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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

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Research on User Behavior Analysis and Growth Strategies in Digital Exhibitions Based on pattern recognition 24

Research on User Behavior Analysis and Growth Strategies in ...

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6th international conference on Information Technology and Computer Communications, ITCC 2024

作者： Zhu, Liuru Sui, Qiao Wu, Zhenlin Sui, Li Liu, Huinan Xu, Yuanlong College of Teacher Education Quzhou University Quzhou China School of Media And Design Hangzhou Dianzi University Hangzhou China Huaiyin Middle School Huaian China College of Architectural Engineering Quzhou University Quzhou China Zhejiang Yuanlai Culture Media Group Co. Ltd Quzhou China

ISBN: (纸本)9798400717789

this paper explores the analysis of user behavior and the development of growth strategies in digital exhibitions using pattern recognition technology. With the rapid advancement of digitalization, particularly in response to global events like the COVID-19 pandemic, digital exhibitions have become increasingly prevalent. By applying pattern recognition, this study identifies and classifies user behavior patterns, enabling companies to better understand user needs and optimize their marketing strategies. the research highlights the significant influence of perceived value on user engagement, decision-making processes, and overall satisfaction. through a comprehensive case study of the Canton Fair’s Digital Exhibitions, the paper demonstrates how companies can leverage behavioral insights to enhance user experience, increase conversion rates, and implement effective personalized marketing strategies. the findings underscore the potential of pattern recognition technology in achieving sustainable user growth and offer actionable recommendations for its application in digital exhibitions. © 2024 Copyright held by the owner/author(s).

关键词： Marketing

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Multi-floor indoor pedestrian pattern recognition based on improved LSTM network 6

Multi-floor indoor pedestrian pattern recognition based on i...

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6th international conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Li, Yaxin Liu, Guoyu Rui, Zeping College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9781510688018

the appearance of many auxiliary devices in multi-storey buildings enriches the movement mode of indoor pedestrians, making the object of indoor pedestrian pattern recognition not only limited to the common three modes of going up, down and walking, but also taking elevator, escalator and automatic sidewalk are new indoor pedestrian movement modes that need to be taken into consideration. In order to recognize the movement pattern of indoor pedestrians in multi-storey buildings as precise comprehensively as possible, this paper proposes a new method of movement pattern recognition based on smart phone. Firstly, the relationship between the indoor pedestrian movement pattern and the changes of air pressure, acceleration data obtained by mobile phone was analyzed, and then nine movement patterns including walking, going up and down stairs, lift, escalator and automatic sidewalk were identified by the CNN-LSTM-BO after Bayesian optimization. the experimental results show that the accuracy can reach 0.96. © 2025 SPIE.

关键词： Escalators

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

6th International Conference on Computational Intelligence i...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

ISBN: (纸本)9789819780891

the proceedings contain 45 papers. the special focus in this conference is on Computational Intelligence in pattern recognition. the topics include: Vietnamese Text Summarization Using Transformer Approach;DQN Trader: Reinforcement Learning for Automated Trading;indirect Feature Extraction for Efficient Fingerprint recognition;A Novel and Intelligent Neuroimage Analysis for Tumor Classification with thermo-Color Segmentation Using VGG-16;a Deep Feature Ensemble Methodology for 2D Biomedical Image Classification;detection of Hardware Trojan in Nanocrossbar-Based Cryptographic Circuit;convolutional Neural Network Segmentation for Satellite Imagery Data to Identify Landforms Using U-Net Architecture;performance Comparison of Different Machine Learning Classifiers for Diabetes Prediction;Intelligent Anti-tamper Systems: Safeguarding Critical Infrastructure in the Era of Proliferating Interconnected Devices and IoBT;contrasting Various Cryptographic Algorithms for Storage and Cloud Computing Services;A New Technique of Cellular-Automata-Based PUF Design in Nanoscale Crossbar Circuits;CrimeScene2Graph: Generating Scene Graphs from Crime Scene Descriptions Using BERT NER;a Squeeze and Excitation Framework Utilizing ResNet-152 for Alzheimer’s Disease Dementia Classification;Identification of Real or Fake Currency Using Semi-supervised Generative Adversarial Networks (SSGANs);efficient Detection of Fake Twitter Profiles on Social Media through Hybrid Deep Learning Model;religious Sentiment Analysis and Detection on Social Media;Visualizing NASDAQ Composite Index Historical Data Using Tableau;typeface Identification for English Using Convolutional Neural Networks;Adaptive Ensemble of ML Regressors and LSTM for Stock Price Prediction;PolyNMT: Neural Machine Translation Model with Seq2Seq Encoder-Decoder System;a Rectangular Slotted Sierpinski Fractal Bowtie Antenna for Multiband Wireless Communications;enhancing Cardiac Health: Machine Learning in Coronary Artery Disease Predi

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ICPR 2024 Competition on Word Image recognition from Indic Scene Images 27th

ICPR 2024 Competition on Word Image Recognition from Indic S...

