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Proceedings of 2022 3rd international Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022

Proceedings of 2022 3rd International Symposium on Artificia...

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3rd international Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022

ISBN: (纸本)9781450398442

The proceedings contain 105 papers. The topics discussed include: the effect of hg content in water bodies for precision medicine of Hg disease;the spatial migration pattern of Hg content for the prediction of disease during a long time;Cryptophycin’s clinical use in xenograft pancreatic tumor cell treatments based on computer science;standardization of clinical terminology based on hybrid recall and Ernie;interaction process between changes in terms of time and space of pH value and the growth of marine organisms in the Jiaozhou Bay;clinical research progress of acupuncture in the treatment of sciatica;research on identification of abnormal foot arch in school-age children based on foot pressure analysis;intelligent diagnosis of vascular anomalies with deep learning;the value of brain-computer interface in stroke upper rehabilitation;and research on abnormal behavior recognition of the elderly based on spatial-temporal feature fusion.

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Automatic 3D Teeth Semantic Segmentation with Mesh Augmentation Network 3

Automatic 3D Teeth Semantic Segmentation with Mesh Augmentat...

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3rd international conference on pattern recognition and Machine Learning, PRML 2022

作者： Zhao, Qijun Wu, Xiao Zhu, Feiyu Liu, Jun Wei, Mingying Peng, Jiahan Lu, Yin Sichuan University College of Computer Science Chengdu China Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision Chengdu China Sichuan University State Key Laboratory of Oral Diseases National Clinical Research Center for Oral Diseases Department of Orthodontics West China Hospital of Stomatology Chengdu China The People's Hospital of Neijiang Department of Stomatology Neijiang China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

In this paper, we propose an approach that improves segmentation networks with automatic augmentation networks for dental mesh data. Since conventional data augmentation is to augment all samples uniformly with predefined parameters, it ignores the unique characteristics of a single tooth sample and cannot make good use of the data set. And the traditional method separates data augmentation and segmentation network training, so the augmented data cannot be well adapted to the network to make it play a good role. We adopt a joint optimization strategy to integrate the augmentation network and the segmentation network, so that the augmented tooth data is the most suitable for the segmentation network. In addition, we design new improved loss functions suitable for augmentation and segmentation networks. Experiments have shown that the automatic augmentation network in our proposed method, named MeshAugNet, can effectively improve the segmentation accuracy after it is used for tooth segmentation. In general, this work achieves a combination of 3D dental data auto- augmentation network and segmentation network, which improves the accuracy of tooth segmentation, and can be used to solve the problem of too few samples in tooth datasets. © 2022 IEEE.

关键词： Semantic Segmentation

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A New Deep Fuzzy Based MSER Model for Multiple Document Images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： Image classification

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Vehicle Theft Detection Using Neural pattern recognition

Vehicle Theft Detection Using Neural Pattern Recognition

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： J. Jane Rubel Angelina S.J Subhashini Anil Kumar Rapuri Vennela Ravva Raghavendra Rasala Sai Dcepak Ray Adurgam Dept of Computer Science and Engineering Kalasalingam Academy Of Research And Education Krishnankoil TamilNadu India Dept of Computer Science and Engineering SRM Madurai College for Engineering and Technology Sivagangai TamilNadu India

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge.

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Improved Model Accuracy Based on a Simple Frame of Temporal-Correlation Representation Method in Sleep Staging 3

Improved Model Accuracy Based on a Simple Frame of Temporal-...

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3rd international conference on pattern recognition and Machine Learning, PRML 2022

作者： Zhao, Liang Liu, Guanzhou Tang, Xianwen Bai, Yang Li, Yuzhi Wang, Xingjun Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen China South China University of Technology School of Electronic and Information Engineering Shenzhen China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

We proposes a new kind of contrast learning method based on Temporal-Correlation characters of electroencephalogram (EEG) used in sleep staging. This method, called simple framework of temporal-correlation representation(simTCR), was studied and tested on Open EEG dataset, sleep-EDF and clinical EEG dataset from polysomnography (PSG). Deep learning used in clinical diagnosis is often faced with problems of deficiency of well labeled labels. And physiological signal, like EEG signal, often shows temporal correlation character, which means the signal maintains one state in a period of time before it transforms into a new state. Therefore, there exist high probability that the nearby frames of EEG signal belong to same categories and shows similar characters. This character can be considered as self-supervised features and a loss function was raised here to pretrain the model. Our result shows that, (1) simTCR can improve the sleep staging accuracy based on EEG signal to 1%-17% in both clinical dataset and Open dataset, sleep-EDF;(2) The improved effect is more obvious when the training data in fine-tuning process is more less;(3) the hyper-parameter batch size plays limited roles in the accuracy improvement;(4) simTCR costs about three magnitudes of more time than the traditional supervised deep learning method. © 2022 IEEE.

关键词： Electroencephalography

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Channels Selection for pattern recognition of Five Fingers Motor Imagery Electroencephalography Signals 3

Channels Selection for Pattern Recognition of Five Fingers M...

