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检索条件"任意字段=7th International Conference on Computer and Information Processing Technology, ISCIPT 2022"

共 4229 条记录，以下是671-680 订阅

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Research on Chinese Text Sumarization Generation Method Based on ERNIE-GEN 7

Research on Chinese Text Sumarization Generation Method Base...

引用

2022 7th international conference on Electronic technology and information Science, ICETIS 2022

作者： Hua, JiXiang Yao, Feng Zhang, Yuan Sun, HaoXen School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9783800757794

Chinese text summarization methods based on transfer learning suffer from insufficient deep information focus and exposure bias issue. this paper propose the ERNIE-GEN-CL method based on ERNIE-GEN[1], which introduces the continual learning mechanism[2]and uses large chinese corpora for secondary training of the pre-trained model to achieve multi-layer focus of the model on lexical, syntactic and semantic information. the noise-aware generation method and the infilling generation mechanism are also used to optimise the exposure bias issue. Finally, for the text summarisation task, the pre-trained model was fine-tuned using the NLPCC2017 dataset, and and the experimental results verify that the method in this paper is effective. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Semantics

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Partial Mixture-of-Experts Similarity Variational Autoencoder for Clustering on Single Cell Data 7

Partial Mixture-of-Experts Similarity Variational Autoencode...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Liu, Chenjian Hong, Libin Liu, Fuchang Hangzhou Normal University School of Information Science and Technology Hangzhou311121 China

ISBN: (纸本)9781665478571

Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE) is a novel generative clustering model which can cluster high-dimensional samples well and generalize to multi-modal distributions. However, high-dimensional feature from biological measurement is often incomplete. the common solution is to fill '0's to those missing elements in high-dimensional feature, which may lead to a decrease in accuracy. In this paper, we propose a data optimization strategy which called 'partial VAE' to overcome the issue caused by missing value using 'maxpooling' operation for partial inference. the improved version of MoE-Sim-VAE is called Partial Mixture-of-Experts Similarity Variational Autoencoder (Partial MoE-Sim-VAE). We evaluate the performance of clustering on public datasets including mouse organ-cell and simulated dataset with different proportions of '0's. the experiments demonstrate that Partial MoE-Sim-VAE outperforms MoE-Sim-VAE. © 2022 IEEE.

关键词： Learning systems

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Physical Layer Secret Key Generation for Spatially Correlated Channels Based on Multi-Task Autoencoder 7

Physical Layer Secret Key Generation for Spatially Correlate...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Zhou, Jingqi Zeng, Xin Tongji University Dept. of Automation Shanghai China Tongji University Dept. of Information and Communication Engineering Shanghai China

ISBN: (纸本)9781665478571

Physics layer key, as an emerging method to enhance information security, is generated based on the reciprocity and spatial decorrelation of wireless channels. However, due to the half-duplex mode of the communication systems, the channels detected by the legitimate sides are not completely reciprocal. Meanwhile, when the eavesdropper is extremely close to the legitimate side, the spatial correlation could be very strong and threaten the information security. this paper combines the autoencoder and the multi-task learning into a novel physical layer key generation scheme, i.e., Multi-Task Autoencoder (MTAE). MTAE extracts the reciprocal features from the weakly correlated channel estimates of the legitimate sides while reducing its correlation with eavesdropper. Simulation results show good randomness and consistency with the primary keys generated based on MTAE for the legitimate sides, while increasing the difference in the extracted features between eavesdropper and its adjacent node. the proposed method has great potential in scenarios requiring high security. © 2022 IEEE.

关键词： Learning systems

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Optimization and Design of Coding Algorithm for Monitoring System Based on Embedded Structure 7

Optimization and Design of Coding Algorithm for Monitoring S...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Huang, Rubing Zhao, Lianyi Anhui Vocational and Technical College School of Information Engineering Hefei China Anhui Water Resources and Hydropower Vocational and Technical College School of Mechanical & Electrical Engineering Hefei China

ISBN: (纸本)9781665478571

In view of the problems of large transmission delay, low video image compression rate, and high network transmission overhead in traditional monitoring systems. According to the characteristics of ARM platform and H.264 encoding algorithm, an algorithm optimization method based on x264 encoder embedded platform is proposed. the New three-step Search method is used to realize motion compensation prediction encoding. Based on streaming media technology, the RTP and RTSP protocol realizes the packaging and real-time transmission of video stream data. the optimized encoder is applied to the embedded video monitoring system, which effectively improves the video encoding efficiency of the system. © 2022 IEEE.

