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检索条件"任意字段=5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024"

共 134 条记录，以下是51-60 订阅

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Complex System pattern recognition Algorithm Based on BP Neural Network 5

Complex System Pattern Recognition Algorithm Based on BP Neu...

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5th international conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Fan, Xue College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798331527662

this article explores the intricate and complex system pattern recognition algorithm based on backpropagation (BP) neural networks, which are efforts to leverage leverage's capabilities. Complicated systems, as seen in this case, are widely characterized by complex, nonlinear, and dynamic interactions and systems that pose challenges to traditional models and patterns of recognition. To this effect, a BP Neural Network that involves an artificial neural network plays a critical role in learning accuracy and efficiency that is integrated in addressing these intricate deficiencies. BP Neural Networks encompass an extensive and comprehensive dataset that helps discern correlations and underlying patterns in a complex system. the iterative adjustment of weights through the minimization of errors helps in accurately classifying and predicting patterns in complex systems. A typical approach involves training the BP Neural Networks through an extensive dataset, which allows underpinning to underpin the correlations and patterns in a complex system. the experimental results of this article revealed that the BP Neural network algorithms efficiently identified complex patterns, thus providing valuable insights into the model's efficiency in bioinformatics, finance, and engineering. © 2024 ieee.

关键词： Neural network models

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Linear Regression Approach for Stock Chart pattern recognition 5

Linear Regression Approach for Stock Chart Pattern Recogniti...

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5th Biennial international conference on Nascent Technologies in Engineering, ICNTE 2023

作者： Tripathi, Amay Mathure, Jignesh Deotarse, Shivam Rai, Darshak Gadhikar, Lakshmi Fr. C. Rodrigues Institute of Technology Department of Information Technology Navi Mumbai India

ISBN: (纸本)9781665465045

Finding stock chart patterns, like channels, triangles, head and shoulders, etc., in high-dimensional stock data is arduous. Technical analysis experts can visualize these patterns in stock candlestick charts based on experience and predict stock price movements. the subjectivity of visualizing these stock patterns by different experts increases the possibility of drawing different variations of the same patterns based on scale and time. Stock market pattern recognition is a complex challenge due to the volatile nature of the stock price. Various machine-learning algorithms can be used to detect stock chart patterns. A linear regression approach for forming these patterns can minimize human errors by automating the process. In this paper, we have implemented linear regression, which is an efficient approach for handling dynamic stock data and drawing a triangle pattern on charts. It has an average accuracy of 98.60% when the top ten stocks of the Information Technology sector and Pharmaceutical sector in terms of market capitalization in the Indian National Stock Exchange are analyzed. © 2023 ieee.

关键词： pattern recognition

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A Gait Perspective Generation Method Based on the 3D Model

A Gait Perspective Generation Method Based on the 3D Model

引用

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

作者： Dongliang Yang Xianglei Xing Changjiang Song Intelligent Manufacturing Research Institute Heilongjiang Academy of Sciences Harbin China College of Intelligent Science and Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Aiming at the problem of gait recognition, a multi- view gait recognition method based on the human pose estimation model is proposed. this method can effectively solve the problem of low gait recognition rates caused by limited viewpoints. the VIBE method is used to extract human pose parameters of video frames, but there are errors in human pose parameters estimated by VIBE, and direct rotation with attitude parameters will inevitably cause greater errors. the attitude parameters were corrected in the design experiment. Finally, we used the Rodrigues rotation matrix to generate human pose parameters from other angles for VIBE to complete the conversion of character angles. the calibration network, including the attitude average and angle correction model, is designed. the attitude parameters under are input as the mean value as our final attitude parameters and then sent into the angle correction model to correct the root node. Finally, the corrected gait sequence is obtained. We can intuitively observe that the human model has obvious effects, and the accuracy of gait recognition is verified by Gaitset, which verifies that the model has good qualitative and quantitative effects. After correcting the correction network, we can generate gait sequences from other perspectives through the existing perspective. this method can well expand the perspective of the database and obtain more accurate gait sequence models from other perspectives.

关键词： Solid modeling Accuracy three-dimensional displays Databases Pose estimation Clothing machine learning pattern recognition Calibration Gait recognition

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A Knowledge Graph Completion Model Combining Graph Convolutional Networks with Self-Attention Mechanisms

A Knowledge Graph Completion Model Combining Graph Convoluti...

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

作者： Yao Chen Shaorong Li Hao Fang Yaokun Li Jianhong Hu Yunnan Transportation Science Research Institute Co. Ltd. Kunming China School of Software Yunnan University Kunming China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Knowledge graph completion aims to predict missing entities or relationships in a knowledge graph, addressing the issue of data sparsity. In recent years, deep learning-based models, especially Graph Convolutional Network (GCN) and Self-Attention mechanisms, have gained attention for their advantages in handling graph-structured data. We propose a knowledge graph completion model, CSANN, which integrates GCN and self-attention mechanisms. First, GCN module is used to learn the initial representations of entities and relationships, considering the neighboring information of entities. Next, the initial representations of entities and relationships are concatenated to form a sequence, which is further refined by introducing the self-attention mechanism. By combining the local graph structure awareness of GCN and the sequential dependency capturing ability of self-attention mechanisms, CSANN improves the accuracy of knowledge graph completion. Experiments conducted on two public datasets, FB15K-237 and WN18RR, show that the proposed model outperforms many classical models, such as TransE, DistMult, ConvE, and RotatE, in terms of Mean Reciprocal Rank (MRR) and Hit@K.

