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

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Construction of hospital big data center for intelligent medical care 7

Construction of hospital big data center for intelligent med...

引用

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

作者： Wang, Jun Wang, Xiu-Lai Yu, Ai-Rong Deng, Bo Nanjing Vocational College of Information Technology Nanjing210007 China General Hospital of Eastern Theater Command PLA Nanjing210002 China The Army Engineering University of PLA Nanjing210007 China

ISBN: (纸本)9783800760565

New technologies of Internet of things and artificial intelligence are rapidly developing in the field of medical big health, and they are widely used in many fields such as text analysis of medical records, image recognition, and analysis of medical procedures. the construction of hospital big data center can give full play to the potential value of medical big data and realize the effective interaction among patients, medical care and treatment institutions through big data mining. By analyzing the classification, composition, characteristics and application scenarios of medical big data, adapting different storage and computing schemes and building hospital big data center according to the scheme, supporting the unified storage management of data resources, realizing the classification application, rapid retrieval, sharing and exchange and intelligent computing of data in different medical fields, maximizing the utilization of medical service resources, taking the construction of the eastern war zone general hospital big data as a typical case. It also promotes the scientific, professional, digital and refinement of hospital operation and management. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Big data

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A Semantic Segmentation Algorithm for Distributed Energy Data Storage Optimization based on Neural Networks 7

A Semantic Segmentation Algorithm for Distributed Energy Dat...

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7th IEEE international conference on Smart Cloud, SmartCloud 2022

作者： Mao, Dong Li, Zhongxu Chen, Zuge Rao, Hanyu Zhang, Jiuding Liu, Zehan New Technology Research Center State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Hangzhou China State Grid Zhejiang Electric Power Company Internet Department Hangzhou China Hangzhou Innovation Institute of Beihang University The Research Center of Big Data and Computational Intelligence Hangzhou China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

there are many kinds of energy data, how to realize unified storage, processing and sharing of energy data is a big problem. As the national energy data center, State Grid aims to build a database that can store distributed heterogeneous asynchronous energy data. the storage of image files in the big energy database will take up a lot of space in the system, but not all parts of the image are needed. therefore, it is very necessary to accurately segment the effective area of the image to store it so as to achieve the purpose of data compression. this paper proposes the Attention U-Net framework, which combines the traditional semantic segmentation network U-Net with the Attention module to focus on the region of interest in the image, emphasize foreground information, and suppress background information. the results show that compared with U-Net, the accuracy is improved by 1.77% and after the segmentation is completed, each image saves an average of 2MB of storage space. © 2022 IEEE.

关键词： Semantic Segmentation

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Accurate and Visual Video Recommendation Based on Deep Neural Network 7

Accurate and Visual Video Recommendation Based on Deep Neura...

引用

7th international conference on computer and Communication Systems, ICCCS 2022

作者： Yang, Fan Li, Gangmin Yue, Yong Payne, Terry Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China University of Bedfordshire School of Computer Science and Technology Luton United Kingdom University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

Video recommendation is vital for a video platform, which provides its users with videos they may be interested in. In this paper, we integrate users' ratings of videos in the video platform and community and crucial information data such as video category, director/actor, predict users' preference for videos through deep neural network, which could improve the accuracy of personalized recommendation. In addition, we use weighted force-directed Graph to show the relationship among users, videos, directors, and other elements, which could display the visualization of data elements and recommended results. Extensive experiments are conducted on three video datasets, and the experimental results demonstrate that the proposed method is more effective than several other recommendation methods. © 2022 IEEE.

关键词： Data visualization

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Robust Adaptive Pseudo Affine Projection Algorithm for Censored Regression 23

Robust Adaptive Pseudo Affine Projection Algorithm for Censo...

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7th international conference on Digital Signal processing, ICDSP 2023

作者： Wang, Bolin Wen, Pengwei Xu, Xinxin Chai, Xuzhao Wang, Yifan School of Electronic and Information Engineering Zhongyuan University of Technology Henan Province Zhengzhou450007 China

ISBN: (纸本)9781450398626

Currently censored observation systems have widely used in various business applications related to adaptive signal processing, but the traditional adaptive processing algorithm has poor convergence performance in identifying censored regression systems. the least mean square algorithm of censored regression (CR-LMS) has been proposed to solve the above problem. However, the CR-LMS algorithm will not achieve good convergence under colored inputs. therefore, a robust pseudo affine projection algorithm based on censored regression (CR-R-PAP) is presented for the parameter estimation problem of censored regression systems under colored inputs. Compared with the pseudo affine projection algorithm based on the censored regression (CR-PAP) algorithm, the CR-R-PAP algorithm can exclude adverse effects and improve convergence performance in environments containing impulse noise. the computer simulation results show that the performance of the proposed algorithm is better than the existing algorithm in the pulse environment with different background noise. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Impulse noise

