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检索条件"任意字段=3rd IAPR International Conference on Pattern Recognition in Bioinformatics"

共 2193 条记录，以下是21-30 订阅

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A novel pattern-based edit distance for automatic log parsing 26

A novel pattern-based edit distance for automatic log parsin...

引用

26th international conference on pattern recognition / 8th international Workshop on Image Mining - Theory and Applications (IMTA)

作者： Raynal, Maxime Buob, Marc-Olivier Quenot, Georges Univ Grenoble Alpes CNRS Grenoble INP LIG F-38000 Grenoble France Nokia Bell Labs France Nozay Ile De France France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

This work aims at inferring a set of regular expressions to parse a text file, like a system log. To this end, we propose a novel edit distance taking advantage of the pattern matching background. Edit distances are commonly used for fuzzy search and in bioinformatics, and compare two strings at the character level. By doing so, edit distances do not consider the nature of the data conveyed by the strings. To address this problem, we propose the following contributions. First, we propose to model strings at the pattern level using a dedicated data structure, called pattern automaton. Second, we design a novel edit distance, operating at the pattern level. Third, we derive a clustering algorithm optimized for this distance. Finally, we evaluate our proposal through experimental validation.

关键词： Biological system modeling Clustering algorithms Automata Data structures Inference algorithms Data models Proposals

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Physical Abuse Activity recognition using Landmarks of Human Posture and DTW Classifier 3

Physical Abuse Activity Recognition using Landmarks of Human...

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3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Bhavani, Y. V. K. Durga Pagi, V.B. Bagalkote Affiliated to Visvesvaraya Technological University Computer Science and Engineering Karnataka Belagavi590 018 India

ISBN: (纸本)9798350364569

Video Surveillance analysis can help to recognize the different types of crime activities i.e., abuse, fighting, robbery, shooting, etc. The main purpose of this study is to recognize physical abuse activities like kicking, dragging, and beating along with their postures. This study uses the values of landmarks (Left_Wrist, Right_Wrist, Left_Hip, Right_Hip, Left_Ankle, Right_Ankle) of human postures in video frames. Euclidean distance (D1 to D10) between the landmark vectors is used as the input for DTW (Dynamic Time Warping) classifier. DTW measures the similarity between training vectors and test vectors to recognize the type of physically abusive activity and classify the posture. The UCF-101 Crime public dataset is used. The empirical analysis indicates a 96% accuracy. © 2024 IEEE.

关键词： pattern recognition

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Application of Wave Cluster Algorithm Based on Kernel Optimization in pattern recognition of High-Frequency Financial Data 3

Application of Wave Cluster Algorithm Based on Kernel Optimi...

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3rd international conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Yue, Dazhi Business School Xiamen Institute of Technology Xiamen China

ISBN: (纸本)9798331506582

High-frequency financial data has the characteristics of high nonlinearity, non-stationarity and noise, which brings great challenges to traditional data mining algorithms. In this study, we propose a Wave Cluster algorithm based on kernel function optimization, which aims to improve its performance in pattern recognition of high-frequency financial data. The Wave Cluster algorithm has attracted much attention for its robustness to noise and multiresolution analysis capability, but it still has limitations when dealing with complex nonlinear patterns. In this paper, kernel-based functions are introduced to map the original high-dimensional financial data to a higher-dimensional feature space. This nonlinear transformation can effectively capture the potentially complex relationships in the data. Subsequently, the modified Wave Cluster algorithm is applied in the transformed feature space, and high-frequency trading data from the China stock market, including price, trading volume and order book information, are used in the empirical study. Experimental results show that compared with traditional methods, the proposed algorithm achieves significant improvement in the tasks of identifying market microstructure, detecting abnormal trading patterns and predicting short-term price movements. Especially during periods of high volatility, the algorithm shows greater stability and accuracy. © 2024 IEEE.

关键词： Multiresolution analysis

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An Automated Attendance System Using Face recognition Technique based on Haar Cascade with LBPH 3

An Automated Attendance System Using Face Recognition Techni...

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3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Chauhan, Kritika Goyal, Sakshi Guru Prasad, M.S. Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338607

In educational institutions, attendance used to be manually recorded on attendance sheets. This method doesn't work since substitute pupils can easily use them. This study introduces a face recognition-based automated attendance system that uses an image of the entire classroom to determine students' presence by matching their faces to a trained set. Everyone's faces are captured by a camera positioned in the classroom and sent to this system, which analyses them to find and recognize individual faces as well as log attendance in the main database. By capturing features on images, the Haar Cascade classifier was used to detect faces, and the Local Binary pattern Histogram (LBPH) was used to recognize faces. This system is capable of recognizing students even if they are wearing glasses or have a beard. © 2023 IEEE.

