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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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Classification of human heart signals by novel feature extraction techniques for rescue application 5

Classification of human heart signals by novel feature extra...

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5th international conference on Image Information processing (ICIIP)

作者： Saha, Rajesh Singh, Ratnesh Kumar Kumar, Rajeev Singh, Ghanapriya Goel, Tushar Pal, Pankaj Kumar NIT Uttarakhand Dept Elect Engn Srinagar Garhwal India

ISBN: (纸本)9781728108995

In the paper, the methodology of detecting the heart rate signal wirelessly with regards to use it as an applications as an emerging tool for health care in emergency and surveillance application is presented. A novel feature extraction technique is presented to detect human heart signal or electrocardiogram (ECG) signals wirelessly and classify the signal as normal or abnormal. By the analysis of the heart rate signal the presence of an alive human person below the debris can be detected. After detection of heart rate signals via sensors, the challenge lies in classifying the human heart rate signals as normal or abnormal. Heart signals or Electrocardiogram (ECG) is the primary signal used to diagnose and detection of heart disease abnormality. In this work, ECG signals are de-noised and then analyzed to extract feature from it by a novel technique which is combining Discrete Wavelet Transform (DWT) and nonlinear, linear statistical feature. To verify the efficiency of extracted feature, here used various machine learning (ML) methods namely as Decision Trees (DT), Discriminant Analysis (DA), Logistic Regression (LR), Nearest Neighbor (KNN), Support Vector machine (SVM) and Ensemble have been used as a classifiers and their results are compared. KNN reports the lowest accuracy of 81.60% on MIT- BIH Arrythmia Database. SVM and Ensemble reports the highest accuracy of 97.40%.

关键词： ECG machine learning Discrete Wavelength Transform Features

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Word Sense Disambiguation of Bengali Words using FP-Growth Algorithm 2

Word Sense Disambiguation of Bengali Words using FP-Growth A...

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

作者： Kaysar, Mohammad Shibli Khaled, Md. Asif Bin Hasan, Mahady Khan, Mohammad Ibrahim Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Department of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (纸本)9781538691113

Word Sense Disambiguation (WSD) is the task of determining the specific meaning of an ambiguous word according to the context. In the realm of natural language processing, WSD is an open problem, and its development can significantly assist in human-level machine translation. In this paper, we have proposed a system for Bengali Word Sense Disambiguation through the FP-Growth algorithm. Moreover, we have also implemented the Apriori algorithm and presented an analysis on both of them to explain how the FP-Growth algorithm outperforms the Apriori algorithm performing Bengali Word Sense Disambiguation. Furthermore, In the testing phase we have found that for 80% of the test sentences, our proposed method can retrieve the exact meaning of an ambiguous word. © 2019 IEEE.

关键词： learning algorithms

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A comprehensive survey of machine learning-based network intrusion detection 2nd

A comprehensive survey of machine learning-based network int...

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2nd international conference on Smart Computing and Informatics, SCI 2018

作者： Chapaneri, Radhika Shah, Seema Mukesh Patel School of Technology Management and Engineering NMIMS University Mumbai India

ISBN: (纸本)9789811319204

In this paper, we survey the published work on machine learning-based network intrusion detection systems covering recent state-of-the-art techniques. We address the problems of conventional datasets and present a detailed comparison of modern network intrusion datasets (UNSW-NB15, TUIDS, and NSLKDD). Recent feature-level processing techniques are elaborated followed by a discussion on supervised multi-class machine learning classifiers. Finally, open challenges are pointed out and research directions are provided to promote further research in this area. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Surveys

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0. 4V, Low Power GmC Integrator and Bandpass Filter Design for EMG Bio Medical signal processing 2

0. 4V, Low Power GmC Integrator and Bandpass Filter Design f...

引用

2nd international conference on Power and Embedded Drive Control, ICPEDC 2019

作者： Javvaji, Veerayya Musala, Sarada Vignan's Foundation for Science and Technology Dept. of Electronics Communication Engineering VadlamudiA.P. India

ISBN: (纸本)9781728124148

This paper presents a novel 0.4 V $G-{m}\mathrm{C}$ integrator design approach and fourth order bandpass filter design for EMG biomedical applications with high DC gain and and low power consumption. The filter and integrator are implemented with a new architecture in 180nm CMOS process. By using negative resistance in proposed integrator the response can be controllable. The negative resistance in this paper is implemented with Operational Trans Conductance Amplifier(OTA) The response of proposed integrator is close to ideal integrator *** OTA used in this work is operated with 0.4 V supply and bulk driven structure. The maximum power consumption of integrator is 45 nW and cut off frequency is 160 Hz. The fourth order bandpass filter is consumes 117nW power with bandwidth 260 Hz. The dynamic range of the filter is 63dB. © 2019 IEEE.

