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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1-10 订阅

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2023 ieee 4th international conference on pattern recognition and machine learning, prml 2023

2023 IEEE 4th International Conference on Pattern Recognitio...

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4th ieee international conference on pattern recognition and machine learning, prml 2023

ISBN: (纸本)9798350324303

the proceedings contain 98 papers. the topics discussed include: self-supervised learning for point clouds through multi-crop mutual prediction;assessment and prediction of corrosion rate of marine railway bridges based on ridge regression model;Chunk-BERT: boosted keyword extraction for long scientific literature via BERT with chunking capabilities;chunking of Setswana noun and verb phrase;RarKGQA: multi-hop question and answering method based on knowledge graph embedding;diagnosis and identification of citrus canker growth rate using machine learning;joint spatial similarity-based attention for retina vessel segmentation with super resolution encoding;mixed attention interleaved execution cascade architecture for fittings instance segmentation in overhead transmission line images;and RFBiCF: a relation-first bidirectional cascade framework for relational triple extraction.

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4th EAI international conference on Data Information in Online Environments, DIONE 2023

4th EAI International Conference on Data Information in Onli...

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4th EAI international conference on Data Information in Online Environments, DIONE 2023

ISBN: (纸本)9783031807121

the proceedings contain 21 papers. the special focus in this conference is on Data Information in Online Environments. the topics include: Deep Image Inpainting Incorporating Texture Prior Based on Gabor Filter;the Improved XdeepFM Algorithm Based on Attention Mechanism and Factorization machine;deep learning-Based Glaucoma Diagnostic Assistance System on Mobile Devices;using Alignment Chain to Boost Genetic Sequence Alignment Process;analysis of the Industrial Internet Industry Chain and Supply Capability Analysis of Key Links Within Jiangxi Province;a Lithium-Ion Battery Cathode Material Literature Entity recognition Method Based on Deep learning;design and Implementation of Improved Multi-objective Genetic Algorithm Based on Uniform Distribution;Wavelet and Kalman Filter-Empowered Traffic Detection for Secure QUIC Network Communication;hyFed: A Hybrid Blockchain Empowered Federated learning Privacy Fair Framework;research on the Capability Status of Industrial Internet Security Supervision Platform;solar System Dynamics with Jet Propulsion Laboratory Ephemeris;elderly Health Care Data Integration Framework: Design and Implementation;challenges and Prospects of Power Network Security Protection in the Context of a New Power System: A Case Study of Jiangxi;enhancing Sequence Alignment Efficiency through Concurrent Utilization of Multiple Arm Processors in a Sequential Processing Framework;Fusion of Multiscale Convolution and LSTM for Stock Price Prediction;development of a Web Application for the Sociocultural Diffusion of the Municipality of Lamas, Peru;A Solution Against Selective Jamming Attack in ieee 802.15.4e Wireless Networks;TDFM and TAFM: Time-Aware and Feature Fusion-Based Deep Recommendation Models for Short Videos;blockchain Based Access Control: A Review and Future Perspectives.

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Assessment and Prediction of Corrosion Rate of Marine Railway Bridges Based on Ridge Regression Model 4

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

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4th ieee international conference on pattern recognition and machine learning, prml 2023

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

ISBN: (纸本)9798350324303

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. © 2023 ieee.

关键词： Corrosion rate

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Robot Debater: Debate-styled Text Auto-generation System Based on Large Foundation Language Models 4

Robot Debater: Debate-styled Text Auto-generation System Bas...

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4th ieee international conference on pattern recognition and machine learning, prml 2023

作者： Zhu, Yu Ling, Yijun Ling, Xufeng Yang, Jie Shanghai China Shanghai Xiangming High School Shanghai China Shanghai Normal University Tianhua College School of Artificial Intelligence Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9798350324303

We use a large foundation language model, which is fine-tuned with debate corpora, to develop a robot debater application. To address the limitations of requiring immense computational power in large base language models, this study takes advantage of the Low Rank Adaption characteristic prevalent in domain expert knowledge. By applying Low Rank Adaption technology and fine-tuning with a dedicated dataset, the computational load is reduced to just one-thousandth of what is needed for a large language model, greatly expanding the application scenarios of robot debaters using large language models. In view of the characteristics of debate competitions, this model can preset a variety of debate scenarios and supports personalized debate processes. It employs intelligent voice recognition technology combined with a multi-channel voice input method, allowing for precise localization of different human debaters and improving the accuracy of voice input recognition. the system can support multiple large-scale language generation models and utilize various different voice broadcasting systems, including male and female voice styles, as well as a range of voice emotions. this model can be applied to debate competitions held in universities, high schools, and various industries. It can support human-machine debates as well as machine-to-machine debates. © 2023 ieee.

