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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61-70 订阅

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A Novel Approach on Detecting Deep Fake Imagery in Images and Videos

A Novel Approach on Detecting Deep Fake Imagery in Images an...

引用

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

作者： N Duraimurugan P Kumar R Jayaprakash Computer Scicence and Engineering Rajalakshmi Engineering College Thandalam Chennai

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Advances in artificial intelligence have made deep fake technologies develop very rapidly, with tremendous requirements for detection systems that guarantee efficiency in maintaining digital integrity. This paper is an approach that outlines the use of state-of-the-art neural networks in the development of a multimodal framework of selecting deepfakes with precise and efficient identification. The framework will be a spatial feature extraction through EfficientNetB7, spatial-temporal analysis using 3D Convolutional Neural Networks, and sequential learning applied for temporal data using the LSTM network. The new approach is sensitive to micro-level anomalies, including visual artifacts, inter-frame dynamics, and long-term temporal relationships that uniquely characterize synthetic media from actual information. This hybrid strategy offers an integrated solution in the battle against emerging techniques of synthetic media creation exploiting multiple analytical skills. Beyond providing a solution to technical problems of deep fakes, the paper contributes to larger social projects toward regaining trust in digital content. The results demonstrate how multimodal architectures could be transformative for the domain of digital forensics thereby opening new fields to new applications in the fight against misinformation.

关键词： Deepfakes Three-dimensional displays visual analytics Neural networks Digital forensics data visualization Feature extraction Convolutional neural networks Artificial intelligence Long short term memory

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Analysis of Market Trends with Sentimental Analysis using Word2Vec and GloVe

Analysis of Market Trends with Sentimental Analysis using Wo...

引用

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

作者： Senthil Pandi, S. Kumar, P. Harinisubashree, S. Helen Shalini, A. Rajalakshmi Engineering College Department of Cse Chennai India Rec Department of Cse Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

This research study implements an aspect-based sentimental analysis of online comments and reviews. It considers customers' opinions based on various aspects of a product such as quality, price, and customer service. Extricating the sentiments related to the product's features gives a nuanced perception of the feedback provided by the customer. This enables the company to filter comments based on the most appropriate details, making it simple to access the areas needing enhancement. The proposed solution uses machine learning techniques like Word2Vec, GloVe etc to inquire into text identifying the important aspects in reviews by categorizing them as positive, negative, or neutral. The system then combines the data to provide deep comprehension enabling the business to acknowledge not just the sentiment but also the sentiment related to each feature. The machine learning model SVM is used to analysis the sentiment of the user comment. The feedback linked with the sentiments, it helps the stakeholders to point out trends and the areas to develop. This allows the companies to make datadriven conclusions swiftly and efficiently. This allows the companies to make data-driven conclusions swiftly and efficiently. This aspect-specific feedback enables stakeholders to identify areas that require improvement to adapt to the trends. This model allows room for enterprises to make room to make informed and data-driven decisions swiftly and effectively which helps their ability to respond to the needs of customers and products. © 2025 IEEE.

关键词： Support vector machines Sentiment analysis Reviews Filtering Decision making data visualization Machine learning Market research Stakeholders Business

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Transforming Sentimental Analysis: Applying Deep Learning with BERT and GPT for X data Insights

Transforming Sentimental Analysis: Applying Deep Learning wi...

引用

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

作者： Varma, Manthan S Ghosh, Soma Coep Technological University Computer Science & Engineering Pune India

ISBN: (纸本)9798331521394

Social media platforms like X have become key channels for expressing opinions and sentiments, creating vast amounts of textual data. Analyzing this data to understand public sentiment is crucial for various applications, including market research, political analysis, and customer feedback. Traditional sentiment analysis methods often struggle with the nuances of language, especially in informal settings like X. However, recent advancements in deep learning, particularly with transformer models such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), have significantly improved the accuracy of sentiment analysis. These models use attention mechanisms to capture the context of words within a sentence, allowing for a more sophisticated understanding of sentiment. Unlike existing studies that predominantly focus on either BERT or GPT separately, this research provides a comparative evaluation of their effectiveness for sentiment analysis on X, highlighting their strengths and limitations in handling informal and context-dependent text. BERT, with its bidirectional approach, and GPT, known for its text generation capabilities, have achieved state-of-the-art results in numerous NLP tasks. This study leverages these models to analyze the Sentiment140 dataset, aiming to enhance sentiment analysis on X by utilizing deep learning techniques and libraries like Hugging Face Transformers, PyTorch, and TensorFlow. © 2025 IEEE.

