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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31-40 订阅

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Traffic pattern recognition Method with XGBoost Based on Multi-scale Features 4

Traffic Pattern Recognition Method with XGBoost Based on Mul...

引用

4th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Song, Yunlong Wang, Hao Institute of Big Data and Artificial Intelligence China Telecom Corporation Limited Beijing Research Institute Beijing China

ISBN: (纸本)9798350314496

Traffic pattern recognition belongs to a branch of scene recognition and has become a hot research field. Correctly identifying the transportation mode used by users to travel plays a vital role in promoting the development of situational recognition. In the field of traffic pattern recognition research, many recognition methods use GPS, the combination of GPS and WiFi, and the combination of GSM and WiFi to obtain data, and use LR, SVM and deep learning models to make predictions. However, these methods have some disadvantages, such as poor WiFi signal in some outdoor environments, resulting in failure to obtain user data, GPS being interfered by the external environment, resulting in inaccurate data acquisition and other issues, and the models they use have problems such as low prediction accuracy or prolonged prediction consumption. In response to these problems, this paper uses multi-source sensors in mobile phones to obtain data, and preprocesses sensor data through statistics and signal processing methods to generate features at different scales, and finally uses XGBoost to identify various traffic modes. Extensive experiments are carried out on the method proposed in this paper, and the effectiveness of the proposed method is demonstrated by comparing with two state-of-the-art methods. © 2023 ieee.

关键词： pattern recognition

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Proceedings - 4th ieee 2023 international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

Proceedings - 4th IEEE 2023 International Conference on Comp...

引用

4th ieee international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

ISBN: (纸本)9798350306118

the proceedings contain 194 papers. the topics discussed include: comparative study of DDoS detection and mitigation techniques;a machine learning-based blockchain model for the storage of maternal health records and safety prediction;enhancing digital investigation: leveraging ChatGPT for evidence identification and analysis in digital forensics;managing metadata in data warehouse for data quality and data stewardship in telecom industry – a compact survey;a review on detection and prevention of the DDoS attacks in the blockchain;analysis of face recognition technique: plastic surgery altered face;image classification using federated averaging algorithm;navigating the gray area: a three-label framework for uncovering uncertainty in fake news;and improvement in validation score with loss function for breast cancer detection using deep learning.

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Digital Circuit Design using Support Vector machines

Digital Circuit Design using Support Vector Machines

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4th international conference on Communication Systems and Network Technologies (CSNT)

作者： Manikandan, J. Agrawal, V. K. PES Inst Technol Crucible Res & Innovat Bangalore 560085 Karnataka India

ISBN: (纸本)9781479930708

Digital circuits are preferred over its analog counterpart with the invention of microprocessors, microcontrollers, digital signal processors and Field Programmable Gate Arrays (FPGA). Digital circuits in the form of digital arithmetic and digital logics are employed for various applications. On the other side, Support Vector machine (SVM) is considered as a state-of-the-art tool for pattern recognition. In this paper, a novel attempt is made to study, assess and design digital circuits using two-class and multi class support vector machine classifiers. It is reported in literature that computational complexity and the classification time for SVM classifier depends on the number of support vectors required for classification. In order to reduce the number of support vectors, an optimum threshold technique based SVM classifier is employed for the design of digital circuits and it is observed that a maximum of 87.5% of total support vectors are pruned on using the technique.

关键词： Digital circuits pattern Classification machine learning Support Vector machines

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Mushroom Classification Based on Deep Residual Network

Mushroom Classification Based on Deep Residual Network

引用

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

作者： Ju Feng Xufeng Ling Yubo Wang Jie Yang School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Institute of Image Processing and Pattern Recognition and Institute of Medical Robotics Shanghai Jiaotong University Shanghai China

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.

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Artificial immune system based machine learning for voltage stability prediction in power system

Artificial immune system based machine learning for voltage ...

引用

4th international Power Engineering and Optimization conference, PEOCO 2010

作者： Suliman, S.I. Abdul Rahman, T.K. Faculty of Electrical Engineering Universiti Teknologi Mara Malaysia

ISBN: (纸本)9781424471287

Voltage instability has recently become a challenging problem for many power system operators. this phenomenon has been reported to be responsible for severe low voltage condition leading to major blackouts. this paper presents the application of Artificial Immune Systems (AIS) for online voltage stability evaluation that could be used as early warning system to the power system operator so that necessary action could be taken in order to avoid the occurrence of voltage collapse. Key features of the proposed method are the implementation of clonal selection principle that has the capability in performing pattern recognition task. the proposed technique was tested on the ieee 30 bus power system and the results shows that fast performance with accurate evaluation for voltage stability condition has been obtained. ©2010 ieee.

