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

共 1804 条记录，以下是331-340 订阅

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Local Binary pattern Based Feature Extraction with KELM for Face Identification

Local Binary Pattern Based Feature Extraction with KELM for ...

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international conference on Signal Processing and Communication (ICSC)

作者： Bhawna Ahuja Virendra P. Vishwakarma University School of Information Communication Technology Guru Gobind Singh Indraprastha University Delhi India

the following topics are dealt with: learning (artificial intelligence); Internet of things; feature extraction; face recognition; convolutional neural nets; error statistics; image classification; MOSFET; iterative m... 详细信息

ISBN: (数字)9781728154930

ISBN: (纸本)9781728154947

关键词： Face Databases Classification algorithms Kernel Training Feature extraction Robustness

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Radar Phrase recognition Algorithm Based on New Word Discovery

Radar Phrase Recognition Algorithm Based on New Word Discove...

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international conference on Computer and Communications (ICCC)

作者： Liu Yang Weigang Zhu Shouye Lv Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE) Xi'an China UOSE Beijing China Beijing Institute of Remote Sensing Beijing China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Aiming at the problem that the previous recognition algorithms based on supervised learning are difficult to apply to non-cooperative multi-function radar, this paper proposes a radar phrase recognition algorithm based on new word discovery. the algorithm can effectively identify radar phrases without prior knowledge. It is verified by simulation experiments that with the increase of the input data size, the algorithm can overcome the problem of decreased phrase recognition rate caused by the loss of data quality. To a certain extent, the algorithm achieves the purpose of compensating for data “quality” with data “quantity”, and has good engineering applicability.

关键词： Data integrity conferences Supervised learning Radar Data models Natural language processing pattern recognition

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Automated Behavioral Modeling and pattern Analysis of Children with Autism in a Joint Attention Training Application: A Preliminary Study 6th

Automated Behavioral Modeling and Pattern Analysis of Childr...

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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： Tang, Tiffany Y. Winoto, Pinata Wenzhou Kean Univ Dept Comp Sci Media Lab Wenzhou Peoples R China

ISBN: (纸本)9783030218133;9783030218140

Although recent research works have highlighted and demonstrated the applicability of the activity and behavioral pattern analysis mechanisms in offering early windows of opportunities in the assessment and intervention for individuals with autism spectrum disorder (ASD), the computational cost and sophistication of such behavioral modeling systems might prevent these automatic and semi-automatic systems from deploying, which might in turn restrict its actual use. As such, in this paper, we proposed an easily deployable automatic system to train joint attention (JA) skills, characterizing and evaluating JA and reciprocity patterns (i.e. the frequency and degree of reciprocity, initiating and responding to JA bids). Our proposed approach is different from most of earlier attempts in that we do not capitalize the sophisticated feature-space construction methodology;instead, the simple designs and in-game automatic data collection offers hassle-free benefits for such individuals as special education teachers and parents to use in both classrooms and at homes.

关键词： Joint attention skills Children pattern Autism Behavioral modeling Puzzle Training application

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6th international conference on Learning and Collaboration Technologies, LCT 2019, held as part of the 21st international conference on Human-Computer Interaction, HCI international 2019

6th International Conference on Learning and Collaboration T...

引用

6th international conference on Learning and Collaboration Technologies, LCT 2019, held as part of the 21st international conference on Human-Computer Interaction, HCI international 2019

ISBN: (纸本)9783030218133

the proceedings contain 60 papers. the special focus in this conference is on Learning and Collaboration Technologies. the topics include: the Neuro-Subject: A Living Entity with Learnability;information and Communication Science Challenges for Modeling Multifaceted Online Courses;Let’s Talk About Tools and Approaches for Teaching HCI;four-Dimensional Learning, a Response to Social Responsibility in Learning;learning Translation in Geometric Transformations through Digital and Manipulative Artefacts in Synergy;fables for Teachers and Pupils: Incrementally Defined Scenario-Based, Interactive and Spatial Stories;proposing an Estimation Method of Mental Fatigue by Measuring Learner’s Leg Movement;collaborative Meaning Construction in Socioenactive Systems: Study with the mBot;gender Difference in Language Learning with Technology;Measuring Students’ Acceptance to AI-Driven Assessment in eLearning: Proposing a First TAM-Based Research Model;cognitive Load Levels While Learning with or Without a Pedagogical Agent;where the User Does Look When Reading Phishing Mails – An Eye-Tracking Study;automated Behavioral Modeling and pattern Analysis of Children with Autism in a Joint Attention Training Application: A Preliminary Study;student Emotion recognition in Computer Science Education: A Blessing or Curse?;creative Learning and Artefacts Making: Promises and Challenges in Practice;effects of Teaching Methodology on the Students’ Academic Performance in an Introductory Course of Programming;Adaptive Learning Case Studies Implementation at Architectural BIM Construction Courses;case Studies of Applications to Encourage Students in Cyber-Physical Environment;supporting the Teaching and Learning for Subject of Computer Sciences;Programming Teaching Tools Feature Assessment Associated with Brazilian Curriculum Base Obtained through BPL Platforms Analysis.