关键词： Deep learning

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An Enhanced Approach to Detect the Eight Stages of Osteoarthritis Using Learning Algorithm

An Enhanced Approach to Detect the Eight Stages of Osteoarth...

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

作者： Kunnaragurubaran, T. Madhumita, P. Kavyashree, B.N. Senthil Pandi, S. Rajalakshmi Engineering College Department of Cse Chennai India Department of Cse Rec Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

This paper introduces an innovative method in the classification of knee osteoarthritis based on an ensemble of convolutional neural networks specifically MobileNet and DenseNet. Osteoarthritis is a common degenerative joint disease that causes much pain, diminished mobility, and poor quality of life, primarily among the older population. Precise and timely diagnosis is an important aspect because it will only lead to successful intervention and the proper planning for treatment. Our approach uses an ensemble model with MobileNet and DenseNet. It significantly outperforms a traditional single-CNN approach for feature extraction in knee radiographs. The ensemble model captures the diverse architectural strengths, thereby catching a wide variety of image patterns while reducing overfitting due to training on different subsets of the dataset. In addition, more advanced ensemble strategies such as bagging and boosting improve the accuracy and robustness of the model. The method was validated using a large dataset of knee radiographs covering the full spectrum of osteoarthritis progression. Experimental results demonstrated higher classification accuracy than traditional techniques and, in some cases, outperformed expert radiologists. This research has shown the power of CNN ensembles in medical imaging, thus underlining the transformative role of machine learning in healthcare and its potential to support clinical diagnostics and improve patient outcomes. © 2025 IEEE.

关键词： Training Accuracy visual analytics Feature extraction Robustness Planning Convolutional neural networks Osteoarthritis Medical diagnostic imaging Diseases

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Optimal Placement of Solar Power Plant by Using MOTHFLAME Algorithm

Optimal Placement of Solar Power Plant by Using MOTHFLAME Al...

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

作者： K. Karthikeyan A. George Klington Department of Electrical and Electronics Engineering St. Mother Theresa engineering college Tuticorin India Department of Computer science Engineering St. Mother Theresa engineering college Tuticorin India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This study recommends the best location for a solar power plant in an IEEE 69-bus system to maintain voltage stability. PV recitation learning is done in an IEEE 69-bus test system using MATPOWER to develop voltage stability. A power device's balance is impacted when it experiences unexpected loading. To increase the stability from the diseases, compensation is required. In this case, the machine is examined utilizing CPF to improve stability. The overall performance of the suggested system is assessed using a variety of operating conditions, including those with and without PV, with a PV system utilising PSO, and with PV placement (using the Mothflame method). The findings indicate that a device with PV (adjusted with Mothflame's assistance) performs better than a device without PV and a device with a PV system installed by PSO.

关键词： Performance evaluation visual analytics Loading Fires Voltage Stability analysis Power generation Load modeling Load flow Diseases

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Predictive Analysis of Kidney Diseases with Artificial Intelligence and data Science

Predictive Analysis of Kidney Diseases with Artificial Intel...

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

作者： Eswar, G. Hariharan, C. Lavein, S. Akilandeswari, V. Velammal College of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798331521394

In the Field of Medical diagnosis, nowadays there are many new methods are getting introduced each day and also gets advanced by many people around the world. The Kidney is one of the most vital organ of a Human body that helps a lot by filtering blood and throws the waste toxic contents through excretory organs. But the food habits and other bad habits can affect the process of Kidney and further even affects kidney. As a result our kidney can also suffer from various disease. Most vital Kidney diseases can include Chronic Kidney Disease(CKD), Diabetic Nephropathy at the 2nd stage of Diabetes, and still many more. In the process of predicting of these diseases, we can use machine learning algorithms like Random Forest with Decision Tree and Supervised Learning algorithms we can train a model that can use the previous datasets to clarify if the new person is diagnosed with the particular Kidney disease. The Random Forest algorithm provides around 90% accuracy that is better in prediction of results for the collected information. Using these techniques we can detect the problem at early stage which can give a greater chance to prevent it from getting worse. © 2025 IEEE.

