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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181-190 订阅

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Predicting Student Attention And Engagement Through Eye-Tracking Using Machine Learning

Predicting Student Attention And Engagement Through Eye-Trac...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Kirubakaran R Suganthi N Lokeshwaran V Akilan V E Shahidha S Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Information Science and Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper presents a comprehensive approach to predicting student engagement and learning behavior by analyzing eye-tracking data combined with behavioral analysis using camera-based recognition algorithms. The proposed system leverages WebGazer for eye-tracking to monitor gaze patterns and uses computer vision algorithms to recognize behaviors and emotional states. Feelings like engagement, frustration, or perplexity are identified using facial expression analysis. The system aims to provide real-time, actionable insights to students and educators by relating to emotional and concentration states by combining different modalities, thereby improving personalized learning strategies and educational outcomes.

关键词： Emotion recognition Computer vision Gaze tracking Machine learning Educational technology Prediction algorithms Real-time systems Telecommunication computing pattern recognition Monitoring

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Detection of Venous Thromboembolism Using Recurrent Neural Networks with Time-Series Data 24

Detection of Venous Thromboembolism Using Recurrent Neural N...

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3rd Asia conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Xu, Can Huang, Yaqin Xiang, Xinni Lei, Haike Yang, Jie Shanghai Jiao Tong University Shanghai China Chongqing University Cancer Hospital Chongqing China West China Hospital Sichuan University Chengdu China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition China Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment China West China School of Medicine China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center China

ISBN: (纸本)9798400716416

Machine Learning (ML) has been widely applied to medical science for decades. As common knowledge, the progress of many diseases is often chronic and dynamic. Longitudinal data, or time-series data, has better descriptions of certain diseases, as it naturally captures the dynamic over time. Venous thromboembolism (VTE), as a regular complication of cancer tumour treatments, has been emphasized in many studies. Recent research has shown that ML algorithms incredibly contribute to VTE risk estimation and prediction. However, existing methods rarely consider the dynamic changes during the development of VTE, and often make decisions based on time-point data, such as the latest blood test results of patients. In this paper, we propose a VTE detection model that takes advantage of capturing dynamic information using time-series data. The model consists of a Recurrent Neural Network (RNN) based framework with time embedding and Attention mechanism to encode longitudinal features of patients. We test our proposed model on a real-world dataset that contains multiple times of blood test results of patients together with other clinical features. The network is compared with several commonly used ML models in previous VTE studies. The results show that our model outperforms all baseline models, and achieved a state-of-art performance with 80.0% accuracy and 0.881 AUC. © 2024 ACM.

关键词： Risk assessment

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Using Convolutional Neural Network to Handle Word Shape Similarities in Handwritten Cursive Arabic Scripts of Pashto Language 3rd

Using Convolutional Neural Network to Handle Word Shape Simi...

引用

3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Shah, Muhammad Ismail Suen, Ching Y. Computer Science and Software Engineering Department Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031090363

The Inter-Class Word Similarities in combination with Intra-Class Variations make it a difficult task for an OCR or any other machine learning system to recognize the handwritten characters and words with high accuracy, especially in the domain of cursive Arabic scripts. Convolutional Neural Network was originally designed to handle the problems of shape resemblance, position shift and distortion in the domain of handwritten characters and digit recognition. In this paper, we have used Convolutional Neural Network (CNN) to recognize multi class handwritten words written in cursive Arabic scripts of Pashto Language. The handwritten word images in Pashto Database [3] contain a high level of shape resemblance, and position shift in terms of diacritic marks. Hence it has been proved to be a good source for properly analyzing the performance of any CNN based recognition system. The model has successfully handled the problem and has not been affected by the level of complexity of Inter-class resemblances. The CNN model has been well tested on three sub data sets of Pashto Database. In each data set the number of classes and level of complexity, i.e., Shape Similarity, increase from one data set to another, i.e., 25, 40 and 68 classes of handwritten words. The average accuracies for the three Data Sets are 97.26%, 96.25% and 95.84%, respectively. © 2022, Springer Nature Switzerland AG.