关键词： Measurement Knowledge engineering Adaptation models Graph convolutional networks Computational modeling Scalability Knowledge graphs machine learning Magnetic heads pattern recognition

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Deep learning in Automatic recognition of Mongolian Compound Nouns

Deep Learning in Automatic Recognition of Mongolian Compound...

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

作者： Ha Si Du La Yang Ding College of Computer Science and Information Engineering Hohhot Minzu College Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Hohhot China School of Software and Big Data Inner Mongolia Electronic Information Vocational Technical College Hohhot China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Mongolian compound nouns often become the vehicle for the creation of new concepts and terms due to their diverse combinations and flexible expressive power. In the field of natural language shallow syntactic analysis, the recognition of Mongolian compound nouns plays an important role, and its performance will directly affect the processing effect of related research. In the current academic research, the research on automatic recognition of Mongolian compound nouns is still insufficient, especially in the automatic recognition of Mongolian compound nouns using deep learning technology, which is almost in a blank state. In this study, we firstly construct a Mongolian compound noun annotated corpus, and on the basis of the constructed annotated corpus, we propose a Mongolian compound noun automatic recognition model based on deep learning for the fact that there is no deep learning method for recognising Mongolian compound nouns at present. the model takes Conditional Random Field (CRF) as the basic framework to make full use of contextual information by considering the joint probability distribution of the whole sequence. During the model construction process, the TextCNN model is incorporated and by designing a multi-scale convolutional kernel, the model is able to effectively capture the local features of Mongolian compound nouns. the experimental results show that the automatic Mongolian compound noun recognition model based on deep learning proposed in this study performs well on the Mongolian compound noun recognition task, with an F1 value of 93.40%.

关键词： Deep learning Natural languages Syntactics Conditional random fields Probability distribution pattern recognition Compounds Kernel Context modeling

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Underwater Plankton Image Enhancement Method Based on Local Histogram Equalization

Underwater Plankton Image Enhancement Method Based on Local ...

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

作者： Jin Yang Wenqiang Fan Longwen Fu Chunlei Xia School of Physics and Electronic Information Yantai University Yantai China Yantai Institute of Coastal Zone Research Chinese Academy of Sciences Yantai China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Classification and identification of marine plankton are important for marine ecological environment monitoring. Due to the tiny size of individual plankton and the weak light intensity in the marine environment, it is challenging for the imaging system to capture a clear image of plankton in the flowing water. Sometimes causing weak brightness and noise interference in the collected images. To this end, we propose an underwater image enhancement method based on local histogram equalization to remove noise while improving image clarity effectively. the proposed method improves the brightness and contrast in HSI color space, and preserves the color information (hue and saturation) in H and S channels. Experiments were carried out on the underwater plankton images. the enhancement performance of the proposed method was evaluated by commonly used metrics and compared with the well-known image enhancement methods, including a deep learning-based method. the experimental results showed that the proposed method was superior to the other methods in noise reduction, structure similarity, and perceptual quality. this work presents a simple and effective method for underwater plankton image enhancement. the proposed enhancement method could provide high-quality images for implementing automatic plankton classification.

关键词： Measurement Histograms Image color analysis Plankton Brightness Noise reduction pattern recognition Colored noise Image enhancement Monitoring

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Research on Multimodality Tibetan-Chinese machine Translation of Text and Image Based on a Dual-Layer Attention Mechanism Decoder

Research on Multimodality Tibetan-Chinese Machine Translatio...

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

作者： Quzong Gesang Chenghao He Mingjun Zhou Gadeng Lobsang Nuo Qun Tashi Nyima School of Information Science and Technology Tibet University Collaborative Innovation Center for Tibet Informatization by MOE and Tibet Autonomous Region Lhasa China School of Information Science and Technology Tibet University Lhasa China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Current Tibetan-Chinese machine translation based on text has achieved significant progress, yet it still faces numerous challenges. One of the major technical challenges is addressing ambiguity in translation. To tackle this issue, this paper proposes a dual-layer attention mechanism decoder-based method for the fusion of text and image multimodal information. In this method, the decoder, while generating words in the target language, learns to independently focus on parts of the source language words and the image through two distinct attention mechanisms. Specifically, the model effectively integrates two separate attention mechanisms: one for the source language words and another for different regions of the image. the empirical findings indicate that the approach introduced within this study has achieved a marked enhancement in translation quality, outperforming the Transformer and other RNN for pure text translation by 2.6 and 4.2 points on the BLEU scale, respectively. Additionally, the method exhibits a robust capacity to assimilate visual information and discern ambiguities within the translation process. Such advancements are conducive to refining the current neural Tibetan-to-Chinese machine translation systems, thereby augmenting the fidelity and applicability of the translations produced. It is hoped that the research presented in this paper can serve as a proof of concept to inspire future studies on Tibetan- Chinese text and image multimodal machine translation.