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Spatio-temporal pattern Analysis for EEG Classification in Rapid Serial Visual Presentation Task 19

Spatio-temporal Pattern Analysis for EEG Classification in R...

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Proceedings of the 6th international conference on bioinformatics Research and Applications

作者： Bowen Li Zhiwen Liu Xiaorong Gao Yanfei Lin School of Information and Electronics Beijing Institute of Technology Beijing China School of Medicine Tsinghua University Beijing China

ISBN: (纸本)9781450372183

this study will explore an algorithm of spatio-temporal pattern analysis for electroencephalographic (EEG) classification in the rapid serial visual presentation (RSVP) task. In this algorithm, the spatial low-rank and temporal-frequency sparse priors are exploited to train the supervised spatial and temporal filters. the discriminant features are extracted by the supervised spatio-temporal filters and classified by support vector machine. the EEG signals were recorded from a total of 12 subjects under RSVP task and were used as training and testing data. the average true positive rate of classification is 79%, and the average false positive rate is only 3.4%. the classification results show that the proposed algorithm has better performance in the target detection than HDCA and SWFP.

关键词： Spatio-temporal pattern Discriminant features EEG Low-rank Sparse prior

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A Review on Various Techniques of Brain Tumor Boundary Detection 6

A Review on Various Techniques of Brain Tumor Boundary Detec...

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

作者： Dharnia, Sunita Wasson, Vikas Chandigarh Univ CSE Dept Ajitgarh Punjab India

ISBN: (纸本)9789380544342

Common and disparate brains may be segmented by registering objective image with an outline. An outline may be described as the grouping of an intensity image and respectively segmented image. After registering the outline model and objective image, outline labels are propagated to objective image. In previous years, researchers have discovered segmentation algorithms to match outlines, subjects, query and also strategies for outline structure. this work presents a survey of several automated techniques for outline based segmentation of MRI images. In this survey researcher tried to highlight the weakness and strengths of outline based techniques using the concepts of artificial intelligence techniques and recommend new research directions in the field of medical science. Basically, paper focused on presenting the different criterion for detection and segmentation methods. Initially, paper described the algorithms on the basis of outline based strategy: label propagation, methods based upon the threshold, region and probabilistic techniques. On the basis of medical objective the methods are described: the brain and its inside structures segmentation with the damaged area of brains. In this paper a comparison of the literature review's result is also available. this paper presents a brief survey on automated segmentation and detection mechanism of damaged area of brains to classify the abnormality of brains.

关键词： Brain Tumor Segmentation Algorithms Magnetic Resonance Imaging (MRI) pattern recognition Machine Learning and Artificial Intelligence

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Sensor Headband for Emotion recognition in a Virtual Reality Environment 6th

Sensor Headband for Emotion Recognition in a Virtual Reality...

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6th international conference on Information Technology in Biomedicine (ITIB)

作者： Kroenert, David Gruenewald, Armin Li, Frederic Grzegorzek, Marcin Brueck, Rainer Univ Siegen Med Informat & Microsyst Engn D-57076 Siegen Germany Univ Siegen Res Grp Pattern Recognit D-57076 Siegen Germany

ISBN: (纸本)9783319912110

Due to global digitalisation, teaching in virtual reality is becoming a growing market. Compared to learning in class, individual learning scenarios are possible. To find out, if a person is currently stressed or overstrained and the training course thus should be adapted, it is necessary to detect the emotional state of the person. therefore in this paper a sensor headband is introduced, which is able to measure certain physiological values such as galvanic skin conductance, blood volume pulse or body temperature. With the help of feature extraction it is then possible to determine, which emotional state relevant in learning scenarios is predominate.

