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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 Time-domain End-to-End Method for Sound Source Localization Using Multi-Task learning

A Time-domain End-to-End Method for Sound Source Localizatio...

引用

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

作者： Yankun Huang Xihong Wu Tianshu Qu Speech and Hearing Research Center Peking University Beijing China

In recent years, many researches focus on sound source localization based on neural networks, which is an appealing but difficult problem. In this paper, a novel time-domain end-to-end method for sound source localization is proposed, where the model is trained by two strategies with both cross entropy loss and mean square error loss. Based on the idea of multi-task learning, CNN is used as the shared hidden layers to extract features and DNN is used as the output layers for each task. Compared with SRP-PHAT, MUSIC and a DNN-based method, the proposed method has better performance.

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Predicting Severity of Autism Spectrum Disorder based on Multi-center Multi-modality Data 19

Predicting Severity of Autism Spectrum Disorder based on Mul...

引用

proceedings of the 2019 2nd international conference on Digital Medicine and Image processing

作者： Sijia Li Huifang Huang School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9781450376983

In recent years, many researchers have done a lot of research on the qualitative diagnosis of autism spectrum disorder (ASD) based on magnetic resonance imaging (MRI). However, the quantitative prediction of ASD severity is clinically more important, but there are few studies focused on the prediction of the ASD severity. In addition, since the heterogeneity between multi-center data for ASD is difficult to eliminate, most of studies are based on single-center and do not make full use of multi-center data. To this end, we propose a multi-modality multi-center regression (M3CR) method to apply multi-task learning to the severity prediction of ASD. Specifically, each center is treated as a task for joint learning. In addition, for each center, two types of modal information are extracted from magnetic resonance imaging for information complementation. More importantly, based on the task-task regularization term and modality-modality regularization term in the existing model, we add the feature-feature regularization term to further consider the relationship between the features. This regularization term enables the learning process to make full use of the data information shared by different centers. The experimental results on the ABIDE database demonstrate the effectiveness of the proposed method in ASD severity prediction.

关键词： Severity prediction Multi-center Multi-task learning Multi-modality ASD Magnetic resonance imaging

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Removal of Motion Artifacts from ECG signals by Combination of Recurrent Neural Networks and Deep Neural Networks

Removal of Motion Artifacts from ECG signals by Combination ...

引用

international conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Muhammad Zubair Gunturi N V S Chandra Mouli Rafi Ahamed Shaik Prasad V. Potluri Siddhartha Institute of Technology Vijayawada Andhra Pradesh India Indian Institute of Technology Guwahati Guwahati Assam India

ISBN: (数字)9781728198774

ISBN: (纸本)9781728198781

Electrocardiogram (ECG) is the graphical portrayal of heart usefulness. The ECG signals holds its significance in the discovery of heart irregularities. These ECG signals are frequently tainted by antiques from various sources. It is basic to diminish these curios and improve the exactness just as dependability to show signs of improvement results identified with heart usefulness. The most commonly disturbed artifact in ECG signals is Motion Artifacts (MA). In this paper, we have proposed a new concept on how machine learning algorithms can be used for de-noising the ECG signals. Towards the goal, a unique combination of Recurrent Neural Network (RNN) and Deep Neural Network (DNN) is used to efficiently remove MA. The proposed algorithm is validated using ECG records obtained from the MIT-BIH Arrhythmia Database. To eliminate MA using the proposed method, we have used Adam optimization algorithm to train and fit the contaminated ECG data in RNN and DNN models. Performance evaluation results in terms of SNR and RRMSE show that the proposed algorithm outperforms other existing MA removal methods without significantly distorting the morphologies of ECG signals.

关键词： Electrocardiography Recurrent neural networks Data models Optimization signal processing algorithms Motion artifacts Heart

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Target Speech signal Enhancement Based on Deep Neural Networks

Target Speech Signal Enhancement Based on Deep Neural Networ...

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

作者： Xin Zhang MingJiang Wang XiaoGuang Xuan FengJiao Sun Electronic and Information Engineering College Harbin Institute of Technology Shenzhen Shenzhen China

This paper presents a speech enhancement system based on deep neural network (DNN) and covers the signal processing methods used in speech enhancement and the fundamentals of DNN. DNNs containing multiple hidden layers have the capability to suppress noise by learning the relationship between noisy speech and target clean speech. A series of experiments were carried out using the Chinese corpus to evaluate the performance of the speech enhancement model. Meanwhile, the model was identified as a result of a good generalization capability in mismatched noise types. In addition, the model is compared with OMLSA in terms of the quality of speech signals.