关键词： Operational amplifiers

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Brain Stroke Detection Using Convolutional Neural Network and Deep learning Models 2

Brain Stroke Detection Using Convolutional Neural Network an...

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2nd international conference on Intelligent Communication and Computational Techniques (ICCT)

作者： Gaidhani, Bhagyashree Rajendra Rajamenakshi, R. Sonavane, Samadhan Sandip Univ Nasik Maharashtra India CDAC Pune Maharashtra India

ISBN: (纸本)9781728117119

For the last few decades, machine learning is used to analyze medical dataset. Recently, deep learning technology gaining success in many domain including computer vision, image recognition, natural language processing and especially in medical field of radiology. This research attempts to diagnose brain stroke from MRI using CNN and deep learning models. The proposed methodology is to classify brain stroke MRI images into normal and abnormal images and delineate abnormal regions using semantic segmentation [4]. In particular, two types of convolutional neural network that are LeNet [2] and SegNet are used. For classification, we passed pre-processed stroke MRI for training, trained all layers of LeNet and classify normal and abnormal patient. Then this abnormal patient data stored into two dimensional array and passed this two dimensional array to SegNet which is auto encoder decoder [3] model for segmentation, trained all layers of SegNet except fully connection layer. The experimental result show that classification model achieve accuracy between 9697% and segmentation model achieve accuracy between 8587%. Through experimental results, we found that deep learning models not only used in non-medical images but also give accurate result on medical image diagnosis, especially in brain stroke detection.

关键词： brain stroke deep learning convolutional neural network semantic segmentation

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Testing Conditional Independence on Discrete Data using Stochastic Complexity 22

Testing Conditional Independence on Discrete Data using Stoc...

引用

22nd international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Marx, Alexander Vreeken, Jilles Max Planck Inst Informat Saarbrucken Germany Saarland Univ Saarbrucken Germany Helmholtz Ctr Informat Secur Saarbrucken Germany

Testing for conditional independence is a core aspect of constraint-based causal discovery. Although commonly used tests are perfect in theory, they often fail to reject independence in practice especially when conditioning on multiple variables. We focus on discrete data and propose a new test based on the notion of algorithmic independence that we instantiate using stochastic complexity. Amongst others, we show that our proposed test, SCI, is an asymptotically unbiased as well as L-2 consistent estimator for conditional mutual information (CMI). Further, we show that SCI can be reformulated to find a sensible threshold for CMI that works well on limited samples. Empirical evaluation shows that SCI has a lower type II error than commonly used tests. As a result, we obtain a higher recall when we use SCI in causal discovery algorithms, without compromising the precision.

关键词： Parallel processing systems

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A Variational Autoencoder Based Generative Model of Urban Human Mobility 2

A Variational Autoencoder Based Generative Model of Urban Hu...

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2nd IEEE international conference on Multimedia Information processing and Retrieval (MIPR)

作者： Huang, Dou Song, Xuan Fan, Zipei Jiang, Renhe Shibasaki, Ryosuke Zhang, Yu Wang, Haizhong Kato, Yugo Univ Tokyo Ctr Spatial Informat Sci Tokyo Japan Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Tsukuba Ibaraki Japan Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Oregon State Univ Civil & Construct Engn Corvallis OR 97331 USA NAVITIME JAPAN Co Ltd Transport Consulting Div Tokyo Japan

ISBN: (纸本)9781728111988

Recently, big and heterogeneous human mobility data inspires many revolutionary ideas of implementing machine learning algorithms for solving some traditional social issues, such as zone regulation, air pollution, and disaster evacuation el at.. However, incomplete datasets were provided owing to both the concerns of violation of privacy and some technique issues in many practical applications, which leads to some limitations of the utility of collected data. Variational Autoencoder (VAE), which uses a well-constructed latent space to capture salient features of the training data, shows a significant excellent performance in not only image processing, but also Natural Language processing domain. By combining VAE and sequence-to-sequence (seq2seq) model, a Sequential Variational Autoencoder (SVAE) is built for the task of human mobility reconstruction. It is the first time that this kind of SVAE model is implemented for solving the issues about human mobility reconstruction. We use navigation GPS data of selected greater Tokyo area to evaluate the performance of the SVAE model. Experimental results demonstrate that the SVAE model can efficiently capture the salient features of human mobility data and generate more reasonable trajectories.

