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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11-20 订阅

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Research on Safety Hazard Identification Method for Construction Sites Based on YOLOv9 Algorithm

Research on Safety Hazard Identification Method for Construc...

引用

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

作者： Hong Zeng Xinjie Wu Yiran Lyu Chunyi Yu Intelligent Construction Department Chongqing Jianzhu College Chongqing China Construction Management Department Chongqing Jianzhu College Chongqing China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

this study establishes a framework system for identifying construction safety hazards, addressing issues such as insufficient perception of construction safety scene information, insufficient utilization of unsafe behavior data, and insufficient analytical capabilities in the current construction industry. through computer vision understanding, various potential safety hazards are automatically identified, and the YOLOv9 algorithm is used for model validation. the identification and positioning of people and hazardous areas are analyzed, with an overall average recognition accuracy of 92%. this method can provide safety warnings and reminders for construction sites, and provide important reference and guidance for active control and management of construction site safety.

关键词： Hazardous areas machine learning algorithms Safety management Object detection machine learning Inspection Safety pattern recognition Object recognition Security

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Be Server in Rotation: Combining Model Reuse with Knowledge Distillation for Heterogeneous Federated learning

Be Server in Rotation: Combining Model Reuse with Knowledge ...

引用

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

作者： Xintong Wen Ming An Tong Wang Chengwei Zhang Rong Chen College of Information Science and Technology Dalian Maritime University China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Dealing with data and model heterogeneity is crucial to federated learning practices. In this work, we introduce a novel mechanism termed SIO, which asks clients to take turn to be the server for aggregating model parameters in the procedure of federated mutual learning. Concretely, we propose a new federated learning framework, called FedSIO, which implements the proposed mechanism and especially exploit channel distil-lation and decoupled knowledge distillation to better address data heterogeneity issues. To assess the effectiveness of FedSIO, we conduct extensive experiments on three datasets involving different heterogeneity settings. Empirical results demonstrate that, compared to state-of-the-art methods, FedSIO significantly improves the generalization performance of the client group models.

关键词： Federated learning conferences Data models pattern recognition Servers

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INDISCON 2024 - 5th ieee India Council international Subsections conference: Science, Technology and Society

INDISCON 2024 - 5th IEEE India Council International Subsect...

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5th ieee India Council international Subsections conference, INDISCON 2024

ISBN: (纸本)9798350376753

the proceedings contain 135 papers. the topics discussed include: LearnSync: navigating learning opportunities;ultra-wideband pattern reconfigurable antenna for base station applications;finite-time frequency control of an islanded microgrid system;blockchain-enabled distributed payment card tokenization system;probabilistic free riding attack in federated learning;dual-band MIMO array for 5G wireless networks;CFD and thermal analysis of electric vehicle battery pack;revolutionizing sentiment analysis: accelerated data science approaches for reddit submissions;defect mediated Sc2CO2 MXene monolayer for sensing small gas molecules: a first principle study;enhancing financial market analysis bitcoin vs gold through machine learning algorithms: a study on risk assessment and portfolio management;two step federated learning approach in brain stroke identification and classification via fine-tuning EfficientNetB0 network at edge nodes;and privacy-preserved stress detection from wearables using federated learning.

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A Vehicle Classification Method Based on machine learning

A Vehicle Classification Method Based on Machine Learning

引用

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

作者： Xinjin Li Jinghao Chang Tiexin Li Wenhan Fan Yu Ma Haowei Ni Columbia University New York NY USA The Kyoto College of Graduate Studies for Informatics Kyoto Japan Xi'an Jiaotong-Liverpool University Suzhou Jiangsu China Independent Researcher New York NY USA Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In this article, we propose a machine learning based fine-grained vehicle classification method VehiClassNet, which effectively solves the challenges of precise vehicle recognition and classification in intelligent transportation systems by combining multi-scale feature extraction, cross modal fusion, and attention mechanisms. Our model demonstrated excellent performance on the Stanford Cars-196 dataset. the innovation of this study lies in its advanced multi-scale feature extraction, cross modal fusion, and attention mechanism. It not only improves classification accuracy, but also lays the foundation for further research on intelligent transportation systems and autonomous vehicle technology.

关键词： Adaptation models Technological innovation Attention mechanisms Accuracy Computational modeling Transportation Feature extraction Robustness pattern recognition Optimization

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Enhancing Security with machine learning-based Finger-Vein Biometric Authentication System 5

Enhancing Security with Machine Learning-based Finger-Vein B...

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5th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Babu, G. Prakash Aswal, Upendra Singh Sindhu, M. Upadhye, Vijay Jagdish Natrayan, L. Patil, Harshal Acharya Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560 107 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601 206 India Parul Institute of Applied Sciences Research and Development Cell Parul University Department of Microbiology Gujarat Waghodia391760 India SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India

ISBN: (纸本)9798350395235

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. the study employs two feature extraction approaches, the Local Binary pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. the simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task. © 2024 ieee.

关键词： Local binary pattern

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Predictive Analysis of Plant Macronutrient Consumption pattern Using machine learning 5

Predictive Analysis of Plant Macronutrient Consumption Patte...

