning;using process enhancement to predict organizational citizenship behavior via the role of sustainable training practices;self-supervised contrastive few-shot learning for motor imagery brain-computer interfaces;and a wearable brain-computer interface system for fatigue detection in driving.

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Fault Detection and Diagnosis Technique for a SRM Drive Based on a Multilevel Converter Using a Machine Learning Approach 12

Fault Detection and Diagnosis Technique for a SRM Drive Base...

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12th international conference on Renewable Energy Research and applications (ICRERA)

作者： Amaral, Tito G. Pires, V. Fernao Foito, Daniel Pires, A. J. Martins, J. F. Inst Politecn Setubal ESTSetubal Setubal Portugal INESC ID Lisbon Portugal CTS UNINOVA Lisbon Portugal Univ Nova Lisboa CTS UNINOVA DEEC FCT Lisbon Portugal

ISBN: (纸本)9798350337938

One of today's well-accepted solutions for the SRM drives is based on multilevel converters. In fact, they present interesting features like an extended voltage range and the capability of fault tolerance. the guarantee of fault tolerance is fundamental in the context of preventive maintenance. However, regarding the power electronic converter, this requires a fault detection and diagnosis algorithm for failures in power semiconductors. thus, this paper proposes a novel detection and diagnostic approach for the failure of those semiconductors. In this case, it will focus on one of the most commonly used topologies, namely the asymmetric neutral point clamped converter. this approach was developed with the purpose to develop specific patterns that are associated with each semiconductor and fault type. In this way, through the image identification of the multilevel converter current patterns, it will be possible to identify a distinct semiconductor and fault type. Several tests obtained from a simulation tool allowed to show the capability of the proposed approach.

关键词： SRM drive Multilevel ANPC topology Fault Diagnosis Detection pattern recognition

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Traffic classification - Towards accurate real time network applications

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12th international conference on Human-Computer Interaction (HCI international 2007)

作者： Li, Zhu Yuan, Ruixi Guan, Xiaohong Tsinghua Univ Ctr Intelligent & Networked Syst Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9783540731092

Timely traffic identification is critical in network security monitoring and traffic engineering. Traditional methods using well-known ports, protocols and precise signature matching are no longer accurate with the proliferation of new applications. Recently, applying pattern recognition methods to classify network application traffic based on the flow parameters (e.g. port, flow duration, etc.) has become increasing popular. However, many methods developed in the previous works are either too complex to be applied in real-time, or suffer from lower accuracy due to the insufficient knowledge of the application. In this paper, we first give an overview on the developments of pattern recognition methods as traffic classification tools. We then develop two separate pattern recognition methods: one with supervised learning, and one with un-supervised learning, and apply them to classify traffic captured from a campus backbone network. the supervised learning method (an optimized SVM method) yields approximately 99.41 % accuracy for the collected traffic. the un-supervised learning method (an entropy based clustering method) gets the average accuracy of 92.41% for the top 20 traffic generating hosts during the same time period. Performance test on a single PC with 3GHz Pentium 4 processors and 1 GB of memory show that both methods can handle more than 10000 network flows per second, close to real time requirements for many situations.

关键词： network flows traffic identification supervised learning patter recognition

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A Hybrid Echo State Network for Hypercomplex pattern recognition, Classification, and Big Data Analysis 12

A Hybrid Echo State Network for Hypercomplex Pattern Recogni...

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12th international conference on Computer and Knowledge Engineering, ICCKE 2022

作者： Jamshidi, Mohammad Behdad Daneshfar, Fatemeh Pilsen Czech Republic University of Kurdistan Department of Computer Engineering and Information Technology Sanandaj Iran

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

Processing big data with high-dimensional forms is one the most challenging parts of analyzing different signals and systems for various applications, including decision-making, pattern recognition, classification, etc. this procedure can be more problematic when interpreting and modeling complex phenomena. On the other hand, the processing speed in machine learning (ML) methods may not be acceptable, especially for real-Time implementations. In other words, the more accurate a signal is, the more latency can occur. Recently, echo state networks (ESN) have shown appropriate precession in signal processing and classifying variables. However, utilization of a reservoir layer affects their speed, particularly for high-dimensional data. this paper presents a powerful ESN method for pattern recognition and classification of complex phenomena based on a new octonion ESN called octonion nonlinear ESN (ONESN). this method includes a modified version of the conventional ESN, in which all computations from the real space are mapped to the octonion space. Consequently, the size of the reservoir and its weights are reduced to 1/8 of its initial amount, increasing the speed of processing. Also, a bilinear filter to improve the performance of the conventional ESN is added to the output of the network. © 2022 IEEE.

关键词： pattern recognition

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