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Modeling and Predicting the COVID-19 Trajectory in India 7

Modeling and Predicting the COVID-19 Trajectory in India

引用

7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Karra, Nagasaikiran Lekhwani, Rohan Singh, Bhupendra Hazra, Tanmoy Indian Institute of Information Technology Department of Computer Science and Engineering Pune India

ISBN: (纸本)9781665421683

the SARS-CoV-2 has a confirmed case count of about 11.3 million and a death count of about 158,000 in India as of March 13th, 2021. Despite the early social distancing and lockdown measures imposed by the government, these counts have continued to rise. Mathematical models prove extremely useful to predict the course of the pandemic and for the government to strategize accordingly. Over due course several models have emerged to predict the number of COVID-19 cases, but a thorough comparison among them is lacking. In this paper, we propose three novel Hybrid Models based on the compartment-based modeling over data from January 22nd, 2020 to December 3rd 2020 and then make comparisons among them and show through experiments that each performs a better fitting and prediction on the Johns Hopkins COVID-19 dataset pertaining to India than all other benchmark models discussed. Comparison of our proposed Hybrid models with the existing compartment models like SIR, SIRD and SEIRD demonstrates that our proposed Hybrid models not only overcome the performance inefficiencies related to the existing compartmental models but also achieve a better fitting on the Johns Hopkins COVID-19 dataset. © 2022 IEEE.

关键词： Coronavirus

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Station-Wise Boarding Passenger Flow Prediction for Public Transport Using Various Machine-Learning Methods 7th

Station-Wise Boarding Passenger Flow Prediction for Public T...

引用

7th international conference of Transportation Research Group of India, CTRG 2023

作者： Patel, Madhuri Patel, Samir B. Swain, Debabrata Patel, Shubh Department of Computer Engineering Pandit Deendayal Energy University Gandhingar India Department of Information Technology L D College of Engineering Ahmedabad India Department of Computer Science & Engineering Nirma University Ahmedabad India

ISBN: (纸本)9789819610365

the migration behavior of regular passengers in city transport refers to the movement patterns and preferences of individuals who use public transportation on a regular basis. these passengers may have specific routes, travel times, and preferences for certain modes of transportation. Passenger flow prediction is crucial for understanding and managing this behavior. Accurate predictions enable transportation operators to optimize their services by adjusting vehicle frequency and capacity, deploying additional services during peak periods, providing real-time information, identifying high-demand areas, and expanding the network. this improves the efficiency and reliability of public transportation, catering to the needs of regular passengers. Data science and machine-learning methods allow us to extract correlations from historical data, improving the accuracy of passenger flow prediction. the passenger flow on a station is highly affected by various factors such as the day of the week, holiday, rain, available routes from that station, and some uncertain events like COVID-19. In this study, we have successfully reported passenger flow prediction at various stations using ensemble machine-learning algorithms. Comparative analysis of implemented work has been carried out with the help of statistical parameters and visual infographic details. For accurate prediction model, accurate data cleaning, pre-processing and feature selection based on correlation have been performed on real dataset of thane Municipal Transport (TMT) from April 2021 to May 2022. We have also compared the performance of prediction models based on month, day and individual station for moderately deviated data and highly deviated data resulted due to effect of COVID-19 pandemic and Taukte cyclone. An exhaustive comparative analysis between train set and test set have been reported with necessary parameters. Comparing to benchmark models, the XGB regressor and Random forest model can reach most accur

关键词： Decision trees

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Multi-camera Enhanced Real-Time Content-Aware Vehicle Detection 7th

Multi-camera Enhanced Real-Time Content-Aware Vehicle Detect...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Mafaz, Mohamed Karthiga, I. Priya, S. Sharon Department of AI and Data Science GST Rd Perungalathur Chennai600063 India Department CSE Grand Southern Trunk Rd Vandalur Chennai603210 India Department Computer Science Engineering Grand Southern Trunk Rd Vandalur Chennai603210 India