关键词： human mobility generative model meachine learning

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Multi-class Document Classification Using Improved Word Embeddings 2019

Multi-class Document Classification Using Improved Word Embe...

引用

2nd international conference on Computing and Big Data (ICCBD)

作者： Rabut, Benedict A. Fajardo, Arnel C. Medina, Ruji P. Technol Inst Philippines Coll Informat Technol Educ Quezon City Philippines Manuel L Quezon Univ Sch Grad Studies Quezon City Philippines

ISBN: (纸本)9781450372909

In this paper, we conducted an experiment to build a classification model that combines different techniques in most of the Natural Language processing Tasks. We used the word embedding method to transform every word in the dataset and to obtain the custom-built word embedding vectors. This is in contrast to the approaches in the previous literature that implement word embedding using the pre-trained word embedding vectors. We enriched the custom-built word embedding vectors by incorporating Part-of-Speech (POS) tag vectors to provide additional semantic information about the word to be used in training our proposed classification model. The proposed model was built using the neural network approach, which is considered to be more efficient and reliable in solving real problems for document classification tasks. We fine-tuned the parameters during the training of our neural network classification model with our aim to increase the performance in terms of classification accuracy. The experimental result demonstrates that our model performs remarkably well and increase the percentage accuracy up to 1.7% compared to the accuracy results obtained by the previous baseline word embedding methods using the same dataset. It was also observed that our model outperforms some other traditional classification models implemented using different techniques and machine learning algorithms.

关键词： Natural Language processing Document Classification Word Embeddings

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Autonomous cognitive gpr based on edge computing and reinforcement learning 2

Autonomous cognitive gpr based on edge computing and reinfor...

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2nd IEEE international conference on Industrial Internet Cloud, ICII 2019

作者： Omwenga, Maxwell M. Wu, Dalei Liang, Yu Yang, Li Huston, Dryver Xia, Tian Department of Computer Science and Engineering University of Tennessee at Chattanooga United States Department of Mechanical Engineering University of Vermont United States Department of Electrical and Biomedical Engineering University of Vermont United States

ISBN: (纸本)9781728129778

Autonomous cognitive ground penetrating radar (ACGPR), carried by drones or other robotic platforms, may perform robust and accurate subsurface object detection and recognition in varying environments based on real-time data processing and decision making. However, limited system computing resources and intelligence generating capability pose significant challenges for the operations of such systems. To address these challenges, in this paper we propose an ACGPR enabled by edge computing (EC) and reinforcement learning. Specifically, an edge computing based system architecture is presented to utilize edge resources for real-time intelligence generation. A reinforcement learning approach is developed as the decision-making model for the ACGPR to adaptively adjust its operational parameters. Simulation results show the accuracy and efficacy of the proposed ACGPR system. The framework also provides insight into the design of autonomous cognitive industrial Internet of things (IoT) supported by edge computing and machine learning. © 2019 IEEE.

关键词： Ground penetrating radar systems

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A Review on Automatic signal Classification Techniques for Software Defined Radios 5

A Review on Automatic Signal Classification Techniques for S...

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

作者： Manisha, Ghunsar Marriwala, Nikhil Kurukshetra Univ Univ Inst Engn & Technol Dept Elect & Commun Engn Kurukshetra Haryana India

ISBN: (纸本)9781728139883

In today's generation automatic modulation recognition for software defined radio (SDR) is a hot topic of research. Their ability like re-configurability, flexibility, high capacity, improved bandwidth efficiency, rapid up-gradation of technology etc., has made it popular and widely acceptable. This paper represents a review about SDR infrastructure, modulation recognition problem and various existing modulation recognition techniques. This paper summarizes the work and contribution of various researchers in this field. This paper also holds discussion about various machine learning tools and their advantages and disadvantages. This paper helps the reader to have brief knowledge about development of SDR and automatic modulation recognition technique.

关键词： Software radio

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