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

共 1869 条记录，以下是321-330 订阅

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Error Rate Analysis of State-of-the-Art Algorithms for Face Detection 6

Error Rate Analysis of State-of-the-Art Algorithms for Face ...

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6th international conference on Computing for Sustainable Global Development (INDIACom)

作者： Danish, Mohammad Chaurasia, Sumit Jain, Aarti AIACTR Dept ECE Delhi India

ISBN: (纸本)9789380544342

Face recognition is an easy task for humans but it is tedious and complex for computers. Currently, Eigenfaces, Local Binary pattern Histograms (LBPH) and Fisherfaces algorithms are considered as state-of-the-art and are widely used algorithms for face detection. Eigenfaces and Fisherfaces uses Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) respectively while LBPH uses histogram to recognize the face in the image. In this paper we have analyzed and compared the Eigenfaces, Local Binary pattern Histograms (LBPH) and Fisherfaces algorithms for different scenarios where there is high probability of getting errors. From the simulation results it has been observed that for these algorithms there is increases in the error rate whenever there is a change in the environment, lighting condition, hair and moustache change etc. in the face. Moreover, from the results it has been found that Eigenfaces and Fisherfaces are more prone to the errors than the LBPH.

关键词： Face recognition face detection principal component analysis PCA local binary pattern Eigenfaces Fisherfaces LBPH

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3D reconstruction and pattern recognition methods for the search activity of missing persons and lost items by tsunami : Keynote Speakers 1

3D reconstruction and pattern recognition methods for the se...

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international conference on Science in Information Technology (ICSITech)

作者： Yasushi Kanazawa Toyohashi University of Technology Aichi Japan

the following topics are dealt with: learning (artificial intelligence); neural nets; computer aided instruction; Internet; text analysis; Internet of things; data mining; feature extraction; diseases; and natural lan... 详细信息

ISBN: (数字)9781728173498

ISBN: (纸本)9781728173504

关键词： three-dimensional displays Shape pattern recognition Image reconstruction Computer science Surface treatment Surface reconstruction

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6th international Workshop on Mining Ubiquitous and Social Environments, MUSE 2015, held in conjunction with the 6th European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2015, 6th international Workshop on Modeling Social Media, MSM 2015, held in conjunction with the 24th international World Wide Web conference, WWW 2015 and 7th international Workshop on Modeling Social Media, MSM 2016, held in conjunction with the 25th international World Wide Web conference, WWW 2016

6th International Workshop on Mining Ubiquitous and Social E...

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6th international Workshop on Mining Ubiquitous and Social Environments, MUSE 2015, held in conjunction with the 6th European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2015, 6th international Workshop on Modeling Social Media, MSM 2015, held in conjunction with the 24th international World Wide Web conference, WWW 2015 and 7th international Workshop on Modeling Social Media, MSM 2016, held in conjunction with the 25th international World Wide Web conference, WWW 2016

ISBN: (纸本)9783030339067

the proceedings contain 7 papers. the special focus in this conference is on Mining Ubiquitous and Social Environments. the topics include: Analyzing big data streams with apache SAMOA;Multimodal behavioral mobility pattern mining and analysis using topic modeling on GPS data;sequential monte carlo inference based on activities for overlapping community models;results of a survey about the perceived task similarities in micro task crowdsourcing systems;Provenance of explicit and implicit interactions on social media with W3C PROV-DM.

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Improved Pathogen recognition using Non-Euclidean Distance Metrics andWeighted kNN 6

Improved Pathogen Recognition using Non-Euclidean Distance M...

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6th international conference on Biomedical and bioinformatics Engineering (ICBBE)

作者： tharmakulasingam, Mukunthan Topal, Cihan Fernando, Anil La Ragione, Roberto Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England Univ Surrey CVSSP Guildford Surrey England Eskisehir Tech Univ Eskisehir Turkey Univ Surrey Sch Vet Med Guildford Surrey England

ISBN: (纸本)9781450372992

the timely identification of pathogens is vital in order to effectively control diseases and avoid antimicrobial resistance. Non-invasive point-of-care diagnostic tools are recently trending in identification of the pathogens and becoming a helpful tool especially for rural areas. Machine learning approaches have been widely applied on biological markers for predicting diseases and pathogens. However, there are few studies in the literature that have utilized volatile organic compounds (VOCs) as non-invasive biological markers to identify bacterial pathogens. Furthermore, there is no comprehensive study investigating the effect of different distance and similarity metrics for pathogen classification based on VOC data. In this study, we compared various non-Euclidean distance and similarity metrics with Euclidean metric to identify significantly contributing VOCs to predict pathogens. In addition, we also utilized backward feature elimination (BFE) method to accurately select the best set of features. the dataset we utilized for experiments was composed from the publications published between 1977 and 2016, and consisted of associations in between 703 VOCs and 11 pathogens. We performed extensive set of experiments with five different distance metrics in both uniform and weighted manner. Comprehensive experiments showed that it is possible to correctly predict pathogens by using 68 VOCs among 703 with 78.6% accuracy using k-nearest neighbour classifier and Sorensen distance metric.

关键词： Distance metrics backward feature elimination (BFE) pathogen detection bioinformatics feature selection machine learning

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Multimodal behavioral mobility pattern mining and analysis using topic modeling on GPS data 1

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作者： Merino, Sebastiaan Atzmueller, Martin Tilburg University Tilburg Netherlands

ISBN: (数字)9783030344078

ISBN: (纸本)9783030339067

Identifying risky driving behavior is of central importance for increasing traffic safety. this paper tackles the task of analyzing real (naturalistic) driving data captured by in-vehicle sensors using interpretable data science methods. In particular, we focus on symbolic time-series abstraction and the subsequent behavioral profile identification using topic modeling approaches. For our experiments, we utilize a real-world dataset. Our results indicate interesting behavioral driving profiles including important patterns and factors for traffic safety modeling. © Springer Nature Switzerland AG 2019.

