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检索条件"任意字段=Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning"

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Video-based violence detection by human action analysis with neural network 2

Video-based violence detection by human action analysis with...

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2nd international conference on Image and Video processing, and Artificial Intelligence (IPVAI)

作者： Zhao, Yunqing Fok, Wilton W. T. Chan, C. W. Univ Hong Kong Dept Elect & Elect Engn Pokfulam Rd Hong Kong Peoples R China

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

In recent years, human action analysis is a focal point in video processing, especially on action recognition and safety surveillance. It always performs as an auxiliary tool to minimize the manpower-resource on special tasks. This paper explores the human action analysis in a specified situation, based on the human posture extraction by pose-estimation algorithm. Deep neural network(DNN) methods was used, composed of residual learning blocks for feature extraction and recurrent neural network for time-series data learning. All these modules can be applied on real-time videos, classifying different security levels of actions between two people, with 91.8% accuracy on test set. Meanwhile, some other classical network structures were compared as baselines. After forward inference process of the neural network model, a logic enhancement algorithm was raised and applied in this paper, due to the prediction error between two classes. Experiments were conducted on real-time videos, achieving satisfying performance.

关键词： Video processing Pose estimation Human action analysis Violence detection Neural network Residual learning Long-Short-Term-Memory

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Semi-Supervised Entity Recognition of Chinese Government Document 19

Semi-Supervised Entity Recognition of Chinese Government Doc...

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2nd international conference on Artificial Intelligence and Pattern Recognition (AIPR)

作者： Chen, Dagang Li, Zeyuan Li, Zesong Liu, Kunnan Song, Yajun Wang, Peng CETC Big Data Res Inst Co Ltd Guiyang Peoples R China State Grid Sichuan Informat & Commun Co Chengdu Peoples R China

ISBN: (纸本)9781450372299

There is a large amount of entity information in government documents. Identifying the entity information in government documents is the core foundation of intelligent document processing tasks, such as word segmentation, semantic analysis and knowledge graph construction. To recognize entity, traditional machine learning algorithm has the advantage of relatively small tagging corpus requirement. However, this feature also means that this algorithm can hardly capture the implicit semantic information in sentences, which leads to the low accuracy of document entity recognition. Also, this method requires tremendous manual work of feature designing. In contrast, Deep learning algorithm needs a large tagging corpus. But it gives the algorithm ability to automatically acquire semantic feature information between context. So, the accuracy performance of entity recognition is greatly improved. Combining respective advantages of these above methods, this paper proposes a semi-supervised Deep learning algorithm framework, which first implement the Conditional Random Field (CRF) and pseudo-labeling to expand the corpus, and then utilize the Dilated Convolution Neural Network (CNN) with Bi-directional Long Short-Term Memory (BiLSTM) plus CRF for extracting entities in official documents. The experimental results show that, compared with other methods, the accuracy, recall rate and F1 value of entity recognition are improved by 5.02%, 5.85% and 5.44% respectively. The proposed method can effectively extract entity information in a document.

关键词： Conditional random field Dilated Convolution Neural Network Entity Recognition Deep learning

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Research of machine learning Algorithm for Broadcasting Spectrum signal processing 2

Research of Machine Learning Algorithm for Broadcasting Spec...

引用

2019 2nd international conference on Advanced Materials, Intelligent Manufacturing and Automation, AMIMA 2019

作者： Sun, Jinyu Zhou, Qun Shang, Jianping Sun, Shuai College of Automation Wuhan University of Technology Wuhan China Radio Management Office of Hubei Wuhan China Huawei Technology Co. Ltd. Wuhan Institute Wuhan China

FM broadcasting is mainly used to transmit sound and other signals in the form of wireless transmission. This thesis is based on a project for a radio department, and mainly focuses on the broadcasting frequency band, uses radio monitoring equipment to scan the spectrum signal of the broadcasting frequency band, and performs the data preprocessing, signal feature extraction and classification processing of the radio frequency spectrum signal on the extracted spectrum signal, so as to extract abnormal signals such as pseudo base stations, black radio, cheats in exams, etc. Firstly, relevant pre-processing is performed on the spectrum information of the broadcast frequency band. Through the improved K-Means algorithm, the original sample data including the glitch signal is eliminated, and the original signal is processed through the wavelet analysis of the spectrum signal. The original signal was decomposed by wavelet and wavelet reconstruction, so as to achieve the purpose of denoising the original signal. Secondly, based on the analysis of signal characteristics and the comparison of a large number of spectrum signals, a method for extracting individual features of spectrum signals is summarized. Finally, the grey relational degree cluster Analysis is used to extract the features of the spectrum signal, which provides a certain basis for the subsequent classification algorithm. © Published under licence by IOP Publishing Ltd.

