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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Learning, PRML 2022"

共 269 条记录，以下是1-10 订阅

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2022 3rd international conference on pattern recognition and machine learning, prml 2022

2022 3rd International Conference on Pattern Recognition and...

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3rd international conference on pattern recognition and machine learning, prml 2022

ISBN: (纸本)9781665499507

The proceedings contain 76 papers. The topics discussed include: ship detection with optical image based on attention and loss improved YOLO;ECG characteristic detection using DenseNet based on attention mechanism and feature pyramid;a method to detect the onsets and ends of paroxysmal atrial fibrillation episodes based on sliding window and coding;dynamic feature extraction using I-vector for video fire detection;bimodal information fusion network for salient object detection based on transformer;person re-identification method based on multi-view and attention mechanism;research on road unevenness recognition method based on off-road vehicle driving characteristic;and vehicle re-identification approach combining multiple attention mechanisms and style transfer.

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2024) 3

3rd International Conference on Visual Pattern Extraction an...

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2024

作者： Amelio, Alessia Cupar, Drahomira Sangiorgio, Valentino InGeo University "G. d'Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DIS University of Zadar Ulica Mihovila Pavlinovića 1 ZadarHR-23 000 Croatia

This document presents the 3nd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2024), a premier forum for presenting academic and industry papers on big data, data mining and knowledge discovery, machine learning and deep learning, 3D modeling and simulation (structural, thermal, functional and acoustic analysis), software engineering, and, more generally, aspects related to digital transition for visual pattern extraction, analysis, and recognition to preserve cultural heritage. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

3rd International Conference on Intelligent Systems and Patt...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

ISBN: (纸本)9783031463341

The proceedings contain 44 papers. The special focus in this conference is on Intelligent Systems and pattern recognition. The topics include: Staged Reinforcement learning for Complex Tasks Through Decomposed Environments;policy Generation from Latent Embeddings for Reinforcement learning;deep learning Models for Aspect-Based Sentiment Analysis Task: A Survey Paper;A Real-Time Deep UAV Detection Framework Based on a YOLOv8 Perception Module;a Deep Neural Architecture Search Net-Based Wearable Object Classification System for the Visually Impaired;Multicarrier Waveforms Classification with LDA and CNN for 5G;Weeds Detection Using Mask R-CNN and Yolov5;Boruta-AttLSTM: A Novel Deep learning Architecture for Soil Moisture Prediction;graph Autoencoder with Community Neighborhood Network;Study of Support Set Generation Techniques in LAD for Intrusion Detection;question-Aware Deep learning Model for Arabic machine Reading Comprehension;a Hybrid Deep learning Scheme for Intrusion Detection in the Internet of Things;identifying Discourse Markers in French Spoken Corpora: Using machine learning and Rule-Based Approaches;A Comparative Study of the Impact of Different First Order Optimizers on the learning Process of UNet for Change Detection Task;Minimal Window Duration for Identifying Cognitive Decline Using Movement-Related Versus Rest-State EEG;modeling Graphene Extraction Process Using Generative Diffusion Models;bird Species recognition in Soundscapes with Self-supervised Pre-training;on the Different Concepts and Taxonomies of eXplainable Artificial Intelligence;Classifying Alzheimer Disease Using Resting State Coefficient of Variance BOLD Signals;proteus Based Automatic Irrigation System;How AI can Advance Model Driven Engineering Method ?.

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

3rd International Conference on Intelligent Systems and Patt...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

ISBN: (纸本)9783031463372

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A Brief Review of Unsupervised learning Algorithms for Zero-Day Attacks in Intrusion Detection Systems 3

A Brief Review of Unsupervised Learning Algorithms for Zero-...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Oluwadare, Sunkanmi ElSayed, Zag Adekoya, Oluwaseun Univ Cincinnati Dept Informat Technol Cincinnati OH 45221 USA Univ Cincinnati Dept Mech Engn Cincinnati OH 45221 USA

