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

共 1754 条记录，以下是381-390 订阅

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A CNN-Based Framework for Underwater Image Noise Reduction and Visibility Enhancement in Marine Applications

A CNN-Based Framework for Underwater Image Noise Reduction a...

引用

International conference on Computer and Communications Technologies (ICCCT)

作者： Ashok P Ragul Sabarish J Vigneash R V Wasiq Rahman M A Antony Vijay J S. Kaliappan Dept of Computer Science and Engineering Sri Sairam Institute of Technology Chennai Dept of Computer Science and Engineering Karpagam College of Engineering Coimbatore Division of Research and Development Lovely Professional University Jalandhar

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Underwater imaging technology serves critical functions in diverse fields that range from marine biological research to underwater archaeological investigations to oceans exploration endeavors. The underwater imaging process typically produces highly degraded visual output due to multiple factors including light absorption and scattering while also affecting clarity because of particulate matter. We propose a Convolutional Neural Network (CNN)-based technique for underwater image noise reduction which works to enhance image visibility while improving perceptual data. Our system applies deep learning principles to master noise patterns while recovering image clarity. The proposed model trained using underwater images with different levels of noise underwent evaluation on the same dataset. Subjective visual assessment together with objective metrics demonstrated that the CNN delivered noise reduction performance at an accuracy range between 85-90% which defeated conventional noise filtering methods. The developed methodology exhibits substantial progress potential for seabed image quality enhancement tools which subsequently results in better underwater cognitive interpretation processes.

关键词： Image quality Deep learning visualization Recurrent neural networks Image color analysis Noise reduction Scattering Biology Convolutional neural networks Image enhancement

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SPLORT: Evaluation of a Web-based Approach for Descriptive and Exploratory analysis of Multivariate Spatio-temporal data 21

SPLORT: Evaluation of a Web-based Approach for Descriptive a...

引用

14th International Symposium on visual Information Communication and Interaction, VINCI 2021

作者： Feijóo-García, Miguel Alfonso Hernández-Peñalosa, José Tiberio Program of Systems and Computing Engineering Universidad de Los Andes Colombia and Program of Systems Engineering Universidad El Bosque Colombia Program of Systems and Computing Engineering Universidad de Los Andes Colombia

ISBN: (纸本)9781450386470

The multivariate Spatiooral data challenges the analysts along the navigation and exploration of the information, using data Analytics techniques. We present SPLORT, as a user-centered web-based solution for segmented visual Analytics. The tool involves a "What-Where-When"pyramidal paradigm to drive Spatiooral analysis towards the findings of new insights. It features multi-layered coordinated views to help the analyst navigate and explore multivariate data simultaneously. We evaluated this web-based tool, and reported the findings and results, considering users' performance and perceptions, basing our analysis on their Likert scale (0-5) responses, regarding their experience and the tool's usability, and the sentiment of their feedback, by using Natural Language Processing (NLP) techniques. Our findings from the pilot study held, suggest that SPLORT successfully allows the users to visually query, explore and analyze contextless multi-scale attributes within space and time, which quickly drives them to new insights and to support easier decision-making processes. Moreover, the tool's flexibility, due to its multi-layered GUI, supports better and more friendly visual analytics. © 2021 ACM.

关键词： data Analytics

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A Pipeline for Immersive data visualization in CAVE Systems

A Pipeline for Immersive Data Visualization in CAVE Systems

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Challa, Koundinya Jaiswal, Chandra Pendem, Abhinav Gokaraju, Balakrishna North Carolina A&T State University Dept. Computational Data Science and Engineering Greensboro United States

ISBN: (纸本)9798331504847

Virtual Reality (VR) has transformed data visualization by enabling immersive and interactive experiences. CAVE (Cave Automatic Virtual Environment) systems provide a unique platform for exploring complex datasets in an intuitive, spatially immersive manner. However, a standardized pipeline for developing VR scripts tailored to CAVE-based data visualization remains underexplored. This paper presents a structured pipeline for creating and deploying immersive data visualization in CAVE systems, specifically using the WorldViz Prism CAVE environment. Our approach facilitates seamless integration of multi-dimensional datasets, enabling real-time interaction with 27 visual graphs within a single VR scene. The system allows dynamic graph manipulation, interactive exploration, and enhanced user engagement. By streamlining VR script development, this pipeline improves accessibility for researchers and practitioners, fostering a more intuitive understanding of complex datasets. Experimental results demonstrate the effectiveness of this approach in enhancing data interpretability and decision-making. © 2025 IEEE.

关键词： Video analysis

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Research on the Application Algorithm of Multimodal Learning in GPT Large Model

Research on the Application Algorithm of Multimodal Learning...

