检索结果-内蒙古大学图书馆

36th Euromicro conference on Real-Time Systems, ECRTS 2024

作者： Srinivasan, Srinidhi Nelissen, Geoffrey Bril, Reinder J. Meratnia, Nirvana Eindhoven University of Technology Netherlands Mälardalen University Västeras Sweden

ISBN: (纸本)9783959773249

IEEE Time-Sensitive Networking (TSN) is one of the main solutions considered by the industry to support time-sensitive communication in data-intensive safety-critical and mission-critical applications such as autonomous driving and smart manufacturing. IEEE TSN standardizes several mechanisms to support real-time traffic on Ethernet networks. Time-Aware Shapers (TAS) (IEEE 802.1Qbv) is the standardized mechanisms of TSN that is usually considered to provide the most deterministic behavior for packet forwarding. TAS regulates when traffic classes may forward incoming packets to the egress of a TSN switch using gates that are opened and closed according to a time-triggered schedule. State-of-the-art solutions to configure or analyze TAS do not allow for multiple traffic classes to have their TAS gates opened at the same time according to any arbitrary schedule. In this paper, we present the first response-time analysis for traffic shaped with TAS where no restriction is enforced on the gate schedule. The proposed analysis is exact. It is a non-trivial variant of the schedule abstraction graph analysis framework [18]. Experiments confirm the usefulness of the proposed analysis and show that it is promising for doing design-space exploration where non-conventional TAS gates configurations are investigated to, for instance, improve average-case performance without degrading the worst-case. © Srinidhi Srinivasan, Geoffrey Nelissen, Reinder J. Bril, and Nirvana Meratnia.

关键词： Accident prevention

来源：评论

学校读者我要写书评

暂无评论

Predictive analysis of Kidney Diseases with Artificial Intelligence and data Science

Predictive Analysis of Kidney Diseases with Artificial Intel...

引用

2025 International conference on visual Analytics and data visualization, ICvADv 2025

作者： Eswar, G. Hariharan, C. Lavein, S. Akilandeswari, v. Velammal College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798331521394

In the Field of Medical diagnosis, nowadays there are many new methods are getting introduced each day and also gets advanced by many people around the world. The Kidney is one of the most vital organ of a Human body that helps a lot by filtering blood and throws the waste toxic contents through excretory organs. But the food habits and other bad habits can affect the process of Kidney and further even affects kidney. As a result our kidney can also suffer from various disease. Most vital Kidney diseases can include Chronic Kidney Disease(CKD), Diabetic Nephropathy at the 2nd stage of Diabetes, and still many more. In the process of predicting of these diseases, we can use machine learning algorithms like Random Forest with Decision Tree and Supervised Learning algorithms we can train a model that can use the previous datasets to clarify if the new person is diagnosed with the particular Kidney disease. The Random Forest algorithm provides around 90% accuracy that is better in prediction of results for the collected information. Using these techniques we can detect the problem at early stage which can give a greater chance to prevent it from getting worse. © 2025 IEEE.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

28th International conference on Engineering of Complex Computer Systems, ICECCS 2024

28th International Conference on Engineering of Complex Comp...

引用

28th International conference on Engineering of Complex Computer Systems, ICECCS 2024

ISBN: (纸本)9783031664557

The proceedings contain 22 papers. The special focus in this conference is on Engineering of Complex Computer Systems. The topics include: Automated Parameter Determination for Enhancing the Product Configuration System of Renault: An Experience Report;optimal Solution Guided Branching Strategy for Neural Network Branch and Bound verification;AccMILP: An Approach for Accelerating Neural Network verification Based on Neuron Importance;Word2vec-BERT-bmu:Classification of RISC-v Architecture Software Package Build Failures;Test Architecture Generation by Leveraging BERT and Control and data Flows;less is More: An Empirical Study of Undersampling Techniques for Technical Debt Prediction;modeling and verification of Solidity Smart Contracts with the B Method;template-Based Smart Contract verification: A Case Study on Maritime Transportation Domain;QuanSafe: A DTBN-Based Framework of Quantitative Safety analysis for AADL Models;a Event-B-Based Approach for Schedulability analysis For Real-Time Scheduling Algorithms through Deadlock Detection;validation of RailML Using ProB;reachability analysis of Concurrent Self-modifying Code;an Iterative Formal Model-Driven Approach to Railway Systems validation;an Efficient Distributed Dispatching vehicles Protocol for Intersection Traffic Control;confidentiality Management in Complex Systems Design;analyzing Excessive Permission Requests in Google Workspace Add-Ons;formal verification Techniques for Post-quantum Cryptography: A Systematic Review;autoWeb: Automatically Inferring Web Framework Semantics via Configuration Mutation;safePtrX: Research on Mitigation of Heap-Based Memory Safety violations for Intel x86-64;towards Efficiently Parallelizing Patch-Space exploration in Automated Program Repair.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of Machine Learning Techniques for Detecting Sentiments in Social Media 2

Comparative Analysis of Machine Learning Techniques for Dete...