关键词： Supervised learning

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Deep Learning for Neurological Brain Disorder Classifications

Deep Learning for Neurological Brain Disorder Classification...

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

作者： Karthick M Krishna N S Jemima Havila Catherine A Department of ECE St.Joseph's Institute of Technology Chennai

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Utilizing Convolutional Neural Networks (CNN), this research study has developed an advanced methodology to classify disorders such as Alzheimer's disease using medical imaging modalities like MRI and CT scans. Our method integrates multi-modal data collaboration by combining structural, functional, and diffusion imaging in order to build detailed connectivity and composition of the brain. We conducted large-scale experiments that proved the efficiency of the model in differentiating Alzheimer, Parkinson, multiple sclerosis, and epilepsy. Classification networks were also used for interpretability, where attention mechanisms addressed important features responsible for classification of the task in a mechanistic type of way. All of these results provide the way for better ways of classification and targeted therapies, eventually leading to better outcomes for the patients.

关键词： Deep learning Accuracy Computational modeling Medical treatment Benchmark testing Brain modeling data models Robustness Convolutional neural networks Alzheimer's disease

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Implementation of ANFIS Assisted Modified CNN Classifier for Autism Spectrum Disorder Detection

Implementation of ANFIS Assisted Modified CNN Classifier for...

引用

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

作者： A Benny Richard J. Friska K. Lakshmi Narayanan Department of ECE Francis Xavier Engineering College Tirunelveli India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This Study presents a Medical Image Processing Technique to detect Autism Spectrum Disorder (ASD) using the Patient's facial image. An Algorithm used here is Deep learning, that is used to detect ASD from the input face image of the Patient. The input image is preprocessed and it is moved to data augmentation process to increase its robustness and diversity, then the SIFT (Scale-Invariant Feature Transform) is used for feature extraction which captures distinctive points in the images regardless of scale, rotation, or illumination changes. These features serve as valuable inputs for training the MCNN model, enhancing its ability to discern patterns associated with ASD. The MCNN model is designed and trained to classify the images either ASD or non-ASD category with high accuracy.

关键词： Training Deep learning Autism Adaptation models Accuracy Transforms Feature extraction data augmentation Mathematical models Convolutional neural networks

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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...

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

作者： Srikanth Lakumarapu P. Ruba Sudha P.J. Beslin Pajila K. Sasirekha R. Vinoth Siva Subramanian R Department of CSE CVR College Of Engineering Telangana India Dept of CSE S.A Engineering College Dept of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept of CSBS R.M.D Engineering college Department of CSE RMK College of Engg and Tech Dept of CSE RMK College of Engg and Tech

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Ethics visual analytics Evaluation models Decision making data visualization Medical services Machine learning Market research Usability Software tools

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visual analytics of the current status and trends in artificial intelligence education based on big data 24

Visual analytics of the current status and trends in artific...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Chen, Yinhong Chen, Yuefeng College of Mathematics and Computer Guangdong Ocean University Guangdong Zhanjiang China

ISBN: (纸本)9798400709999

More researchers are starting to focus on the intersection of artificial intelligence and education as a result of the technology's ongoing advancements. The paper conducts a thorough analysis of the research hotspots, and development lineage in international artificial intelligence education over the last ten years using the visualization analytic tools VOSviewer and CiteSpace. The following findings are obtained from the analysis of keyword co-occurrence and clusters: the flipped classroom, dropout prediction, and large language models are the main subjects of international research on artificial intelligence;the practical application of artificial intelligence in education, ethical and moral challenges, and the realization of the successful integration of intelligent technology and ideological and political education are the main research trends of future. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： visual analytics

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