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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1741-1750 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 ...

引用

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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Research on Action pattern recognition Based on DeepConvLSTM

Research on Action Pattern Recognition Based on DeepConvLSTM

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international conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Pengkai Guo Zhuo Li Yiyan Sun School of Artificial Intelligence and Automation Hohai University Nanjing China College of Energy and Electrical Engineering Hohai University Nanjing China

pattern recognition is an important way to improve the quality of life and promote scientific research. through the big data captured by the three-axis sensor, this paper focuses on the establishment of relevant models for the accurate classification of 19 motion modes , and analyses of the generalization ability and overfitting. this paper establishes DeepConvLSTM model by combining the original deep learning convolutional neural network (CNN) and long- term short-term memory recursion (LSTM). the model code about DeepConvLSTM was written by Python and relevant data was imported. In terms of methods, this paper evaluates the generalization ability of the model by using setaside method and K-fold cross-validation method respectively from the algorithm level. In terms of indicators, this paper selects accuracy, accuracy rate, recall rate and F1 value. Finally, the conclusion is drawn as follows: when epoch increases to 40, Train loss and Val loss basically fall together, indicating that the accuracy and stability of the model are *** paper use logistic regression model to solve the classification problem of human behavior. It is found that the classification effect of DeepConvLSTM model was better than that of LR model with higher accuracy and no overfitting phenomenon. in addition, Recall is used to analyze the sensitivity of the model. After several tests, it is found that the model shows good sensitivity and stability when the training times reaches 60. On this basis, considering the cost and efficiency of training comprehensively, the number of training is set as 60 in this paper, and the sensitivity and robustness of the model are both good. In conclusion, this paper establishes a scientific action pattern recognition model according to the conditions of the subject and the data given. the model is simple and easy to popularize. After verification and analysis, the model in this paper has strong accuracy, robustness and sensitivity, and has certain pra

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Enhancing Financial Trading Strategies with pattern recognition: A Systematic Literature Review of Methods, Trend and Challenge

Enhancing Financial Trading Strategies with Pattern Recognit...

引用

international conference on Cybernetics and Intelligent System (ICORIS)

作者： I Ketut Dedy Suryawan Kusrini Arief Setyanto Ema Utami Department of Informatics Doctorate Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

this study investigates how pattern recognition in financial markets can enhance trading strategies through a systematic literature review (SLR). the research focuses on the most frequently studied financial markets, commonly used pattern recognition methods, identified chart patterns, the integration of trading strategies with pattern recognition, and future challenges. this study is significant because many approaches to pattern recognition analysis are subjective and lack scientific evidence, leading to varied interpretations and uncertainties. the methodology includes formulating research questions, defining inclusion and exclusion criteria, conducting a comprehensive literature search, critically assessing included studies, data extraction and management, and result analysis and interpretation. this SLR covers 46 articles from databases such as Scopus, ACM Digital Library, IEEE Xplore, and Science Direct, published between 2015 and 2024, relevant to the topic. the findings show a dominant use of deep learning technologies, such as Convolutional Neural Networks (CNN), segmentation techniques, and fuzzy logic in recognizing patterns, particularly classic patterns like head and shoulders, triangles, double tops, and others, which contribute to enhancing trading strategies.

关键词： Deep learning Adaptation models Accuracy Uncertainty Magnetic heads pattern recognition Convolutional neural networks Tuning thermal stability Systematic literature review

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Robust Pedestrian detection for semi-automatic construction of a crowded person re-identification dataset 1

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10th international conference on Articulated Motion and Deformable Objects, AMDO 2018

作者： Huang, Zengxi Feng, Zhen-Hua Yan, Fei Kittler, Josef Wu, Xiao-Jun School of Computer and Software Engineering Xihua University Chengdu China Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Machine Intelligence Jiangnan University Wuxi China