引用

2nd International conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Yogi, Kottala Sri Dankan Gowda, v. Poornima, Galiveeti Prasad, K.D.v. Srinivas, D. Prasanth, P vishnu International University Symbiosis Institute of Business Management Hyderabad Symbiosis Department of Operations Hyderabad Telangana Pune India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Presidency University School of Computer Science Karnataka Bangalore India Deemed University Symbiosis Institute of Business Management Hyderabad Symbiosis International Pune India SR University School of Business Telangana Warangal India Mohan Babu University Department of Physics Andra Pradesh Tirupati517102 India

ISBN: (纸本)9798350349221

This paper provides an extensive discussion of the machine learning algorithms applied to the sentiment analysis on the social media, involving the use of the Naive Bayes, Support vector Machines (SvM), and Deep learning models evaluation and comparison. The growth of the social network content at the rates exponential, computerized assessment and interpretation of the valuer of the client have become extremely important to serve the areas of the market researches from the social opinion monitoring. We conducted a systematic study on the viability of various machine learning methodologies that aim to deal with the complicated characteristics as well as peculiar nature of Language Processing Natural (LPN), which are most-likely caused by the enormous amount of data on social media platforms. According to our results, the Deep Learning based models incorporate more complex neural network structure and thus, CNNs and RNNs are more likely to outperform other explanations. The critical point of their success lies in the power of models to capture the semantic and contextual nature of language. Nevertheless, the exploration also outlines the computational needs of Deep Learning methods, which imply some critical requirements for their use in the contemporary sentiment analysis applications. We cover issues of processing speed and discussion of trade-offs in terms of classification accuracy versus computational efficiency, thus point out the implementation problems that scaled adoption of the models brings. © 2024 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Eurovis 2022 - 24th Eurographics conference on visualization 2022

EuroVis 2022 - 24th Eurographics Conference on Visualization...

引用

24th Eurographics conference on visualization, Eurovis 2022

The proceedings contain 44 papers. The topics discussed include: of course it’s political! a critical inquiry into underemphasized dimensions in civic text visualization;visual analytics of contact tracing policy simulations during an emergency response;effective use of Likert scales in visualization evaluations: a systematic review;how accessible is my visualization? evaluating visualization accessibility with chartability;seeing through sounds: mapping auditory dimensions to data and charts for people with visual impairments;ModelWise: interactive model comparison for model diagnosis, improvement and selection;infographics wizard: flexible infographics authoring and design exploration;reusing interactive analysis workflows;leveraging analysis history for improved in situ visualization recommendation;and visual parameter selection for spatial blind source separation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Music emotion prediction based on multidimensional feature fusion 3

Music emotion prediction based on multidimensional feature f...

引用

3rd IEEE International conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Zhou, Xuanye Su, Zhibin Ren, Hui Communication University of China School of Information and Communication Engineering Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

ISBN: (纸本)9781665490788

Audience sentiment analysis is a popular research task in Music Information Retrieval (MIR). In this work, we aim to lean the prediction model of fine-grained music emotion for intelligent editing and retrieval. To satisfy the properties of time-series of music, the RNN-based model of BILSTM was selected as the bone. In particular, the traditional features and deep features extracted from the CNN architecture of spectrogram was fused with attention mechanism to enhance the recognition ability for different emotions. The ablation experiment has proved the necessary of the multi-dimensional features. Finally, the contrasting experiments on recent algorithms demonstrate that our proposed method for emotion prediction is effective, which is also suitable for small data sets. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Two and More Dimensional Representations of Human Body Composition Parameters 25

Two and More Dimensional Representations of Human Body Compo...

引用

25th International conference on Digital Signal Processing and its Applications, DSPA 2023

作者： Nikolaev, Dmitry v. Shchelykalina, Svetlana P. Kolesnikov, Md vladimir A. Starunova, Olga A. Romanova, vera S. Development Deptartment Src Medas Moscow Russia Pirogov Russian National Research Medical University Biomedical Faculty Moscow Russia

ISBN: (纸本)9798350337235

The practical application of the bioimpedance analysis of human body composition (BIA) technology began four decades ago. The first two-dimensional representation of the dependence of one bioimpedance parameter on another was proposed in 1994 in the work of Piccoli et al. This representation is referred to as bioimpedance vector analysis (BIvA). The position of the point in BIvA coordinates has an unambiguous relationship with values of the phase angle (PA), as BIvA coordinates can be used for calculating PA. In this case, the two-dimensional BIvA graph can be supplemented with values of the phase angle ranges, thus forming a pseudo-three-dimensional representation. Continuing with the BIvA logic, you can use other pairs of BIA parameters to build two-dimensional representations (and in some cases, pseudo-three-dimensional), including body composition parameters. This technology can be referred to as the two-dimensional bioimpedance analysis of the human body composition (2D BIA or BIvA HBC - bioimpedance vector analysis of human body composition). The informativeness of BIvA reports and BIvA HBC reports, in the case when the values deposited along the axes are clinically related, will increase. Nevertheless, the practice of using various BIA techniques indicates that from 6 to 10 parameters are used to obtain a complete picture of the processes of changing body composition. In terms of BIvA HBC, it is 3-5 visual representations, and with the partial use of BIvA HBC+ technology (with the introduction of the third parameter), in the monitoring window, you can limit yourself to tracking 2-4 forms of visual representation of body composition parameters. © 2023 IEEE.