关键词： Signal encoding

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Fast Abnormal Heart Sound Detection Method Based on Multi-struct Neural Network 7

Fast Abnormal Heart Sound Detection Method Based on Multi-st...

引用

7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Yang, Siyu Tong, Jijun Ali, Anwar Zeng, Yajing Xia, Shudong Yue, Xingyu Zhejiang Sci-Tech University School of Information and Technology Hangzhou China Zhejiang University School of Medicine Yiwu China

ISBN: (纸本)9781665478571

Cardiovascular Disease is the most fatal disease in human history and the number one cause of human death. Many techniques are used to investigate the heart health but the most effective one is based on heart sound signals which contains significant information that reflect heart health. Studying heart sound signals is helpful for early diagnosis and prevention of cardiovascular diseases. In order to make full use of the global information of heart sounds, this paper proposes an end-to-end abnormal heart sound classification neural network based on LSTM and 1D-CNN, which can upgrade, rectify, and map time-frequency features. this model fully detects the hyperparameter space, so that it can effectively provide the fitting ability. A Performance test is executed on the PhysioNet dataset and this model shown the accuracy is near to 100. Besides, a data compression algorithm based on 1D-CNN is used, which can reduce the amount of calculation by 60%. Compared with the existing methods, the HSC-LNet shows excellent robust and speed in the classification of heart sound signals. © 2022 IEEE.

关键词： Cardiology

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A Review on Sentiment Analysis of Twitter data for Diabetes Classification and Prediction 7

A Review on Sentiment Analysis of Twitter data for Diabetes ...

引用

7th international conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Akhila, Amudala Meghana Shree Gayathri, Chitluri Srinivas, B. Devi, B.S. Kiruthika Aditya College of Engineering and Technology Department of Computer Science and Engineering A.P. Surampalem India Cl Educate Ltd New Delhi India

ISBN: (纸本)9781665454018

Data mining based on Twitter data can be used to analyses and draw meaningful information from the tweets. In order to convey your innermost thoughts and feelings about life, you might use the sentiment keyword. Sentiment analysis is carried out to provide insights from online conversations about a company's products, brands, and services. Identifying and classifying emotions in written texts is possible using advanced machine learning algorithms. Sentiment analysis has its impact in the field of the medical industry. Based on the tweets, the disease can be categorized and predicted. People can be made aware of the risk, and it provides an opportunity for early diagnosis. this article discusses several machine learning and natural language processing algorithms in the field of sentiment analysis based on Twitter tweets. the various existing systems are discussed in detail and their impact on detection and prediction of diabetes is highlighted. © 2022 IEEE.

关键词： Sentiment analysis

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Research on Big Data Platform for Railway Transport Station 7

Research on Big Data Platform for Railway Transport Station

引用

2022 7th international conference on information Science, computer technology and Transportation, ISCTT 2022

作者： Xu, Chunjie Li, Jun Zhang, Yawei Chen, Ruifeng Li, Chao Yang, Guoyuan Lv, Xiaojun Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing100081 China Depart of Electromechanical Equipment and System Beijing Jingwei Information Technology Co. Ltd Beijing100081 China

ISBN: (纸本)9783800760565

In view of the status that current systems for railway transport station meanly focus on functional integration, partial information sharing and business-based data mining, which can’t meet the requirements of scientific decision and intelligent execution. the architecture of big data platform for railway transport station is proposed in this paper. Firstly, the online real-time data collection technology based on Internet of things is adopted, the operating parameters of the systems and equipment is collected by the Internet of things gateway. Using StreamSets component, data visualization ETL is realized, and the data quality and reliability is improved. Secondly, based on the unified data flow management, the data collection, integration and transparent access are realized, and the processing logic global scheduling for the platform is implemented. Finally, the data development models by the visual studio suite are constructed rapidly. And the platform supports secondary development for railway transport station business. It realizes efficient and low-cost data asset construction and data analysis application. And based on data-driven it provides technical support for quality improvement and business innovation. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Internet of things

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A Machine Learning Approach for Predicting Execution Statistics of Spark Application 7

A Machine Learning Approach for Predicting Execution Statist...

