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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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A Vision-based Human Action Recognition System for Moving Cameras Through Deep learning 19

A Vision-based Human Action Recognition System for Moving Ca...

引用

proceedings of the 2019 2nd international conference on signal processing and machine learning

作者： Ming-Jen Chang Jih-Tang Hsieh Chiung-Yao Fang Sei-Wang Chen National Taiwan Normal University Taipei City Taiwan

ISBN: (纸本)9781450372213

This study presents a vision-based human action recognition system using a deep learning technique. The system can recognize human actions successfully when the camera of a robot is moving toward the target person from various directions. Therefore, the proposed method is useful for the vision system of indoor mobile robots. The system uses three types of information to recognize human actions, namely, information from color videos, optical flow videos, and depth videos. First, Kinect 2.0 captures color videos and depth videos simultaneously using its RGB camera and depth sensor. Second, the histogram of oriented gradient features is extracted from the color videos, and a support vector machine is used to detect the human region. Based on the detected human region, the frames of the color video are cropped and the corresponding frames of the optical flow video are obtained using the Farnebäck method (https://***=.org/3.4/d4/dee/ tutorial_optical_***). The number of frames of these videos is then unified using a frame sampling technique. Subsequently, these three types of videos are input into three modified 3D convolutional neural networks (3D CNNs) separately. The modified 3D CNNs can extract the spatiotemporal features of human actions and recognize them. Finally, these recognition results are integrated to output the final recognition result of human actions. The proposed system can recognize 13 types of human actions, namely, drink (sit), drink (stand), eat (sit), eat (stand), read, sit down, stand up, use a computer, walk (horizontal), walk (straight), play with a phone/tablet, walk away from each other, and walk toward each other. The average human action recognition rate of 369 test human action videos was 96.4%, indicating that the proposed system is robust and efficient.

关键词： Color information Optical flow Deep learning 3D convolutional neural network VGG net Indoor mobile robots Depth information Human action recognition Moving camera

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Eeg signal analysis for mild alzheimer’s disease diagnosis by means of spectral- and complexity-based features and machine learning techniques 2nd

Eeg signal analysis for mild alzheimer’s disease diagnosis ...

引用

2nd international conference on Data Engineering and Communication Technology, ICDECT 2017

作者： Kulkarni, Nilesh STES’s Sou. Venutai Chavan Polytechnic Pune India

ISBN: (纸本)9789811316098

Alzheimer’s disease (AD) is one of the common and fastest growing neurological diseases in the modern society. Biomarker techniques for diagnosis of Alzheimer’s disease and its progression in early stage are key issues for development. Electroencephalogram is one of the powerful techniques which can be used for screening of Alzheimer’s disease and dementia in early stage. The objective of this paper is to analyze the EEG signal by means of spectral and complexity features to serve EEG as a biomarker for Alzheimer’s diagnosis. The research is carried on experimental database obtained from hospital. EEG relative power, spectral entropy, spectral flux, and spectral centroid are analyzed, compared, and classified for separating the data between two groups by means of support vector machine (SVM) classifier and K-nearest neighbor (KNN) classifier. The obtained results indicate severity observed in AD patients reflected in EEG signals which can be treated as benchmark for Alzheimer’s diagnosis. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Sequential Segmentation of EEG signals for Epileptic Seizure Detection using machine learning

Sequential Segmentation of EEG Signals for Epileptic Seizure...

引用

international conference on signal processing and Communication (ICSPC)

作者： Zeba Karin Ahmad Vikram Singh Yusuf Uzzaman Khan Aligarh Muslim Unversity Aligarh India

The problem of epilepsy has grown exponentially and is now considered as one of the most prevailing neurological disorders affecting around 50 million people around the globe. Epilepsy is identified by analyzing the interictal activity present in the EEG signal. Visual analysis of EEG is a tedious process and subject to human error. This work proposes a robust method to ease the burden of intractable seizures by automatic recognition of ictal epileptiform activity in the EEG of epileptic patients. The classification between EEG having an epileptic seizure and non-seizure is done using various machine learning algorithms. The classifiers used are Simple Decision tree, Quadratic Discriminant, Medium Gaussian SVM, Bagged Trees, and Subspace k-NN. The performance is assessed using 10-fold cross-validation.