关键词： Biochemistry

来源：评论

学校读者我要写书评

暂无评论

Learning Task-Agnostic Action Spaces for Movement Optimization

引用

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2022年第12期28卷 4700-4712页

作者： Babadi, Amin van de Panne, Michiel Liu, C. Karen Hamalainen, Perttu Aalto Univ Dept Comp Sci Espoo 02150 Finland Univ British Columbia Dept Comp Sci Vancouver BC V6T 1Z4 Canada Stanford Univ Dept Comp Sci Stanford CA 94305 USA

We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Like several previous article, we parameterize actions as target states, and learn a short-horizon goal-conditioned low-level control policy that drives the agent's state towards the targets. Our novel contribution is that with our exploration data, we are able to learn the low-level policy in a generic manner and without any reference movement data. Trained once for each agent or simulation environment, the policy improves the efficiency of optimizing both trajectories and high-level policies across multiple tasks and optimization algorithms. We also contribute novel visualizations that show how using target states as actions makes optimized trajectories more robust to disturbances;this manifests as wider optima that are easy to find. Due to its simplicity and generality, our proposed approach should provide a building block that can improve a large variety of movement optimization methods and applications.

关键词： Optimization Task analysis Aerospace electronics Trajectory optimization Reinforcement learning Training Splines (mathematics) Movement optimization trajectory optimization policy optimization hierarchical reinforcement learning action space

来源：评论

学校读者我要写书评

暂无评论

Evaluating and Forecasting the Operational Performance of Road Intersections 22

Evaluating and Forecasting the Operational Performance of Ro...

引用

30th ACM SIGSPATIAL International conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Nematichari, Ali Pechlivanoglou, Tilemachos Papagelis, Manos York Univ Lassonde Sch Engn Toronto ON Canada

ISBN: (纸本)9781450395298

Road intersections represent one of the most complex configurations encountered when traversing road networks. It is therefore of vital importance to improve their operational performance, as that can significantly contribute towards the efficiency of the whole transport network. Traditional approaches to improve the efficiency of intersections are based on analysis of static data or expert opinions. However, due to the advancements on vehicle-to-vehicle (v2v) and vehicle-to-Infrastructure (v2I) communication technologies, it is possible to enhance safety and improve road intersection efficiency by continuously monitoring traffic conditions and enabling situational awareness of vehicle drivers. Towards this end, we design, develop and evaluate a system for evaluating and forecasting the operational performance of road intersections by mining streams of v2I data. Our system makes use of graph mining and trajectory data mining methods to continuously evaluate a set of well-defined measures of effectiveness (MOEs) for traffic operations at different levels of road network abstraction. In addition, the system enables interactive analysis and exploration of the various MOEs. The system architecture and methods are general and can be used in various settings requiring continuous monitoring and/or forecasting of the road network state.

关键词： trajectory data mining stream mining road network road intersection machine learning time series

来源：评论

学校读者我要写书评

暂无评论

Design and Practice of a Flight data Application System 2

Design and Practice of a Flight Data Application System

引用

2nd International conference on Big data Engineering and Education, BDEE 2022

作者： Wang, Haozhi Che, Jiumei Zhu, Wenlong Li, Min College of Aeronautics Yantai Nanshan University Yantai China Qingdao New Nanshan Air Training Co. Ltd. Yantai China

ISBN: (数字)9781665481847

ISBN: (纸本)9781665481847

Flight data can not only be applied to safety analysis, condition monitoring and model establishment, but also play a very important role in improving the effect of theory teaching and students' own ability. Based on flight simulation software and visual programming language, a flight data application system is developed in this paper. The system includes three modules: flight status monitoring, verification experiment and data customization. It can realize the objective evaluation of flight skills, the analysis and verification of flight data of theoretical knowledge, as well as the functions of classroom teaching demonstration and independent design experiment of teachers and students. It solves the current situation of insufficient application of simulated flight data in theoretical teaching and students' ability training. The teaching practice shows that the system can improve students' flight technology, deepen their understanding of abstract knowledge, stimulate their independent exploration ability, and assist teachers' classroom teaching, which has high application value. © 2022 IEEE.

关键词： Students

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：