检索结果-内蒙古大学图书馆

10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Stress Analysis and Care Prediction for Hostellers

Stress Analysis and Care Prediction for Hostellers

引用

International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： A. Vishnukumar A. Kavitha G. Akshaya Computer Science and Business System Rajalakshmi Engineering College Rajalakshmi Nagar Chennai

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Stress is one of the major concerns for students in recent days. The rapid changes in the environment, lack of adoptability, life style habits, social media interactions, education, finance in all means, students build a resilient stress thinking about various issues. The proposed study considers the specific concern on hostel students and their impact of stress due to various reasons. Students staying away from the parents initially have certain level of stress and emotional imbalance. The presented study considers the measures taken so far to analyse the human stress level in changing environment, using various existing tools and techniques etc. the role of technology and recent developments impact the analysis pattern of these problems are discussed. The study considers the benefits of internet of things (IoT), Embedded systems and devices enabled to detect the stress levels. The system considerations, challenges in stress analysis models are explored.

关键词： Analytical models Technological innovation Social networking (online) Reviews Anxiety disorders Finance Telecommunication computing

来源：评论

学校读者我要写书评

暂无评论

Classification And Prediction of Brain Tumors and its Types using Deep Learning 1

Classification And Prediction of Brain Tumors and its Types ...

引用

1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Bajaj, Madhvan Rawat, Priyanshu Bhatt, Aastha Sharma, Vikrant Jain, Ayushi Kumar, Navneet Graphic Era Hill University Computer Science and Engineering Dehradun India BSc Nursing Uttar Pradesh Lucknow India Jawaharlal Nehru University School of Computer & System Sciences New Delhi India

ISBN: (纸本)9798350338027

Brain tumors are a major source of illness and mortality globally, with about 11,700 persons diagnosed with one each year. Brain tumor diagnosis and categorization are critical for proper treatment planning, which can increase patients' life expectancy. Due to the intricacy of brain tumors and their attributes, the traditional approach of manual assessment of brain MRI images might be error-prone. Automated classification approaches based on Machine Learning (ML) and Artificial Intelligence (AI) have been shown to be more accurate than manual categorization. Deep Learning techniques including Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Transfer Learning (TL) have shown promising results for the automatic categorization of brain MRI images in this context. These automated procedures can save time and lessen the possibility of misdiagnosis, but they should always be used in tandem with clinical experience. The suggested method has the potential to significantly improve the accuracy and efficiency of brain tumor identification and categorization for doctors and radiologists globally. © 2023 IEEE.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Chatbots4Mobile: Feature-oriented Knowledge Base Generation Using Natural Language

Chatbots4Mobile: Feature-oriented Knowledge Base Generation ...

引用

Joint of REFSQ-2023 Workshops, Doctoral Symposium, Posters and Tools Track and Journal Early Feedback, REFSQ-JP 2023

作者： Motger, Quim Franch, Xavier Marco, Jordi Department of Service and Information System Engineering Universitat Politècnica de Catalunya Spain Department of Computer Science Universitat Politècnica de Catalunya Spain

Chatbots4Mobile is a research project from the GESSI research group (UPC-BarcelonaTech) which aims at designing and developing a task oriented, knowledge based conversational agent to support mobile users in the process of managing and integrating the functionalities exposed by their own application portfolio. To support the design of the required knowledge base, the project focuses on the application of Natural Language Processing (NLP) techniques to infer extended knowledge about the features exposed by a subset of mobile applications, including feature extraction from app-related documents, syntactic and semantic similarity analysis between features, and intent/entity classification focused on functionality identification from user requests. As next steps, we are focusing on the evaluation of embedded linguistic knowledge in large language models, as well as the application of granular sentiment analysis techniques to discern biased documents and process sentiment-based user feedback. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Leveraging Adaptive Algorithms for Real-Time Data Analysis 15

Leveraging Adaptive Algorithms for Real-Time Data Analysis

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Hybrid Routing for Different Energy Levels Using Fuzzy Logic

Energy Efficient Hybrid Routing for Different Energy Levels ...

引用

2023 International Conference on IoT, Communication and Automation Technology, ICICAT 2023

作者： Singh, Kalpana Pandey, Prashant Ahamad, Firoj Rajkiya Engineering College Department of Computer Science Engineering Uttar Pradesh Sonbhadra231206 India Rajkiya Engineering College Department of Electronics Engineering Uttar Pradesh Sonbhadra231206 India School of Computer and System Sciences Jawaharlal Nehru University New Delhi Delhi 110067 India