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IEEE International conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Xinhui Guo Jiasheng Zhang Ying Ren Yulai Ling Huiming Tang Polytechnic Institute Zhejiang University Hangzhou China Innovation Institute Huaxin Consulting Co. Ltd Hangzhou China College of Information Science & Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

This paper discusses the research on the application algorithm of multimodal learning based on the GPT model. Combined with the Transformer architecture, a cross-modal alignment algorithm is proposed to optimize the deep fusion of text and visual information. By introducing the generative adversarial network (GAN), the generation and conversion capabilities of multimodal data are further improved, and efficient collaboration between different modalities is achieved. In the system design, the multimodal data is first preprocessed, and then feature extraction and information aggregation are performed through the encoder and decoder structure based on the Transformer. Then, the cross-modal alignment algorithm is used to match and convert the features of different modal data. In the final stage, the performance of the architecture is improved by strengthening the adversarial generative network, aiming to enhance the system’s performance in dealing with complex cross-modal tasks. This study selected a series of public databases for testing. The actual test results show that the proposed technology has shown significant improvements in topics such as image text creation and image category judgment. For the image-to-text synthesis task, the matching rate between the output text and the calibrated image increased by 15.7%. For the image category classification task, the judgment accuracy increased by 12.4%. The experimental evidence has conclusively proved that this text technology has substantial effects in the field of cross-media learning, opening a new perspective for the subsequent in-depth exploration of cross-media learning principles.

关键词： visualization Accuracy Generative adversarial networks Transformers data models Stability analysis Classification algorithms System analysis and design Image classification Testing

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The status quo of the development of multidisciplinary and "double innovation" talent training models in the field ofeducation in China 2

The status quo of the development of multidisciplinary and "...

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2nd International conference on Information Science and Education, ICISE-IE 2021

作者： Yang, Weimin Shi, Siqi Zhan, Jin Department of Mechanical and Electrical Engineering Beijing University Of Chemical Technology Beijing China

ISBN: (纸本)9781665438292

Based on the data from China HowNet, this paper uses citespace visual analysis software to draw a map of scientific knowledge in this research field. Through literature clustering and data analysis, the development status of the multidisciplinary "double innovation" talent training model in my country's education field is sorted out, and the new talent training model is sorted and summarized from the three perspectives of the country, enterprises, and universities. © 2021 IEEE

关键词： Information analysis

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A Bibliometric analysis of visualizing Knowledge Mapping for Learning Management System

A Bibliometric Analysis of Visualizing Knowledge Mapping for...

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International conference on Educational and Information Technology (ICEIT)

作者： Xiquan Ye Jiaming Wang Danya Liu Qinggui Qin Guangdong Business and Technology University Zhaoqing China GXNU School of Design Guangxi Normal University Guilin China Foreign Language School Guangzhou Institute of Science and Technology Guangzhou China School of Art and Design Guangzhou Institute of Science and Technology Guangzhou China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

With the advancement of technology and methodologies, Learning Management System (LMS) has seen a profound and extensive application in the field of education, thereby rendering scientific and systematic research on it particularly significant. The primary objective of this study is to comprehensively explore the current application status and developmental trajectory of LMS in educational practice. To this end, the authors screened approximately 1,347 high-quality related articles from the Web of Science (WOS) and conducted a visual analysis using the CiteSpace. By analyzing countries, institutions, journals, and keywords, this study reveals the research hotspots and evolving trends of LMS over the past two decades. The results show that the research focus of LMS has gradually expanded from the initial exploration of basic theories on system design to a wide range of applications in educational practice. Furthermore, it has increasingly centered on personalized learning experiences, the improvement of learning efficiency, and the enhancement of learning outcomes. Additionally, the analysis reveals that different countries and regions exhibit diversity and variability in their research emphasis and developmental trends regarding LMS. The conclusions and findings of this study provide some references and insights for future research endeavors and practical implementations of LMS in the field of education. These contributions have the potential to promote ongoing innovation and facilitate the development of LMS in education.

关键词： Learning management systems visualization Systematics databases Soft sensors Education Bibliometrics data visualization Market research Trajectory

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Manufacturing Process Improvement through FMEA analysis and Fuzzy Logic 30

Manufacturing Process Improvement through FMEA Analysis and ...

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30th International Scientific conference Electronics, ET 2021

作者： Ivanova, Malinka Tsenev, Valentin Dimitrov, Borislav Technical University of Sofia Sofia1000 Bulgaria

ISBN: (纸本)9781665445184

The paper presents a method for improvement a robotized manufacturing process of electronic devices. It includes statistical Failure Mode and Effects analysis (FMEA), fuzzy logic and visual technique for results interpretation. Collected data from experts are processed according to FMEA and used for development of intelligent fuzzy inference system, which result is visualized through heatmap. Such approach can support engineers for fast and correct decision making at manufacturing process with dynamic changes. © 2021 IEEE.