关键词： Convolution

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Electrical Power Enterprise Training System Based on Interactive 3D Virtual Reality 3

Electrical Power Enterprise Training System Based on Interac...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Li, Qin Electric Power of Henan Zhengzhou China

ISBN: (纸本)9798331508456

In recent years, the electrical power enterprises require skilled personnel to operate and maintain the complex systems. The trainees need effective training for the systems to ensure safety, efficiency and reliability. The existing electrical power system faced several challenges due to complex systems and high voltage risks with limited hands-on experience. To overcome these issues, interactive 3D Virtual Reality (3D-VR) is proposed for maintaining realistic experience for the trainees. Initially, the Building Information Modelling (BIM) is utilized to generate 115 kV substation data for an accurate 3D model of electric power training system. Further, the BIM model is integrated with VR simulator, where it enables immerse experience for trainees in the substation environment. Then, the interactive 3D simulator is employed to create a realistic environment for enterprise training system to the trainers. Finally, Convolutional Neural Network (CNN) model is incorporated to evaluate the performance with complex pattern recognition. At last, the CNN model detects the unusual behaviors of trainees based on probability threshold and provides personalized feedback. From the results, the proposed interactive 3D-VR attained better results in terms usability (68.23%) and Learnability (45.32%) of when compared to existing VR. © 2025 IEEE.

关键词： Convolutional neural networks

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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

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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High-performance computing system and smart speech pattern findings for intangible cultural heritage art analysis considering visual information 3

High-performance computing system and smart speech pattern f...

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3rd international conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Zhou, Mingyong Fang, Nannan School of Humanities Xi'an Shiyou Univercity Shaanxi Xian710065 China

ISBN: (纸本)9780738146270

This paper studies the visual application of highperformance computing systems and artificial language recognition in intangible cultural heritage art design. First, this article introduces the advantages, disadvantages, problems and performance evaluation of high-performance computing systems in the new era. Then, a high-performance computer-based artificial language recognition algorithm is used to classify and recognize intangible cultural heritage art. Finally, use the network database and artificial language recognition algorithm to visualize the intangible cultural heritage art design. © 2021 IEEE.

关键词： Visual languages

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3rd international conference on Spatial Data and Intelligence, SpatialDI 2022

3rd International Conference on Spatial Data and Intelligenc...

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3rd international conference on Spatial Data and Intelligence, SpatialDI 2022

ISBN: (纸本)9783031245206

The proceedings contain 19 papers. The special focus in this conference is on Spatial Data and Intelligence. The topics include: GWmodelS: A High-Performance Computing Framework for Geographically Weighted Models;spatio-Temporal Variability of Global Aerosol Optical Depth During 2000–2019;research on the Prediction Model of Sites in Kashgar Based on Logistic Regression Analysis;research on the Spatial Characteristics of High-Density Urban Road Network and Functional Agglomeration Taking the Northern Part of Macau as an Example;Hot Public Appeal Extraction and Visual Analysis Combined BERT and Spatio-Temporal Location;study on the Differences of Urban Thermal Environment Under the Framework of Local Climate Zones;response of Urban Park Visitor Behavior to Water Quality in Beijing;MAHGE: Point-of-Interest Recommendation Using Meta-path Aggregated Heterogeneous Graph Embeddings;a Novel Method for Groups Identification Based on Spatio-Temporal Trajectories;jointly Modeling Intersections and Road Segments for Travel Time Estimation via Dual Graph Convolutional Networks;we-Map Orientation Method Suitable for General Public with Weak Sense of Orientation;feature Extraction and Marking Method of Inertial Navigation Trajectory Based on Permutation Entropy Under Road Constraints;spatio-Temporal Disparity of Traffic Accidents Between the Elderly Involved and Non-elderly Groups in Kumamoto, Japan;Using DBSCAN-RF Algorithm and Multi-source Features to Model Deep Convection in Hubei, China;diversified Top-k Spatial pattern Matching;information Diffusion Prediction Based on Deep Attention in Heterogeneous Networks;AIS Data Driven CNN-BiGRU Model for Ship Target Classification.

