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

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Knowledge-based Graph exploration analysis on Customer’s Preference of Insurance Services

Knowledge-based Graph Exploration Analysis on Customer’s Pr...

引用

International ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Yingfan Chen Chalermpon Kongjit College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

With the increasing number of Chinese students in Thailand, the demand for insurance services from these students has gradually shown diversity and personalized characteristics. However, the current research on the insurance demand of overseas students is relatively insufficient. Based on text analysis and knowledge graph technology, this study focuses on the needs and preferences of Chinese students in Thailand in terms of insurance choices, and presents the research results in a visual form. The researchers collected chat history content of some target groups, as well as public discussion data on social media platforms and forums frequently used by Chinese students in Thailand. The researchers used the text analysis method to extract high-frequency keywords related to international student insurance, and carried out sentiment analysis to obtain the main concerns of current international students in insurance selection. Then, a knowledge graph centered on insurance demand is constructed to visualize key nodes and relationships such as insurance categories, budget preferences, and demand characteristics.

关键词： visualization Sentiment analysis Text analysis Social networking (online) Knowledge based systems Insurance Knowledge graphs History

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Fraud Detection in Financial Transaction Using GNN

Fraud Detection in Financial Transaction Using GNN

引用

visual Analytics and data visualization (ICvADv), International conference on

作者： v. Sivakumar Akash Kumar verma Piyush Kumar Nitesh Saw v. Hari Krishnan Information Technology Kalasalingam Academy of Research and Education Virudhunagar India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

As financial transactions increasingly shift to digital platforms, fraud has become a growing concern, impacting individuals and institutions alike. Conventional fraud detection systems, relying on rule-based or statistical approaches, often fall short of adapting to the sophisticated tactics fraudsters employ. This paper introduces a Graph Neural Network (GNN) model for detecting fraudulent transactions by capturing the intricate relationships between users, accounts, and transactional patterns. Unlike traditional machine learning algorithms, GNNs are inherently suited to handle graph-structured data, where entities are connected through various types of interactions that can reveal suspicious behavior. Transactions in fraud approach are presented as a graph where the nodes represent users or accounts, and edges represent the transactions themselves, encoded in the features of transaction amount, frequency, location, and time of day. This graph-based structure enables the GNN to take advantage of both local and global dependencies, thereby detecting otherwise hard-to-detect abnormal transaction patterns. For more accurate detection, we include additional features that capture certain behaviors that are specific indicators of fraud, such as repeated transactions within short intervals or location-based anomalies. proposed model is evaluated on a structured financial transaction dataset, and the results indicate significant improvements in accuracy, recall, and precision over traditional models, underscoring the efficacy of GNNs in fraud detection.

关键词： Time-frequency analysis Accuracy visual analytics QR codes Graph neural networks User experience Real-time systems Fraud Security Reliability

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AI based Efficient analysis and Monitoring of Sugarcane Buds Detection and Leaf Disease Classification

AI based Efficient Analysis and Monitoring of Sugarcane Buds...

引用

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

作者： Ananthajothi, K. Kumar, P. Koteeswaran, v. Rajalakshmi Engineering College Rajalakshmi Nagar Thandalam Computer Science and Engineering Department Chennai602 105 India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

Sugarcane is a significant crop, and its productivity is greatly affected by the early identification of bud problems and leaf diseases. The challenge with present agricultural practices is that these issues are often identified at late stages, which leads to loss of yield. This study puts forward an AI-based system for efficient analysis and monitoring of sugarcane bud detection and leaf disease classification. The system uses Convolutional Neural Networks (CNNs) to detect unhealthy buds and classify leaf diseases at an early stage. The proposed solution makes use of image processing techniques for capturing high-resolution images of sugarcane buds and leaves followed by training a CNN model for disease classification. It mainly aims to create an automated system that can quickly and accurately identify issues, thereby reducing the dependency on manual inspection. The system has the advantages of early diagnosis, increased accuracy in disease detection, and increased crop yield through timely interventions. © 2025 IEEE.

