检索结果-内蒙古大学图书馆

HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

来源：评论

学校读者我要写书评

暂无评论

Enhancing Metaverse using Quantum Technologies

Enhancing Metaverse using Quantum Technologies

引用

2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Doshi, Jash Shaikh, Sanober Sultana Thadomal Shahani Engineering College Dept.of Computer Engineering Mumbai India Thadomal Shahani Engineering College Dept.of Artifical Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350388800

Recently, metaverse technology has seen a boom in users. However, data privacy, security, connectivity, and computational complexity are still the main issues that ruin the user experience and make the metaverse less likely to be used. In this context, we propose a 3-layer quantum-enabled architecture system for the metaverse that holds the promise of overcoming these issues. We also cover the applications of the metaverse, the current challenges faced by the metaverse, and the exciting potential of Quantum Computing to revolutionize the metaverse. © 2024 IEEE.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

data Masking for Chinese Electronic Medical Records with Named Entity Recognition

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 3657-3673页

作者： Tianyu He Xiaolong Xu Zhichen Hu Qingzhan Zhao Jianguo Dai Fei Dai School of Computer Science Nanjing University of Information Science and TechnologyNanjing21000China Geospatial Information Engineering Research Center Xinjiang Production and Construction CorpsShihezi832003China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming650224China

With the rapid development of information technology,the electronifi-cation of medical records has gradually become a *** China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical *** medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical ***,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open ***,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this ***,the text is vectorized to satisfy the required format of the model ***,since the input sentences may have a long or short length and the relationship between sentences in context is not *** this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is ***,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and *** addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this *** experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.

关键词： Named entity recognition Chinese electronic medical records data masking principal component analysis regular expression

来源：评论

学校读者我要写书评

暂无评论

Learning dynamics of kernel-based deep neural networks in manifolds

引用

science China(Information sciences) 2021年第11期64卷 105-119页

作者： Wei WU Xiaoyuan JING Wencai DU Guoliang CHEN School of Computer Science Wuhan University Institute of Deep-sea Science and Engineering Chinese Academy of Sciences School of Computer Guangdong University of Petrochemical Technology Institute of Data Science City University of Macau College of Computer Science and Software Engineering Shenzhen University

Convolutional neural networks(CNNs) obtain promising results via layered kernel convolution and pooling operations, yet the learning dynamics of the kernel remain obscure. We propose a continuous form to describe kernel-based convolutions through integration in neural manifolds. The status of spatial expression is proposed to analyze the stability of kernel-based CNNs. We divide CNN dynamics into the three stages of unstable vibration, collaborative adjusting, and stabilized fluctuation. According to the system control matrix of the kernel, the kernel-based CNN training proceeds via the unstable and stable status and is verified by numerical experiments.

关键词： learning dynamics kernel-based convolution manifolds control model network stability

来源：评论

学校读者我要写书评

暂无评论

Real Time Fruit Recognition and Calories Estimation Using CNN and Model Deployment on Amazon's AWS 3

Real Time Fruit Recognition and Calories Estimation Using CN...

引用

3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Roy, Vikas Kumar Roy, Ganpati Kumar Thakur, Vasu Baliyan, Nikhil Goyal, Nupur Chauhan, Yash Ruprecht Karl University of Heidelberg Department of Data and Computer Science Germany Sharda University Department of Computer Science and Engineering Greater Noida India RIT Roorkee Department of Computer Science and Engineering Uttarakhand Roorkee India Graphic Era Deemed to Be University Department of Mathematics Dehradun India Graphic Era Deemed to Be University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350303360

To carry out vigorous and effective fruit recognition in farming is difficult since there are a number of variations. Lately, various deep-learning approaches presented an outstanding result in numerous visual-guided agricultural usage. These approaches need labelling on training data, which is a tedious work. This study presented a customized five layers CNN architecture and various pretrained deep learning algorithms i.e., VGG-16, DenseNet-121 and InceptionV3are used to recognise the fruit and their calories for helping farmers, patients and peoples to estimate the fruit calories for their health and in farming. The model is trained and validated on integrated datasets which is downloaded from Kaggle and IEEE dataport having 10 classes of different fruits. After extensive experiments, highest accuracy obtained was 99.60% by five layers of CNN and proposed deep learning model has been deployed on Amazon AWS EC2 instance using WinSCP, PuTTY and PuTTYgen. Flask (Python Framework), HTML, CSS and JavaScript is used for website development and connected to its parent website which help users to recognize the fruit and their calories. © 2023 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal data 4

