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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101-110 订阅

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Novel Approach for Early-Stage Ovarian Cancer Prediction and Reducing Recurrence: A Comprehensive Review

Novel Approach for Early-Stage Ovarian Cancer Prediction and...

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

作者： Daruvuri, Rajesh Puli, Balaram Sundaramoorthy, Pandian Vamsilala, Purushothapatnapu Naga Venkata Jebaranjani, B. Sathya, R. University of Cumberlands AtlantaGA United States Everest Computers Inc Department of Engineering and Data Science RoswellGA30076 United States El CIC-1W-AMI Ibm Sandy SpringsGA30328 United States Vignan's Lara Institute of Technology and Science Guntur district Andhra Pradesh 522213 India Vsb College of Engineering Technical Campus Coimbatore India Kongunadu College of Engineering and Technology Department of Information Technology Trichy India

ISBN: (纸本)9798331521394

Ovarian cancer is one of the most difficult cancers to diagnose at an early stage because its symptoms are subtle. Accurate classification of histological subtypes of ovarian cancer is crucial for effective treatment planning and improving survival rates. This project employs advanced deep learning methods, namely Convolutional Neural Networks (CNNs), to analyze and classify ovarian cancer subtypes based on medical imaging data. The system makes use of pre-processing techniques, such as resizing, normalization, and augmentation, to improve the quality and diversity of training data. A custom-trained CNN model is used to classify subtypes such as high-grade serous carcinoma, low-Grade Serous, endometrioid, mucinous Carcinoma, clear cell carcinoma, among others. In the final application, an intuitive interface is created for clinicians to upload images with real-time subtype predictions, supporting quicker and more informed decisions. The proposed survey points to the potential of AI-driven tools in advancing cancer diagnostics. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing data Security Through Cryptographic Transformation Using a Graph CNN and LSTM Based Model

Enhancing Data Security Through Cryptographic Transformation...

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

作者： Kalva, Yamini Senthil Kumar, S. Senthilnathan, S. Kumar, A. Sasi Kavitha, C.T. Pulipeti, Srikanth TamilNadu India Sns College of Technology Department of Management Studies TamilNadu India Bannari Amman Institute of Technology Department of Information Technology TamilNadu Sathyamangalam India Karnataka India St. Joseph's Institute of Technology Omr Department of Ece TamilNadu Chennai India Mpstme SVKM's Nmims University Department of It Maharashtra India

ISBN: (纸本)9798331521394

These days, with more and more people using the internet, cloud computing, and other types of networked information systems, protecting people's and companies' privacy and security is crucial. Looking at it this way, encryption is a sort of efficient technology that can secure the transport of public information and hence satisfy these demands. The researchers achieved their objectives by making use of a wide variety of encryption methods tailored to the requirements of this area. In order to safeguard sensitive data while minimizing the possibility of assaults, it also focused on complex mathematical challenges to create an extremely complex encrypted communication mechanism. The proposed method consists of three phases, which are preprocessing, feature selection, and model training. The formulation of the sample data and group data is necessary to establish the model's setup prior to primary processing. When reducing the dimensionality of a dataset for feature selection, Principal Component Analysis (PCA) is often used. The model was trained using the GCNN-LSTM. On average, our suggested model has a 95.33% accuracy rate, which is better than competing methods such as GCNN and CNN. © 2025 IEEE.

关键词： Sensitive data

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AI-Powered Real-Time Big data analytics for ICU Resource Management

AI-Powered Real-Time Big Data Analytics for ICU Resource Man...

引用

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

作者： Kalaichelvi, S. Mohankumar, N. Uthayakumar, C. Barkathulla, A. Manojee, K.S. Murugan, S. Department of Computer Science and Applications Tamil Nadu Tiruchengode India Nagpur Campus Pune India Vivekanandha College of Engineering for Women Department of Electronics and Communication Engineering Tamil Nadu Namakkal Tiruchengode India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Trichy India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331521394

Improving patient outcomes and operational efficiency in intensive care units (ICUs) relies heavily on efficient resource management. This work presents a novel LSTM-based methodology for real-time optimization of ICU resources, using big data to enhance decision-making. Patient vitals, equipment utilization, and staff activities are just a few examples of the real-time data gathered from the many ICU sensors and monitoring devices the system incorporates using LSTM networks. LSTM networks excel in processing time-series data, allowing the system to anticipate resource requirements, spot bottlenecks, and allocate resources optimally. The proposed system offers predicted insights together with practical suggestions by analyzing historical and real-time data streams. Simulations and case studies show that the system can increase operational efficiency and predict demand. Findings point to major gains in patient care, savings, and decision-making assistance in fast-paced ICUs. This method uses LSTM networks to tackle the intricacies of contemporary hospital environments, revolutionizing ICU management with innovative AI and big data analytics. LSTM networks improve real-time decision-making, resource management, and patient outcomes in ICUs. © 2025 IEEE.

关键词： Resource allocation

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Optimizing Crop Traits with ANN and Genetic Innovations for Sustainable Agriculture

Optimizing Crop Traits with ANN and Genetic Innovations for ...

