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

共 517 条记录，以下是21-30 订阅

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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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A data-Driven Approach for Early Detection and Prevention of Diabetes Mellitus Using Machine Learning

A Data-Driven Approach for Early Detection and Prevention of...

引用

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

作者： Nithya, B. Asha, V. Sreeja, S.P. Amit Yadav, V. Aditya, M. Rai, Abhishek New Horizon College of Engineering Department of Mca Bengaluru India

ISBN: (纸本)9798331521394

Diabetes is a common health issue in India that impacts the quality of life and overburdens healthcare resources. Prevention and early detection are the keys to controlling its effects. This research applies Indian demographic and health statistics to forecast diabetes risk through machine learning. KNearest Neighbors (KNN), Logistic Regression, Decision Tree, Support Vector Machine (SVM), and Random Forest were experimented upon for predictive efficiency. Great preprocessing of data was done to manage missing values and features normalization to make the model reliable. Random Forest yielded the maximum accuracy of 94.50% among the algorithms tested, proving the efficiency in predicting diabetes. The results unveil the promise of machine learning in medicine for early diagnosis and focused treatment approaches. © 2025 IEEE.

关键词： Diagnosis

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Improving Handwritten Stenography Recognition Using Domain Specific Encoding and Synthetic data Augmentation

Improving Handwritten Stenography Recognition Using Domain S...

引用

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

作者： Nivetha, S. Kamatchi, C. Bala Hindustan Institute of Technology and Science Department of Computer Applications India

ISBN: (纸本)9798331521394

Handwritten form of stenography recognition is a challenging task due to high variations in shorthand symbols and a lack of annotated datasets. A strong baseline for stenographic text recognition, leveraging the newly created LION dataset of shorthand manuscripts. To alleviate symbol ambiguity as well as low-resource constraints in this domain, incorporating stenographic domain expertise is proposed through encoding schemes and synthetic data generation. In the experiments, a Gated-CNN-BGRU architecture is used, trained with Connectionist Temporal Classification (CTC) loss. Pre-train on synthetic data and fine-tuning on real data greatly improve CER and WER. The experiments showed the effectiveness of stenography-specific encoding schemes in improving WER (Word Error Rate) over baseline models by up to 10% when using Melin-based encodings. This work opens pathways for transcribing historical stenographic manuscripts more effectively. © 2025 IEEE.

关键词： Deep learning

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Automated Blood Group Detection System Using Image Processing for Non-Invasive Antigen Feature Extraction and Classification

Automated Blood Group Detection System Using Image Processin...

引用

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

作者： Bala Weslin D S. Sophia Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The project intends to design an RFID based automated blood group detection system through image processing and a specific CNN. First, the provided images of blood cells are processed and the system extracts features relevant to the given antigen for the purpose of identifying the blood group. With the help of a set of 12,500 augmented images of blood cells, the System processes and normalizes these images for classification. The presented method can be considered as more effective to detect blood groups in comparison with the traditional method that requires invasive procedures and requires manual inspection. It is a system that optimizes the detection of blood groups leading to improvements in medical diagnosis and medical imaging in general.

关键词： Training Antigens visual analytics Neural networks Cells (biology) Feature extraction Medical diagnosis Convolutional neural networks Blood Radiofrequency identification

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A Simple Framework for Predicting Student Dropout Analysis Using data Mining Algorithms

A Simple Framework for Predicting Student Dropout Analysis U...

引用

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

作者： Balraj, E. Manikandan, P. Sakthivel, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331521394

Dropout is an essential concern in the educational sector with implications for students as well as the institutions. This paper proposed a predictive model that uses machine learning techniques to identify at-risk students for dropout. Four years of data were used for 270 students on an online LMS by using different models such as Logistic Regression, Decision Tree, Naive Bayes, SVM, Random Forest, and ANN. The key features like LMS access, grades, and performance of assignments were evaluated after the data preprocessing involved cleaning and one-hot encoding. AUC measures were used for evaluating the performance of the model. Naive Bayes turned out to be the most effective model and followed by SVM. The results demonstrate the utility of machine learning for early dropout prediction, equipping educators with an important resource for intervention to help atrisk students. © 2025 IEEE.