关键词： Spectrum analysis

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Grape Leaf Disease Identification using machine learning Techniques 2

Grape Leaf Disease Identification using Machine Learning Tec...

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2nd international conference on Computational Intelligence in Data Science (ICCIDS)

作者： Jaisakthi, S. M. Mirunalini, P. Thenmozhi, D. Vatsala Vellore Inst Technol SCOPE Vellore Tamil Nadu India SSN Coll Engn Dept CSE Chennai Tamil Nadu India Barclays Eon IT Pk Pune Maharashtra India

ISBN: (纸本)9781538694718

Having diseases is quite natural in crops due to changing climatic and environmental conditions. Diseases affect the growth and produce of the crops and often difficult to control. To ensure good quality and high production, it is necessary to have accurate disease diagnosis and control actions to prevent them in time. Grape which is widely grown crop in India and it may be affected by different types of diseases on leaf, stem and fruit. Leaf diseases which are the early symptoms caused due to fungi, bacteria and virus. So, there is a need to have an automatic system that can be used to detect the type of diseases and to take appropriate actions. We have proposed an automatic system for detecting the diseases in the grape vines using image processing and machine learning technique. The system segments the leaf (Region of Interest) from the background image using grab cut segmentation method. From the segmented leaf part the diseased region is fruther segmented based on two different methods such as global thresholding and using semi-supervised technique. The features are extracted from the segmented diseased part and it has been classified as healthy, rot, esca,and leaf blight using different machine learning techniques such as Support Vector machine (SVM), adaboost and Random Forest tree. Using SVM we have obtained a better testing accuracy of 93%.

关键词： Grape Leaves Disease Identification machine learning SVM

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Designing and Implementation of a Wireless Gesture Controlled Robot for Disabled and Elderly People 2

Designing and Implementation of a Wireless Gesture Controlle...

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2nd international conference on Electrical, Computer and Communication Engineering, ECCE 2019

作者： Alam, Mahbuba Yousuf, Mohammad Abu Institure of Information Technology Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9781538691113

Gesture controlled robot should perform its job with the embedded system, combination of hardware and software that is designed for performing dedicated tasks for specific users which can be controlled by hand gestures. In this paper, we propose a gesture controlled robot for physically challenged and elderly people. The robot uses motion sensors to recognize five gestures of hand. These five hand gestures are employed to control five directions such as stop or steady, forward, backward, left and right. In this paper, the gesture has been recognized by motion sensors and without any image processing to keep the system simple efficient. The prototype device can be modified into a wheelchair, trolley bed or any physical platform consisting of more than two wheels. An Arduino Nano v3 embedded system with accelerometer gyroscope held by the user and the signals will be transferred wirelessly to the motors of the platform intended to move it as per the users hand gestures in order to carry themselves in their surrounding environment. machine learning is used to classify hand gesture accurately. The result shows that the classification accuracy is near to 94% (93.8%). © 2019 IEEE.

关键词： Image processing

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Sleep Stages Classification in a Healthy People Based on Optical Plethysmography and Accelerometer signals via Wearable Devices 2

Sleep Stages Classification in a Healthy People Based on Opt...

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IEEE 2nd Ukraine conference on Electrical and Computer Engineering (UKRCON)

作者： Fedorin, Illia Slyusarenko, Kostyantyn Lee, Wonkyu Sakhnenko, Nataliya Samsung R&D Inst Ukraine Hlth Innovat Kiev Ukraine Samsung Elect Hlth HW Suwon South Korea

ISBN: (纸本)9781728138824

This paper proposes a novel automatic classifier of rapid-eye-movement (REM), non-rapid-eye-movement (NREM, combined light and deep sleep stages (SS)), and wake stages based on wrist photoplethysmography and wrist 3-axis accelerometer data. The quality and robustness of the classifier are achieved by a multilevel learning framework, which consists of features extraction, preprocessing, signal treatment, machine learning, and post-processing modules. Subject-specific Z-score normalization and exponential smoothing are applied to reduce intra-subject and inter-subject variability. The novel features are based on heart rate variability and motion statistics. Leave-one-subject-out cross-validation technique is applied to estimate the quality of the model. Our solution, based on linear discriminant analysis (LDA), shows the Cohen's Kappa coefficient of 0.58 and accuracy of 77 % for the four class (wake, REM, light, deep) classification problem and Cohen's Kappa coefficient of 0.67 and accuracy of 85 % for the three class (wake, REM, NREM) classification problem. Comparison of different ML algorithms, like LDA, support vector machines, and random forest is done. The obtained results of sleep stages classification exceed the analogous state of the art solutions.