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Cyberbullying Detection Using Deep learning and Word Embeddings: An Empirical Study 2019

Cyberbullying Detection Using Deep Learning and Word Embeddi...

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

作者： Mohammed Al-Hashedi Lay-Ki Soon Hui-Ngo Goh Multimedia University School of Information Technology Monash University Malaysia

ISBN: (纸本)9781450372596

Cyberbullying detection has become a pressing need in Internet usage governance due to its harmful consequences. Different approaches have been proposed to tackle this problem, including deep learning. In this paper, an empirical study is conducted to evaluate the effectiveness and efficiency of deep learning algorithms, coupled with word embeddings in detecting cyberbullying texts. Three deep learning algorithms were experimented, namely GRU, LSTM and BLSTM. Data pre-processing steps, including oversampling were performed on the selected social media datasets. For feature representations, four different word embeddings models were explored, including word2vec, GloVe, Reddit and ELMO models. Elmo cares of word context by capturing information from the word surroundings which eliminates some of the shortcomings of pre-trained word embeddings models. For more accurate results, 10-fold cross-validation technique was implemented. The experimental results show that BLSTM performs best with ELMO in detecting cyberbullying texts. The efficiency of each model is also measured by calculating the average time taken for training each model. GRU outperforms in terms of time efficiency. Based on the analysis done on false negative cases, three observations were made, which highlight the limitations of word embeddings models on top of GRU algorithm in cyberbullying detection.

关键词： ELMo Cyberbullying Deep learning Word Embeddings

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Optimal Jamming Frequency Selection for Cognitive Jammer based on Reinforcement learning

Optimal Jamming Frequency Selection for Cognitive Jammer bas...

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

作者： Lulu Wang Jinlin Peng Zhidong Xie Yi Zhang Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang China

In this paper, a cognitive jammer is developed which can adaptively and optimally jam the radar and protect the target from being detected. The interaction of the cognitive jammer and the environment is modeled as a finite Markov Decision Process based on the framework of reinforcement learning. Q-learning algorithm is used to solve the optimal jamming frequency selection problem. After several interactions, the jammer can learn the radar's strategy and optimize its jamming frequency to achieve a larger jamming-plus-noise to signal ratio (JNSR) during the whole process. Numerical results are given to illustrate the effectiveness of the proposed method. Compared with a random jamming frequency selection method, the JNSR of the proposed method is significantly larger.

关键词： Jamming Radar cross-sections Reinforcement learning signal to noise ratio Interference Numerical models

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2nd international conference on data engineering and communication technology, ICDECT 2017

2nd international conference on data engineering and communi...

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

ISBN: (纸本)9789811316098

The proceedings contain 71 papers. The special focus in this conference is on Data Engineering and Communication Technology. The topics include: S-LSTM-GAN: Shared Recurrent Neural Networks with Adversarial Training;hybrid Approach for Recommendation System;discussion on Problems and Solutions in Hardware Implementation of Algorithms for a Car-type Autonomous Vehicle;software Test Case Allocation;seamless Vertical Handover for Efficient Mobility Management in Cooperative Heterogeneous Networks;sentence Similarity Estimation for Text Summarization Using Deep learning;minimization of Clearance Variation of a Radial Selective Assembly Using Cohort Intelligence Algorithm;m-Wallet Technology Acceptance by Street Vendors in India;explore-Exploit-Explore in Ant Colony Optimization;application of Blowfish Algorithm for Secure Transactions in Decentralized Disruption-Tolerant Networks;an Attention-Based Approach to Text Summarization;enhancement of Security for Cloud Data Using Partition-Based Steganography;large Scale P2P Cloud of Underutilized Computing Resources for Providing MapReduce as a Service;topic Modelling for Aspect-Level Sentiment Analysis;an Image Deblocking Approach Based on Non-subsampled Shearlet Transform;an Effective Video Surveillance Framework for Ragging/Violence Recognition;DoT: A New Ultra-lightweight SP Network Encryption Design for Resource-Constrained Environment;a Distributed Application to Maximize the Rate of File Sharing in and Across Local Networks;obstacle Detection for Auto-Driving Using Convolutional Neural Network;Leakage Power Improvement in SRAM Cell with Clamping Diode Using Reverse Body Bias Technique;a Rig-Based Formulation and a League Championship Algorithm for Helicopter Routing in Offshore Transportation;an Investigation of Burr Formation and Cutting Parameter Optimization in Micro-drilling of Brass C-360 Using Image processing;emotion Recognition from Sensory and Bio-signals: A Survey.