关键词： Support vector machines

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Natural Language Processing-Driven Learning Platform for Personalized Education

Natural Language Processing-Driven Learning Platform for Per...

引用

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

作者： Subitsha R S Vetrivel M Shrimaulik N Saravanan K Department of Information Technology K.S.Rangasamy College of Technology Tiruchengode Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The NLP-driven Learning Platform for Personalized Education will revolutionize educational experiences through the integration of Artificial Intelligence in improved adaptive learning. Other than typical systems, this platform powered by adaptive learning models, sentiment analysis, and gamification creates personalized instruction that best aligns with each student's cognitive and emotional state. For instance, the platform makes use of NLP models in the forms of Rasa for dialogue management and TensorFlow for adaptive algorithms [2]; it measures learning patterns, emotional signals, and progress for its system of adjusting difficulty levels, feedback, and engagement. Preliminary test results indicate that this system can improve academic outcomes and increase student satisfaction through enhanced interactivity and support [4]. This paper covers the design, implementation, and expected impacts of such an innovative approach to the educational landscape, particularly in view of the Education 4.0 and 5.0 frameworks that are turning more personalized and adaptive.

关键词： Sentiment analysis Adaptive learning Adaptation models Analytical models Conversational artificial intelligence visual analytics data visualization Learning (artificial intelligence) Turning

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Autonomous Fire-Fighting Robot with Night Vision Capabilities

Autonomous Fire-Fighting Robot with Night Vision Capabilitie...

引用

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

作者： Amit Kumar Singh Gurjot Singh Dankan Gowda V Archi Kothari Aman Kumar Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This research work presents the designs an automatic fire suppression robot that includes night-vision technology's. The robot uses an Arduino Nano microcontroller to connect flame sensors gas and pump equipment plus a voice control Bluetooth module. By using night vision technology and enhanced components the system detects fires effectively and fights them in risky settings. The robot system functions in industrial, residential, and commercial spaces to improve fire security while protecting human safety and reducing property harm.

关键词： Night vision Bluetooth Service robots visual analytics Machine learning Robot sensing systems Real-time systems Sensors Security Protection

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Performance Analysis of Tea Leaf Disease Detection by Utilizing Hybrid ResNet and BiLSTM

Performance Analysis of Tea Leaf Disease Detection by Utiliz...

引用

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

作者： Aadith A Muthukumaran N Abijith N Aravind S Jeevaprakash M Department of ECE Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This research study aims to build the ability to scan and differentiate whether a tea leaf is diseased or not. With the software built, leaf disease can be easily diagnosed and treated, which is the biggest dilemma of agriculture as sometimes its determination whether a plant is diseased or healthy gets quite hard. According to the proposed model, the disease diagnosis shall be done by an image processing and machine learning algorithm, which will offer its result through the creation of software. In this research, the optimized code is used to reduce the time complexity and space complexity of the output, and results produced are more accurate when compared to many machine learning techniques. This research study has proposed a coherent image processing system driven by machine learning techniques that is able to detect sick leaves from given tea leaf images.

关键词： Codes Image processing visual analytics Plants (biology) Machine learning Software Real-time systems Performance analysis Time complexity Diseases

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The Role of Features in Image Retrieval System: A Survey

The Role of Features in Image Retrieval System: A Survey

引用

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

作者： Gowmari M Murugeswari G Venkatalakshmi K Department of Computer Science and Engineering Manonmaniam Sundaranar University Tirunelveli India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In recent years, digital images have gained a significant role through the growth of computer technology and multimedia information systems. Image retrieval systems assist in retrieving images similar to query images from vast databases. However, retrieving similar images from a large database remains a challenging task, highlighting the demand for successful image retrieval techniques. The effectiveness of such systems depends on the features and techniques employed for retrieval. In this paper, the existing works on image retrieval systems are analyzed and present a survey addressing three key aspects: features, datasets, and performance metrics. The features used in image retrieval systems are discussed under various categories, including Content Based Image Retrieval (CBIR), Text Based Image Retrieval (TBIR), and Semantic Based Image Retrieval (SBIR) systems. Additionally, the datasets utilized for image retrieval and the performance metrics used to evaluate these systems are also presented.

关键词： Deep learning Surveys Shape visual analytics Scalability Image retrieval Semantics Natural language processing Web sites Faces

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