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检索条件"任意字段=Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition"

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international Joint conference: 12th international conference on Computational Intelligence in Security for Information Systems, CISIS 2019 and 10th international conference on European Transnational Education, ICEUTE 2019

International Joint Conference: 12th International Conferenc...

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international Joint conference: 12th international conference on Computational Intelligence in Security for Information Systems, CISIS 2019 and 10th international conference on European Transnational Education, ICEUTE 2019

ISBN: (纸本)9783030200046

the proceedings contain 35 papers. the special focus in this conference is on Computational Intelligence in Security for Information Systems. the topics include: Deep Wavelet Extreme Learning Machine for Data Classification;palm Vein Age and Gender Estimation Using Center Symmetric-Local Binary pattern;neuro-Evolutionary Feature Selection to Detect Android Malware;improving Blockchain Security Validation and Transaction Processing through Heterogeneous computing;anomaly Detection on Patients Undergoing General Anesthesia;an Internet Voting Proposal Towards Improving Usability and Coercion Resistance;linearization of Cryptographic Sequences;On-the-Fly Testing an Implementation of Arrow Lightweight PRNG Using a LabVIEW Framework;a Novel Construction for Streaming Networks with Delays;comparison of System Call Representations for Intrusion Detection;hyot: Leveraging Hyperledger for Constructing an Event-Based Traceability System in IoT;data Mining for Statistical Evaluation of Summative and Competency-Based Assessments in Mathematics;looking for the Antidote for Contaminated Water: Learning through an Escape Game;enhancing Collaborative Learning through Pedagogical Alignment;rules_Math: Establishing Assessment Standards;evaluate Mathematical Competencies in Engineering Using Video-Lessons;statistical Methods – What Is the Problem Here?;enhancing the Learning of Cryptography through Concept Maps;analysis of Student Achievement Scores: A Machine Learning Approach;analysis of Teacher Training in Mathematics in Paraguay’s Elementary Education System Using Machine Learning Techniques;Efficient Verification of Security Protocols Time Properties Using SMT Solvers;cooperative Evaluation Using Moodle;automatic Categorization of Introductory Programming Students;using Scratch to Improve Undergraduate Students’ Skills on Artificial Intelligence;project-Based Methodology to Lecture on Web Frameworks Applied to the Management of Health-Related Data;mirkwood: An Online Parallel Craw

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the Study of PSO-SVM and PSO-GRNN Algorithm Used in the Fault pattern Classification of Transformer 2020

The Study of PSO-SVM and PSO-GRNN Algorithm Used in the Faul...

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12th international conference on Machine Learning and computing, ICMLC 2020

作者： Shiling, Zhang State Grid Chongqing Electric Power Company Chongqing Electric Power Research Institute Chongqing Yubei China

ISBN: (纸本)9781450376426

Support Vector Machine and GR neural network are widely used in the fault pattern classification. For large data samples and fault types, these methods have high classification accuracy. In order to further improve the practical range and operational efficiency of numerical classifier, the Particle Swarm Optimization Algorithm is used to the optimize penalty and kernel function parameters of SVM, weight and threshold parameter of GRNN. By numerical verification, it is found that the classifier is applied to multi-peak complex curved surface function, has nice classification ability. the classifier is used in classification of DGA data of actual oil immersed, and it is found: running time of PSO-SVM classifier increases with the increase of the fault mode, and the recognition accuracy shows the trend of the overall decline. the classification accuracy of the PSO-GRNN classifier is relatively stable, but the fault classification is more time-consuming. through the research and analysis of this paper, validity of optimization classification algorithm is verified by the simulation, compared performance of the PSO-GRNN and PSO-SVM classification algorithms, which provides ideas and methods for the fault classification method of oil immersed transformer. © 2020 ACM.

关键词： Support vector machines

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Automated Analysis of Mitral Inflow Doppler Using Deep Neural Networks 12th

Automated Analysis of Mitral Inflow Doppler Using Deep Neur...