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

作者： Radha Mahendran Ramanakar Reddy Danda Asma Anjum Rashmi Abhay Kamde Bhagyashree Shailesh Firake Jayshree Suhas Wasnik Department of Bioinformatics Vels Institute of Science Technology and Advanced Studies Chennai India Architect Hyderabad Telangana India Department of Computer Science and Engineering HKBK College of Engineering Visvesvaraya Technological University Bangalore India Department of Electrical Engineering K J Somaiya Polytechnic Mumbai Maharashtra India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Sustainable agriculture requires new strategies to tackle difficulties such as rising food demand, environmental pressures, and resource constraints. This study introduces a sophisticated approach that combines artificial neural networks (ANNs) with genetic advancements to enhance crop characteristics. The system attains enhanced accuracy (98.1%), F1 score (0.97), and reduced RMSE (0.012) by utilising high-dimensional genetic, phenotypic, and environmental data, surpassing baseline models. The technique integrates ANN predictions with gene-editing frameworks, guaranteeing accurate interventions and effective trait optimisation. The results demonstrate considerable improvements in forecast accuracy and resource efficiency compared to current techniques, creating a scalable, data-driven framework to promote sustainable agriculture practices worldwide.

关键词： Precision agriculture Technological innovation Adaptation models Accuracy visual analytics Crops Artificial neural networks Genetics Sustainable development Optimization

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Evaluation of Teacher Performance using HybPSOFA - A Hybrid Approach

Evaluation of Teacher Performance using HybPSOFA - A Hybrid ...

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

作者： Sanskriti Agarwal Pitchika P N G Phani Kumar Junali Jasmine Jena Suchismita Das Manas Ranjan Nayak Mahendra Kumar Gourisaria School of Computer Engineering KIIT Deemed to be University Bhubaneshwar Odisha India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Evaluating teacher performance is essential for enhancing educational quality, but accurately estimating the performance is very challenging due to the complexity and subjectivity of the assessment criteria. It is a good real-time problem where optimization algorithms have utmost application. This paper investigates the use of metaheuristic algorithms, such as Particle Swarm Optimization (PSO), the Firefly Algorithm (FA) and proposed HybPSO-FA approach, to estimate the teacher performance. By combining the strengths of both PSO and Firefly algorithms, the hybrid technique aims to optimize performance estimation while minimizing computational complexity. The results of the proposed method indicate that the hybrid algorithm significantly outperforms the standalone methods in terms of achieving the optimal value for teacher performance evaluation and convergence speed. This study highlights the potential of using hybrid optimization techniques for performance evaluation in academics having various criteria, making them more reliable and efficient for use in educational institutions.

关键词： Performance evaluation visual analytics Metaheuristics Estimation data visualization Real-time systems Reliability Particle swarm optimization Computational complexity Convergence

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A Predictive Modelling for Breast Cancer Classification Using Machine Learning Technique

A Predictive Modelling for Breast Cancer Classification Usin...

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

作者： Aradhana Pattnaik Junali Jasmine Jena Jay Prakash Singh Amiya Ranjan Panda Anjan Bandyopadhyay Mahendra Kumar Gourisaria School of Computer Engineering KIIT Deemed to be University Bhubaneswar Odisha India Dept of Computer Science & Engineering Manipal University Jaipur Jaipur Rajasthan India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

One of the important health concern all over the globe is Breast Cancer in women. It is a complex disease that requires a thorough diagnosis before treatment can begin. This research paper explores the intervention of AI in treatment of breast cancer leveraged by using data balancing techniques. Using clinical and pathological data, machine learning is a potent tool for predicting its different forms. The effectiveness of several ML algorithms for classification of the same is investigated in this work. Models including Logistic Regression, Support Vector Machines etc. were trained and evaluated on a dataset with pertinent attributes. Some of the evaluation metrics were used to assess the models. Logistic Regression and AdaBoost classifier gave the most accurate outcome with an accuracy of 0.98 and outperformed other models. Efficacy of the models were supported by providing ROC curve of all models and confusion matrix of the best performing models.

关键词： Support vector machines Logistic regression Analytical models Accuracy visual analytics Machine learning Predictive models Prediction algorithms Breast cancer data models

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Parking Reservation and Smart Allocation using IoT

Parking Reservation and Smart Allocation using IoT

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

作者： JaiAmudhan Dhakshinamoorthy Vishwa Varshini Viswanathan Yamuneswar Annadurai V.P. Dhivya Department of Information Technology K.S. Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Smart Parking System with Dynamic Slot Allocation Using IoT and Paging Method: The aim of this proposed parking system is enhancing the user experience within an urban environment and the development of parking efficiency. A traditional system is inefficient regarding poor slot utilization and also cannot perform dynamic re-allocation. The proposed parking system utilizes IR sensors to detect the space in real-time, and it uses Paging Method to optimize slot allocation. A mobile application allows slot pre-booking, receives updates, and processes secure payments. Automatic Number Plate Recognition ensures that only authorized people get access. With a dynamic reallocation of slots depending on the users, it manages time slots effectively. The Billing Module integrates within it to calculate fees automatically for seamless payment processing. This reduces congestion, increases utilization, and provides a scalable solution to the infrastructure of modern urban life.