ISBN: (纸本)9783031699818

this paper introduces an open-source solution for real-time object awareness in the context of intelligent vehicles, designed to seamlessly adapt to simulation environments. the system transcends traditional vehicle detection, offering a comprehensive framework that includes object classification, precise location, and rotation estimation. Leveraging a multi-camera setup, our approach provides a comprehensive understanding of the surrounding environment, extending beyond vehicle recognition to include various other objects. What sets our solution apart is its capability to efficiently reformat and transition to simulation environments like Blender, enabling integration and testing within virtual contexts. the core components of our system encompass data collection, preprocess-ing, deep learning model selection, Use of Look up table, model training, efficient inference, and post-processing techniques. Designed for open-source collaboration, this solution is positioned for continuous improvement, adaptation to evolving needs, and addressing emerging challenges in the field of intelligent transportation and related domains. this paper represents a foundational step towards establishing an accessible and adaptable real-time object awareness system, encouraging innovation and research in the realm of intelligent vehicles and their applications, both in the real world and in simulated environments. © IFIP international Federation for information processing 2024.

关键词： Table lookup

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Detection of Conflicts between APP's Privacy Policy and Actual Behavior: A Security Analysis System 7

Detection of Conflicts between APP's Privacy Policy and Actu...

引用

7th international conference on Cryptography, Security and Privacy, CSP 2023

作者： Wu, Tong Liu, Qinbo Li, Binchang Luo, Feng Li, Weilong Liu, Yang Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory China

ISBN: (纸本)9798350323368

Leaks of user privacy in the mobile cloud environment have been common in recent years. Common APP stores require apps to have a user privacy policy that complies with applicable laws. Due to the problem of lengthy papers or difficult-to-understand sentences in privacy policies, users frequently skip reading them or fail to comprehend them. Moreover, there may be conflicts between the privacy policy and the actual behaviors. In order to alleviate these problems above, we design a security analysis system that employs natural language processing to detect conflicts between an APP's privacy policy and its actual behaviors. Experimental results show that the analysis accuracy is improved compared with existing methods. © 2023 IEEE.

关键词： Risk analysis

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Virtual Reality-based Emergency Robot Rescue Training Simulation System 7

Virtual Reality-based Emergency Robot Rescue Training Simula...

引用

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

作者： Yang, Guotian Su, Tian Wang, Xiaowei Xie, Ruibiao Zhi, Jingwen Yan, Xingcheng School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9783800757794

Power is the indispensable main energy and power in modern society and the power distribution room is the key place. If the power danger situation cannot be found in time and take counter measures, it will cause irreparable losses. With the development of robot and artificial intelligence technology, the emergency robot is widely used in field of rescue, and the operation training is particularly important. Virtual reality exercise is an efficient, flexible, and economical method. through three-dimensional scene reconstruction of power distribution room, danger situation simulation, rescue path intelligent planning guidance, combining training and assessment, this paper discusses the method and application of constructing a rescue training simulation system, which provides theoretical support for emergency rescue strategy formulation and rescue personnel training and assessment. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Personnel training

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SPSQL: Step-by-step Parsing Based Framework for Text-To-SQL Generation 7

SPSQL: Step-by-step Parsing Based Framework for Text-To-SQL ...

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7th international conference on Machine Vision and information technology, CMVIT 2023

作者： Shen, Ran Sun, Gang Shen, Hao Li, Yiling Jin, Liangfeng Jiang, Han State Grid Zhejiang Electric Power Co Marketing Service Center Ltd Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9781665464857

Converting text into the structured query language (Text2SQL) is a research hotspot in the field of natural language processing (NLP), which has broad application prospects. In the era of big data, the use of databases has penetrated all walks of life, in which the collected data is large in scale, diverse in variety, and wide in scope, making the data query cumbersome and inefficient, and putting forward higher requirements for the Text2SQL model. In practical applications, the current mainstream end-To-end Text2SQL model is not only difficult to build due to its complex structure and high requirements for training data, but also difficult to adjust due to massive parameters. In addition, the accuracy of the model is hard to achieve the desired result. Based on this, this paper proposes a pipelined Text2SQL method: SPSQL. this method disassembles the Text2SQL task into four subtasks-table selection, column selection, SQL generation, and value filling, which can be converted into a text classification problem, a sequence labeling problem, and two text generation problems, respectively. then, we construct data formats of different subtasks based on existing data and improve the accuracy of the overall model by improving the accuracy of each submodel. We also use the named entity recognition module and data augmentation to optimize the overall model. We construct the dataset based on the marketing business data of the State Grid Corporation of China. Experiments demonstrate our proposed method achieves the best performance compared with the end-To-end method and other pipeline methods. © 2023 IEEE.

关键词： Pipelines

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