关键词： pattern recognition

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2022 ieee 4th international conference on Power, Intelligent Computing and Systems, ICPICS 2022

2022 IEEE 4th International Conference on Power, Intelligent...

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4th ieee international conference on Power, Intelligent Computing and Systems, ICPICS 2022

ISBN: (纸本)9781665467735

the proceedings contain 191 papers. the topics discussed include: virtual inertia control strategy for PV using dc capacitive and electrochemical energy storage;liquid drip detection in power plant based on machine vision;intelligent recognition algorithm of specific pattern content information based on mobile terminal equipment;research on power communication defect diagnosis technology based on unsupervised learning;key technologies of high speed modulator for satellite data transmission;research on privacy fraud detection of logistic regression based on homomorphic encryption;application research on x-ray imaging technology for online detection of gas insulated switchgear;power transformer state identification method based on operational deflection shapes and visual measurement technology;research on identification method of potential abnormal station for miniature circuit breaker;trajectory tracking error optimization based on iterative learning;research and design of the intelligent bulk transfer laboratory interconnection;energy management cloud platform in softwarized energy internet;simulation and application of a new power stealing method based on the comparison of two types of transformers;lane line detection based on machine vision;an analysis method of GIS equipment fault causes based on online monitoring and joint diagnosis;and virtual prototype design of flexible exoskeleton based on pattern recognition technology.

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Optimization Study of KNN Classification Algorithm on Large-Scale Datasets: Real-Time Optimization Strategy Based on Balanced KD Tree and Multi-threaded Parallel Computing 4

Optimization Study of KNN Classification Algorithm on Large-...

引用

4th international conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Cao, Kangjie Wu, Jinyao Huang, Qianhang Gan, Yichao Minzu University of China Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350343113

the K-Nearest Neighbors (KNN) algorithm is a classical supervised learning method widely used in classification and regression problems. However, the KNN algorithm faces serious challenges when dealing with high-dimensional large-scale datasets, mainly in terms of high computational complexity, low search efficiency, and difficulty in real time. In this study, an optimization strategy based on balanced KD tree and multithreaded parallel computing is proposed to address this problem. Experimental results show that the optimized KNN algorithm significantly improves the search speed while maintaining classification accuracy, realizes real-time classification of large-scale datasets, and provides a powerful tool for pattern recognition, data mining, and other fields in the era of big data. © 2023 ieee.

关键词： pattern recognition

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

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

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

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

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.

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Face recognition using interest points and ensemble of classifiers 4

Face recognition using interest points and ensemble of class...

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4th ieee international conference on Recent Advances in Information Technology, RAIT 2018

作者： Vinay, A. Sampat, Pratik Rajesh Belavadi, Sagar V. Pratik, R. Rao, B. S. Nikitha Ragesh, Rahul Murthy, K. N. Balasubramanya Natarajan, S. Centre for Pattern Recognition and Machine Intelligence PES University Bangalore Karnataka India

ISBN: (纸本)9781538630396

In human beings, it is the responsibility of the temporal lobe of the brain for recognition of faces. Certain features of the face trigger the neurons of the temporal lobe which are then stored. these eventually lead to the identification of the face. In machine learning system, a huge dataset of images is used as a reference by the face recognition algorithms. the most prominent features are extracted from what is referred to as landmarks on the face and are analyzed based on their relative position amongst other features. the main challenge in computer vision lies in the identification and extraction of these features which in turn assist in face recognition owing to the exponentially large degree of variability that can be seen in appearances. Speed and accuracy are some of the most prominent challenges for the same. We explore ELM critically and study its performance. Owing to its extremely fast learning, it finds potential in real-time applications. the goal of this paper is to evaluate the performance of the extreme learning machine algorithm for facial recognition when used as a standalone model as well as when it is used with an ensemble learner. We then conclude with a voting classifier which is responsible for the final predictions made. Various combinations of ensembles are explored to come up with the best performing model. © 2018 ieee.

关键词： Face recognition

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

2023 4th International Conference on Computer Vision, Image ...

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

ISBN: (纸本)9798350326444

the proceedings contain 136 papers. the topics discussed include: comparison of image generation methods based on diffusion models;research on image preprocessing and target detection of vehicle-machine cooperative system;a research on traditional tangka image classification based on visual features;research on image recognition based on neural network model learning algorithm;research on image recognition based on reinforcement learning;low-quality image binarization method based on threshold array system;comprehensive study of image processing techniques for low-altitude target recognition;visual cryptography scheme by freeform optics based on optimal mass transport;SPECT bone scan image classification by fusing multi-attention mechanism with deep residual networks;fine-grained image recognition method based on input perception joint probability prediction;deep learning in image classification: an overview;auricular images with annotations for segmenting key-organ mapping regions in auricular diagnosis;insulator image dataset generation based on generative adversarial network;structure-preserving domain adaptation network for generating pencil sketches;underwater image recovery method considering target polarization characteristics;and a dual attention network for multimodal remote sensing image matching.

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