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Functional Imaging and Modeling of the Heart - 12th international conference, FIMH 2023, proceedings

作者： Jevsikov, Jevgeni Lane, Elisabeth S. Alajrami, Eman Naidoo, Preshen Serej, Nasim Dadashi Azarmehr, Neda Aleshaiker, Sama Stowell, Catherine C. Shun-shin, Matthew J. Francis, Darrel P. Zolgharni, Massoud School of Computing and Engineering University of West London London United Kingdom National Heart and Lung Institute Imperial College London United Kingdom

ISBN: (纸本)9783031353017

Doppler echocardiography is a widely applied modality for the functional assessment of heart valves, such as the mitral valve. Currently, Doppler echocardiography analysis is manually performed by human experts. this process is not only expensive and time-consuming, but often suffers from intra- and inter-observer variability. An automated analysis tool for non-invasive evaluation of cardiac hemodynamic has potential to improve accuracy, patient outcomes, and save valuable resources for health services. Here, a robust algorithm is presented for automatic Doppler Mitral Inflow peak velocity detection utilising state-of-the-art deep learning techniques. the proposed framework consists of a multi-stage convolutional neural network which can process Doppler images spanning arbitrary number of heartbeats, independent from the electrocardiogram signal and any human intervention. Automated measurements are compared to Ground-truth annotations obtained manually by human experts. Results show the proposed model can efficiently detect peak mitral inflow velocity achieving an average F1 score of 0.88 for both E- and A-peaks across the entire test set. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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proceedings - 2019 IEEE Intl Conf on Parallel and Distributed Processing with Applications, Big Data and Cloud computing, Sustainable computing and Communications, Social computing and Networking, ISPA/BDCloud/SustainCom/SocialCom 2019

Proceedings - 2019 IEEE Intl Conf on Parallel and Distribute...

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17th IEEE international conference on Parallel and Distributed Processing with Applications, 9th IEEE international conference on Big Data and Cloud computing, 9th IEEE international conference on Sustainable computing and Communications, 12th IEEE international conference on Social computing and Networking, ISPA/BDCloud/SustainCom/SocialCom 2019

ISBN: (纸本)9781728143286

the proceedings contain 232 papers. the topics discussed include: convolutional neural network-based approach for citrus diseases recognition;using temporal conceptual graphs and neural networks for big data-based attack scenarios reconstruction;taxi demand prediction with LSTM-based combination model;counting attention based on classification confidence for visual question answering;an optimization method of WebP images lossy compression algorithm for FPGAs;self-adaptive address mapping mechanism for access pattern awareness on DRAM;a high-performance self-learning antelopes migration algorithm (SAMA) for global optimization;and using resource use data and system logs for HPC system error propagation and recovery diagnosis.

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Faculty Facial recognition Using Convolutional Neural Network a Tool for Smart Academic Monitoring

Faculty Facial Recognition Using Convolutional Neural Networ...

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Jaymer Jayoma John Carlo Camba College of Computing and Information Sciences Caraga State University Butuan City Philippines

ISBN: (纸本)9781665429979

Faculty Academic Monitoring is one thing a university has to perform to ensure quality delivery of instructions. that includes proper attendance monitoring of classes, faculty consultations, and others. To address attendance monitoring in CCIS - College of computing and Information Sciences, a facial detection and recognition was developed to identify a faculty face's important feature from a real-time captured image. the study uses a pre-trained model called facenet but acquires new training data sets to generate the desired classifier for a localized recognition system. the data are cleaned by eliminating duplicates, resizing, cropping into the desired dimension, and labeling each image data. the system used image pre-processing techniques like face alignment algorithm, landmarks, face detection, and local binary pattern. the study achieved a validation rate of 96.45% using the test validation function and an accuracy rate of 90% in the actual testing.

关键词： Face recognition Training data Tools Real-time systems Face detection Monitoring Testing

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A Multi-Sensor Algorithm for Activity and Workflow recognition in an Industrial Setting 19

A Multi-Sensor Algorithm for Activity and Workflow Recogniti...

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12th ACM international conference on PErvasive Technologies Related to Assistive Environments (PETRA)

作者： thomay, Christian Gollan, Benedikt Haslgruebler, Michael Ferscha, Alois Heftberger, Josef Res Studios Austria Pervas Comp Applicat Vienna Austria Johannes Kepler Univ Linz Inst Pervas Comp Linz Austria Fischer Sports GmbH Ried Austria

ISBN: (纸本)9781450362320

In the recent revival of human labour in industry, and the subsequent push to optimally combine the strengths of man and machine in industrial processes, there is an increased need for methods allowing machines to understand and interpret the actions of their users. An important aspect of this is the understanding and evaluation of the progress of the workflows that are to be executed. Methods for this require both an appropriate choice of sensors, as well as algorithms capable of quickly and efficiently evaluating activity and workflow progress. In this paper we present such an algorithm, which provides activity and workflow recognition using both depth and RGB cameras as input. the algorithm's main purpose is to be used in an industrial training station, allowing novice workers to learn the necessary steps in assembling nordic ski products without the need for human supervision. We will describe how the algorithm recognizes predefined workflows in the sensor data, and present a comprehensive evaluation of the algorithm's performance on a real data recording of operators performing their work in an industrial setting. We will show that the algorithm fulfills the necessary requirements and is ready to be implemented in the training station application.