ISBN: (纸本)9798350302820

Collection of sensor nodes and base station called as Wireless Sensor Networks. While, communicating with other nodes within network this type of network uses huge amount of energy. And, node has limited life time due to battery power. For efficiently utilizing these nodes energy and power, clustering was considered as a best approach. In proposed paper, we work on heterogeneous node, in such environment nodes are differ by their energy level, power, computation, communication etc. The protocol proposed for Energy Efficient Hybrid Routing in different energy levels using fuzzy logic provides three different network for three different initial energy level of nodes such that. 3J,. 4J and. 5J under same area (500∗500) m2 in which super node region uses multipath routing and clustering approach uses for transmitting packet/ data to base station and normal type nodes will uses single path routing transmitting packet/ data to Base station. The proposed protocol discussed different energy levels for finding best initial energy value of node in network and improved throughput, stability, lifetime of network on. 4J energy level as compare to. 3J and. 5J. To optimize the cluster formation we used predetermined technique i.e. inverted binary tree. This technique helps in balancing the load of super nodes region. The proposed model simulated in MATLAB, result shows better performance at 0.4J initial energy. © 2023 IEEE.

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

BuildingMap-GAN: A Map Translator From Road to Building Footprint 3

BuildingMap-GAN: A Map Translator From Road to Building Foot...

引用

3rd International Conference on Internet of Things and Smart City, IoTSC 2023

作者： Wei, Jing Zuo, Zejun Yang, Lin Yang, Ruolin School of Computer Science China University of Geosciences Wuhan China National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan China

ISBN: (纸本)9781510666375

Building footprint information is crucial for urban-related applications. Due to the rapid growth of the number and types of community-level buildings, the completeness and coverage of building footprint data at the community level are lagging. Efficiently and accurately generating community-level building footprint maps for the update of community electronic maps, post-disaster assessment, navigation services, etc. is an urgent and challenging task. To fill the data gaps of community-level building footprint with high accuracy, we design a building map translator named BuildingMap-GAN that is configured with a novel generator: MACU-Net. It has multi-scale connections with channel attention and asymmetric convolution blocks (ACB), which are used for fully capturing the spatial structure information of buildings and roads. An experiment is conducted on real-world community dataset in Wuhan, China, in which the street network data of 700 communities were used for training and the street network data of 42 communities were used for verification. Compared with the two baseline models, F1 scores are improved by 6.7% and 4.5%, respectively, and IoU values increase by 5.0% and 3.2%, respectively. Our model can generate refined building footprint data with higher precision and is closer to the real building distribution. © 2023 SPIE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

引用

21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

来源：评论

学校读者我要写书评

暂无评论

Deep Optimal VGG16 Based COVID-19 Diagnosis Model

引用

computers, Materials & Continua 2022年第1期70卷 43-58页

作者： M.Buvana K.Muthumayil S.Senthil kumar Jamel Nebhen Sultan S.Alshamrani Ihsan Ali Department of Computer Science and Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologyChennai600073India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia

Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December *** was tagged as a pandemic soon by the WHO being a serious public medical *** spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)*** this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various *** features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of *** optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning *** task is accomplished by exceptional mating conduct of Black Widow *** this strategy,cannibalism is *** this phase,fitness outcomes are rejected and are not satisfied by the proposed *** results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing ***16 model identifies the imagewhich has a place with which it is dependent on the distinctions in *** impact of the distinctions on labels during training stage is studied and predicted for test *** proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.

关键词： COVID 19 multi-feature extraction vgg16 optimal learning rate

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Disruptive Instances in Cloud Environment

Performance Analysis of Disruptive Instances in Cloud Enviro...

引用

International Communication systems and Networks and Workshops, COMSNETS

作者： Pranab Nandy Rounak Saha Anurag Satpathy Sandip Chakraborty Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka Surathkal India Department of Computer Science and Engineering Cloud and Smart System Services Lab Missouri University of Science and Technology USA Department of Computer Science and Engineering Cloud and Smart System Services Lab Indian Institute of Technology Kharagpur India

Virtualization enables the service providers (SPs) to logically partition the resources into virtual machines (VM) instances. Real-world SPs such as Amazon, Google, Microsoft Azure, IBM, and Oracle provide different flavors of VM instances, such as on-demand, reserved, and low-cost or spot, depending on the type of application hosted. The on-demand instances are short-term and typically incur a higher cost than reserved instances that are provisioned for a longer duration at a discounted rate. Low-cost or spot instances are cost-effective compared to on-demand but are reclaimable by the SPs. The SPs often claim that the on-demand and spot instances achieve similar performance, but it is far from that. This paper studies the performance of spot instances via rigorous experimentation over commercial SPs such as Amazon AWS and Microsoft Azure. Real-world evaluations affirm that spot instances perform poorly compared to their on-demand counterpart concerning memory, CPU, disk read, and write operations. We identify such instances as disruptive and name them so because it does not fulfill the performance, durability, and flexibility expectations like an on-demand instance having the same configuration. We also perform hypothesis testing over the experimental data obtained to corroborate our claim further.

关键词：

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：