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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

共 5114 条记录，以下是441-450 订阅

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A Systematic Approach to Tuning Cover Parameters in Mapper for Improved TDA Representation 3

A Systematic Approach to Tuning Cover Parameters in Mapper f...

引用

3rd IEEE International conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： vannoni, Stefano Tassi, Emma Sampaio, Inês Won Bianchi, Anna M. Maggioni, Eleonora Politecnico di Milano Dept. of Electronics Information and Bioengineering Milan Italy Fondazione Irccs Ca' Granda Osp. Maggiore Policlinico Dept. of Neurosciences and Mental Health Milan Italy

ISBN: (纸本)9798350378009

Topological data analysis (TDA) offers a powerful framework for analyzing complex datasets by leveraging algebraic topology to unveil underlying structures. Within TDA, the Mapper algorithm plays a pivotal role in transforming data into a simplified graph representation, known as a simplicial complex. However, selecting appropriate parameters within the Mapper algorithm remains a challenge due to the absence of a standardized approach. In this study, we propose a systematic methodology for choosing Cover parameters by integrating quantitative assessment, using the Netsimile metric and graph topological features, with qualitative visual analysis. We demonstrate the efficacy of our approach using brain features from the Human Connectome Project dataset. Through a comprehensive evaluation, we identify parameter configurations that enhance graph properties stability producing informative graph structures. Our findings highlight the importance of integrating quantitative metrics into the parameter selection process, paving the way for enhanced data exploration and understanding. The open-source nature of our methodology promotes broader adoption and application across diverse datasets and analytical workflows. © 2024 IEEE.

关键词： data Analytics

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analysis of visualization Tools for Team Sports

Analysis of Visualization Tools for Team Sports

引用

2023 International conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Gayathri, M. Kavitha, v. SRM Institute of Science and Technology Department of Data Science and Business System Chennai India

ISBN: (纸本)9798350334357

visualization is a major topic in sports and other professions that deal with performance. All the enthusiastic spectators and an increasing number of athletes across all sports credit visualisation for their better performance and success. Understanding the facts now in existence and the challenges posed by picturing team sports are the main goals of this work. For the time being, team sports visualisations do not have any defined criteria, according to my review. Tools and approaches for visualising team sports are examined in this work. © 2023 IEEE.

关键词： Sports

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Bio Inspired Approaches for Indoor Path Navigation and Spatial Map Formation by Analysing Depth data

Bio Inspired Approaches for Indoor Path Navigation and Spati...

引用

2022 IEEE International conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Chaudhuri, Rapti Deb, Suman Shubham, Shubham National Institute of Technology Cse Department Agartala India

ISBN: (纸本)9781665483162

Indoor Navigation presents a significant domain for carrying out with research issues. Literature review depicts inspite of having various existing graph theoretic optimization approaches for safe path exploration, the mobile robot faces difficulties in achieving robust and efficient computation. The cause of difficulties include limitation in sensor availability, limitation in reachability, having dynamic on path obstacles, and space-time complexity. The paper presents a working model for mobile robot navigation by detecting objects analysing depth data in the concerned environment followed by optimized path exploration using bio inspired path planning techniques. This is accompanied by spatial map formation incorporating with Robot Operating System (ROS) environment for getting an idea of visual inference of the taken indoor environment. analysis of working principle and respective methodology of the algorithms have been done and experimental results are noted. Comparative ease in computation and optimization are obtained and presented neatly in subsequent sections. The analysis would prove to be remarkable citation for future research in the field of Optimized Collision-free Indoor Navigation (OCIN). © 2022 IEEE.

关键词： Motion planning

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Advancing Brain Image Segmentation: A Comprehensive exploration of Enhanced vGG16 Architecture for Precise Neuroanatomical Mapping 2

Advancing Brain Image Segmentation: A Comprehensive Explorat...

引用

2nd International conference on Computing and data Analytics, ICCDA 2024

作者： Sheeja Kumari, v. Wilfred Blessing, N.R. Palarimath, Suresh Gunasekaran, Hemalatha Poorana Senthilkumar, S. Sutherlin Subitha, G. Saveetha School of Engineering Simats University Department of Computational Intelligence Chennai600 025 India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Dr. N. G. P Arts and Science College Department of Computer Applications Tamil Nadu Coimbatore India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798331542573