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27th international conference on pattern recognition, ICPR 2024

作者： Lunia, Harsh Mondal, Ajoy Jawahar, C.V. Centre for Vision Information Technology International Institute of Information Technology Hyderabad500032 India

ISBN: (纸本)9783031801389

Scene text recognition has historically concentrated on English, with limited advancements in developing solutions that perform well across multiple languages. Previous efforts in multilingual scene text recognition have predominantly targeted languages with considerable syntactic and semantic differences. However, Indian languages, while diverse, share numerous common features that remain largely underutilized. this competition aims to address the often-overlooked challenge of scene text recognition within the Indian context and to advance robust word image recognition across ten Indian languages. the dataset provided for this competition is one of the most comprehensive multilingual datasets, encompassing 10 languages, each with 17,500 training samples, 2,500 validation samples and 5,000 test word-image samples. the task was to correctly recognize the word-images, for which we received forty-nine registrations and five final submissions from industrial and research communities. the winning team achieved an average Character recognition Rate (CRR) of 92.85% and a Word recognition Rate (WRR) of 84.01% across the ten languages. this paper details the proposed dataset and summarizes the submissions for the competition- WIRIndic-2024. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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6th international conference on Knowledge Graphs and Semantic Web, KGSWC 2024

6th International Conference on Knowledge Graphs and Semanti...

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6th international conference on Knowledge Graphs and Semantic Web, KGSWC 2024

ISBN: (纸本)9783031812200

the proceedings contain 23 papers. the special focus in this conference is on Knowledge Graphs and Semantic Web. the topics include: Towards Complex Ontology Alignment Using Large Language Models;enhancing Knowledge Graph Construction: Evaluating with Emphasis on Hallucination, Omission, and Graph Similarity Metrics;views, Semantic Data Catalog and Role-Based Access Control for Ontologies and Knowledge Graphs;accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment;ontoSeer - A Recommendation System to Improve the Quality of Ontologies;Adaptive Planning on the Web: Using LLMs and Affordances for Web Agents;Enriching RDF Data with LLM Based Named Entity recognition and Linking on Embedded Natural Language Annotations;TEDME-KG Metrics Framework: A Metrics Framework for TEmporal Data Modelling Evaluation in Knowledge Graphs;SFARDE: A Knowledge-Centric Semantic Strategic Framework for Heritage Artifact Recommendation Integrating Generative AI and Differential Enrichment of Ontologies;leveraging Graph Models for Comprehensive Visual Analytics of Equine Heritage;OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment;Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF and Smart Energy Appliances;YOKO ONtO: You only KNIT One Ontology;CoKGLM: Detecting Hallucinations Generated by Large Language Models via Knowledge Graph Verification;autOnto: Towards A Semi-Automated Ontology Engineering Methodology;Enhancing Question Answering Systems with Generative AI: A Study of LLM Performance and Error Analysis;disjointness Violations in Wikidata;enhancing WebProtégé with Version Control Systems;visual Presentation and Summarization of Linked Data Schemas;a Proposed Ontology Evaluation Tool to Assist Ontology Engineers in Selecting Ontologies During the Reuse Phase;manufacturing Commonsense Knowledge.

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YCB-Ev: Event-Vision Dataset for 6 DoF Object Pose Estimation 4th

YCB-Ev: Event-Vision Dataset for 6 DoF Object Pose Estimati...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Rojtberg, Pavel Pöllabauer, thomas Fraunhofer IGD Darmstadt Germany

ISBN: (纸本)9789819787043

Our work introduces the YCB-Ev dataset, which contains synchronized RGB-D frames and event data that enables evaluating 6DoF object pose estimation algorithms using these modalities. this dataset provides ground truth 6DoF object poses for the same 21 YCB objects [1] that were used in the YCB-Video (YCB-V) dataset [19], enabling the evaluation of algorithm performance when transferred across datasets. the dataset consists of 21 synchronized event and RGB-D sequences, amounting to a total of 7:43 min of video. Notably, 12 of these sequences feature the same object arrangement as the YCB-V subset used in the BOP challenge [16]. Our dataset is the first to provide ground truth 6DoF pose data for event streams. Furthermore, we evaluate the generalization capabilities of two state-of-the-art algorithms, which were pre-trained for the BOP challenge, using our novel YCB-V sequences. the proposed dataset is available at https://***/paroj/ycbev. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object recognition

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Accuracy Assessment and Calibration of a 6-DOF Robotic Arm through Simulation Data Analysis 6th

Accuracy Assessment and Calibration of a 6-DOF Robotic Arm T...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Kailash, M. Jones, Aida Babu, K. Dinesh Praveen, S. Department of Artificial Intelligence and Data Science KCG College of Technology Tamil Nadu Chennai India

