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检索条件"任意字段=2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025"

共 496 条记录，以下是11-20 订阅

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Secure Attendance System Leveraging Machine Learning and Blockchain

Secure Attendance System Leveraging Machine Learning and Blo...

引用

2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Daniel, R. Nidumolu, Akhila Manikonda, Kumara Sai Movva, Manoj Kumar Majji, Sivani Prasad v Potluri Siddhartha Institute of Technology Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331521394

The system enables schools to reach maximum operational success together with reliable accountability in attendance handling. The tracking solution unites decentralized blockchain with facial recognition technology to provide a safe contemporary monitoring platform. The system provides precise identification along with security risk elimination which surpasses traditional insufficient performance methods. The blockchain system protects data integrity by stopping unauthorized modifications so that only authorized personnel maintain exclusive access to confidential attendance records. The system resolves privacy matters by using an organized data management system that combines sensitive information protection with adaptable scalability options. The system maintains high reliability according to performance evaluations while biometric authentication with blockchain integration establishes it as a leading solution for modern automated attendance tracking. © 2025 IEEE.

关键词： Sensitive data

来源：评论

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Predictive Maintenance for Electric Motors, RPM data, Machine Learning for Proactive Life Cycle Management

Predictive Maintenance for Electric Motors, RPM Data, Machin...

引用

2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Kumar, Mamilla Chetan Brindha, D. Reddy, Ettedi Rishwanth Karunya University Department Cse Coimbatore India

ISBN: (纸本)9798331521394

The research develops predictive maintenance as a solution which prevents unexpected electric motor breakdowns. The objective remains to develop a machine learning algorithm for Remaining Useful Life (RUL) prediction which utilizes RPM (Revolutions Per Minute) values as input data. Past RPM data training of an autoencoder allows detection of anomalies while it predicts motor performance for proactive planning of maintenance activities. Real-time monitoring and visualization becomes possible through the implementation of this solution which functions using Python and streamer application. Their predictive model connects motor deterioration patterns to create double benefits of operational savings through decreased device downtime coupled with accident failure prevention and reactive maintenance improvement to predictive maintenance. The proposed model should be expanded to include new equipment types while new sensor integration will boost accuracy levels. © 2025 IEEE.

关键词： Predictive maintenance

来源：评论

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Smart Energy Forecasting: Energy Consumption Prediction in data Centers

Smart Energy Forecasting: Energy Consumption Prediction in D...

引用

2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Kumar, Kanapareddy Nithin Chethan, C. Priya, S. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur603203 India

ISBN: (纸本)9798331521394

The rapid expansion of data centers has been driven by the growing demand for digital services. As a consequence, the energy usage follows the real challenges in terms of cost and environmental effect. As a result, this study proposes a hybrid technique for predicting energy consumption in data centers that combines machine learning approaches, notably Long Short-Term Memory (LSTM) networks and Auto-Regressive Integrated Moving Average (ARIMA) models. This approach builds a model capable of accurately capturing both short-term variations and long-term trends in energy consumption. As a model for optimizing energy use in data centers, the suggested approach would save costs while potentially contributing to sustainability through improved operational efficiency. This research also shows the need for real-time prediction techniques in energy management while making decisions that support processes with a visual dashboard and alert system for mitigating risks of overconsumption of energy. © 2025 IEEE.

关键词： Risk perception

来源：评论

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AI Based Early Detection of Parkinson's and Alzheimer's Disease Using EEG Signals

AI Based Early Detection of Parkinson's and Alzheimer's Dise...

引用

visual analytics and data visualization (icvadv), international conference on

作者： K. Sunil Kumar Suneetha Madduluri Manoj Kumar Myakala KR Anudeep Laxmikanth Department of Electronics and Communication Engineering NIT Warangal Telangana India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In this study, a new approach to the diagnosis of Parkinson's and Alzheimer's diseases is introduced by employing EEG signals and applying PCA and training through the CNN model. Although the focus is on classification, 1D CNN generated from EEG-based signal values are applied independently and results are compared in the proposed method. Based on the classification results, the EEG signals obtained from EEG signals were able to accurately diagnose Parkinson's and Alzheimer's. From these data, the highest classifier accuracy was achieved to be 95%.

关键词： Training Accuracy visual analytics data visualization Brain modeling Electroencephalography Alzheimer's disease Artificial intelligence Principal component analysis

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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

来源：评论

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Design and Development of a Mobile Application for Direct Agricultural Market Access

Design and Development of a Mobile Application for Direct Ag...