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Modified Baum Welch Algorithm for Hidden Markov Models with Known Structure 2nd

Modified Baum Welch Algorithm for Hidden Markov Models with ...

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2nd international conference on Intelligent Human Systems Integration (IHSI) - Integrating People and Intelligent

作者： Schmidt, Kim Hoffmann, Karl Heinz Tech Univ Chemnitz Str Nationen 62 D-09111 Chemnitz Germany

ISBN: (纸本)9783030110512;9783030110505

Hidden Markov Models (HMMs) are widely used in speech and handwriting recognition, behavior prediction in traffic, time series analysis, biostatistics, image and signal processing, and many other fields. For some applications in those real world problems, a-priori knowledge about the structure of the HMM is available. For example the shape of the state transition matrix and/or the observation matrix might be given. We might know that some entries in these matrices are equal and others are zero. For training such a model, we have two options: use the common Baum Welch Algorithm (BWA) and enforce the given structure after training or modify the BWA to enforce it during training. This paper shows several approaches for modifying the BWA and compares the results of all training methods.

关键词： Hidden Markov Model HMM Baum Welch Algorithm Multiple sequence learning A-priori knowledge

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Reduce False Positive Alerts for Elderly Person Fall Video-Detection Algorithm by convolutional neural network model 2

Reduce False Positive Alerts for Elderly Person Fall Video-D...

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2nd international conference on Intelligent Computing in Data Sciences (ICDS)

作者： El Kaid, Amal Baina, Karim Baina, Jamal Mohammed V Univ ENSIAS Rabat IT Ctr Alqualsadi Rabat Morocco Angel Assistance Nancy France

Currently, image acquisition and understanding has become a necessity. In fact, it's what allows machines to become one of the most powerful tools. Nowadays, machines that replace humans and experts in making decisions in several areas have seen their success from what so-called deep learning, a powerful learning in machine learning tool for processing, classifying and object recognition tasks. The idea behind deep learning is training machines, adapting their skills and applying them to many tasks. In the same way that human brain learns, the information is entered through our senses (eyes...) and goes through billions of neurons before it was processed to get an output, deep learning also takes information as in input then start proceeding through several hidden layers before an output layer. For that, we choose to profit from this powerful learning to improve the Video fall detection algorithm which suffers from generating a huge amount of false alarms. So we propose in our work to minimize these false alerts using a CNN model that can classify a person sitting in a wheelchair from others to eliminate them. We present in this paper, on the one hand, a survey of the most recent and powerful architectures of CNN, on the other, we propose to add a CNN model into the elderly person fall Video-Detection Algorithm to improve its accuracy. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Deep learning Convolutional neural networks (CNN) Classification CNN architectures Reduce false positives

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IESC 2019 Author Index

IESC 2019 Author Index

引用

international conference on Innovations in Electronics, signal processing and Communication (IESC)

Presents an index of the authors whose articles are published in the conference proceedings record.

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A machine learning Approach for Disease Surveillance and Visualization using Twitter Data 2

A Machine Learning Approach for Disease Surveillance and Vis...

引用

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

作者： Ashok, Ashwin Guruprasad, M. Prakash, C. O. Shylaja, S. S. PES Univ Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781538694718

Insights from real-time disease surveillance systems are very useful for the public to take preventive measures against the diseases and it also benefits the pharmaceutical manufacturers in improving the sales of medicines for the particular disease and ensuring adequate availability of medicines when they are needed. A disease outbreak is an event wherein there is a rise in the number of positive cases for a disease in a short span of time. An outbreak can be limited to a particular region or time of the year. Diseases can be detected by several approaches, social media being preferred method due to availability of real-time data. Hence, data from social media, especially Twitter can be used to detect live events and monitor them efficiently. In order to detect diseases precisely, this paper proposes an approach wherein tweets, which are collected and pre-processed, can be effectively vectorized and clustered into the appropriate diseases with the use Agglomerative Clustering technique. The tweets can also be visualized using their geo information in order to generate zones which have high density of diseases. Such a surveillance system can be of use for early prediction of disease outbreaks, in turn facilitating faster and better handling of the situation.