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5th international conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Nayana, V. Bhan, Madhu Sowmya, B.J. Ramiah Institute of Technology Department of Mca Karnataka Bangalore India Ramaiah Intitule of Technology Department of Mca Karnataka Bangalore India Ramaiah Intitule of Technology Department of Ai and Ds Karnataka Bangalore India

ISBN: (纸本)9798331528539

this study incorporates many machine learning (ML) methods to predict and analyze the nutrient intake as it explores the macronutrient consumption patterns of different plants in few different states of India. Important macronutrient factors for a wide variety of plant species were recorded using data gathered from a variety of agricultural online sources. Creating precise prediction models to improve nutrition management strategies was the main goal. K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Classification (SVC) were the three machine learning techniques used. Based on how well these algorithms predicted intake of macronutrients, their performance was assessed. the outcomes showed that, at 95.87%, Naive Bayes had the best accuracy, followed by KNN at 89.65% and SVC at 83.85%. Our results demonstrate that Naive Bayes is the best appropriate machine learning approach for this dataset analysis and show its promise for efficient nutrition prediction. Finally, by promoting sustainable farming practices, this research helps optimise nutrient management systems © 2024 ieee.

关键词： KNN machine learning Macro Nutrients Naïve Bayes SVC

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2024 5th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

2024 5th International Conference on Information Science, Pa...

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5th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

ISBN: (纸本)9798350352719

the proceedings contain 138 papers. the topics discussed include: research on near-infrared spectroscopy calibration transfer algorithm based on piecewise direct standardization and K-Nearest neighbor;research on intelligent data operation and maintenance management scheme;GAIPPTSC: an efficient container tasks orchestration and scheduling algorithm;enhancing network security: machine learning evaluation for intrusion detection in power load management system;UAV anomaly detection model based on integrated multi-modal neural network and neural architecture search;frequency hopping modulation format recognition based on higher-order cyclic cumulants and neural network classifier;cluster-based two-stage client selection strategy for personalized federated learning;and implementation of fir digital filter algorithm in C language on DSP platform.

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Infrared Small Target Detection Based on Segmented Contrast Feature Enhancement

Infrared Small Target Detection Based on Segmented Contrast ...

引用

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

作者： Yuan Wang Shuxian Liu Hankiz Yilahun Askar Hamdulla College of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In this paper, we explore the importance of infrared small target detection (ISTD) techniques in harsh weather and complex environments, especially in military and civilian domains such as sea rescue and night surveillance. Given the challenges of ISTD such as low signal-to-noise ratio and complex background, we propose an improved YOLOv8 algorithm called YOLOv8-SCFE, which is specially designed with Segmented Contrast Feature Enhancement (SCFE) module to enhance feature contrast. the SCFE module significantly improves the feature differentiation and expressiveness by introducing a contrast enhancement computation in the neck network. Experimental results on two datasets, NUTD-SIRST and Combine-SIRST, show that compared with the baseline algorithm YOLOv8, our proposed algorithm improves the mAP metrics by 4.5% and 3.2%, respectively.

关键词： Measurement Target recognition Surveillance Object detection machine learning Feature extraction pattern recognition Neck Signal to noise ratio Meteorology

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Enhanced Detection of Sunspots Using machine learning Techniques

Enhanced Detection of Sunspots Using Machine Learning Techni...

引用

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

作者： Wanida Panup Acharaporn Bumrungkit Rungnapa Kaewthongrach Jaruwan Sutthana Sittiporn Channumsin Space Technology Research Center Geo-Informatics and Space Technology Development Agency (GISTDA) Chonburi Thailand

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In this work, we develop a method for sunspot detection based on the YOLOv5 and YOLOv8 networks. We apply this method to a dataset of HMIIF solar disk images from the Solar Dynamics Observatory (SDO) satellite, renowned for its comprehensive collection of sun images. Sunspot detection is crucial for predicting solar flares and potential coronal mass ejections (CMEs), which can have significant effects on the Earth. By automating this detection process, our aim is to provide a reliable tool for continuously monitoring these phenomena and studying their evolution over time. Our experimental findings demonstrate that YOLOv8 models outperform YOLOv5, particularly YOLOv8n, which offers enhanced detection capabilities and promising improved object detection across various applications.

关键词： YOLO Earth Satellites Observatories machine learning pattern recognition Reliability Sun Monitoring

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Efficient English Text Comprehension and pattern Understanding Model with ChatGPT 5

Efficient English Text Comprehension and Pattern Understandi...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Lin, Wei Chengdu Technological University School of Foreign Languages and International Education Sichuan Chengdu611730 China

ISBN: (纸本)9798331530013

In the context of intelligent information system models, in order to further promote the development of natural language processing and explore its application scenarios in different fields, this study studied the English text understanding and pattern recognition model based on ChatGPT middleware from the modeling and analysis level. the model proposed in this study integrates the information of text and audio input as the modeling of multi-modal semantic understanding to improve the accuracy and universality of machine language understanding in complex environments. In order to transform human natural language into structured data and hierarchy that can be understood by machines, this study first explored the significance of natural language processing and the processing challenges in real applications. Subsequently, this study gave an in-depth explanation of the design and methods of the model, focusing on the key role of improving ChatGPT middleware in simplifying the modal fusion process and thus ensuring the accuracy and applicability of the output results. In the experimental verification part, this study used a variety of self-collected data sets to evaluate the model. the experimental results show that the proposed model has an accuracy of 97.5% for text semantic understanding, an accuracy of 98.2% for pattern recognition tasks, and an overall accuracy of 99.2%. Compared with traditional algorithms, proposed model has better comprehensive performance. © 2024 ieee.

关键词： Middleware

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