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Software Tools and Evaluation Models for Visual Analytics: Trends, Challenges, and Future Directions

Software Tools and Evaluation Models for Visual Analytics: T...

引用

2025 International Conference on Visual Analytics and data visualization, ICVADV 2025

作者： Lakumarapu, Srikanth Sudha, P. Ruba Pajila, P.J. Beslin Sasirekha, K. Vinoth, R. Siva Subramanian, R. Cvr College of Engineering Department of Cse Telangana India S.A Engineering College Dept of Cse India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept of Cse India R.M.D Engineering College Dept of Csbs India Rmk College of Engg and Tech Department of Cse India Rmk College of Engg and Tech Dept of Cse India

ISBN: (纸本)9798331521394

Visual analytics (VA) is a complex interdisciplinary field which applies data visualization and analysis methods in order to extract knowledge from Big data. This paper aims at presenting an overview of the most common software tools and assessment frameworks for VA with an emphasis on the open source, commercial and specific tools for BI, healthcare and scientific purposes. It discusses the issues that VA has concerning varied data types, their usability and how the system can be expanded to accommodate large data sets. The paper also considers recent trends in AI and machine learning, virtual and augmented reality, and synchronous shared environments. Privacy and data bias issues are also drawn out as ethical concerns. Last, the paper presents the directions of further research to enhance the efficiency and usability of VA tools and make them more ethical in data decision-making. © 2025 ieee.

关键词： Video analysis

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Ensemble-Driven Multilingual Sentiment analysis Framework for YouTube Comments with Dashboard

Ensemble-Driven Multilingual Sentiment Analysis Framework fo...

引用

2025 International Conference on Visual Analytics and data visualization, ICVADV 2025

作者： Dharini, N. Madhuvanthi, M. Aswini, C.S. Lakshya, R. Triumbika, M. Saranya, N. Tiruvallur India R.M.K. College of Engineering and Technology Dept of Al & Ds Tiruvallur India

ISBN: (纸本)9798331521394

In the present-day digital age, sites like YouTube produce a vast volume of multilingual user comments. These comments are perfect for sentiment analysis because they contain a wealth of viewpoints and feelings. This article investigates how sentiments might be extracted from multilingual YouTube comments to improve user engagement and content comprehension. Our method divides sentiments into categories such as positive, negative, mixed feelings, and non-relevant using sophisticated natural language processing (NLP) techniques. This study illustrates the difficulties related to linguistic diversity and context sensitivity while also showcasing the efficacy of sentiment analysis in capturing the subtleties of user thoughts through the use of a large dataset of YouTube comments. The generated insights are meant to inform marketers and content creators about the preferences and opinions of their audiences in order to inform strategic decision-making. The development of an interactive dashboard will also facilitate the visualization of sentiment patterns in real-time, enabling stakeholders to engage with their audiences on a deeper level. This study demonstrates the potential of multilingual sentiment analysis as a tool for understanding global audience dynamics, developing the field, and enhancing the performance of automated content evaluation systems. © 2025 ieee.

关键词： Emotion Recognition

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Lightweight Contextual LLMs for IoT data Interpretation in Smart Cities

Lightweight Contextual LLMs for IoT Data Interpretation in S...

引用

2025 International Conference on Visual Analytics and data visualization, ICVADV 2025

作者： Kannadasan, Tamilarasan Independent Researcher TX United States

ISBN: (纸本)9798331521394

While the populations in cities are growing, the need for smart cities to optimize resources and life quality is increasingly a pressing matter. At the heart of this transformation lie the IoT devices creating massive volumes of data to be interpreted in real time. This paper introduces the Lightweight Contextual large Language Models designed for efficient processing of IoT data in Smart Cities. In the approach proposed, advanced model pruning and knowledge distillation are used, keeping the solution computationally efficient with no sacrifice in interpretative power. We validate our model's effectiveness on the UrbanSensing dataset in anomaly detection, predictive maintenance, and analytics of engagement on tasks involving citizens. Our framework allows feasibility on edge devices with a 40% reduction in inference time and up to 50% decrease in computational overhead, thus guaranteeing scalability and sustainability in real-world smart city applications. The findings bridge the gap from computational efficiency to contextual understandings, thereby offering the premise for enhanced urban resilience and wiser decision-making. © 2025 ieee.