ISBN: (数字)9783319945446

ISBN: (纸本)9783319945439

the problem of re-identification of people in a crowd commonly arises in real application scenarios, yet it has received less attention than it deserves. To facilitate research focusing on this problem, we have embarked on constructing a new person re-identification dataset with many instances of crowded indoor and outdoor scenes. this paper proposes a two-stage robust method for pedestrian detection in a complex crowded background to provide bounding box annotations. the first stage is to generate pedestrian proposals using Faster R-CNN and locate each pedestrian using Non-maximum Suppression (NMS). Candidates in dense proposal regions are merged to identify crowd patches. We then apply a bottom-up human pose estimation method to detect individual pedestrians in the crowd patches. the locations of all subjects are achieved based on the bounding boxes from the two stages. the identity of the detected subjects throughout each video is then automatically annotated using multiple features and spatial-temporal clues. the experimental results on a crowded pedestrians dataset demonstrate the effectiveness and efficiency of the proposed method. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Gesture recognition

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Geometric Numerical Integral Method in Compact Lie Group

Geometric Numerical Integral Method in Compact Lie Group

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Chao Liu Shengyi Yang Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guizhou Minzu University Gui Yang China

ISBN: (纸本)9781665447300

three dimensions special orthogonal group SO (3) is widely used to describe the rotation kinematics of the rigid body without local coordinates, which can avoid rotation singularity and unwinding in traditional methods. Propagating the rotation kinematics in SO (3) with a specific geometric integration method is not only to obtained numerical results with improved qualitative behavior, but also provided more accurate long-time integration results. While many studies have focused on geometric integration algorithms to preserve the geometric structure, this work has the additional objective of studying result accuracy. Integral curves on SO (3) obtained using the third-order Crouch-Grossman Lie group method are compared with numerical results using the third-order RKMK algorithms, the exponential coordinates method, and the simple projection method. Results show that the use of the Crouch-Grossman Lie group method better preserves the geometric structure of SO (3) for the larger time steps considered. It is also found that the third-order Crouch-Grossman algorithm is more accurate than the RKMK except for the smallest time step used.

关键词： Heuristic algorithms Signal processing algorithms Kinematics Signal processing

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A convolutional neural network combined with aggregate channel feature for face detection 6

A convolutional neural network combined with aggregate chann...

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6th international conference on Wireless, Mobile and Multi-Media, ICWMMN 2015

作者： Wang, Shuo Yang, Bin Lei, Zhen Wan, Jun Li, Stan Z. School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China China Research and Development Center for Internet of Thing China Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781849199940

Face detection has been studied intensively over the past several decades and achieved great improvements via convolutional neural network (CNN) which has greatly improved the performance in image classification and object detection. In this paper, similar to the idea of R-CNN [1], we present a new method that combines the aggregate channel features (ACF) [2] and CNN for face detection. the proposed method uses ACF to select the possible human face regions and then trains a CNN model to filter out non-face candidates. then we merge the results of ACF and CNN to get the final detection window(s). Evaluations on two popular face detection benchmark datasets show that our method outperforms the ACF method and has achieved competitive performance against the state-of-the-art algorithms.

关键词： Aggregates

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Quantification of Epileptic Electrical Sustained Activity during Sleep in BECT Patients

Quantification of Epileptic Electrical Sustained Activity du...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Guohan Mei Zhezhao Zhang Dinghan Hu Danping Wang Tiejia Jiang Tao Jiang Junfeng Zhang Jiuwen Cao Machine Learning and I-Health International Cooperation Base of Zhejiang Province Hangzhou Dianzi University China Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang China Plateforme d’Etude de la Sensorimotricité (PES) BioMedTech Facilities Université Paris Cité Paris France Department of Neurology Children’s Hospital Zhejiang University School of Medicine China Department of Neurosurgery The First Affiliated Hospital of Anhui Medical University China Department of Neurosurgery Anhui Public Health Clinical Center China School of Information and Communication Engineer Hainan University China

