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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151-160 订阅

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Enhanced Transformers with Dilated Convolutional Attention for Predicting Lung Cancer Outcomes

Enhanced Transformers with Dilated Convolutional Attention f...

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

作者： Senthilnathan Chidambaranathan Pandian Sundaramoorthy Balaram Puli Rajesh Daruvuri Purushothapatnapu Naga Venkata VamsiLala R. Sathya Architecture and Design Company name is Virtusa Corporation USA (country) Piscataway NJ EL CIC-1W-AMI IBM GA USA Department of Engineering and Data Science Everest Computers Inc Roswell GA University of Cumberlands USA Vignan's Lara Institute of Technology and Science Vignan's Lara Institute of Technology and Science Andhra Pradesh Department of Information Technology Kongunadu College of Engineering and Technology Trichy

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The study in investigation of the usage of dilated convolutional attention functions with transformers to improve the accuracy of the prognosis of lung cancer. The suggested model exploits the benefits of the dilated convolutions to seize multi-scale spatial features in medical related data while it is integrating the human attention of the transformers that are focused on the most important information. The predicted model needs to cover collected clinical data patterns from which the best results for lung cancer will be achieved. These patterns are including early detection and tracking of tumor progression. this alternative the most effective way to improve feature extraction, but it also ensures presenting the most important trends in medical data to achieve an exact prognosis. The proposed methodology is an structured methodology that health experts use to diagnose tumor at variable stages of patient's testing while also giving hints of the possibility of Lyme Infections.

关键词： Heart Analytical models visual analytics Lung cancer Transformers Feature extraction data models Prognostics and health management Medical diagnostic imaging Tumors

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AI-Powered Dynamic Task Allocation for Agile Work Environments

AI-Powered Dynamic Task Allocation for Agile Work Environmen...

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

作者： J. Gayathri S Nevashine H. U Deeksha Sahana Ashok R Nithyasree S. Divya Saveetha School of Law Saveetha Institute of Medical and Technical Science (SIMATS) Chennai Saveetha School of Law Saveetha Institute of Medical and Technical Science Chennai

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Remote work success has created new management issues related to worker productivity as well as task assignments and team collaboration requirements especially for companies working with agile approaches. The research investigates an AI -based task allocation framework which optimizes efficiency of remote workers operating in agile work environments. The system employs Reinforcement Learning (RL) and Natural Language Processing (NLP) to distribute tasks through real-time data regarding team performance as well as workload and skillsets. Preprocessing activities prepared data obtained from Jira, Asana along with Slack and Microsoft Teams for training the AI model. The implemented system showed remarkable results by reducing average task completion time by 29.3% alongside achieving a 45.4% improvement in workload balance. The task-alignment model enabled higher team satisfaction levels of 26-28% which proved its capability to match work tasks to worker abilities and minimize inefficient processes. The AI system performed task allocation with 91.4% accuracy that exceeded traditional methods which managed only 72.1 % efficiency. A task management system that runs on AI technology helps both teams and their productivity when working from different locations.

关键词： Productivity Training visual analytics Collaboration Reinforcement learning Remote working Natural language processing Real-time systems Resource management Predictive analytics

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Deep Learning-Based 3D Indoor Room Layout Prediction with ConvBi-LSTM Models

Deep Learning-Based 3D Indoor Room Layout Prediction with Co...

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

作者： Narendra Mohan Manoj Kumar Department of Computer Engineering and Applications GLA University Mathura India Department of Information Technology GGV University Bilaspur Chhattisgarh India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Estimating room arrangement has become more and more significant in recent years due to its many uses. This method is very challenging because of the many factors that affect the room image, such as clutter, occlusions, illuminations, etc. It is essential to accurately determine the 3D layout of the area from a single 2D room image. The available approaches were mostly focused on determining the 3D arrangement and lacked many features. Many features must be added to a model in order for it to generate good predictions. To this degree, a strong foundation for estimating 3D layouts in interior environments was introduced by the suggested model. The input image is first pre-processed before being placed via layout estimation, where our suggested model forecasts the image's edge maps and semantic labels. The suggested framework used the Deep ConvBi-LSTM model for prediction, and the remora optimization algorithm (ROA) developed and maximized a score function to extract the best 2D layout from the candidate set. Lastly, using the layout coordinates and camera orientations, the 3D output is recreated from the 2D layout. The suggested model's effectiveness in delivering the intended performance was demonstrated by the experimental findings.

关键词： Solid modeling Three-dimensional displays visual analytics Layout Semantics Estimation Predictive models Prediction algorithms Optimization Long short term memory

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

Ensemble-Driven Multilingual Sentiment Analysis Framework fo...

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

作者： N Dharini M Madhuvanthi C S Aswini R Lakshya M Triumbika N Saranya Dept of CSE (Cyber Security) R.M.K. College of Engineering and Technology Thiruvalllur India Dept of Al & DS R.M.K. College of Engineering and Technology Thiruvalllur India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Sentiment analysis Video on demand Social networking (online) Computational modeling data visualization Predictive models Real-time systems data models Multilingual Web sites

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IoT-Based AI-Driven Solutions for Automated Classification and Segregation of Ayurvedic Leaves in Agricultural Applications

IoT-Based AI-Driven Solutions for Automated Classification a...

