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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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Local, Mid-Level and Convolutional Features Fusion Using Multiple Kernel learning for Image Classification 2

Local, Mid-Level and Convolutional Features Fusion Using Mul...

引用

2nd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Lu, Yao Zhang, Hui Xie, Bojun Hebei Univ Key Lab Machine Learning & Computat Intelligence Coll Math & Informat Sci Baoding Peoples R China

ISBN: (纸本)9781728151021

Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel learning method. Experimental results show that the local features, mid-level features and convolutional features can be fused effectively to improve the classification performance about 4%-6% on several popular benchmarks.

关键词： convolutional feature mid-level feature multiple kernel learning image classification

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Sign Language Interpreter Detection Method for Live TV Broadcast Content 22nd

Sign Language Interpreter Detection Method for Live TV Broad...

引用

22nd international conference on Human Computer Interaction, HCII 2020

作者： Reiser de Melo, João Guilherme Paes de Souza, Leandro Frossard, Davi de Oliveira, Rodrigo Ribeiro Sidia Institute of Science and Technology ManausAM69055-035 Brazil

ISBN: (纸本)9783030601485

Recent technological improvements in computing and embedded systems have enabled the development and improvement of systems and applications in a way that makes people’s lives simpler, safer and more accessible. The development of small and powerful hardware devices for signal processing, together with the use of techniques of artificial intelligence and machine learning can assist people with all kinds of limitations in the use of multimedia content on a TV broadcast video. Some broadcasters insert sign language interpreters on the transmitted content. The size and the position of the interpreter on the screen are not always the more appropriate for the best use of such tool. The use of artificial intelligence techniques make it possible to identify such elements on an image with high accuracy, reliability and repeatability, when compared to traditional systems. On this paper, a machine learning based method was developed with the aim of identify the presence and the position of a sign language interpreter on the screen, take an instance of its content and make it available for magnification, providing greater accessibility and experience improvement on watching TV for people with special needs. The developed system achieved final accuracy greater than 80% with inference time between 2 to 3 s, depending on the used hardware, running on a live broadcast environment. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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Screening of Moderate Traumatic Brain Injury from Power Feature of Resting State Electroencephalography using Support Vector machine 2019

Screening of Moderate Traumatic Brain Injury from Power Feat...

引用

2nd international conference on Electronics and Electrical Engineering Technology (EEET) / 3rd international conference on Biomedical Engineering and Bioinformatics (ICBEB)

作者： Lai, Chi Qin Abdullah, Mohd Zaid Abdullah, Jafri Malin Azman, Azlinda Ibrahim, Haidi Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia Univ Sains Malaysia Dept Neurosci Sch Med Sci Kubang Kerian 16150 Kelantan Malaysia Univ Sains Malaysia Sch Social Sci George Town 11800 Pulau Pinang Malaysia

ISBN: (纸本)9781450372145

Traumatic brain injury (TBI) needs to be identified faster, so that suitable treatment can be planned properly. Normally, the severity of TBI is evaluated through the study from computed tomography (CT) or magnetic resonance imaging (MRI). Unfortunately, the number of CT scanners and MRI scanners is limited. Therefore, it is impractical to directly do CT or MRI scan to all patients without screening. Thus, this research investigates a method for screening moderate TBI patient. Data from resting state 63-channels electroencephalography is used in this work. Power of the signal is extracted from alpha, beta, theta and gamma frequency bands. This work utilizes a support vector machine, which is one of machine learning approaches, to identify moderate TBI patients. From the experimental results, it is shown that the average power from alpha or theta band gives the best accuracy score, which is at 70.83%.

关键词： Biomedical signal processing Feature extraction machine learning Moderate traumatic brain injury Power feature Resting state electroencephalography

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Speech signal Analysis for Language Identification Using Tensors 2nd

Speech Signal Analysis for Language Identification Using Ten...

引用

2nd international conference on machine learning, Image processing, Network Security and Data Sciences, MInd 2020

作者： Jain, Shubham Parabattina, Bhagath Das, Pradip Kumar IIT Guwahati North Amingaon GuwahatiAssam781039 India

ISBN: (纸本)9789811563171

Language detection is the first step in speech recognition systems. It helps these systems to use grammar and semantics of a language in a better way. Due to these reasons, active research is being carried out in language identification. Every language has specific sound patterns, rhythm, tone, nasal features, etc. We have proposed an approach based on Tensor that uses MFCCs for determining the characteristic features of a language that can be used to identify a spoken language. Tensor based algorithms perform quite well for higher dimensions and scale quite well as compared to classic maximum likelihood estimation (MLE) used in latent variable modeling. Also, this approaches does not suffer from slow convergence and require fewer data points for learning. We have conducted language identification experiments on native Indian English and Hindi for some chosen speakers, and an accuracy of around 70% is observed. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Tensors

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Chinese Texts Classification System 2

Chinese Texts Classification System

引用

2nd IEEE international conference on Information and Computer Technologies (ICICT)

作者： Zhu, Meng Yang, Xudong Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781728133232

In this article, we designed an automatic Chinese text classification system aiming to implement a system for classifying news texts. We propose two improved classification algorithms as two different choices for users to choose and then our system uses the chosen method for the obtaining of the classified result of the input text. There are two improved algorithms, one is k-Bayes using hierarchy conception based on NB method in machine learning field and another one adds attention layer to the convolutional neural network in deep learning field. Through experiments, our results showed that improved classification algorithms had better accuracy than based algorithms and our system is useful for making classifying news texts more reasonably and effectively.