关键词： Decision making

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Accelerating Semantic Segmentation Labeling of Time Sequence data via Assistive Label Generation with Conditional GANs

Accelerating Semantic Segmentation Labeling of Time Sequence...

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Computing and Machine Intelligence (ICMI), International conference on

作者： Amila Akagić Emir Buza Stefani Kecman Rijad Sarić Mathew G. Lewsey Edhem Čustović James Whelan Faculty of Electrical Engineering University of Sarajevo (UNSA) Sarajevo Bosnia and Herzegovina Dept. of Animal Plant and Soil Sciences La Trobe Institute for Sustainable Agriculture & Food (LISAF) Melbourne VIC Australia Australian Research Council Research Hub for Medicinal Agriculture La Trobe University Melbourne VIC Australia Scientific Instruments Australia (SIA) Melbourne VIC Australia State Key Laboratory of Plant Environmental Resilience College of Life Sciences Zhejiang University Hangzhou China Provincial International Science and Technology Cooperation Base on Engineering Biology Zhejiang University Haining China

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

This paper presents a robust exploration of the capabilities of conditional Generative Adversarial Networks (GANs) in harnessing labeled data to produce high-quality labels for unlabeled samples. By leveraging conditional information, our approach guides the network to generate contextually relevant labels for specific time series data, accelerating the labeling process. A comprehensive evaluation of our model's performance, incorporating diverse metrics, visual representations, and his-tograms, illuminates the effectiveness of conditional GANs for the Assistive Label Generation (ALG) of time series Arabidopsis thaliana images. The Structural Similarity Index (SSIM) high-lights an average similarity of 98.89 % between the generated and manually labeled images. This innovative methodology holds the promise of significantly reducing labeling efforts.

关键词： Measurement visualization Histograms Semantic segmentation Time series analysis Generative adversarial networks Labeling

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Semantics-Based, Automated Preparation of Exploratory data analysis for Complex Systems

Semantics-Based, Automated Preparation of Exploratory Data A...

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IEEE International conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Noor Al-Gburi Attila Klenik Imre Kocsis Department of Measurement and Information Systems Budapest University of Technology and Economics Budapest Hungary

visual Exploratory data analysis (EDA) is a key step in data analysis – however, after decades of research, recommending visualizations and their sequences for a human analyst in an exploration-supporting, efficient and repeatable way is still not a solved problem. However, EDA in the empirical assessment of performance and dependability of complex IT systems has key differences from the general setting: system structure and behavior have at least partial specifications, and the EDA process tends to follow established engineering processes (e.g., for diagnosis). Utilizing these differences, in this paper, we propose a novel, semantically driven approach for rapidly setting up analytic notebooks for the IT performance and dependability EDA of complex systems. An ontology-based knowledge base connects observed and inferred operational data with operational semantics and deployment topology; rule-based inference on the knowledge base creates a model of EDA notebook structure and plot recommendations. The model is automatically translated to notebook code and connected to the input data. We also present an open, end-to-end proof of concept implementation of the approach for the transaction duration analysis of Hyper-ledger Fabric, a complex, cross-organizational distributed ledger platform.

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Enhanced Underwater Object Detection Using Deep Neural networks

Enhanced Underwater Object Detection Using Deep Neural netwo...

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International conference on Computing and Networking Technology (ICCNT)

作者： I. R. Oviya Shaik Atheeq Rasool Balu Bhasuran Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Bakar Computational Health Sciences Institute (BCHSI) University of California San Francisco (UCSF) California United States

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The identification of marine animals has become a significant area of data processing and analysis due to the growing volume of underwater visual data and deep sea exploration. The imperative nature of safeguarding the environment underscores the significance of this undertaking; nonetheless, challenges persist due to factors such as intricate underwater scenery, subpar image and video clarity, and the diverse range of motions shown by marine organisms. To solve these challenges, this study used deep learning algorithms to recognize certain marine species and indicate their presence in submitted photos and videos using bounding boxes. The primary objective is to provide practical techniques for identifying marine species, so offering essential support for more research in this field. The study made considerable advancements in detection speed and mean average precision (mAP) for marine species identification by using the key deep learning frameworks, YOLOv3, YOLOv8, and Faster-RCNN. This study project not only resolves the present issues with marine animal detection, but it also lays the foundation for future studies aimed at understanding and safeguarding the delicate underwater ecosystem.

关键词： Deep learning YOLO visualization Ecosystems Artificial neural networks data processing Organisms Object recognition Marine animals Motion control

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