This study introduces a novel approach to brain image segmentation by enhancing the vGG16 architecture. Neuroanatomical mapping is a critical aspect of understanding brain structures, and our research focuses on refining the vGG16 model to achieve more accurate segmentation results. We address the intricacies of brain image analysis, optimizing the architecture for the nuanced structures inherent in neuroimaging data. Our enhanced vGG16 model is thoroughly evaluated across diverse datasets, demonstrating superior performance compared to state-of-the-art segmentation methods. The framework proves robust and efficient in accurately delineating complex brain regions. We also delve into the interpretability of the segmentation results, shedding light on the learned features that contribute to the model's efficacy. The significance of our work extends beyond improved segmentation, offering valuable insights into the interpretability of deep learning models in neuroimaging. This research contributes to the broader field of biomedical image analysis and holds promise for applications in neuroscience and medical diagnostics. The proposed enhanced vGG16 framework emerges as a potent tool for advancing our understanding of brain structures and enhancing the capabilities of neuroimaging technologies. © 2024 IEEE.

关键词： Brain mapping

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Unlocking Kubernetes Networking Efficiency: Exploring data Processing Units for Offloading and Enhancing Container Network Interfaces 4

Unlocking Kubernetes Networking Efficiency: Exploring Data P...

引用

4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Nagendra, T.P. Hemavathy, R. Department of Computer Science and Engineering R. V. College of Engineering Bengaluru India

ISBN: (纸本)9798350305258

This paper delves into the realm of Kubernetes networking, exploring the pivotal role of Container Network Interface (CNI) plugins in orchestrating containerized applications. The core of the paper focuses on CNI plugins, encompassing the basics of Kubernetes networking, CNI architecture, and an in-depth analysis of prominent plugins such as Cilium, Flannel, Project Calico, SR-IOv CNI, and Weave Net. Leveraging insights from past performance studies, the paper navigates the advantages and limitations of these plugins. The exploration extends to data Processing Units (DPUs), showcasing their potential to revolutionize Kubernetes networking. Through case studies and use cases, the paper unveils the innovative utilization of DPUs for offloading networking tasks. Considering the offloading capabilities inherent in DPUs, we showcase different CNIs for specific Kubernetes cluster requirements. © 2023 IEEE.

关键词： Cilium CNI Container Network Interface data Processing Unit DPU Kubernetes Offload SmartNIC SR-IOv

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ExTravis: exploration of Traffic Incidents Using a visual Interactive System 25

ExTraVis: Exploration of Traffic Incidents Using a Visual In...

引用

25th International conference Information visualisation (Iv) - AI and visual Analytics and data Science

作者： Zerafa, Joshua Islam, Md Rafiqul Kabir, Muhammad Ashad Xu, Guandong Univ Technol Sydney Adv Analyt Inst AAi Sydney NSW Australia Charles Sturt Univ Sch Comp & Math Bathurst NSW Australia

ISBN: (纸本)9781665438278

The impact of road traffic incidents (e.g., road accidents, vehicle breakdowns) have become progressively worse over the years, being a major cause of many adverse issues such as serious injury, economic loss, and lifelong disabilities. Thus, it is essential to acknowledge these issues and proactively construct appropriate solutions to mitigate the impact of these issues in the future. This study outlines the history of traffic incident research and covers several solutions such as machine learning, mathematical modeling, and visualization system to traffic incident analysis. In this paper, we design a unique visualization system, ExTravis, for incident data exploration and analysis that can be used to help traffic management controllers, aid to make decisions, and help them to understand how past incidents affected and where incidents may occur. The key features of this system are visual exploration and analysis to overcome the problems linked with road traffic incidents and to encourage future work and improvements. Additionally, we gather various custom queries for free text search feature. We find that people ask questions and our system provide 90% correct visual insights. Finally, we demonstrate the effectiveness and robustness of ExTravis by comparing with three different incident visualization dashboards and a user study.

关键词： Traffic incidents Interactive system visualization Information system

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From Raw data to Informed Decisions: The Development of an Online data Repository and visualization Dashboard for Transportation data 14

From Raw Data to Informed Decisions: The Development of an O...

引用

14th International conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Tsouros, Ioannis Polydoropoulou, Amalia Tsirimpa, Athena Karakikes, Ioannis Tahmasseby, Shahram Mohammad, Anas Ahmad Nemer Alhajyaseen, Wael K.M. University of the Aegean Department of Shipping Trade and Transport Chios82132 Greece Qatar Transportation and Traffic Safety Center Department of Civil and Environmental Engineering Qatar University P.O.Box 2713 Doha Qatar Department of Civil and Environmental Engineering Qatar University P.O.Box 2713 Doha Qatar