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Firearm Detection from Surveillance Cameras Using Image processing and machine learning Techniques 2nd

Firearm Detection from Surveillance Cameras Using Image Proc...

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2nd international conference on Smart Innovations in Communications and Computational Sciences ICSICCS-2018

作者： Gelana, Fraol Yadav, Arvind Department of Electronics and Communication Engineering Parul Institute of Engineering and Technology VadodaraGujarat India

ISBN: (纸本)9789811324130

The increasing number of terrorist acts and lone wolf attacks on places of public gathering such as Hotels and Cinemas has solidified the need for much denser Closed-circuit Television (CCTV) systems. The increasing number of CCTV cameras has deemed it almost impossible for a human operator to inspect all the video streams and detect possible terror events. One of the common types of terror event is called "Active Shooter". Events such as the 2008 Mumbai shooting, shooting at the movie theater in Colorado (USA), Oslo (Norway) and recently an attacker opened gun fire at an outdoor music festival in Las Vegas on Oct. 1, 2017, USA. Therefore in this work, the detection of an "Active Shooter" carrying a non-concealed firearm and alerting the CCTV operator of a potentially dangerous event both visually and audibly has been carried out. The proposed approach of gun detection uses a feature extraction techniques and a convolutional neural network classifier for classifying objects as either a gun or not a gun. And the classification accuracy achieved by the proposed approach is 97.78%. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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A survey on acronym–Expansion mining approaches from text and web 2nd

A survey on acronym–Expansion mining approaches from text a...

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

作者： Menaha, R. Jayanthi, Ve. Department of Information Technology Dr. Mahalingam College of Engineering & Technology PollachiTamil Nadu India Department of Electronics and Communication Engineering PSNA College of Engineering & Technology DindigulTamil Nadu India

ISBN: (纸本)9789811319204

An acronym is a textual form used to refer an entity and to stress the important concepts. Over the last two decades, many researchers worked for mining acronym expansion pairs from plain text and Web. This is mainly used in language processing, information retrieval, Web search, ontology mapping, question answering, SMS, and social media posting. Acronyms are dynamically growing day by day, and discovering its definition/expansion is becoming a challenging task because of its diversified characteristics. Manually edited online repositories have acronym definition pairs, but it is an overwhelming task to update all possible definitions systematically. To extend the support, different approaches are employed for the automatic detection of acronym definitions from text and Web documents. This paper presents those approaches and also reveals the Web-based methods used for disambiguating, ranking, finding popularity score, and context words of the expansions. The scope for the future work in this research area is also conferred in this paper. © Springer Nature Singapore Pte Ltd. 2019.

关键词： machine learning

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A Semantic Internet of Things Framework using machine learning Approach based on Cloud Computing 2018

A Semantic Internet of Things Framework using Machine Learni...

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2nd international conference on Digital signal processing (ICDSP) / international conference on Computer Graphics and Virtuality (ICCGV)

作者： Ding, Pei-Wun Hsu, I-Ching Natl Formosa Univ 64 Wenhua Rd Huwei Township 632 Yunlin Taiwan

ISBN: (纸本)9781450364027

A major limitation of existing Semantic Web applications is the lack of automatic generation linked data for personal needs. Internet of Things (IoT) can provide automatic sensing data to improve this limitation. The study addresses this issue by defining a Semantic Internet of Things Framework (SIOTF), which is implemented on Hadoop-based cloud computing ecosystem to provide efficiency in dealing with a mass of sensing data. The SIOTF is composed of four modules: Internet of Things module, Naive Bayesian Classification module, Open Data Service module, and Semantic Web module. The proposed SIOTF is used to develop a Culture Sharing Cloud Platform (CSCP) that provides customized culture information for personnel needs. To demonstrate the feasibility of CSCP, the experimental results illustrate the efficiency and effectiveness of the proposed approach.

关键词： Cloud Computing IoT Semantic Web machine learning

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