关键词： Convolutional neural networks

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Skin Lesion Detection Using Partial Differential Equations

Skin Lesion Detection Using Partial Differential Equations

引用

visual Analytics and data visualization (ICvADv), International conference on

作者： D. Sree Lakshmi A. Divya Gayathri Yadav Mugada Tanusha Koduru Mownika Bingi Ishwarya Madupu Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Vijayawada India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The technique used for image interest segmentation is known as segmentation. Processing pixels with matching attributes helps to accomplish this segmentation. Regions of this image become easier to process through part-based methods while avoiding complete image processing. The standard use of segmentation in pictures involves detecting objects as well as their edges. Technical image segmentation functions to break images into basic elements before creating a simplified understanding for easy analysis. A version of the image with reduced dimensions composed of uniform areas displaying similar visual patterns would serve as the best preference. Every image region must have a smooth intensity value that changes abruptly only at the region boundaries. The main segmentation approaches include one method for detecting original image objects and their contours. Edge detection systems produce matching results through the comparison of deformable curves with object contours by defining energy functional principles.

关键词： Image segmentation Accuracy Partial differential equations Image edge detection visual analytics Feature extraction Wavelet analysis Skin Mathematical models Lesions

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Optimizing vo-viso: A Modified Methodology to Parallel Computing with Isolating data in Memristor Arrays 20th

Optimizing Vo-Viso: A Modified Methodology to Parallel Comp...

引用

20th IFIP WG 10.3 International conference on Network and Parallel Computing, NPC 2024

作者： Wang, Wenqing Chen, Yabo Xu, Nuo Hu, Yihong Huang, Chenglong Wei, Shaojun Fang, Liang National University of Defense Technology Changsha410073 China Tsinghua University Beijing100084 China

ISBN: (纸本)9789819628292

The MAGIC NOR gate-based memristor array is a promising processing-in-memory (PIM) approach due to its massive parallelism, flexible logical operation, and non-volatile memory characteristics. The vo-viso MAGIC NOR gate operation mode is proposed to achieve high-throughput parallel computing and isolate other data within a memristor array. However, viso has a cumulative effect on the node voltage of the active row, calling into question the feasibility of vo-viso operation mode. In this paper, circuit analysis defines the necessary circuit parameter conditions for enabling parallel computing and isolation units in the MAGIC NOR gate array. By algorithms in Python and CUDA C++ exploration, we determined that achieving the necessary conditions within the array parameters ranges—including the tolerance margin vΔ, array size, vo, viso and memristor parameters suitable for real-world circuit requirements—is unfeasible. In response to these challenges, we modified the memristor array architecture. The modified architecture employs transistors to activate compute units while safeguarding the rest selectively. The feasibility of this architecture is validated by SPICE with selected transistors fabricated using a 65 nm process, taking into account transistor resistance and the fluctuations in vset and vreset in the 8×8 modified memristor array. To accommodate the design requirement of the state-of-the-art memristor array, the feasibility of 64×64 modified memristor array, taking into account the impact of wire resistance, is further verified. This paper bridges the gap between reliable high-throughput parallel computing operation and MAGIC NOR PIM architecture. © IFIP International Federation for Information Processing 2025.

关键词： SPICE

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AI Powered Ayurvedic Leaf Prediction and Disease Classification: A Comprehensive analysis

AI Powered Ayurvedic Leaf Prediction and Disease Classificat...

引用

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

作者： Sathya, R. Suganthi, M. Trisha, M. Janani, E. Dhivya, S. viswanathan, R.v. Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798331521394

Ayurvedic medicine, a fundamental component of India's traditional healthcare framework, is heavily dependent on the quality and efficacy of medicinal plants. Nevertheless, the medicinal properties of these plants can be adversely affected by diseases stemming from pathogens and environmental factors. This research explores the utilization of artificial intelligence (AI) methodologies, such as deep learning (DL), machine learning (ML), and technologies, for the prompt identification and diagnosis of plant diseases. The review emphasizes recent developments in this field, evaluates their success in disease detection, and pinpoints areas where further research is needed. By merging AI technologies with traditional practices, the integrity of medicinal plants can be preserved, promoting sustainable agricultural practices and improved disease management © 2025 IEEE.

关键词： Deep learning

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Enhancing Customer Experience in Retail for Adaptive Store Layouts with Reinforcement Learning

Enhancing Customer Experience in Retail for Adaptive Store L...

