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检索条件"任意字段=4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023"

共 108 条记录，以下是21-30 订阅

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Parallel algorithm for control chart pattern recognition

Parallel algorithm for control chart pattern recognition

引用

ICMLA 2005: 4th international conference on machine learning and Applications

作者： Wani, M. Arif Rashid, Sumia California State University Bakersfield

ISBN: (纸本)0769524958

Fast control chart pattern recognition aids in instantaneous detection of abnormal functioning of a system. In this paper, we present a parallel algorithm for fast control chart pattern recognition. It addresses three major issues of control chart pattern recognition: (i) transparency (ii) accuracy and (iii) fast detection of abnormal patterns. the algorithm uses novel shape features extracted from a control chart pattern (CCP) instead of the unprocessed CCP data or its statistical properties. these shape features can be extracted in parallel. A parallel algorithm that is based on distributed and synergistic neural network structure for recognition of CCPs is described. the paper presents the results of analyzing several hundred control chart patterns and gives a comparison with those reported in previous work. © 2005 ieee.

关键词： pattern recognition

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Research on Radar PRI Modulation pattern recognition Based on Recurrent Neural Network 4

Research on Radar PRI Modulation Pattern Recognition Based o...

引用

4th international conference on Computer Vision, Image and Deep learning, CVIDL 2023

作者： Qu, Wei Yao, Gang Meng, Lei Space Engineering University Department of Electronics and Optics Beijing China Beijing Institute of Tracking and Telecommunications Technology Beijing China Beijing Remote Sensing Information Research Instituteline Beijing China

ISBN: (纸本)9798350326444

In this paper, the Recurrent Neural Network (RNN) model is introduced into the identification of radar emitters, which verifies the feasibility of RNN application to radar emitter identification, and the design process is simplified and the radar emitter identification method is easy to apply in engineering. Based on the radar emission source recognition model, the Pulse Repetition Interval(PRI) modulation pattern recognition research based on recurrent neural network was carried out. Firstly, aiming at the problems of gradient vanishing and gradient explosion in RNN, a PRI modulation pattern recognition method based on LSTM is proposed, and the recognition method process is introduced in detail. According to the characteristics of pulse train data, a data preprocessing method suitable for recurrent neural network is proposed. Finally, simulation verifies the effectiveness of the proposed method and tests the performance of the algorithm under different interference conditions. © 2023 ieee.

关键词： Long short-term memory

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Assessment and Prediction of Corrosion Rate of Marine Railway Bridges Based on Ridge Regression Model

Assessment and Prediction of Corrosion Rate of Marine Railwa...

引用

pattern recognition and machine learning (prml), ieee international conference on

作者： Huan Wang Zhengbin Chen Rail Transit Branch of Sinohydro 7th Engineering Bureau Co. LTD Chengdu China

this investigation employs ridge regression analysis to assess and forecast the corrosion rate of maritime railway bridges. We have gathered and organized pertinent data, devising a regression model encompassing multiple attributes, intending to scrutinize seawater’s influence on railway bridge corrosion. the model’s performance is appraised using cross-validation techniques, and the significance of each point is measured via ridge regression coefficients. Experimental findings demonstrate that the ridge regression model excels in predicting the corrosion rate of maritime railway bridges, exhibiting minimal discrepancies between predicted and actual values. Upon analysis of the model’s attributes, we discern that salinity and sulfide content in seawater are critical determinants of railway bridge corrosion rates. the findings of this study provide valuable reference points for marine engineering and railway bridge construction while offering novel insights into the application of ridge regression models for predicting and analyzing maritime environmental issues.

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Application of Complete Local Binary pattern Method for Facial Expression recognition

Application of Complete Local Binary Pattern Method for Faci...