关键词： Speech recognition

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Mushroom Classification Based on Deep Residual Network 4

Mushroom Classification Based on Deep Residual Network

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4th ieee international conference on pattern recognition and machine learning, prml 2023

作者： Feng, Ju Ling, Xufeng Wang, Yubo Yang, Jie Shanghai Normal University Tianhua College School of Artificial Intelligence Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Inst. of Image Proc. and Pattern Recog. and Inst. of Medical Robotics Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9798350324303

Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classification and identification, this paper proposes a mushroom classification method based on residual networks. Firstly, a network architecture with multiple residual blocks is designed, and it is trained using an image dataset. then, a transfer learning strategy is employed to initialize the network parameters from a pre-Trained model, followed by fine-Tuning to adapt to the mushroom classification task. Finally, multiple testing experiments are conducted to evaluate the effectiveness of the proposed method. the experimental results demonstrate excellent performance of the proposed method in mushroom classification tasks. Compared to traditional feature extraction methods, it can better capture the details and texture features of mushrooms, thereby improving classification accuracy. In conclusion, the mushroom classification method based on residual networks exhibits high accuracy and generalization capability. this method has potential applications in the field of mushroom classification, aiding in the better identification and differentiation of poisonous mushrooms, thereby protecting human health. © 2023 ieee.

关键词： Convolutional neural networks

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A MobileNetV2-CBAM-based Model for Forest Fire Classification Using UAV Imagery 4

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

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4th ieee international conference on pattern recognition and machine learning, prml 2023

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

ISBN: (纸本)9798350324303

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. © 2023 ieee.

关键词： Antennas

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the New Answer to Drug Discovery: Quantum machine learning in Preclinical Drug Development 4

The New Answer to Drug Discovery: Quantum Machine Learning i...

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4th ieee international conference on pattern recognition and machine learning, prml 2023

作者： Wong, Yew Kee Zhou, Yifan Liang, Yan Shing Qiu, Haichuan Wu, Yu Xi He, Bin Basis International School Guangzhou Guangzhou China

ISBN: (纸本)9798350324303

the Research & Development (R&D) phase of drug development Drug discovery and development (D&D) is a complex and costly endeavor, typically requiring six to nine years [1] and four hundred to fourteen hundred million USD [2] for the research and development (R&D) phase alone. To transform this process, we propose a novel concept that combines Quantum-based machine learning network (QML) and Quantum Computing Simulation (QS) to expedite the R&D phase to three to six months and reduce the cost to merely fifty to eighty thousand USD. Our approach takes as inputs the target protein/gene structure and the primary assay [3]. For Hit Generation [3], the QML network generates potential hits [4] based on the molecular structure of the target protein, while the QS filters molecules from the primary assay according to their reaction and binding efficacy with the target protein. then, for Lead Optimization [3], the resulting molecules from QML and QS are compared, and the ones that are common to both processes are subjected to dozens of molecular variations, while others are only modified slightly. Finally, all optimized molecules undergo multiple rounds of QS filtering with high standards for reaction efficacy and safety, producing a few dozen pre-clinical-trial-ready drugs. Our concept of QML and QS integration can also be applied to other fields, such as agricultural research, genetic editing, and aerospace engineering. Keywords: Quantum Computer, Hit to Lead, Lead Optimization Simulation, machine learning. © 2023 ieee.

关键词： Molecules

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thalamic Bursts Mediate pattern recognition

Thalamic Bursts Mediate Pattern Recognition

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4th international ieee/EMBS conference on Neural Engineering

作者： Jandel, Magnus Swedish Def Res Agcy Stockholm Sweden

ISBN: (纸本)9781424420728

A new functional model for burst firing in the dorsal thalamus is proposed where thalamocortical pattern recognition systems, based on kernel machine principles, are connected by burst signaling. the systems include input trapping in the dorsal thalamus, cortical learning state memory and processing in the thalamic reticular nucleus. Misclassified events are captured as training examples in the waking state and the pattern recognition systems are trained by extensive thalamic bursting in deep sleep.