关键词： Local binary pattern

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Proceedings of 2023 3rd international conference on bioinformatics and Intelligent Computing, BIC 2023

Proceedings of 2023 3rd International Conference on Bioinfor...

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3rd international conference on bioinformatics and Intelligent Computing, BIC 2023

ISBN: (纸本)9798400700200

The proceedings contain 69 papers. The topics discussed include: research on the characteristics of free-living nematode community in the Dagu Estuary of Qingdao based on mathematical and intelligent analysis tools;preliminary investigation of a reactors core design optimization;a multi-operator-based large-scale multi-objective optimization evolutionary algorithm;convolutional hybrid kernel network for in-vitro transcription factor binding sites;three-dimensional point cloud reconstruction and body ruler measurement of pig body under multi-angle KinectV2;a deep learning based end-to-end surface defect detection method for industrial scenes;research on the development of a digital conservation system for cultural relics based on image recognition technology;parametric design of orthotics for abnormal arches in school-age children;and evaluation method of object control capability for children based on nonlinear dynamics.

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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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Development of Methods and Algorithms for Dimension Reduction of Space Description for pattern recognition Problem 1

引用

3rd international conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2023

作者： Narzullaev, D.Z. Baydullaev, A.S. Abdurakhmanov, B.A. Tursunov, A.T. Ilhamov, Kh. Sh. Tashkent Pharmaceutical Institute 45 Oybek Street Tashkent100015 Uzbekistan

ISBN: (数字)9783031603181

ISBN: (纸本)9783031603174

The essence of the dimensionality reduction process is the transition to a more concise set of indicators in such a way that the associated loss of information present in the source data is minimized. This article proposes methods and algorithms for reducing the dimension of the original feature description space for the problem of pattern recognition. Four types of applied problems of reducing the dimension of the analyzed feature space are defined. The mathematical model underlying the construction of one or another dimension reduction method has been determined. For each method discussed below, a step-by-step algorithm is given that allows you to quickly and efficiently translate these methods into programming languages for various types of computers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： pattern recognition

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A Review on EMG-based pattern Identification Methods for Effective Controlling of Hand Prostheses 3

A Review on EMG-based Pattern Identification Methods for Eff...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Ramkumar, S. Rema, Dhanusha Devi, T. Archana Elavarasi, K. Selvaganapathi, T. Gokila, S. Department of Computer Science Bangalore India Karpagam Academy of Higher Education Department of Food Technology Coimbatore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Karpagam Institute of Technology Department of CSE Coimbatore India

ISBN: (纸本)9798350343632

The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion. © 2023 IEEE.

关键词： Classification Algorithms Deep Learning Electromyography Hand Prosthesis recognition Accuracy

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Movement pattern recognition in Boxing Using Raw Inertial Measurements 3rd

Movement Pattern Recognition in Boxing Using Raw Inertial Me...

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3rd international conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Puchalski, Radoslaw Giernacki, Wojciech Poznan Univ Tech Poznan Poland

ISBN: (纸本)9783031530357;9783031530364

In the paper, a new machine-learning technique is proposed to recognize movement patterns. The efficient system designed for this purpose uses an artificial neural network (ANN) model implemented on a microcontroller to classify boxing punches. Artificial intelligence (AI) enables the processing of sophisticated and complex patterns, and the X-CUBE-AI package allows the use of these possibilities in portable microprocessor systems. The input data to the network are linear accelerations and angular velocities read from the sensor mounted on the boxer's wrist. By using simple time-domain measurements without extracting signal features, the classification is performed in real-time. An extensive experiment was carried out for two groups with different levels of boxing skills. The developed model demonstrated high efficiency in the identification of individual types of blows.

关键词： activity recognition artificial intelligence artificial neural networks edge computing microcontrollers

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Development of an emotion and age recognition model based on the combination of local binary patterns and squeeze-and-excitation block method 3

Development of an emotion and age recognition model based on...

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3rd international conference on Digital Technologies, Optics, and Materials Science, DTIEE 2024

作者： Soma, Guedes Kopanitsa, Georgy ITMO University St. Petersburg197101 Russia

ISBN: (纸本)9781510681590

This paper presents models for improving accuracy and efficiency in recognizing a person's emotional state and age based on facial expressions using the combination of Local Binary patterns and Squeeze-and-Excitation Block methods. The proposed models were trained on the AffectNet dataset for emotion recognition and the Adience dataset for age recognition with four classes [angry, happy, neutral, sad] and [6-20, 25-30, 42-48, 60-98]. The accuracy and speed of these models, achieved after training, were compared with the results described in scientific articles published in the Scopus database. According to the obtained experimental data, the first and second developed models achieved validation accuracy of 81.5% and 72.3%, respectively. © 2024 SPIE.

关键词： Local binary pattern

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