引用

4th international conference on Intelligent Human Computer Interaction (IHCI)

作者： Singh, Suryakant Maurya, Ritesh Mittal, Ajay GLA Univ Dept Comp Sci & Engn Mathura India PEC Univ Tech Dept Comp Sci Chandigarh India

ISBN: (纸本)9781467343695;9781467343671

We propose a novel approach using Complete Local Binary pattern feature generation method for facial expression recognition with the help of Multi-Class Support Vector machine. Complete Local Binary pattern method is an extended version of Local Binary pattern method with a little difference. LBP feature considers only signs of local differences, whereas CLBP feature considers both signs and magnitude of local differences as well as original center gray level value. CLBP and LBP have same computational complexity while CLBP performs better facial expression recognition over LBP using SVM training and multiclass classification with binary SVM classifiers. the experimental result demonstrate the average efficiency of recognition of propose method (35 images) with CLBP is 86.4%, while with LBP and CCV is 84.1255% and 75.83% in the JAFFE database.

关键词： Color Coherence Vector (CCV) Local Binary pattern (LBP) Completed Local Binary pattern (CLBP) Support Vector machine learning and classification

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machine learning Techniques for pattern recognition in High-Dimensional Data Mining 4

Machine Learning Techniques for Pattern Recognition in High-...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Li, Pochun Northeastern University Boston United States

ISBN: (纸本)9798331534622

this paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent pattern mining algorithms in high-dimensional and sparse data environments. By converting the frequent pattern mining task into a classification problem, the SVM model is introduced to improve the accuracy and robustness of pattern extraction. In terms of method design, the kernel function is used to map the data to a high-dimensional feature space, so as to construct the optimal classification hyperplane, realize the nonlinear separation of patterns and the accurate mining of frequent items. In the experiment, two public datasets, Retail and Mushroom, were selected to compare and analyze the proposed algorithm with traditional FP-Growth, FP-Tree, decision tree and random forest models. the experimental results show that the algorithm in this paper is significantly better than the traditional model in terms of three key indicators: support, confidence and lift, showing strong pattern recognition ability and rule extraction effect. the study shows that the SVM model has excellent performance advantages in an environment with high data sparsity and a large number of transactions, and can effectively cope with complex pattern mining tasks. At the same time, this paper also points out the potential direction of future research, including the introduction of deep learning and ensemble learning frameworks to further improve the scalability and adaptability of the algorithm. this research not only provides a new idea for frequent pattern mining, but also provides important technical support for solving pattern discovery and association rule mining problems in practical applications. © 2024 ieee.

关键词： Adversarial machine learning

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A Privacy-preserving Crowd Movement Analysis by k-member Clustering of Face Images 4

A Privacy-preserving Crowd Movement Analysis by <i>k</i>-mem...

引用

4th international conference on Informatics, Electronics & Vision (lCIEV)

作者： Honda, Katsuhiro Omori, Masahiro Ubukata, Seiki Notsu, Akira Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan

ISBN: (纸本)9781467369022

Crowd movement analysis is an important issue in social design. this paper studies an machine learning approach to crowd movement estimation through face image recognition. Although high performance face recognition is a powerful tool in individual authentication with surveillance camera images in public spaces, utilization of personal information is often hesitated under fear of privacy violation. In this paper, a privacy preserving framework for crowd movement analysis is proposed considering k-anonymization of face image features. k-anonymity is a quantitative measure of secureness in data mining and is expected to enhance the utility of personal information. An experimental result demonstrates the applicability of the secure framework in capturing crowd movement characteristics even if individual features are k-aonymized so that each individual is not distinguishable from others k - 1 ones.

关键词： cameras data mining data privacy face recognition learning (artificial intelligence) parallel processing high performance face image recognition k-anonymization k-member clustering machine learning approach privacy violation privacy-preserving crowd movement analysis surveillance camera image Cameras Data privacy Face Face recognition Image recognition Logic gates pattern matching Data privacy Facial recognition Cameras data mining Image recognition Face Face pattern matching Logic gates PARALLEL PROCESSING (COMPUTERS) Movements

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Interactive classification using a granule network

Interactive classification using a granule network

引用

4th ieee international conference on Cognitive Informatics (ICCI 2005)

作者： Zhao, Y Yao, YY Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada

ISBN: (纸本)0780391365

Classification is one of the main tasks in machine learning, data mining and pattern recognition. Compared with the extensively studied data-driven approaches, the interactively user-driven approaches are less explored A granular computing model is suggested for re-examining the classification problems. An interactive classification method using the granule network is proposed, which allows multi-strategies for granule tree construction and enhances the understanding and interpretation of the classification process. this method is complementary to the existing classification methods.