关键词： natural language processing machine learning deep learning Naive bayes CNN automatic system

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A Received signal Strength Based Indoor Localization Algorithm Using ELM Technique and Ridge Regression 2

A Received Signal Strength Based Indoor Localization Algorit...

引用

IEEE 2nd international conference on Electronic Information and Communication Technology (ICEICT)

作者： Feng, Zhiyue Cao, Yanhua Yan, Jun Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing Peoples R China Project Construct Bur Water Resources Dept Jiangsu Prov Nanjing Peoples R China

ISBN: (纸本)9781538692981

In this paper, a received signal strength (RSS) based localization algorithm by extreme learning machine (ELM) technique is proposed. In the offline phase, in order to cope with the environmental noise of the training data set, the improved ridge regression based ELM (IRR-ELM) algorithm is proposed to obtain more stable prediction which has better generalization ability, because the ridge parameter is obtained with the variance of the training error. In the online phase, the obtained prediction model is straightly used for position prediction. Since it can reduce the deviation in the off-line training phase, the proposed algorithm has more stable and accurate position estimation result in online phase. At last, the RSS measurements obtained from field test are used for performance evaluation. It shows that the localization performance of the proposed is better than that of the existing ELM based localization methods.

关键词： extreme learning machine localization received signal strength ridge regression

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Pattern Recognition by Pattern Inversion 2

Pattern Recognition by Pattern Inversion

引用

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

作者： Mikhailov, Alexei Karavay, Mikhail Russian Acad Sci ICS Moscow Russia

ISBN: (数字)9781510634107

ISBN: (纸本)9781510634107

Alternative approach to pattern recognition is discussed that amounts to operations on inverse patterns and resembles working of Google-type search engines. Unlike neural networks that iteratively calculate weights within many a learning cycles, inverse patterns-based paradigm (neural cortex) does not use weights and follows a challenging learning trend that attempts to achieve a human-like generalization from a single example.

关键词： machine learning neural networks pattern recognition inverse patterns neural cortex search engines

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Overview of the 2nd international Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM'19) 19

Overview of the 2nd International Workshop on Exploiting Art...

引用

ACM SIGMOD international conference on Management of Data (SIGMOD)

作者： Bordawekar, Rajesh Shmueli, Oded IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Technion Dept Comp Sci IL-32000 Haifa Israel

ISBN: (纸本)9781450356435

Recently, the Artificial Intelligence (AI) field has been experiencing a resurgence. AI broadly covers a wide swath of techniques which include logic-based approaches, probabilistic graphical models, and machine learning/deep learning approaches. Advances in hardware capabilities, such as Graphics processing Units (GPUs), software components (e.g., accelerated libraries, programming frameworks), and systems infrastructures (e.g., GPU-enabled cloud providers) has led to a wide-spread adaptation of AI techniques to a variety of domains. Examples of such domains include image classification, autonomous driving, automatic speech recognition (ASR) and conversational systems (chatbots). AI techniques not only support multiple datatypes (e.g., free text, images, or speech), but are also available in various configurations, from personal devices to large-scale distributed systems.

关键词： Database Systems Artificial Intelligence machine learning Neural Networks

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Glottal signal Analysis for Voice Pathology 2

Glottal Signal Analysis for Voice Pathology

引用

2nd international conference on Innovations in Electronics, signal processing and Communication, IESC 2019

作者： Mittal, Vikas Sharma, R.K. NIT School of VLSI Design and Embedded System Kurukshetra India NIT Department of Electronics and Communication Engineering Kurukshetra India

ISBN: (纸本)9781728107448

The work present in this paper is focusing on a method for the detection of voice disorders related to the pathologies of vocal folds. It extracts glottal signal parameters by using inverse filtering method. Support Vector machine (SVM) and k-nearest neighboring (k-NN) classifiers are used for differentiating healthy and non-healthy persons. The voice samples of the person belonging to both the categories were acquired using a specialized voice recorder. SVM provided 98.5 % whereas k-NN provided 88.2 % accuracy. Voice signals of patients affected with vocal cord nodules were also collected and tested with same classifier. The Accuracy of 78.9 % and 70.4% were obtained with SVM and k-NN respectively. © 2019 IEEE.

关键词： Support vector machines

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Classification of ECG signal Using machine learning Techniques 2

Classification of ECG Signal Using Machine Learning Techniqu...

引用

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

作者： Syama, S. Sweta, G. Sai Kavyasree, P.I.K. Reddy, K.Jagan Mohan Bengaluru Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amrita School of Engineering India

ISBN: (纸本)9781728124148

Electrocardiogram (ECG) signals are the impulses generated by the heart which are used to analyze the proper functioning of heart. Our work deals with the efficient analysis of Electrocardiogram (ECG) signals imported from MIT-BIH database into MATLAB platform, generation of the imported ECG signal, pre-processing the generated signal to remove the noises mainly the baseline wandering and power line interference from which features are extracted. For adequate study of the ECG signal Daubechies and Haar wavelet techniques are compared. Proper decomposition of the signal is achieved using Db4 and Db5 Daubechies wavelets as their scaling functions are analogous to ECG signal. PAN TOMPKINSONS algorithm is considered in our study as it serves best for precise identification of most prominent features namely QRS complexes, RR interval's as they constitute the major data required for clinical analysis and research. After feature extraction ECG signals are trained using machine learning techniques for detecting the presence of Arrhythmia using different classifiers adopting Weka software. © 2019 IEEE.

关键词： Feature extraction

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