引用

2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Manikandan, G. Nivetha, S.B. Sri Siva Swathika, S.T. Narmthaa, J.B. Rm, Srijhha R.M.K. Engineering College Department of Artificial Intelligence and Data Science Tiruvallur Kavaraipettai India

ISBN: (纸本)9798331521394

In developing countries, the conventional agricultural supply chain relies significantly on middlemen, which often leads to farmers receiving merely 20-40% of the retail price. This system not only diminishes farmers' earnings but also raises prices for consumers. This paper investigates the advantages of direct market access through digital platforms, which eliminate intermediaries and allow farmers to connect directly with consumers and retailers. Utilizing these platforms can boost farmers' incomes by as much as 50%, while also improving transparency and efficiency in the supply chain. The research offers a comprehensive examination of the economic, technological, and social effects of direct market access, as well as addressing major challenges concerning platform adoption, scalability, and connectivity in rural areas. © 2025 IEEE.

关键词： Crops

来源：评论

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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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Integrated Deep Learning Framework for Automated Glaucoma Detection, Optic Disc/ Cup Segmentation, and CDR Calculation

Integrated Deep Learning Framework for Automated Glaucoma De...

引用

2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Tina Brivitha, K.J. Sophia, S. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

The earliest stage of ocular glaucoma is often the leading cause of irreversible vision loss globally, primarily due to late diagnosis and its typically asymptomatic nature. This makes early diagnosis and timely intervention critical for preventing or at least partially slowing vision deterioration. Traditional diagnostic techniques, which are often time-consuming, remain widely used. However, manual estimation by ophthalmologists frequently leads to significant variability in intra- and inter-subject results. This paper proposes a novel deep learning-based system for optic disc (OD) and optic cup (OC) segmentation, as well as cup-to-disc ratio (CDR) calculation, to approximate glaucoma levels. The system utilizes a CNN-based approach that ensures accurate segmentation of the nerve fiber layer, reduces reliance on manual CDR inspection, and facilitates early glaucoma diagnosis. © 2025 IEEE.

关键词： Clock and data recovery circuits (CDR circuits)

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Dimensionality Reduction-based Interactive visual analytics Approach for Investigating Ensemble Weather Simulations 25

Dimensionality Reduction-based Interactive Visual Analytics ...

引用

8th international conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2025

作者： Tamura, Go Kobayashi, Sena Sakamoto, Naohisa Maejima, Yasumitsu Nonaka, Jorji Photron Limited Tokyo Japan Kobe University Kobe Japan Riken R-CCS Kobe Japan

ISBN: (纸本)9798400713354

Torrential rains causing infrastructure damage and threats to human lives have unfortunately become more frequent worldwide. HPC-based numerical weather predictions, by using ensemble simulation and data assimilation, have become an indispensable tool for dealing with such extreme weather phenomena. However, ensemble simulations can generate large time-varying, multivariate, and multivalued outputs, posing a particular challenge to the visualization and analysis tasks. In this paper, we present a visual analytics approach combining unsupervised machine learning and a combination of 2D and 3D visual representation techniques to assist interactive visual analysis of such HPC-based simulation results. In addition to the investigation of how ensemble members evolve over time, this also makes possible the investigation of how these members compare with the verification truth data, which can contribute to a better understanding of the simulation models. We developed a prototype visual analytics system, and through some experimental evaluations with a domain scientist in numerical weather prediction, we could confirm the usefulness of the proposed approach in the better understanding of the spatiotemporal behavior of the ensemble members themselves, as well as in comparison to the verification truth data, which was not possible by using traditional visual analysis based on ensemble mean and spread. © 2025 Copyright held by the owner/author(s).

关键词： visual analytics

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A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for Secure Cloud data Management

A Hybrid B-Tree and Enhanced Merkle Hash Tree Approach for S...

引用

2025 international conference on visual analytics and data visualization, icvadv 2025

作者： Thiruneelakandan, S. Eliyas, Sherin Sathish Kumar, M. Hindustan Institute of Technology and Science Department of Computer Application TamilNadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications TamilNadu Chennai India

ISBN: (纸本)9798331521394

data security, integrity verification, and efficient retrieval issues are much more pronounced with large datasets in cloud storage systems. The angle of this paper is the proposal of hybrid architecture of AES, EMHT, B-tree indexing, and POR techniques to complete the optimization of cloud operations. The way the system was evaluated against the traditional approaches was based on several key metrics such as encryption time, integrity verification time, update time, and memory usage for datasets up to the 15GB limit. The experimental results show that the proposed system has 6.50% less verification time and 38.44% better update time when compared to existing methods without compromising the effectiveness of encryption. These indicate that the proposed approach is cost-effective, secure, and scalable in a large-scale cloud storage environment. The contribution of this paper is a practical solution for future enhanced data management and security in modern cloud systems. © 2025 IEEE.

关键词： Information management

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