ISBN: (纸本)9789819780891

this study focuses on refining the real-time positional accuracy of a 6-degree-of-freedom (6-DOF) robotic arm’s end effector by comparing the actual X, Y, and Z axis positions with those calculated from joint angles using DH parameters. through the application of linear regression analysis, variations between these datasets are identified and quantified. the derived variations are then used to adjust the DH parameter-based calculations, resulting in significantly improved accuracy for the robotic arm’s end-effector positioning. this approach offers a systematic method for enhancing the precision of calculated positional data, thereby contributing to the advancement of robotic arm operations across diverse fields. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Degrees of freedom (mechanics)

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Semi-structured Pruning of Graph Convolutional Networks for Skeleton-Based recognition 27th

Semi-structured Pruning of Graph Convolutional Networks for...

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27th international conference on pattern recognition, ICPR 2024

作者： Sahbi, Hichem Sorbonne University CNRS LIP6 Paris75005 France

ISBN: (纸本)9783031781650

Deep neural networks (DNNs) are nowadays witnessing a major success in solving many pattern recognition tasks including skeleton-based classification. the deployment of DNNs on edge-devices, endowed with limited time and memory resources, requires designing lightweight and efficient variants of these networks. Pruning is one of the lightweight network design techniques that operate by removing unnecessary network parts, in a structured or an unstructured manner, including individual weights, neurons or even entire channels. Nonetheless, structured and unstructured pruning methods, when applied separately, may either be inefficient or ineffective. In this paper, we devise a novel semi-structured method that discards the downsides of structured and unstructured pruning while gathering their upsides to some extent. the proposed solution is based on a differentiable cascaded parametrization which combines (i) a band-stop mechanism that prunes weights depending on their magnitudes, (ii) a weight-sharing parametrization that prunes connections either individually or group-wise, and (iii) a gating mechanism which arbitrates between different group-wise and entry-wise pruning. All these cascaded parametrizations are built upon a common latent tensor which is trained end-to-end by minimizing a classification loss and a surrogate tensor rank regularizer. Extensive experiments, conducted on the challenging tasks of action and hand-gesture recognition, show the clear advantage of our proposed semi-structured pruning approach against both structured and unstructured pruning, when taken separately, as well as the related work. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tensors

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Visualizing NASDAQ Composite Index Historical Data Using Tableau 6th

Visualizing NASDAQ Composite Index Historical Data Using Tab...

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6th international conference on Computational Intelligence in pattern recognition, CIPR 2024

作者： Safuan, Halawati Abd Jalil Nyein, Chit Ko Ko Riskhan, Basheer Ashfaq, Farzeen School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Computer Science SCS Taylors University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

the study explores the visualization and analysis of historical data for the NASDAQ Composite Index (^IXIC) using Tableau. It examines trends in daily closing prices and volume distribution and compares performance with the NYSE index. Findings reveal significant fluctuations in the NASDAQ index over 22 years, suggesting potential future price changes. Forecasted graphs offer insights into potential economic decline and recovery, highlighting the dynamic nature of the market. Volume distribution analysis identifies periods of heightened trading activity. Comparative performance analysis emphasizes different trends observed in the NASDAQ and NYSE indices. Due to market unpredictability and external influences, additional research is crucial for validation. this study showcases the value of data visualization and analysis in finance through Tableau. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inflation

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Exploring the Role of Artificial Intelligence for pattern recognition of Textile Sorting and Recycling for Circular Economy 6

Exploring the Role of Artificial Intelligence for Pattern Re...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Karmali, Irfan Mohammed Valilai, Omid Fatahi Constructor University Bremen Campus Ring 1 Bremen28759 Germany

In an era where sustainability is a vital concern, the textile industry is a major obstacle that stands in the way of addressing environmental challenges, climate change, pollution, and biodiversity loss. the integration of Artificial Intelligence (AI) into the textile recycling process holds significant promise for advancing the circular economy agenda. AI technologies offer innovative solutions for automating pattern recognition tasks, thereby enhancing the efficiency and accuracy of textile sorting and recycling processes. this paper endeavors to explore AI algorithms specifically tailored for soiled cloth detection in textile products. through a comprehensive analysis of existing literature studies, the paper aims to propose an approach that addresses the unique challenges associated with identifying and classifying soiled cloth. this research focuses on investigating and developing unsupervised computer vision techniques for the initial detection and segmentation of stains on textile materials. the paper focuses on the capabilities of two methods, (1) Otsu's thresholding and (2) K-Means clustering, which do not require extensive training data or deep learning models. Practical conditions are examined for complex backgrounds and uneven lighting conditions and capability for accurate separation of stains from fabrics with similar colors or textures. the results show that for both methods to work efficiently, they require even and bright lighting conditions on the textiles. Moreover, the paper has discussed the challenges faced for real implementation of models for textiles being sorted on factory line. © 2025 the Authors. Published by Elsevier B.V.

关键词： Circular economy

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