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3rd international conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Azizah, R.N. Zakaria, H. Hermanto, B.R. Department of Biomedical Engineering Bandung Institute of Technology Bandung Indonesia

Research about pattern recognition on electroencephalography (EEG) signal of finger motor imagery (MI) plays a critical role in Brain-Computer Interfaces (BCI) based hand prosthetics development. However, the previous research still used irrelevant channels to finger MI. This work proposed optimal EEG channels combination for five-finger MI. It is achieved by subject-dependence channel selection using One versus Rest Common Spatial pattern (CSP-OVR) combined with sequential searching algorithms due to specific neural activation areas of MI. Optimal channels combinations are of great importance to reduce channels number. It supports the development of practical BCI-based hand prosthetics that can help hand handicapped to do daily activities easier. Experimental results show 4 out of 19 channels are relevant to five-finger MI with 0,6% accuracy degradation compared with EEG-MI pattern recognition using 19 channels. This result is better than the Principal Component Analysis (PCA) channel selection method that only selects 11 out of 19 channels with 1 % accuracy degradation. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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Structure-Aware Photorealistic Style Transfer Using Ghost Bottlenecks 3rd

Structure-Aware Photorealistic Style Transfer Using Ghost Bo...

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

作者： Bui, Nhat-Tan Nguyen, Ngoc-Thao Cao, Xuan-Nam International Training and Education Center University of Science Ho Chi Minh City Viet Nam Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9783031090363

Photorealistic style transfer synthesizes a new image from a pair of content and style images. The transfer should express the visual patterns in the former while preserving the content details following the latter. However, most existing methods may generate an image that suffers disrupted content details and unexpected visual cues from the style image;hence, they do not satisfy the photorealism. We tackle this issue with a new style transfer architecture that effectively unifies a content encoder, an Xception style encoder, a Ghost Bottlenecks subnet, and a decoder. In our framework, the style features extracted from the Xception module balance well with the content features obtained from an encoder;the Ghost Bottlenecks subnet then integrates these features and feeds them into a decoder to produce the resulting style transferred image. Experimental results demonstrate that our model surmounts the structure distortion problem to satisfy photorealistic style transfer and hence obtains impressive visual effects. © 2022, Springer Nature Switzerland AG.

关键词： Signal encoding

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Research on decline pattern recognition of hydraulic system 3

Research on decline pattern recognition of hydraulic system

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3rd international conference on Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field, ICMSP 2021

作者： Ma, Qi Song, Dong Liu, Bobing Northwestern Polytechnical University School of Civil Aviation Xi'an China Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665437158

This paper investigates the method of identifying decline patterns based on condition monitoring data. The paper firstly investigates the process of decline pattern recognition from the inevitability of system decline;then uses the time-domain feature extraction method to extract features for hydraulic system health characteristic parameters;finally uses the ML-KNN algorithm and the recurrent neural network-based algorithm to identify decline patterns on the experimental data, and compares the two algorithms. The results demonstrate that the recurrent neural network-based model has stronger generalization ability compared with other classification algorithms, and characterizes the data more accurately and fits the distribution of the data more realistically. © 2021 IEEE.

关键词： Feature extraction

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Fault pattern recognition Method of Rolling Bearing Based on MTF-2DWDCNN 3

Fault Pattern Recognition Method of Rolling Bearing Based on...

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3rd international conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Xun, Zhiwen Miao, Xiaodong Yu, Hu Gu, Yinji Nanjing Tech University School of Mechanical and Power Engineering Nanjing China Nanjing Tech University School of computer science and technology Nanjing China

ISBN: (纸本)9781665492812

2-D fault feature images have advantages as neural network input for fault pattern recognition, but the most deep image recognition algorithms are non-sensitive to the time-series features of images. This paper proposes a new algorithm named MTF-2DWDCNN based on Markov transfer field(MTF) jointly improved deep convolutional neural networks with wide first-layer Kernel(WDCNN). Firstly, the original 2-D sequence signal is converted into a 2-D time series image by MTF, used as the input of the neural network;secondly, the convolution mode of the original WDCNN is improved to adapt to 2-D time series;finally, the accuracy and error experiments are carried out to verify its effectiveness. Experimental results show that the method proposed in this paper has certain advantages over traditional algorithms regarding recognition accuracy. © 2022 IEEE.

关键词： Bearing deep learning fault diagnosis MTF

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Harmonic Representation for CNN-LSTM Automatic Chord recognition 3

Harmonic Representation for CNN-LSTM Automatic Chord Recogni...

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3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Ito, Tsuyoshi Arai, Shuichi Tokyo City Univ Grad Sch Engn Setagaya Ku 1-28-1 Tamazutsumi Tokyo 1588557 Japan

ISBN: (纸本)9781665425803

Paper Since Chord progression is the element that determines the harmony of a piece of music, Automatic Chord recognition (ACR) from audio signals is a crucial task in the field of Music Information Retrieval(MIR). Recently, various models using deep learning have been proposed, but there are few studies on their input features. Notes parts of the chord are the fundamental note, and its overtone ringed simultaneously. In order to model these audio signals efficiently, feature transforms such as "Constat-Q-Transform(CQT)" is used. However, due to the super-position of fundamental notes and overtones of various instruments in polyphonic music, it is considered difficult to model chords even by deep learning. Therefore, we focused on the structure, including fundamental notes are on the logarithm and its overtones are on the linear. In this paper, we propose a feature representation that can represent overtone structure for each fundamental note. Based on these feature representations, data-driven approach to learn the chord by CNN-LSTM model. We evaluated performance using 383 songs with publicly available annotations, and achieved the same performance with approximately one-tenth of the number of parameters than the existing methods.

关键词： Deep Learning Signal Processing pattern recognition Music Information Retrieval Automatic Chord recognition

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