引用

7th international conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Sewal, Piyush Singh, Hari Jaypee University of Information Technology Computer Science and Engineering Department Himachal Pradesh Solan India

ISBN: (纸本)9781665454018

Apache Spark is one of the most popular, widely used and open-source distributed processing framework that can process huge site datasets in time efficient manner due to its in-memory computational capabilities. However, there are several factors that can affect the performance of an application which include the nature and size of the input dataset, computational capability of the system and nature and design of the algorithm. Hence, there are different parameters that are required to correctly predict the execution statistics of a Spark application which include execution time of jobs, stages and tasks, memory requirement and usage at the execution level and I/O cost in the form of read/ write shuffling of data. To address these challenges, a simulation and machine learning based prediction model is presented in this paper that takes only a few initial samples of execution statistics and predicts the performance and execution statistics of the Spark application with high accuracy. the proposed model is evaluated on the Wordcount application and Spark standalone mode and accuracy metrics show that the proposed model achieves high accuracy in predicting execution statistics. © 2022 IEEE.

关键词： Machine learning

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Automatic Detection Method of High Altitude Falling Object Based on Interframe Difference Method 7

Automatic Detection Method of High Altitude Falling Object B...

引用

7th international conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2022

作者： Nan, Jiang Tao, Lin Yabo, Zhang Biao, Chen Yu, Shi Ruixia, Wang Shanghai Institute of Technology School of Computer Science and Information Engineering China

ISBN: (数字)9781665482295

ISBN: (纸本)9781665482295

In order to effectively detect the high-altitude falling behavior in the surveillance video, an automatic detection method of high-altitude falling based on the improved frame difference method(IFD) is proposed. Firstly, the video data are processing for debounce, and the optimal number of interval frames is determined with the proposed method. then, the motion prospect is obtained by using the optimal number of interval frames. Finally, a fall judgment criterion is proposed to determine whether the motion prospect is a high-altitude fall or not. the experimental results show that the proposed method can process surveillance video efficiently compared with frame difference method and three frame method. In this paper, the problem of low efficiency of other methods in detecting high-altitude falling objects is solved with IFD. © 2022 IEEE.

关键词： Object detection

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Enhancing Low-Dose CT Imaging Reconstruction through Fusion of MLEM and Deep Convolutional Neural Network Priors 7

Enhancing Low-Dose CT Imaging Reconstruction through Fusion ...

引用

7th international conference on Image information processing, ICIIP 2023

作者： Gothwal, Ritu Tiwari, Shailendra Shivani, Shivendra Thapar Institute of Engineering And Technology Computer Science And Engineering Department Punjab Patiala147004 India

ISBN: (纸本)9798350371406

the process of reconstructing low-dose CT imaging involves dealing with noisy data, which is characterized as an ill-posed inverse problem. Traditional methods like iterative reconstruction rely on manually designed priors. Recently, deep learning-based CT image reconstruction techniques have gained importance in filed of CT image reconstruction, but deep learning techniques face limitations due to the substantial data requirements for training and testing purposes. Our research exploit the efficiency of deep Convolutional Neural Networks (Deep CNNs) in identifying meaningful patterns amidst noise. We propose an iterative reconstruction approach for low-dose CT using the Maximum Likelihood Expectation Maximization (MLEM) algorithm, enhanced by a Deep Neural Network acting as a prior. the proposed technique demonstrates improved performance when compared to traditional methods i.e. Maximum Likelihood Expectation Maximization (MLEM), Simultaneous Algebraic Reconstruction Technique (SART), and SART+TV (Total Variation). © 2023 IEEE.

关键词： Image reconstruction

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