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Munirathnam, Rajesh Maindola, Meenakshi Bhargav, H.K. Patil, Prachi Ramesh Bhatt, Pooja Natrayan, L. TechnoGen Inc. Data & Analytics Division NJ United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Department of Computer Science and Engineering Tumakuru India Jagdambha College of Engineering and Technology Department of Electronics and Telecommunication Engineering Yavatmal India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Saveetha School of Engineering Simats Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019-2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

A Systematic Survey of Automatic Detection of Lung Diseases from Chest X-Ray Images: COVID-19, Pneumonia, and Tuberculosis

引用

SN computer science 2024年第2期5卷 1-20页

作者： Koyyada, Shiva Prasad Singh, Thipendra P. School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun 248007 India Data Sciences uGDXIT (Formerly INSOFE) Telangana Hyderabad 500082 India School of Computer Science Engineering and Technology Bennett University NCR-Delhi Greater Noida 201310 India

Chest X-ray images (CXR) can convey a great deal about a patient’s condition;hence, the standard chest radiograph should be reconsidered. Interpretation of radiographs is challenging and requires skilled people to determine lung disease without false positives and negatives. A detailed investigation addressing lung diseases COVID-19, Pneumonia, and Tuberculosis is presented here with the goal of assisting investigators in constructing models that automatically identify lung diseases. This paper is presented in three folds. The first is an exploration of how research has progressed from classic feature engineering approaches to deep learning methods;the second is how these are used to identify the listed diseases using radiology images such as Chest X-rays (CXRs);and the third is the future path way of research to detect these diseases. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Chest X-ray image Convolutional neural networks COVID-19 Pneumonia Transfer learning Tuberculosis

来源：评论

学校读者我要写书评

暂无评论

On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

引用

应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Effectiveness of Deep Learning Models in Network Intrusion Detection

Evaluating the Effectiveness of Deep Learning Models in Netw...

引用

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kumar, Gaurav Gupta, Pankaj Yadav, Ganesh Kumar Verma, Richa Bhati, Jai Prakash Bhakuni, Vihan Singh IIMT College of Engineering Department of CSE Uttar Pradesh Greater Noida India Manager Data Engineer Discover Financial Services United States JSS Academy of Technical Education Department of Computer Science and Engineering UP Noida201301 India Computer Science and Engineering BBDITM Lucknow India Greater Noida India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (纸本)9798331542344

Network security is now a significant concern in the current digital era since the increasing volume of cyber-attacks in day-to-day life has become a potential threat for the integrity and privacy of data. Most traditional intrusion detection systems face problems when it comes to detecting new and sophisticated attacks. The paper goes ahead to elaborate on how the concepts of machine learning and deep learning can be utilized to enhance the process of detecting and classifying network attacks. For instance, we experimented on the UNSW-NB15 dataset with different models: Decision Tree, Random Forest, SVM, KNN, MLP, and DNN. We have proved through our results that such kinds of models can considerably enhance the accuracy and robustness of IDS. With an average accuracy of 94.91%, the Decision Tree model took second place as the Deep Neural Network was closely followed. We will outline performance metrics based on the respective strengths and weaknesses of the models. This research is used to construct more effective and reliable network intrusion detection systems, which lead further into advancements of cybersecurity. © 2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Role of IoT in Smart Cities: A Review, Applications, Open Challenges and Solutions 3

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vivek, Kolla Swetha, Gurrala Kumar M, V. T. Ram Pavan Veni, Anusuri Krishna Praveen, Neti Kale, Masthan Rao Dept. of Cse Andhra Pradesh Ongole India Dept. of Cse Telangana India Kakaraparti Bhavanarayana College Dept. of Computer Science Andhra Pradesh Vijayawada India Madanapalle Institute of Technology & Science Dept. of Computer Science & Engineering - Data Science Andhra Pradesh Madanapalle517325 India Srkr Engineering College Dept. of Computer Science and Design Andhra Pradesh Bhimavaram India Dept. of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798331509675

Smart cities have emerged as a response to the fast expansion of metropolitan areas;these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions. © 2025 IEEE.

关键词： Digital Technology Drainage Monitoring Fire detection Smart City Waste Management

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：