关键词： Sentiment analysis

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analysis of VF-TLP ESD Measurements Using Machine Learning

Analysis of VF-TLP ESD Measurements Using Machine Learning

引用

ieee International symposium on Quality Electronic Design

作者： Seyed Hossein Hosseini Mehrdad Nourani Theo Smedes Charvaka Duvvury Dept. of ECE Univ. of Texas at Dallas Dallas USA NXP Semiconductors Nijmegen Netherlands iT2 Technologies Dallas USA

ISBN: (数字)9798331509422

ISBN: (纸本)9798331509439

In this paper, we propose a machine learning-powered approach to explore analysis of electrostatic discharge (ESD) protection device measurements taken with high-current pulse method. Specifically, the data analyzed was collected under a very fast transmission line pulse (VF - TLP) on a grounded gate NMOS (ggNMOS) protection device. The analysis is done by applying K-means clustering in two complementary steps. First, we use the raw data for a holistic pattern-based clustering to filter out the possible measurement errors. In the second step, the subset that seems error-free is further analyzed through a feature-based clustering that provides insight into parametric variations and stability of the protection design. We also employ visualization techniques, i.e. t-SNE dimensionality reduction and statistical boxplots, to make interpretation of results easier for ESD test and quality engineers. We have validated the proof of concept using measurement data obtained in Kelvin setup for 18 devices.

关键词： Dimensionality reduction Measurement errors Power transmission lines Statistical analysis Pulse measurements Statistical distributions Power system stability Transmission line measurements Electrostatic discharges Stability analysis

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Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound

Weakly Supervised Contrastive Learning for Better Severity S...

引用

ieee International symposium on Biomedical Imaging

作者： Gautam Gare Hai Tran Bennett DeBoisblanc Ricardo Rodriguez John Galeotti Carnegie Mellon University Pittsburgh USA LSUHSC Internal Medicine New Orleans USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis. Several AI-based patient severity scoring models have been proposed that rely on scoring the appearance of the ultrasound scans. AI models are trained using ultrasound-appearance severity scores that are manually labeled based on standardized visual features. We address the challenge of labeling every ultrasound frame in the video clips. Our contrastive learning method treats the video clip severity labels as noisy weak severity labels for individual frames, thus requiring only video-level labels. We show that it performs better than the conventional cross-entropy loss based training. We combine frame severity predictions to come up with video severity predictions and show that the frame based model achieves comparable performance to a video based TSM model, on a large dataset combining public and private sources.

关键词： COVID-19 visualization Ultrasonic imaging Lungs Biological system modeling Contrastive learning Predictive models Noise measurement Labeling Artificial intelligence

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RDMP: A Reference Detection and Mask Propagation Pipeline for Numerous 3D Glomeruli Segmentation in large Volumes

RDMP: A Reference Detection and Mask Propagation Pipeline fo...

引用

ieee International symposium on Biomedical Imaging

作者： Ziling Lin Zehua Li Zifan Chen Yao Lu Fangxu Zhou Bin Dong Li Zhang Haifeng Li Center for Data Science Peking University Beijing China Peking University First Hospital Beijing China College of Future Technology Peking University Beijing China Beijing International Center for Mathematical Research Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Quantitative analysis of numerous repetitive structures in large volumes is one of the most challenging tasks in biomedical imaging, primarily due to its intensive manual requirements. Kidney glomeruli exemplify this challenge due to their dispersion across the cortical region, high density and microscopic size. To address this, we present a Reference Detection and Mask Propagation (RDMP) pipeline that combines object detection with 2D and 3D mask propagation, gradually achieving efficient segmentation of the entire volume by using a 2D reference example. Our method demonstrates robust performance, achieving a Dice Similarity Coefficient (DSC) of 0.9096 on microCT data, and when generalized to Synchrotron Radiation X-ray (SRX) data, reaching a DSC of 0.8431 with only minimal bounding box fine-tuning. Overall, this pipeline shows strong potential for efficient, large-scale kidney analysis, significantly reducing labor demands and accelerating kidney disease research.

关键词： Image segmentation Three-dimensional displays Statistical analysis Microscopy Pipelines Object detection Manuals Kidney Synchrotron radiation Biomedical imaging

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Evaluating Multi-Modal LLMs for Automatically Recognizing Semantic Elements in UML Use Case Diagram Images

Evaluating Multi-Modal LLMs for Automatically Recognizing Se...