关键词： Emotion recognition Virtual reality Sensors GSR BVP

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6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 and 4th international Workshop on Visual Performance Analysis, VPA 2017 and Workshop on Extreme-Scale Programming Tools, ESPT 2018 and 5th international Workshop on Visual Performance Analysis, VPA 2018

6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 a...

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6th Workshop on Extreme-Scale Programming Tools, ESPT 2017 and 4th international Workshop on Visual Performance Analysis, VPA 2017 and Workshop on Extreme-Scale Programming Tools, ESPT 2018 and 5th international Workshop on Visual Performance Analysis, VPA 2018

ISBN: (纸本)9783030178710

the proceedings contain 17 papers. the special focus in this conference is on Extreme-Scale Programming Tools. the topics include: Gotcha: An Function-Wrapping Interface for HPC Tools;Projecting Performance Data over Simulation Geometry Using SOSflow and ALPINE;Visualizing, Measuring, and Tuning Adaptive MPI Parameters;visual Analytics Challenges in Analyzing Calling Context Trees;paScal Viewer: A Tool for the Visualization of Parallel Scalability Trends;using Deep Learning for Automated Communication pattern Characterization: Little Steps and Big Challenges;visualizing Multidimensional Health Status of Data Centers;generic Library Interception for Improved Performance Measurement and Insight;improved Accuracy for Automated Communication pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning;Moya—A JIT Compiler for HPC;polyhedral Optimization of TensorFlow Computation Graphs;CAASCADE: A System for Static Analysis of HPC Software Application Portfolios;visual Comparison of Trace Files in Vampir;understanding the Scalability of Molecular Simulation Using Empirical Performance Modeling;Advanced Event-Sampling Support for PAPI.

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DeepSignCX: Signature complexity detection using recurrent neural networks 15

DeepSignCX: Signature complexity detection using recurrent n...

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15th iapr international conference on Document Analysis and recognition, ICDAR 2019

作者： Vera-Rodriguez, Ruben Tolosana, Ruben Caruana, Miguel Manzano, Gustavo Gonzalez-Garcia, Carlos Fierrez, Julian Ortega-Garcia, Javier Universidad Autonoma de Madrid Biometrics and Data Pattern Analytics-BiDA Lab Madrid Spain

ISBN: (纸本)9781728128610

this paper proposes a novel approach for on-line signature complexity detection based on Recurrent Neural Networks (RNNs). Complexity of handwritten signatures can vary from very simple ones (just a simple flourish) to very complex signatures (including the handwritten full name and complex flourish). three different complexity levels are proposed: low, medium, and high. Time functions are extracted from the on-line signatures and a system based on RNNs (BLSTM in particular) is trained to classify the three levels of complexity over a ground truth manually labelled database (BiosecurID with 400 subjects). this initial model is used to automatically label a very large database (DeepSignDB) containing over 1500 subjects, which is then used to train the proposed RNN for signature complexity detection. Promising results ca. 85% of accuracy are achieved. this complexity detector could be used as a first stage in a signature verification system in order to train a specific biometric system per signature complexity level and improve the overall system performance. © 2019 IEEE.

关键词： Biometrics

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the Research on Detection of Crop Diseases Ranking Based on Transfer Learning 6

The Research on Detection of Crop Diseases Ranking Based on ...

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

作者： Yang, Mengji Zhang, Haiqing Bouras, Abdelaziz Tang, Yiqian He, Yu Li, DaiWei Yu, Xi Chengdu Univ Sch Informat Sci & Engn Chengdu Peoples R China Chengdu Univ Informat Technol Dept Software Engn Chengdu 610103 Sichuan Peoples R China Qatar Univ Comp Sci Dept ictQATAR Box 2731 Doha Qatar Key Lab Pattern Recognit & Intelligent Informat P Chengdu Peoples R China

ISBN: (纸本)9781728157122

Crop diseases are a major global threat to food security. Because the lack of agriculture experts or necessary facilities, it is difficult to determine the type of disease, as well as the degree of disease in time, which became the major factor affecting in crop production. In recent years, with the development of the transfer learning in deep learning domain, the experience of experts can be simulated to detect crop diseases in time. In this paper, we have proposed an improved transfer learning method based on ResNet 50 in crop disease diagnosis. the AI Challenger 2018 dataset has been deeper analyzed, the degree of crops diseases are detected. Comparing with non-transfer learning, the proposed transfer learning method achieved better results, which can significantly improve accuracy results by 5.1%similar to 1.87% with reducing half of the running time.

关键词： crop disease deep learning transfer learning ResNet 50

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