ISBN: (纸本)9798350372977;9798350372984

This research paper presents a brief review of ten popular unsupervised algorithms widely utilized in pattern recognition publications. The algorithms are assessed based on their popularity, strengths, limitations, and resource requirements. Considering these factors, we propose two most-preferred algorithms suitable for adoption in IDS (Intrusion Detection Systems) to address the problems associated with Zero Day exploits or attacks. Our review of the surveyed algorithms facilitated the recommendation of specific algorithms that can enhance IDS capabilities in detecting and mitigating Zero-Day attacks and anomalous intrusion attempts. These algorithms leverage unsupervised learning techniques to overcome the limitations of traditional signature-based approaches. By incorporating these algorithms, IDS can better handle sophisticated and evolving attacks that often evade detection. In conclusion, this research provides valuable insights into the strengths, limitations, and resource requirements of popular unsupervised algorithms used in pattern recognition. It highlights the potential of adopting these algorithms in IDS systems to bolster their ability to detect and respond to Zero-Day attacks. By recommending the integration of these algorithms, we contribute to the development of intelligent IDS solutions that can adapt to dynamic threat landscapes.

关键词： Intrusion Detection Zero-Day Attacks machine learning Unsupervised learning pattern recognition

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Spam recognition Model Based on TextCNN 3

Spam Recognition Model Based on TextCNN

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3rd international conference on pattern recognition and machine learning, prml 2022

作者： Yixuan, Gao Yuanyuan, Zhang Ye, Cheng Fuzhou University Maynooth International Engineering College Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

With the advancement of modern Internet technology, email is widely used in people's daily lives and has become one of the common communication tools. However, at the same time, email-based spam has also been widely spread, flooding people's lives and not only wasting public resources but also infringing on people's legitimate rights and interests. Based on this, we propose a Spam recognition model based on TextCNN by combining Convolutional Neural Network and Natural Language Processing, where natural language processing techniques are used to pre-process the text in the spam corpus to obtain text features, which are then imported into the constructed Convolutional Neural Network analysis model. A CNN spam recognition model is then designed and analyzed hierarchically using the TensorFlow framework. After extracting the features from the email dataset, the model was fed into the convolutional neural network again for several iterations of training. The results indicated that the model achieved an accuracy of 97.7% in spam recognition, which is an exceptionally significant improvement in accuracy and stability compared to the SVM model used previously, demonstrating good filtering performance. © 2022 IEEE.

关键词： Convolutional neural networks

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An Improved ICP Algorithm for 3D Point Cloud Registration 3

An Improved ICP Algorithm for 3D Point Cloud Registration

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3rd international conference on pattern recognition and machine learning, prml 2022

作者： Chen, Anguo Zhuang, Jinlei Han, Xiuqin Harbin Institute of Technology School of Mechatronics Engineering Harbin China Hit Wuhu Robot Technology Research Institute Wuhu China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

The rigid registration of two point clouds is a fundamental task in many areas, such as 3D reconstruction and robot navigation. The Iterative Closest Point (ICP) algorithm has been widely for this task. The basic principle of ICP algorithm is to match corresponding points between the two point clouds and compute an optimal transformation matrix that minimizes the Euclidean distance between corresponding points. A major drawback of the ICP algorithm is the sensitivity to partial overlaps often observed in 3D scans. In this regard, we propose a new rigid registration algorithm based on ICP. Firstly, the Super 4PCS algorithm is used to implement the initial alignment of point clouds to reduce the probability of ICP algorithm falling into a local optimal solution. Then, we propose a local refinement registration method by adaptively eliminating the boundary points of the overlap region of two point clouds. Our algorithm effectively solves the problem that ICP algorithm achieves poor registration accuracy or even produces an incorrect result when dealing with partial overlap data. According to the experimental results, our algorithm achieves better registration accuracy than the classical ICP algorithm. © 2022 IEEE.

关键词： Iterative methods

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A Real-time Visual Target Detection and Automatic Tracking Algorithm Based on YOLOv3 and DWSiamRPN 3

A Real-time Visual Target Detection and Automatic Tracking A...