关键词： photoplethysmography heart rate variability sleep stages machine learning

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Dataset of Raw and Pre-processed Speech signals, Mel Frequency Cepstral Coefficients of Speech and Heart Rate Measurements 5

Dataset of Raw and Pre-processed Speech Signals, Mel Frequen...

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5th IEEE international conference on signal processing, Computing and Control (ISPCC)

作者： Usman, Mohammed Ahmad, Zeeshan Wajid, Mohammad King Khalid Univ Dept Elect Engn Abha Saudi Arabia Aligarh Muslim Univ Dept Elect Engn ZHCET Aligrah 202002 India

ISBN: (纸本)9781728139883

Heart rate is an important vital sign used in the diagnosis of many medical conditions. Conventionally, heart rate is measured using a medical device such as pulse oximeter. Physiological parameters such as heart rate bear a correlation to speech characteristics of an individual. Hence, there is a possibility to measure heart rate from speech signals using machine learning and deep learning, which would also allow non-invasive, non-contact based and remote monitoring of patients. However, to design such a scheme and verify its accuracy, it is necessary to collect speech recordings along with heart rates measured using a medical device, simultaneously during the recording. This article provides a dataset as well as the procedure used to create the dataset which could be used to facilitate research in developing techniques to estimate heart rate accurately by observing speech signal.

关键词： Heart rate measurement speech as a biomedical signal heart rate from speech speech biomedical dataset speech-heart rate dataset

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2019 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2019 - proceedings

2019 IEEE International Conference on Acoustics, Speech, and...

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44th IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2019

ISBN: (纸本)9781479981311

The proceedings contain 1730 papers. The topics discussed include: SIGNPROX: one-bit proximal algorithm for nonconvex stochastic optimization;multi-feature fusion based on supervised multi-view multi-label canonical correlation projection;face landmark-based speaker-independent audio-visual speech enhancement in multi-talker environments;spatial-Fourier retrieval of head-related impulse responses from fast continuous-azimuth recordings in the time-domain;a fast and robust paradigm for Fourier compressed sensing based on coded sampling;generative adversarial speaker embedding networks for domain robust end-to-end speaker verification;robust subspace clustering by learning an optimal structured bipartite graph via low-rank representation;model change detection with application to machine learning;dilated residual network with multi-head self-attention for speech emotion recognition;and on using 2D sequence-to-sequence models for speech recognition.

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Prediction of CKDu using KDQOL score, Ankle Swelling and Risk Factor Analysis using Neural Networks

Prediction of CKDu using KDQOL score, Ankle Swelling and Ris...

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Advancements in Computing (ICAC), 2019 international conference on

作者： D.N. Lokuarachchi J. V. Tharindu Manoj M.N. H. Weerasooriya M.N.M. Waseem Fahim Aslam Nishantha Kumarasinghe Dharshana Kasthurirathne Sri Lankan Institute of Information Technology Malabe Sri Lanka University of Colombo Colombo Sri Lanka General Sir John Kotalwala Defense University Sri Lanka

The following topics are dealt with: learning (artificial intelligence); natural language processing; computer aided instruction; Internet; diseases; neural nets; mobile computing; handicapped aids; health care; featu... 详细信息

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

关键词： Sensitivity Filtration machine learning Convolutional neural networks Kidney Diseases Testing

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Finger Recognition Using a Wearable Device while Typing 2019

Finger Recognition Using a Wearable Device while Typing

引用

proceedings of the 2019 2nd international conference on Computational Intelligence and Intelligent Systems

作者： Daisuke Hamazaki Tatsuhito Hasegawa Graduate School of Engineering University of Fukui Fukui Japan

ISBN: (纸本)9781450372596

In the information society, the ability to use a computer is important. To use a computer, users commonly need a keyboard as an input device. If users place their fingers on the home keys and stroke each key using the correct finger, they will lead to improve their typing skills. In this study, we develop a stroked finger recognition method for keyboard typing using Myo, a wearable device that can simply measure the surface electromyography (EMG) signal of the user's arm. Our method detects the user's stroked finger through machine learning that uses the measured EMG. We introduced window functions during feature extraction in order to suppress the influence of the keystroke speed. Our method was capable of recognizing six categories (five fingers and a neutral state) with an accuracy of about 80% when our method was evaluated by a 10-fold cross validation for each subject's data.

关键词： Surface electromyography Wearable device Typing training support machine learning

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