关键词： visual analytics Urban areas Dynamic scheduling Real-time systems User experience Sensor systems Sensors Mobile applications Resource management License plate recognition

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AI- Powered Emotion Recognition: A Multimodal Approach to Analyzing Human Sentiment

AI- Powered Emotion Recognition: A Multimodal Approach to An...

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

作者： Shalma H Shreya Mukherjee Jainulabudeen S A K Borra Surya Thirumal Reddy Bhanu Budhati Nakka Shiva Krishna Department of Artificial Intelligence and Machine Learning Sphoorthy Engineering College Nadergul Hyderabad Department of Computer Science and Engineering College of Engineering and Technology Anna University Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

AI-Powered Emotion Recognition: A Multimodal Approach to Analyzing Human Sentiment, we develop an advanced emotion detection system using cutting-edge AI techniques. By leveraging attention-based models, the system effectively analyzes and integrates multiple modalities, including facial expressions, voice tone, and textual sentiment, to deliver an accurate and nuanced understanding of human emotions. Attention mechanisms allow the model to focus on the most significant features across these modalities, capturing tiny emotional cues that may otherwise be overlooked. This multimodal approach not only enhances the accuracy of emotion detection but also improves engagement analysis, making it particularly suited for applications in mental health, education, customer service, and human-robot interaction. Through this project, we aim to showcase the power of AI in providing a more empathetic and emotionally- aware interface, pushing the boundaries of intelligent systems in understanding complexhuman sentiment

关键词： Emotion recognition Accuracy Attention mechanisms Customer services visual analytics Scalability Education Mental health Speech recognition Robustness

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HSLNF: Hybrid Socio-Legal Neural Framework for data Analysis

HSLNF: Hybrid Socio-Legal Neural Framework for Data Analysis

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

作者： Kumar, R. Arun Kamalakhannan, S.K. Sundaram, Asha Tamil Nadu Chennai India Saveetha School of Law Saveetha Institute of Medical and Technical Sciences Chennai77 India Saveetha School of Law Simats Chennai77 India

ISBN: (纸本)9798331521394

The enactment of the Transgender Persons Act marked a pivotal step in addressing the socio-economic challenges faced by transgender individuals in the labor market. This study, titled 'HSLNF: Impact Analysis of Labour Laws on Transgender Persons Post-Enactment of the Transgender Persons Act Using a Hybrid Socio-Legal Neural Framework,' evaluates the socio-economic and legal impact of the Transgender Persons Act on employment trends, wage equality, and workplace discrimination. A Hybrid Socio-Legal Neural Framework (HSLNF) was designed, integrating socio-economic variables such as employment rates and wage gaps with legal metrics like compliance rates and resolved cases. Primary data was collected through surveys, interviews, and focus groups with transgender individuals, while secondary data included government reports and legal documents. Sentiment analysis and text analysis of legal documents were conducted using natural language processing (NLP). The neural model achieved an accuracy of 94.5% in predicting trends and assessing labor law impacts. Results indicate a 12.7% increase in employment rates and a 25.4% decrease in severe workplace discrimination. Wage disparities, however, remain significant, with the gap reducing from 360 in 2015 to 320 in 2024. Legal awareness improved significantly, with compliance rates rising to 70% in 2024. These findings underscore the Act's effectiveness while highlighting the need for further reforms to achieve full inclusivity and equity. The study concludes with recommendations for policy development and advocacy for transgender rights. © 2025 IEEE.

关键词： data reduction

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A Review on Development of Farmers Disease Diagnostics and Reporting Using Machine Learning Techniques

A Review on Development of Farmers Disease Diagnostics and R...

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

作者： Moresh M Mukhedkar Vaishali V Thorat Bhakti A Mukhedkar Sonali V Jadhav Nitin A Dawande Chetana V Thorat E&TC Engineering Department School of Engineering and Technology D Y Patil University Pune India Computer Engineering Department School of Engineering and Technology D Y Patil University Pune India Computer Engineering Department Pimpri Chinchwad College of Engg. SPPU Pune University India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The Agricultural Portal is a helpful online tool designed to make farming easier and more productive. It gives farmers quick access to important agricultural information, resources, and tools. With features like weather forecasts, advice on managing pests and diseases, tips for keeping soil healthy, crop planning tools, and updates on market prices, this portal covers a wide range of needs. This paper explains how the Agricultural Portal was developed, describes its main features, and shows how it benefits farmers by boosting productivity, helping them make better decisions, and increasing their profits. Built on reliable technology, the portal can be adapted to support farmers of different sizes and locations. It's designed to be easy to use on various devices, including mobile phones and tablets, making it accessible to more people.

关键词： Support vector machines Productivity Reviews visual analytics Weather forecasting Soil Planning Reliability Portals Diseases

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