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3rd international conference on pattern recognition and machine learning, prml 2022

作者： Zhong, Yexiu Qiu, Senhui Zhao, Zhipeng He, Fuyun Guilin Institute of Information Technology School of Electronic Engineering Guilin China Guangxi Normal University School of Electronic Engineering Guilin China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

The current single-target trackers cannot detect the categories of the targets, and multi-object trackers have low performances due to the missed and false detections. Most of these trackers do not have the capabilities for the interesting target selections. To overcome these problems, a real-time visual target detection and automatic tracking algorithm is proposed. First, the video image is detected by using YOLOv3. Then the interesting target is obtained by the proposed intelligent selection strategy. Finally, the selected target is tracked in real-time by the Deeper and Wider Siamese Networks (DWSiamRPN). Experiments show that the proposed YOLOv3-DWSiamRPN method can effectively detect, automatically obtain and track targets in real-time (26 frames per second), with 55.5 mean Average Precision (mAP) on COCO dataset, 0.296 Expect Average Overlaprate (EAO) on VOT-2017 and 0.66 Area Under Curve (AUC) on OTB-2015, respectively. Furthermore, it also addresses the problems of short-term and long-term target losses. © 2022 IEEE.

关键词： Target tracking

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Improved Model Accuracy Based on a Simple Frame of Temporal-Correlation Representation Method in Sleep Staging 3

Improved Model Accuracy Based on a Simple Frame of Temporal-...

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3rd international conference on pattern recognition and machine learning, prml 2022

作者： Zhao, Liang Liu, Guanzhou Tang, Xianwen Bai, Yang Li, Yuzhi Wang, Xingjun Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen China South China University of Technology School of Electronic and Information Engineering Shenzhen China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

We proposes a new kind of contrast learning method based on Temporal-Correlation characters of electroencephalogram (EEG) used in sleep staging. This method, called simple framework of temporal-correlation representation(simTCR), was studied and tested on Open EEG dataset, sleep-EDF and clinical EEG dataset from polysomnography (PSG). Deep learning used in clinical diagnosis is often faced with problems of deficiency of well labeled labels. And physiological signal, like EEG signal, often shows temporal correlation character, which means the signal maintains one state in a period of time before it transforms into a new state. Therefore, there exist high probability that the nearby frames of EEG signal belong to same categories and shows similar characters. This character can be considered as self-supervised features and a loss function was raised here to pretrain the model. Our result shows that, (1) simTCR can improve the sleep staging accuracy based on EEG signal to 1%-17% in both clinical dataset and Open dataset, sleep-EDF;(2) The improved effect is more obvious when the training data in fine-tuning process is more less;(3) the hyper-parameter batch size plays limited roles in the accuracy improvement;(4) simTCR costs about three magnitudes of more time than the traditional supervised deep learning method. © 2022 IEEE.

关键词： Electroencephalography

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Time Series pattern Matching Technique for Low-Energy Arrhythmia Classification in Implantable Cardiac Devices 3

Time Series Pattern Matching Technique for Low-Energy Arrhyt...

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3rd international conference on Computing and machine Intelligence (ICMI)

作者： Sengupta, Prianka John, Eugene B. Texas A&M Univ Dept Elect & Comp Engn Coll Satation TX 77843 USA Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA

ISBN: (纸本)9798350372977;9798350372984

Implantable cardiac devices (ICDs) are often used as an effective treatment for arrhythmia. Although these devices have access to a live Electrocardiogram (ECG) stream, currently they do not offer on-device classification of arrhythmia due to the limited computing capability and severe power constraints. In this paper we propose a low-energy computing method for extracting shape-based features from ECG in combination with machine learning techniques for classifying nine different cases of arrhythmia. This is achieved by using a Localized Longest Common Subsequence (LLCS) algorithm which has low computational requirements that allows on-device execution. The proposed method strongly focuses on maintaining minimal energy and computational footprint, in line with the operating constraints of implantable devices. To demonstrate the energy efficiency and low computation load of the proposed method we implement the classification pipeline on a low-power RISC microcontroller and compare its performance with existing classification techniques. The classification accuracy and energy of the proposed method is compared with state-of-the art research in arrhythmia classification.

关键词： Low-energy Computing machine learning Localized Longest Common Subsequence Low-energy Arrhythmia Classification Time Series pattern recognition

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