关键词： Accident prevention

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Fault pattern Mining Based on Fault Information Event Model 6

Fault Pattern Mining Based on Fault Information Event Model

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6th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Wen, Yijun Zhang, Li Zhu, Huimi Sun, Kan Tao, Ye Tsinghua Univ Sch Software Beijing 100084 Peoples R China Naval Res Acad NVRA POB1303 Beijing Peoples R China

ISBN: (纸本)9781728105215

Faced with a large number of fault management data, whether there are some important and interesting but unknown patterns in the event data has always attracted the attention of fault management engineer. Based on the analysis of fault management process and daily fault management data of engineering machinery and equipment, a fault management information event model is put forward, which includes six elements: description elements, object elements, time elements, space elements, modification elements and condition elements. Based on information event model we propose a fault pattern mining method, and we also tested the proposed approach on the practical production data of a construction machinery data service provider, the experimental results show that our approach is correct and effective.

关键词： Data mining Circuit faults Data models Machinery Object recognition Maintenance engineering Correlation

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Multi-Directional Ternary pattern for Facial Expression recognition 6

Multi-Directional Ternary Pattern for Facial Expression Reco...

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6th IEEE international conference on Advances in Computing, Communication and Control, ICAC3 2019

作者： Dorage, Kiran Sudam Raveendran, Smitha Ramrao Adik Institute of Technology Department of Electronics Engineering Sector 7 Nerul Navi Mumbai India

ISBN: (纸本)9781728123868

Multi-Directional Ternary pattern (M-DTP) is the face descriptor which reduces the noise and extracts the key expression related characteristics of the input face image to overcome the recent challenges encountered in unconstrained face recognition. In M-DTP, the derivative of the Gaussian operator is utilized to transform the gray scale facial image into the multi-directional gradient image that is vigorous to changes in the illumination. this pre-processing is introduced to minimize the challenges due to the variations in illumination and unconstrained face expression recognition. Further, by making use of directional information and ternary pattern the emotion-related features are extracted from the face. the proposed multi-level histogram features extracted from the ternary face code technique delivers the finer grain description of facial emotions to improve the facial expression performance under various illumination conditions. the features extracted using M-DTP are high-dimensional, therefore to reduce the dimensionality Principal Component Analysis (PCA) is used. the Support Vector Machine (SVM) classifier is utilized to perform and evaluate the classification performance using different datasets. © 2019 IEEE.

关键词： Principal component analysis

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Welcome Message from the conference Chairs

Conference Proceedings - IEEE International Conference on Sy...

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conference Proceedings - IEEE international conference on Systems, Man and Cybernetics 2020年 2020-October卷 ii-iii页

作者： Gabbar, Hossam A. Trajkovic, Ljiljana Ontario Tech University Canada Simon Fraser University Canada

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Immersive Community Analytics for Wearable Enhanced Learning 6th

Immersive Community Analytics for Wearable Enhanced Learning

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6th international conference on Learning and Collaboration Technologies (LCT) Held as Part of the 21st international conference on Human-Computer Interaction (HCII)

作者： Klamma, Ralf Ali, Rizwan Koren, Istvan Rhein Westfal TH Aachen Lehrstuhl Informat 5 Adv Community Informat Syst Grp ACIS Ahornstr 55 D-52074 Aachen Germany

ISBN: (纸本)9783030218164;9783030218171

Nowadays, we can use immersive interaction and display technologies in collaborative analytical reasoning and decision making scenarios. In order to support heterogeneous professional communities of practice in their digital transformation, it is necessary not only to provide the technologies but to understand the work practices under transformations as well as the security, privacy and other concerns of the communities. Our approach is a comprehensive and evolutionary socio-technological learning analytics and design process leading to a flexible infrastructure where professional communities can co-create their wearable enhanced learning solution. In the core, we present a multi-sensory fusion recorder and player that allows the recordings of multi-actor activity sequences by human activity recognition and the computational support of immersive learning analytics to support training scenarios. Our approach enables cross-domain collaboration by fusing, aggregating and visualizing sensor data coming from wearables and modern production systems. the software is open source and based on the outcomes of several national and international funded projects.

关键词： Visual analytics Communities of practice Immersive analytics Human activity recognition

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Hypervelocity damage pattern recognition on Aluminum alloy based on acoustic emission 6

Hypervelocity damage pattern recognition on Aluminum alloy b...

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6th international conference on Information Science and Control Engineering (ICISCE)

作者： Zhang, Kai Pang, Baojun Chi, Runqiang Jiao, Hongyu Changshu Inst Technol Dept Mech Engn Changshu Jiangsu Peoples R China Dalian Univ Technol Changshu Res Inst Co Ltd Dalian Liaoning Peoples R China Harbin Inst Technol Dept Space Engn Mech Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781728157122

Space debris is a threat to manned spacecraft due to its high velocity, a method based on BP neural network is presented to estimate the degree of hypervelocity impact damage. In this research, projectiles were fired by two stage light gas gun to impact single aluminum plate, the damage formed on target plate was a crater or a perforating hole. the impact signals were obtained by ultrasonic sensors. According to the signal characteristics of different damage mode, signal amplitude, S2 modal energy, proportion of high-frequency energy and signal propagation distance were chosen as the input characteristic parameters to build a neural network. Experimental results showed that the trained network can be used to identify damage patterns, the correct rate was higher than 85%. It can also predict the depth of crater and hole diameter.

关键词： hypervelocity impact damage pattern recognition BP neural network

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