Electrical status epilepticus during sleep (ESES) is a specific EEG phenomenon induced by sleep with near-constant spike and slow wave emission. In the clinical diagnosis of its associated syndromes, the quantification of abnormal electroencephalography (EEG) during sleep, i.e. spike-wave index (SWI), is often used as an important reference standard. It is based on the characteristics of spikes and slow-spikes in the central temporal region. EEG signals can identify the presence of seizures for patients and provide information about the severity and extent of abnormal EEG activity during sleep, which helps doctors to better understand the patient’s physiological function and to develop individualised treatment and prognosis plans. In this paper, we proposed a novel method that combines deep learning and morphological operations to identify and quantify epileptic electrical sustained activity during sleep. the proposed method provides the mean SWI error of 6.04%, the Recall of 87.37% and the Precision of 56.11%. Besides, 15.2%, 64.6% and 85.3% could be achieved for PCT (1%), PCT (5%) and PCT (10%), respectively. the experimental results show that the proposed method has great potential for the clinical diagnosis and prognosis of children with epilepsy. It will help to provide long-range EEG detection for patients with ESES syndrome, thus offering the possibility of early treatment for patients.

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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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Research on Partial Discharge pattern recognition of Transformer Bushings Based on KPCA and GTO-SVM Algorithm

Research on Partial Discharge Pattern Recognition of Transfo...

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international conference on Power and Energy Technology (ICPET)

作者： Qingdong Zhu Mengzhao Zhu Qihui Cui Jian Wang Zhaoliang Gu State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

Transformer bushings are critical components for the safe operation of power grids, and their failures can lead to significant risks. In order to improve the accuracy of diagnosing Partial Discharge (PD) types in transformer bushings, this paper proposes a PD pattern recognition algorithm based on Kernel Principal Component Analysis (KPCA) and Gorilla Troops Optimizer (GTO)-Support Vector Machine (SVM). Firstly, KPCA is used to reduce the dimensionality of high-frequency signals, and then the GTO algorithm is employed to optimize the selection of SVM parameters to improve the classification and recognition performance. Experimental results show that the proposed algorithm has a higher recognition rate in PD defect diagnosis. Compared with traditional SVM and BP neural network algorithms, it improves the overall recognition rate by 5% and 5.83% respectively, verifying its effectiveness and superiority in diagnosing transformer bushing defects. this study provides a new approach and method for the early defect diagnosis of transformer bushings, and has important engineering application value.

关键词： Support vector machines Partial discharges Accuracy Neural networks Transformers Insulators Vectors Classification algorithms pattern recognition Principal component analysis

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A new visualization method for virtual colonoscopy 4th

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4th international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001

作者： Vos, F.M. Serlie, I. Van Gelder, R.E. Post, F.H. Truyen, R. Gerritsen, F.A. Stoker, J. Vossepoel, A.M. Pattern Recognition Group Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands Department of Radiology University Hospital Rotterdam Dr. Molewaterplein 40 Rotterdam3015 GD Netherlands Computer Graphics and CAD/CAM Group Delft University of Technology Zuidplantsoen 4 Delft2628 BJ Netherlands Department of Radiology Academic Medical Centre Amsterdam P.O. Box 22700 Amsterdam1100 DE Netherlands EasyVision Advanced Development Group Philips Medical Systems Nederland B.V. P.O. Box 10000 Best5680 DA Netherlands

ISBN: (纸本)3540426973

Virtual colonoscopy or ‘colonography’ is a patient-friendly, modern screening technique for polyps. Automatic detection of polyps can serve to assist the radiologist. this paper presents a method based on clustering the principal curvatures. Via automatic polyp detection 5/6 polyps (>5 mm) were detected at the expense of 9 false positive findings per case. For visualization, the bowel surface is presented to the physician in a ‘panoramic’ way as a sequence of unfolded cubes. Conventionally, only 93% of the colon surface is available for examination. In our approach the area in view is increased to 99.8%. the unfolded cube visualization is another step to optimize polyp detection by visual examination. Experiments show a sensitivity of 10/10 (on a per patient basis) for any polyp. the specificity was 7/10. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Visualization

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