关键词： Twitter Disease Surveillance Natural Language processing Clustering Visualization

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An Artificial Intelligent Flexible Gas Sensor Based on Ultra-Large Area MoSe2 Nanosheet 62

An Artificial Intelligent Flexible Gas Sensor Based on Ultra...

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62nd IEEE international Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Guo, Shiqi Yang, Dong Li, Baichen Dong, Quan Li, Zhenyu Zaghloul, Mona E. George Washington Univ Sch Engn & Appl Sci Washington DC 20052 USA

ISBN: (纸本)9781728127880

Flexible electronic applications based on transition metal dichalcogenides (TMDCs) nanosheet are extremely impressive recently. Synthesis of ultra-large area atomic layer molybdenum diselenide (MoSe2) on a flexible substrate is the kernel of the fabrication process. In this work, we successfully transferred the ultra-large area of MoSe2 atomic layer flakes on Kapton film with gold-mediated exfoliation method. The large area MoSe2 nanosheet works as an active chemical gas sensing channel for flexible functional devices. The two terminal flexible gas sensor exhibited high responsivity under concentration for NH3 and NO2 of 5ppm and 1ppm, respectively. The flexible electronics were bent and further tested with an external circuit. In addition, an artificial intelligent data processing method with machine learning is applied to localize the toxic gas source. These results suggested that wearable large area MoSe2 atomic layer is promising for wearable environmental detection applications.

关键词： Gas Sensors MoSe2 Flexible Electronics Data processing

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Utalk: Sri Lankan Sign Language Converter Mobile App using Image processing and machine learning

Utalk: Sri Lankan Sign Language Converter Mobile App using I...

引用

Advancements in Computing (ICAC), 2019 international conference on

作者： I.S.M Dissanayake P.J Wickramanayake M.A.S Mudunkotuwa P.W.N Fernando Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

Deaf and mute people face various difficulties in daily activities due to the communication barrier caused by the lack of Sign Language knowledge in the society. Many researches have attempted to mitigate this barrier using Computer Vision based techniques to interpret signs and express them in natural language, empowering deaf and mute people to communicate with hearing people easily. However, most of such researches focus only on interpreting static signs and understanding dynamic signs is not well explored. Understanding dynamic visual content (videos) and translating them into natural language is a challenging problem. Further, because of the differences in sign languages, a system developed for one sign language cannot be directly used to understand another sign language, e.g., a system developed for American Sign Language cannot be used to interpret Sri Lankan Sign Language. In this study, we develop a system called Utalk to interpret static as well as dynamic signs expressed in Sri Lankan Sign Language. The proposed system utilizes Computer Vision and machine learning techniques to interpret sings performed by deaf and mute people. Utalk is a mobile application, hence it is non-intrusive and cost-effective. We demonstrate the effectiveness of the our system using a newly collected dataset.

关键词： Computer vision Assistive technology Natural languages Gesture recognition machine learning Mobile applications Videos

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Melanoma Skin Cancer Detection Using Image processing and machine learning Techniques

Melanoma Skin Cancer Detection Using Image Processing and Ma...

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Advancements in Computing (ICAC), 2019 international conference on

作者： MA. Ahmed Thaajwer UA. Piumi Ishanka Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9781728184128

ISBN: (纸本)9781728184135

In humans, skin cancer is the most common and severe type of cancer. Melanoma is a deadly type of skin cancer. If it identifies early stages, it can be easily cured. The formal method for diagnosing melanoma detection is the biopsy method. This method can be a very painful one and a time-consuming process. This study gives a computer-aided detection system for the early identification of melanoma. In this study, image processing techniques and the Support vector machine (SVM) algorithms are used to introduce an efficient diagnosing system. The affected skin image is taken, and it sent under several pre-processing techniques for getting the enhanced image and smoothed image. Then the image is sent through the segmentation process using morphological and thresholding methods. Some essential texture, color and shape features of the skin images are extracted. Gray Level Co-occurrence Matrix (GLCM) methodology is used for extracting texture features. These extracted GLCM, color and shape features are given as input to the SVM classifier. It classifies the given image into malignant melanoma or benign melanoma. High accuracy of 83% is achieved when we combine and apply the shape, color and GLCM features to the classifier.

关键词： Support vector machines Thresholding (Imaging) Shape Image color analysis Melanoma Feature extraction Skin

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