This paper presents the design, implementation, and practical use of a specialized online data repository and an interactive visualization of a transportation dashboard. Specifically tailored to handle and interpret large-scale transportation data within the Qatari context, the combined platform serves as a comprehensive solution for managing extensive datasets, including GPS traces from taxis and e-scooters, which are examined as primary use-cases in this paper. The online data repository provides a centralized hub for efficient data storage and management of public transport including Mobility-as-a-Service (MaaS) data. Concurrently, the visualization dashboard fosters an intuitive, user-friendly interface for data exploration and analysis. Through real-world applications within Qatar's transportation ecosystem, we elucidate how these innovative developments can inform data-driven policy decisions in crucial areas such as infrastructure development, resource allocation, and safety measures. Ultimately, this study underscores the pivotal role of effective data management and advanced visualization in maximizing the potential of big data, providing valuable insights for urban mobility planning and enhancing the landscape of policy-making in Qatar and worldwide. © 2024 Elsevier B.v.. All rights reserved.

关键词： visualization

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visual Analytics of Streaming data in Concept Drift Detection

Visual Analytics of Streaming Data in Concept Drift Detectio...

引用

Electrical, Electronic and Computing Engineering (IcETRAN), International conference on

作者： Aleksandra Stojnev Ilic Dragan Stojanovic dept. Computer Science Faculty of Electronic engineering University of Nis Nis Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

Recent developments in sensor technology have enabled real-time data acquisition, high-frequency and multimodal data capturing thus underlying the need for monitoring physical or operational conditions in various aspects of the data. High dimensionality and volume of the data poses significant statistical challenges creating a need for accurate concept drift detection and prompt responses, a task that can be performed either by an algorithm or by human expert. visual analytics plays a crucial role in concept drift detection by enabling analysts to visually explore, analyze, and interpret complex data streams, facilitating real-time monitoring, decision-making, and communication with stakeholders. This paper examines the usability of radar graphs for concept drift exploration, as well as their usage in other steps of analysis. For the purpose of demonstration, a pipeline for real-time health data analysis is presented and steps are visualized using radar graphs.

关键词： data analysis visual analytics Concept drift Pipelines Decision making Radar detection data visualization

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An Integrated Secure Rail System For Accident Prevention and Anti-Hijacking Measures

An Integrated Secure Rail System For Accident Prevention and...

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2024 International conference on Advances in data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Pradeep, Ganesula Kiran, Samineni Uday Sathyadurga, v. Hindustan Institute Of Technology & Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

India has one of the world's longest rail systems, covering an amazing distance. However, Indian railways fall short of international norms in terms of dependability and passenger safety. In order to solve this, an improved interface solution this paper has been started to improve railway safety and prevent collisions. This method uses Arduino microcontrollers in conjunction with fingerprint sensor modules. In order to maintain safe and controlled train operations, trains fitted with these specialised fingerprint devices won't start their engines until after authenticating the fingerprints of authorised users. And also the system recognises when two trains are about to collide on the same track when fingerprint modules are activated simultaneously. The GSM module quickly sends the latitude and longitude coordinates to a specified web site after detecting something. The position and seriousness of the collision risk may be quickly determined thanks to this data interchange, which is essential for further processing and analysis. In addition, the apparatus features an LCD screen that offers instantaneous visual notifications to users on potential collision scenarios. By enabling proactive steps for collision prevention and easy connection with external monitoring systems, this novel solution strengthens overall railway safety rules. It makes use of web-based connectivity and cutting-edge sensor technology to do this. © 2024 IEEE.

关键词： Liquid crystal displays

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Automated Mechanism for Image Capturing, Processing, and Evaluation for various programs using Deep Learning 4

Automated Mechanism for Image Capturing, Processing, and Eva...

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4th International conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： vijay Anand, K. Sai Sharan, P. Nehith, M. Rajashekar, v.S. R.M.K. Engineering College Thiruvallur India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

This paper presents an exploration of object detection, a critical application in deep learning characterized by its robust feature learning and representation capabilities. It focuses on typical methodologies such as YOLO, detailing their underlying principles and evaluating their real-time performance and detection accuracy. The existing model, trained for human detection, requires manual data updates and lacks the ability to identify duplicate or inappropriate images. Our project addresses these challenges by developing an enhanced detection system capable of identifying image types, ensuring image clarity by filtering out blurred images and detecting duplicates ensuring, for example, that an image from Day 1 of a workplace is not reuploaded for subsequent days. Additionally, the system is designed to detect trained objects more accurately. This project proposes the development of software that, during image capture, integrates geotagging, pixel analysis for blur detection, duplicate identification, and image type classification to improve the overall efficiency and accuracy of object detection. © 2024 IEEE.

关键词： Adversarial machine learning

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