引用

ieee International Conference on Software analysis, Evolution and Reengineering (SANER)

作者： Jameleddine Hassine Information and Computer Science Department KFUPM Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Requirements engineering commonly employs UML Use Case Diagrams (UCD) to visually capture system interactions and functionality, facilitating clear communication between stakeholders. Recognizing and extracting semantic information from UCDs is essential for applications such as automated requirements extraction and system design validation, which improves software analysis accuracy, and streamlines model understanding for both developers and stakeholders. Recent advancements in large language models (LLMs) with visual processing capabilities enable interpreting intricate diagrammatic content. This paper evaluates multi-modal LLMs, specifically GPT-4o and GPT-4o-mini, in accurately identifying semantic elements within UCDs. We conducted experiments on a new dataset of UCDs and other diagrams collected from online sources. Experimental results show that both models struggled to accurately identify and interpret key UCD elements, often misclassifying or overlooking essential ones.

关键词： visualization Image recognition large language models Unified modeling language Semantics Software Stakeholders Requirements engineering data mining System analysis and design

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Path Overlap Detection and Property Graph Construction from TCX data for Advanced analysis

Path Overlap Detection and Property Graph Construction from ...

引用

International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Tilen Hliš Iztok Fister Faculty of Electrical Engineering and Computer Science University of Maribor iD

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This paper presents a novel approach for processing cycling data from TCX files using a custom-built Julia library, TCX2Graph.j1. The method includes constructing property graphs from GPS track points and detecting overlapping path segments. These detected overlapping points are enriched with track point metrics, enabling detailed analysis of cycling sessions. The proposed approach processes large datasets efficiently, pro-viding insights into overlapping paths and athlete performance.

关键词： Measurement Heart rate Wind Predictive models Libraries Robustness Numerical models Machine intelligence Global Positioning System Sports

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Exploring Foundation Models Fine-Tuning for Cytology Classification

Exploring Foundation Models Fine-Tuning for Cytology Classif...

引用

ieee International symposium on Biomedical Imaging

作者： Manon Dausort Tiffanie Godelaine Maxime Zanella Karim El Khoury Isabelle Salmon Benoît Macq ICTEAM Université Catholique de Louvain Belgium ILIA Université de Mons Belgium CMMI Université Libre de Bruxelles Belgium

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Cytology slides are essential tools in diagnosing and staging cancer, but their analysis is time-consuming and costly. Foundation models have shown great potential to assist in these tasks. In this paper, we explore how existing foundation models can be applied to cytological classification. More particularly, we focus on low-rank adaptation, a parameter-efficient fine-tuning method suited to few-shot learning. We evaluated five foundation models across four cytological classification datasets. Our results demonstrate that fine-tuning the pre-trained backbones with low-rank adaptation significantly improves model performance compared to fine-tuning only the classifier head, achieving state-of-the-art results on both simple and complex classification tasks while requiring fewer data samples. Our source code is available on GitHub https://***/mdausort/Cytology-fine-tuning.

关键词： Adaptation models visualization Head Foundation models Histopathology Source coding Transfer learning data models Few shot learning Software development management

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Identifying Representation Bias in large Language Models Used in Financial Sentiment analysis

Identifying Representation Bias in Large Language Models Use...

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Computational Intelligence for Financial Engineering and Economics (CiFer), ieee symposium on

作者： Alpay Sabuncuoglu Carsten Maple The Alan Turing Insitute University of Warwick The Alan Turing Institute

ISBN: (数字)9798331508319

ISBN: (纸本)9798331508326

Financial sentiment analysis is the task of evaluating and quantifying the emotions and opinions expressed in financial news, reports, or social media to help investors and institutions make informed decisions. Financial institutions have been actively exploring the use of large language models (LLMs) to analyse market sentiment signals for a more nuanced understanding of a broader context. However, issues such as the scale of training data, model complexity, and the potential for human oversight can introduce or even amplify bias in these systems. Representation bias is a common challenge for LLMs as training data fail to properly represent the target groups, hence causes harmful bias in general-purpose use. Therefore, replacing current solutions with LLMs in financial organisations requires a robust evaluation methodology to ensure fairness. This paper investigates a three-level bias evaluation approach that specifically focuses on representation bias and presents a baseline evaluation of the FinBERT model. Step 1 uses a synthetic dataset that explicitly reveals sources of bias, structured as probability- and embedding-based evaluation recipes. Step 2 evaluates the model against data released by another country (e.g. Indian News dataset) to assess its performance in relation to more implicit biases. Step 3 examines individual problematic samples using token-based interpretability methods (e.g. integrated gradients). This paper presents the application of this structured bias evaluation process and its results on the FinBERT model. The evaluation code and dataset are available on GitHub (https://***/asabuncuoglu13/faid-test-financial-sentiment-analysis).

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