关键词： Visualization Attention mechanisms Image resolution Refining Transformers Decoding pattern recognition machine translation Faces

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A Review of Image Enhancement Methods Based on Stochastic Resonance of Neuronal Networks

A Review of Image Enhancement Methods Based on Stochastic Re...

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

作者： Ximin Sun Pingjuan Niu Yong Li Xiaoming Li Bingzhen Zhao Bin Zhang Dan Liu Bo Sun Tiangong University Laboratory of Metaverse in Electric Power Industry State Gride Ecommerce Technology CO.LTD Tian'jin China Laboratory of Metaverse in Electric Power Industry Tiangong University Tian'jin China Laboratory of Metaverse in Electric Power Industry State Gride Ecommerce Technology CO.LTD Tian'jin China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Stochastic resonance has always been a research hotspot in the field of image processing. the ability of the nervous system to detect signals is closely related to nonlinear and collective behavior in neural mediators. Neuronal noise is the most significant factor influencing this ability, and it is a natural endogenous fluctuation in brain activity. Experimental and theoretical studies have shown that noise is beneficial, not harmful, for the normal functioning of the nervous system. In this regard, it is generally agreed that noise of appropriate intensity can participate in the rhythmic activity of the brain, and that noise-induced oscillations enhance the performance of weak signal processing, especially when the frequency of the signal is approximately noise-induced rhythmic oscillations. this behavior in the biological nervous system can be explained by the concept of “stochastic resonance”. Correspondingly, the stochastic resonance mechanism of neurons is used to denoise the image, which has a good effect. In this paper, we first introduce an image processing method using random resonance of neurons. then, the commonly used evaluation indicators in the field of image processing are sorted out. Finally, the development trend of image processing using random resonance of neurons is prospected.

关键词： Reviews Stochastic resonance Noise Neurons Resonant frequency machine learning Market research pattern recognition Oscillators Image enhancement

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Improving wafer map classification in Industry 4.0 5

Improving wafer map classification in Industry 4.0

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ieee 5th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Scheiber, Michael Nartey, Clement Zernig, Anja Kaestner, Andre KAI GmbH Villach Austria Flextron Int GmbH Althofen Austria Infineon Technol Austria AG Villach Austria

ISBN: (纸本)9781665497701

At the heart of Industry 4.0. lies the automation of manufacturing processes and interoperability of corresponding applications, in most cases utilizing Cyber-Physical Systems and Internet of things. Used within diverse industrial areas across the world, these concepts gained more importance in recent years. In the semiconductor industry, continuous improvement of all involved processes is achieved by utilizing these concepts in many different stages of the manufacturing process. this work proposes a machine learning framework that on one hand enhances wafer map classification in the testing stage of semiconductor devices, and on the other hand fulfills the requirements and demands of all involved stakeholders in adopted engineering processes. the core of the proposed system is the Wafer Health Factor, a machine learning framework that detects process deviations in analog wafer test data through pattern recognition. Additional integration of Radon-based features, as well as the use of an ensemble classification framework, boosts model performances in the presented real-world application scenario significantly. Moreover, we also show that the proposed framework performs well on two benchmark data sets from literature, even on small training data sets. We conclude, that the presented framework improves the performance of pattern recognition tasks in real-world applications and thus, enables the automatic and early detection of deviations within semiconductor manufacturing processes.

关键词： Arrowhead Tools analog wafer test data engineering process ensemble classification machine learning pattern recognition Radon transform

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A Method for Chinese Sarcasm Detection Based on Enhanced Cross-Entropy and Regularization

A Method for Chinese Sarcasm Detection Based on Enhanced Cro...

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

作者： Hang Ren Jing Zhang Yongqiang A Nuo Qun Shengqiao Ni Jin Yang Collaborative Innovation Center for Tibet Informatization by MOE and Tibet Autonomous Region School of Information Science and Technology Tibet University Lhasa China School of Information Science and Technology Tibet University Lhasa China School of Mechanical Engineering Sichuan University Chengdu China School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In the context of social media, the automatic identification of sarcastic discourse presents a complex challenge within the field of natural language processing. Due to the significant disparity between the literal and implied meanings of sarcastic statements, traditional text analysis algorithms struggle to accurately detect them. this paper introduces a method for detecting Chinese sarcastic comments on social media, which integrates an improved cross-entropy loss function with regularization techniques. the method employs a dynamic adjustment factor to optimize weighted cross-entropy loss function, calibrating the model's penalty intensity for high-confidence predictions, and enhances the consistency of the model's output through batch-averaged Kullback-Leibler divergence. Experiments were conducted using the Chinese-RoBERTa model as a baseline, validated on full data and small sample sizes (50%, 10%). the results demonstrate that, compared to the baseline model, the proposed method achieved accuracy improvements of 3.7%, 2.5%, and 2.3% across different data volumes, confirming its effectiveness and applicability in the detection of Chinese sarcastic discourse.

关键词： Visualization Text analysis Social networking (online) machine learning Predictive models Prediction algorithms Data models Natural language processing pattern recognition Optimization

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