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visual Analytics and data visualization (ICvADv), International conference on

作者： P. Suresh T Sivakumar N. Revathy M. Pandi Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Master of Computer Applications Hindustan College of Arts and Science Coimbatore Tamil Nadu India Department of Information Technology SRM Madurai College for Engineering and Technology Sivagangai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In the competitive retail industry, improving customer experience is essential for increasing sales and cultivating brand loyalty. A crucial determinant of consumer happiness is the shop layout, which impacts customers' navigation, exploration, and interaction with merchandise. Traditional store layouts are often inflexible and do not adjust to evolving customer behaviour, hence diminishing their efficacy in enhancing the shopping experience. This paper examines the use of Reinforcement Learning (RL) to create adaptable shop layouts that adjust based on real-time consumer interactions. Through the analysis of customer behaviour and feedback, RL algorithms may dynamically modify the layout of merchandise, aisles, and promotional areas, therefore optimizing the retail environment for both consumers and merchants. The RL algorithm analyses historical data, progressively optimizing store layouts to correspond with consumer preferences, so improving the entire shopping experience. It provides substantial advantages, such as enhanced consumer involvement, more operational efficiency, and elevated revenue designs. It examines the potential of RL in revolutionizing retail settings in optimizing adaptable shop layouts and its effects on consumer happiness and corporate performance.

关键词： Technological innovation IEEE merchandise Heuristic algorithms visual analytics Layout Reinforcement learning Real-time systems Internet of Things Artificial intelligence Optimization

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Proceedings of SPIE: visual data exploration and analysis v

Proceedings of SPIE: Visual Data Exploration and Analysis V

引用

visual data exploration and analysis v

This volume 3298 of the conference proceedings contains 32 papers. Topics discussed include web based visualization, virtual reality and sound, multidimensional visualization, uncertainty visualization, timed-based vi... 详细信息

关键词： data reduction

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Optimizing Soccer Match Prediction with Deep Convolutional Neural Networks and Grey Wolf Optimization 5

Optimizing Soccer Match Prediction with Deep Convolutional N...

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5th IEEE International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shukla, Tushar Dhar Rani, v.Kanpur Kumar, Indrajeet Hussein, Omar Mohsen Banu, Shaik Balkhis Bhavani, P. University Institute of Physical Education & Sports Chandigarh University Mohali India Hindustan Institute of Technology and Science Department of Eee Chennai India Department of Cse Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Fatima College of Health Sciences Department of Physiotherapy Al Ain United Arab Emirates K. Ramakrishnan college of Engineering Dept of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

Predicting soccer match outcomes has become an essential area of interest in sports analytics, yet many existing studies rely on traditional machine learning methods that may not adequately capture the complex dynamics inherent in the sport. This study aims to address these limitations by proposing a novel approach utilizing a Deep Convolutional Neural Network (DCNN) optimized through Grey Wolf Optimization (GWO) to significantly enhance prediction accuracy. The methodology leverages a comprehensive dataset from the European Soccer database, gathered from Kaggle, which includes detailed match statistics, player attributes, and betting odds, allowing for a richer analysis. The results of the study indicate that the proposed DCNN model outperforms several baseline models, achieving notable improvements in accuracy, precision, and F1-scores, thereby showcasing its ability to learn complex patterns and relationships within the data effectively. Despite these promising findings, the study acknowledges certain limitations, such as the potential for further enhancement through the integration of additional features like spatiotemporal tracking data and more detailed player performance metrics. The outcomes suggest that the combination of advanced deep learning techniques with effective optimization strategies can yield substantial improvements in predictive analytics in sports. In conclusion, this research contributes significantly to the field by demonstrating the potential of deep learning in the accurate prediction of soccer match outcomes. It encourages future exploration of diverse datasets and methodologies to further refine predictive capabilities, ultimately supporting more informed decision-making processes for teams and coaches within the competitive landscape of soccer. Overall, the study underscores the transformative potential of machine learning in sports analytics. © 2025 IEEE.

关键词： Convolutional neural networks

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Developing a Comprehensive Task Framework for Effective Workforce analysis 25

Developing a Comprehensive Task Framework for Effective Work...

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Companion Proceedings of the ACM on Web conference 2025

作者： Rebecca Dew Mingzhao Li Weidong Liu Sandya Baratha Raj Pearson Melbourne Australia Pearson Sydney Australia

ISBN: (纸本)9798400713316

Effective workforce analysis, planning, and management require a deep understanding of the tasks and skills associated with different roles. This paper introduces a novel methodology for developing a comprehensive task framework that leverages Large Language Models (LLMs) and large-scale job ads data. We first propose an innovative approach to task taxonomy design, which involves the decomposition and reconstruction of tasks into a hierarchical structure based on action-object pairings, systematically refined using LLMs. The methodology extends to integrating the taxonomy with occupation and skill linkages derived from job ads, ensuring alignment with real-world workforce dynamics. Finally, we demonstrate the practical value of this framework through a visual analytics system that enables interactive exploration and analysis of tasks, occupations, and associated skills, highlighting its potential to transform workforce analysis. Demo video: https://***/41txBZK

关键词： gpt

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