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

作者： R. Sathya M. Shivaani V.Nikitha Vardhini D. Ramyadharshini S. Parthiban K.S. Manojee Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In fields like Ayurvedic medicine, where the quality and purity of leaves have a direct impact on the effectiveness of medicinal formulations, the quality and appropriate sorting of leaves are crucial. Large-scale operations cannot use manual sorting techniques since they are frequently laborintensive, time-consuming, and prone to errors. The Ayurvedic Leaf Classification and Segregation System uses Internet of Things (IoT) technology to provide an automated, effective, and accurate solution to these constraints. Based on their current state of health, the technology automatically classifies and separates Ayurvedic leaves. Real-time photos of leaves are taken by a conveyor belt equipped with a camera. These images are then evaluated and contrasted with an existing dataset that includes information on healthy, afflicted, and non-Ayurvedic leaves. A Raspberry Pi processes and transmits the data for verification, and the system's IoT-based design allows for real-time monitoring and decision-making, greatly reducing manual effort while ensuring higher accuracy. The system's design directs healthy leaves to a dedicated storage area, while non-Ayurvedic and affected leaves are automatically removed and collected in a tray attached to the system stand. The system's efficiency and consistency modernize traditional practices, benefiting the agricultural and Ayurvedic medicine industries.

关键词： Accuracy visual analytics Process control Quality control Manuals Real-time systems Internet of Things Usability Sorting Biomedical imaging

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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...

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

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Social networking (online) Generative Pre-trainer transformer Bidirectional control Transformers Encoding data models

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Advancing NICU Monitoring with IoT and Real-Time AI analytics

Advancing NICU Monitoring with IoT and Real-Time AI Analytic...

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

作者： N. Vini Antony Grace S Balakrishna Reddy D Sahaya Lenin N. Mohankumar L.M. Merlin Livingston M. Rajmohan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering (Data Science) CVR College of Engineering Hyderabad Telangana India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering DMI College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In neonatal intensive care units (NICUs), the prompt and accurate observation of sick and premature infants is of the most critical significance. Using fuzzy logic and the Internet of Things (IoT), this research demonstrates an innovative method for NICU monitoring. Our system constantly records data on critical factors, including temperature, oxygen saturation, and heart rate, via an IoT sensor network. To facilitate proactive management of newborn health, this data is analysed using Artificial Intelligence (AI) algorithms based on fuzzy logic. The results of this processing include predictive analytics and real-time insights. To provide more adaptable and precise decision-making assistance, fuzzy logic is used to deal with physiological data's inherent uncertainties and unpredictability. The proposed approach aims to have a more responsive NICU staff, better patient outcomes, and fewer critical events. This integrated strategy is useful for improving NICU operations and providing the best care for premature newborns via clinical trials and simulations.

关键词： Fuzzy logic Pediatrics Uncertainty visual analytics Decision making Real-time systems Internet of Things Artificial intelligence Predictive analytics Monitoring

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Deep Learning for Skin Cancer Detection - A Review

Deep Learning for Skin Cancer Detection - A Review

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

作者： N Raghava Rao D Vasumathi Department of Computer Science and Engineering University College of Engineering Science & Technology Hyderabad JNTU Hyderabad Telangana India Department of Computer Science and Engineering University College of Engineering Science & Technology Hyderabad JNTU Hyderabad Telangana India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Skin cancer is also one of the most fatal diseases among all other types of cancer. Diagnosing it in the early stage can reduce the risk factors. visual assessment and skin biopsy are the traditional ways of diagnosing skin cancer. The results provided by this method are inaccurate and sometimes misdiagnoses occur due to the incapability of human experts. Due to the advancement of technologies, Deep Learning (DL) and Machine Learning (ML) are highly utilized in medical diagnosis, which also produces more accurate results. Many researchers have utilized DL methodologies for skin cancer detection. In this paper, an attempt is made to provide a detailed survey on skin cancer detection methods. For this survey, 25 standard research journals are accessed from sites like Springer, IEEE, and so on. The accessed papers on skin cancer detection are classified based on DL methodologies. Moreover, the DL techniques are also categorized into four categories based on the methods employed for the detection process. This analysis shows that DL-based techniques are the most employed techniques for the detection process in research journals. The majority detection process has been implemented using the Python tool. Moreover, accuracy is the most commonly utilized evaluation metric.

关键词： Measurement Deep learning Surveys Standards publications Accuracy visual analytics Skin Convolutional neural networks Skin cancer Systematic literature review

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Advanced Energy Consumption Prediction for Smart Cities Using Meta-Learning Layers

Advanced Energy Consumption Prediction for Smart Cities Usin...

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

作者： Neeraja B Wendrila Biswas M. Sreelaxmi Bhagyashree Shailesh Firake Jayshree Suhas Wasnik Rashmi Abhay Kamde Department of Electrical and Electronics Engineering Government Polytechnic Nalgonda Telangana India Department of Management and Social Science Haldia Institute of Technology West Bengal India Department of CSE (Data Science) Sphoorthy Engineering College Hyderabad Telangana India Department of Electrical Engineering K J Somaiya Polytechnic Mumbai Maharashtra India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Predicting energy consumption in smart cities possesses challenges such as data heterogeneity, scalability, and model inefficiency. This study introduces an innovative meta-learning architecture that integrates advanced feature engineering to tackle these challenges. The model analyses datasets obtained from Kaggle, attaining outstanding metrics: MAE of 0.028, MSE of 0.002, AUC-ROC of 0.985, accuracy of 96.8%, and precision of 97.2%. The suggested method significantly outperforms baseline models (Linear Regression, Random Forest, LSTM). Significant advancements encompass the integration of a meta-learning layer for adaptive learning and the implementation of robust feature engineering for energy data. These developments provide a scalable and economical method for optimising energy in smart cities. The results support its excellence, facilitating sustainable urban planning and promoting smart city growth. Future study intends to improve computing efficiency and investigate more extensive datasets to increase applicability.

关键词： Metalearning Adaptation models Energy consumption Smart cities Scalability Computational modeling Computer architecture Predictive models Energy efficiency data models

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Fraud Detection in Financial Transaction Using GNN

Fraud Detection in Financial Transaction Using GNN

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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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