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An Adaptive pattern Prediction Framework for Solar PV Power Forecasting Using Cloud Image

An Adaptive Pattern Prediction Framework for Solar PV Power ...

引用

Energy Engineering and Power Systems (EEPS), international conference on

作者： Xinyang Zhang Ming Wen Yuchong Liu Zongchao Yu Bo Wen Jiaxi Li Hunan Key Laboratory of Energy Internet Supply-demand and Operation State Grid Hunan Electric Power Company Limited Economic & Technical Research Institute Changsha Hunan China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

The global energy crisis has hastened the development and adoption of renewable energy sources. The rapid expansion of photovoltaic (PV) installed capacity exacerbates the challenge of unit forecasting errors, particularly amidst instantaneous irradiance fluctuations caused by cloud movement. To address the limitations of individual models in accurately characterizing power fluctuations, this paper proposes a pattern prediction framework based on cloud image for refined enhancement of PV power forecasting. The framework incorporates a classifier for adaptive pattern classification of cloud images, aligning them with corresponding PV power periods, and a predictor comprising two models: an ultra-short-term PV power forecasting model and a pattern recognition model. Simulation results demonstrate the framework's ability to accurately predict target period patterns, thereby enhancing forecasting precision, thus providing crucial technical support for constructing a secure and manageable new power system.

关键词： Adaptation models Fluctuations Accuracy Clouds Simulation Statistical learning pattern classification Predictive models pattern recognition Forecasting

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Application of pattern recognition in Airport Target Conflict Alert 3rd

Application of Pattern Recognition in Airport Target Conflic...

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3rd international conference on Artificial Intelligence, 2022

作者： Liu, Yan State Key Laboratory of Air Traffic Management System and Technology Nanjing210007 China

ISBN: (纸本)9789811694226

The operational safety of aircraft and vehicles at the airport currently mainly relies on the ATC and drivers, and there is a risk of collision caused by negligence of personnel. At present, control voice and video surveillance are important information that can be obtained from mode information, but they are not fully integrated into the control automation system application. Through the intelligent pattern recognition technology, the control voice and video monitoring information are recognized and processed, and the control system is integrated into the control system to accurately determine the operation trend of the airport target, and promptly give warnings, which has important practical significance. In response to this need, this paper studies structured command generation based on control voice, three-dimensional relationship calculation based on target semantics, future trajectory prediction and target conflict warning methods, to achieve knowledge-incorporated flight conflict detection between airport targets, and automate air traffic control System application method is designed to provide support for airport operation safety guarantee. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Feature Fusion with Non-local for Pedestrian Attribute recognition 2022

Feature Fusion with Non-local for Pedestrian Attribute Recog...

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2nd international conference on bioinformatics and Intelligent Computing, BIC 2022

作者： Lv, Jiping Xiong, Zhenghua Zou, Rongfang Wen, Zhangying Lin, Hongli 3rd Construction Co. Ltd China Construction 5th Engineering Bureau Hunan Changsha410004 China Information Management Department 5th Engineering Bureau Hunan Changsha410004 China College of Computer Science and Electronics Engineering Hunan University Overland park Changsha China

ISBN: (纸本)9781450395755

In the Pedestrian Attribute recognition (PAR) research topic, how to extract comprehensive features to represent pedestrian attributes is still an open problem due to its multi-label nature. In order to tackle this problem, we proposed a new end-to-end PAR network based on Feature Fusion with a Non-local operation(FFNL), named FFNL-net. Compared with existing feature fusion approaches that only mechanically pays attention to feature maps from multiple levels, it also exploits the strongest semantic but still higher resolution feature maps. Firstly, the pedestrian image is extracted from a backbone network. Then, three feature maps from different levels and scales are obtained, and the Non-local operation on the above multiple feature maps are applied respectively to further extract spatial information. Finally, a novel feature fusion strategy is performed on them to fetch a fused feature map. Our experimental results on existing popular pedestrian attribute datasets of PETA, PA-100K, and RAP prove that our proposed approach achieves state-of-the-art results. © 2022 ACM.

关键词： Semantics

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