关键词： learning systems

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Scopira: A pattern recognition application framework for biomedical datasets

Scopira: A pattern recognition application framework for bio...

引用

ICMLA 2005: 4th international conference on machine learning and Applications

作者： Vivanco, Rodrigo A. Demko, Aleksander Pizzi, Nick J. Institute for Biodiagnostics National Research Council Canada

ISBN: (纸本)0769524958

machine learning techniques are widely used in the analysis of biomedical datasets. Modern devices tend to produce voluminous, high-dimensional datasets for which medical practitioners require high-performance, user-friendly programs and researchers need effective algorithm development and testing platforms. Interactive development systems, such as MATLAB, provide for rapid prototyping of algorithms and visualization but at the cost of computational efficiency. We present Scopira, a C++, open source programming framework for the development of biomedical data analysis applications. © 2005 ieee.

关键词： pattern recognition

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A MobileNetV2-CBAM-based Model for Forest Fire Classification Using UAV Imagery

A MobileNetV2-CBAM-based Model for Forest Fire Classificatio...

引用

pattern recognition and machine learning (prml), ieee international conference on

作者： Xinjie Deng Burhan Khan Chee Peng Lim Ming-Yu Liao Chongqing Jianzhu College Institute for Intelligent Systems Research and Innovation Deakin University Chongqing China Institute for Intelligent Systems Research and Innovation Deakin University Waurn Ponds VIC Australia Department of International Business National Kaohsiung University of Science and Technology Taiwan

Forest fires are one of the worldwide severe natural disasters causing human and environmental losses. Early and accurate fire detection is critical to minimize damages caused by forest fires and reduce human risks in firefighting efforts. Recently, Unmanned Aerial Vehicles (UAVs) mounted with cameras and edge computing devices with Artificial Intelligence (AI)-based algorithms have been employed to tackle forest fire detection problems in real time. Considering that edge computing is limited in resources and power consumption, the developed AI algorithms need to be lightweight. this paper proposes lightweight deep learning (DL) algorithms based on MobileNetV2 and attention mechanism to detect forest fires using UAV imagery. Based on several experiments, the proposed model yields an accuracy rate of 86.14% and an F1-score of 0.8719, outperforming other related methods in forest fire detection and classification.

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learning to Optimize Quantum Neural Networks Without Gradients 4

Learning to Optimize Quantum Neural Networks Without Gradien...

引用

4th ieee international conference on Quantum Computing and Engineering, QCE 2023

作者： Kulshrestha, Ankit Liu, Xiaoyuan Ushijima-Mwesigwa, Hayato Safro, Ilya Newark United States Fujitsu Research of America Sunnyvale United States

ISBN: (纸本)9798350343236

Quantum machine learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. this extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer. the similarities in training of these hybrid algorithms and classical neural networks has further led to the development of Quantum Neural Networks (QNNs). However, in the current training regime for QNNs, the gradients w.r. t objective function have to be computed on the quantum device. this computation is highly non-scalable and is affected by hardware and sampling noise present in the current generation of quantum hardware. In this paper, we propose a training algorithm that does not rely on gradient information. Specifically, we introduce a novel meta-optimization algorithm that trains a meta-optimizer network to output parameters for the quantum circuit such that the objective function is minimized. We empirically and theoretically show that we achieve a better quality minima in fewer circuit evaluations than existing gradient based algorithms on different datasets. © 2023 ieee.

关键词： pattern recognition

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