关键词： Industrial assistance systems activity recognition pattern recognition pervasive computing sensor fusion

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the research of Intelligent Fault Diagnosis System Based on Internet of things 12

The research of Intelligent Fault Diagnosis System Based on ...

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IEEE Int Congr on Cybermat / 12th IEEE Int Conf on Cyber, Phys and Social Comp (CPSCom) / 15th IEEE Int Conf on Green computing and Communications (GreenCom) / 12th IEEE Int Conf on Internet of things (ithings) / 5th IEEE Int Conf on Smart Data

作者： Wen, Yingli Zhu, Zhiliang Dai, Yuxing Hu, Jing Chen, Changan Wenzhou Univ Coll Math & Elect Informat Engn Wenzhou Peoples R China Zhejiang Ind & Trade Vocat & Tech Coll Wenzhou Peoples R China YaLong Intelligent Equipment Grp Wenzhou Peoples R China

ISBN: (纸本)9781728129808

In order to obtain accurate real-time system state recognition under the supervision of the universal mobile internet of things management platform, an intelligent fault diagnosis system is proposed. And the intelligent fault diagnosis system is presentation based on ZigBee technology and particle filter in this paper. Real-time collection of multi-variable data is realized by adopting ZigBee wireless sensor network, and the accurate estimation of the state of monitored objects and intelligent prediction of faults are based on the particle filtering algorithm. the proposed approach is implemented on a circuit fault identification system. the application results show that the proposed approach can achieve the real-time remote monitoring, accurate state estimation and fault diagnosis, meanwhile, effectively enhance applicable scope and diagnostic level of the fault diagnosis system.

关键词： Fault diagnosis Particle filter Zigbee pattern recognition Internet of things

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Predicate Clustering-Based Entity-Centered Graph pattern recognition for Query Extension on the LOD 12th

Predicate Clustering-Based Entity-Centered Graph Pattern Rec...

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12th international conference on Innovative Mobile and Internet Services in Ubiquitous computing (IMIS)

作者： Kim, Jongmo Kong, Junsik Park, Daeun Sohn, Mye Sungkyunkwan Univ Suwon South Korea

ISBN: (纸本)9783319935546;9783319935539

In this paper, we propose a method to reduce the difficulties of query caused by lack of information about graph patterns even though the graph pattern is one of the important characteristics of the LOD. To do so, we apply the clustering methodology to find the RDF predicates that have similar patterns. In addition, we identify representative graph patterns that imply its characteristics each cluster. the representative graph patterns are used to extend the users' query graphs. To show the difficulties of the query on the LOD, we developed an illustrative example. We propose the novel framework to support query extension using predicate clustering-based entity-centered graph patterns. through the implementation of this framework, the user can easily query the LOD and at the same time collect appropriate query results.

关键词： pattern recognition

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Complex Activity recognition Using Polyphonic Sound Event Detection 12th

Complex Activity Recognition Using Polyphonic Sound Event De...

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12th international conference on Innovative Mobile and Internet Services in Ubiquitous computing (IMIS)

作者： Kang, Jaewoong Kim, Jooyeong Kim, Kunyoung Sohn, Mye Sungkyunkwan Univ Dept Ind Engn Suwon South Korea

ISBN: (纸本)9783319935546;9783319935539

In this paper, we propose a method for recognizing the complex activity using audio sensors and the machine learning techniques. To do so, we will look for the patterns of combined monophonic sounds to recognize complex activity. At this time, we use only audio sensors and the machine learning techniques like Deep Neural Network (DNN) and Support Vector Machine (SVM) to recognize complex activities. And, we develop the novel framework to support overall procedures. through the implementation of this framework, the user can support to increase quality of life of elders'.

关键词： Complex activity pattern recognition Polyphonic sound event detection Machine learning

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proceedings - 2019 IEEE international conference on Cloud computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services

Proceedings - 2019 IEEE International Conference on Cloud Co...

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12th IEEE international conference on Cloud computing, CLOUD 2019

ISBN: (纸本)9781728127057

the proceedings contain 84 papers. the topics discussed include: data protection as a service in the multi-cloud environment;robust management of trans-cloud applications;an adaptive approach for dealing with flow disruption in virtualized water-cooled data centers;cloud VM provisioning using analytical performance models;QuADD : quantifying accelerator disaggregated datacenter efficiency;TrIMS: transparent and isolated model sharing for low latency deep learning inference in function-as-a-service;STRETCH: in-memory storage with autoscaling for cluster computing;exploiting serverless runtimes for large-scale optimization;and non-intrusive cloud application transaction pattern discovery.

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