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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91-100 订阅

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Design of a Squaring Architecture Based on Yavadunam Sutra of Vedic Mathematics

Design of a Squaring Architecture Based on Yavadunam Sutra o...

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

作者： C.Gangaiah Yadav G.Sreenivasula Reddy K.Venkata Subba Reddy ECE Chaitanya Bharathi Institute of Technology Proddatur India CSE Chaitanya Bharathi Institute of Technology Proddatur Proddatur India ECE KLM College of Engineering for Women Kadapa India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

The Yavadunam Sutra, a computational method from Vedic mathematics renowned for its effectiveness in squaring numbers, served as the inspiration for the VISI design of a squaring architecture. The goal is to create a VLSI architecture that improves computational speed and efficiency by utilizing the Yavadunam Sutra's parallel processing and optimization ideas. Important parts of the architecture, including registers, adders, and multiplication units, will be designed and optimized. While optimizing performance measures like speed and accuracy, special focus will be paid to reducing power consumption and space use. Modern VLSI design tools and methodologies will be used to implement the design. The goal is to investigate and evaluate the possible advantages of incorporating ideas from Vedic mathematics into VLSI architectures for computational tasks, with a particular emphasis on squaring operations. By integrating fundamental mathematical concepts and refining hardware implementations for increased performance and efficiency.

关键词： Power measurement Power demand Scientific computing visual analytics Refining Computer architecture Very large scale integration Parallel processing Registers Optimization

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

Improving Handwritten Stenography Recognition Using Domain S...

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

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Deep learning Handwriting recognition Text recognition Error analysis Symbols Computer architecture Logic gates Writing Encoding Synthetic data

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Enhanced Real-Time Pothole Detection Using YOLOv8 with EfficientNet Backbone: A Deep Learning Approach for Road Safety

Enhanced Real-Time Pothole Detection Using YOLOv8 with Effic...

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

作者： Riya George Ambiliy Francis Binet Rose Devassy Dept. of Electronics and Communication Engineering Sahrdaya College of Engineering and Technology Thrissur Kerala

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Potholes present substantial problems to road safety and infrastructure maintenance, frequently resulting in accidents, vehicle damage, and increased repair costs. Manual inspection methods are inefficient and unfeasible for large roadway networks. This paper presents a new method to real-time pothole identification based on a customized YOLOv8 segmentation model that replaces the usual backbone with EfficientNet-B0. EfficientNet-B0, noted for its remarkable feature extraction and parameter efficiency, has been integrated to improve the precision of detection while retaining computational feasibility. The approach suggested outperforms the baseline YOLOv8 architecture in terms of mAP, precision, and recall, with a fitness score of 0.976. Extensive testing demonstrates the approach's resilience as well as utility in identifying various pothole scenarios. This study highlights the potential of deep learning-driven breakthroughs for automated transportation maintenance systems.

关键词： visual analytics Transportation Object detection Manuals Maintenance engineering Road safety Real-time systems Maintenance Testing Resilience

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Improving Mall Traffic Flow with IoT and Reinforcement Learning Techniques in Footfall analytics

Improving Mall Traffic Flow with IoT and Reinforcement Learn...

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

作者： Muthumarilakshmi, S. Sairam, A. Mohankumar, N. Hemalatha, D. Velmurugan, S. Ganeshbabu, T.R. Chennai Institute of Technology Department of Cse Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Nagpur Campus Pune India Chennai Institute of Technology Department of Artificial Intelligence & Data Science Chennai India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Department of Electronics and Communication Tamil Nadu India

ISBN: (纸本)9798331521394

An innovative method for improving mall traffic flow utilizing IoT devices and reinforcement learning (RL) methods in footfall analytics is presented. Inefficiencies, congestion, and less-than-ideal consumer experiences are common outcomes of the conventional wisdom about managing mall foot traffic. Our proposed system uses Internet of Things (IoT) sensors to track shoppers' flow and density in real time. This data is then input into an RL model to optimize and predict traffic flow patterns. Aiming to enhance the shopping experience and distribute foot traffic more equitably, the system automatically adjusts parameters, including digital signage, escalator functioning, and store promotions. Simulation and pilot deployment results show substantial improvement in lowering congestion and boosting mall visitors' time spent there. By integrating IoT with powerful machine learning (ML) algorithms, this integrated technique demonstrates how smarter and more responsive retail environments may be created. IoT and RL improves mall traffic flow by analyzing footfall trends, optimizing routing, mitigating congestion, and enhancing customer experience. © 2025 IEEE.

关键词： Reinforcement learning

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Etherdoc: Ensuring Security & Integrity for Digital Certificates using Blockchain

Etherdoc: Ensuring Security & Integrity for Digital Certific...

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

作者： Mohanakrishnan S S. Gokila Department of Computer Applications Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

A decentralised system, blockchain technology guarantees strong data security, dependability, and transparency. Etherdoc: Ensuring Security and Integrity for Digital Certificates Using Blockchain is a study that builds on these advantages. This platform assesses the effectiveness of several consensus algorithms, including Proof of Stake (PoS) and Proof of Authority (PoA), using the Sepolia testnet of Ethereum and the VeChain testnet. These chains were selected for testing due to their cost-effective implementation and compatibility with mainnet capabilities. PoS-based Sepolia provides a decentralised method with enhanced scalability and energy efficiency, producing blocks roughly every 15 seconds. However, VeChain's PoA requires faith in the validators and leverages a pre-approved set of authority nodes to achieve faster block times (10 seconds) with lower network overhead. Etherdoc illustrates the benefits of each mechanism through experimentation, emphasising PoS for its decentralisation and security and PoA for its speed and effectiveness. By combining the advantages of blockchain technology's transparency, efficiency, and dependability, this comparison study offers insightful information for future deployments of scalable and secure digital certificate management systems.

关键词： Scalability data security visual analytics Smart contracts Public key Organizations Blockchains Fraud Reliability Testing

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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

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

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Support vector machines Machine learning algorithms Prevention and mitigation Medical services Nearest neighbor methods Predictive models Prediction algorithms Diabetes Wearable devices Random forests

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Smart Tunnel Traffic Control System using IoT and Machine Learning

Smart Tunnel Traffic Control System using IoT and Machine Le...

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

作者： Subramani V Vishnu Kumar S Rishikanth S R Ramadoss Poonkuzhali Department of Information Technology K S Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Smart Tunnel Traffic Control System Using IoT and Machine Learning” is designed to offer safety and effective management of traffic in single-lane tunnels. The conventional tunnel system does not prohibit big vehicles from entering the tunnel through either end, which may lead to congestion and increase the likelihood of accidents. The designed system uses an ESP32-CAM module to sense the images of the vehicles in real time at any entry point of the tunnel. Using a CNN model, the system classifies incoming vehicles by size, and if a large vehicle is detected, it triggers a red “STOP” signal and message at the opposite end. In this way, only one large vehicle occupies the tunnel at any time. The system further calculates the transit time in terms of the length of the tunnel and updates it dynamically to handle incoming vehicles. As soon as the tunnel becomes free, a green “GO” signal allows the next vehicle to move forward. Keywords: loT, ESP32-CAM, CNN, traffic, tunnel safety, automation, image processing, classification, monitoring, accident prevention, congestion, ML.

关键词： Automation visual analytics Image processing data visualization Machine learning Traffic control Real-time systems Safety Monitoring Accidents

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Real Time Eavesdropping Attack Detection by Multivariate Correlation Analysis

Real Time Eavesdropping Attack Detection by Multivariate Cor...

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

作者： T. Mukilan C.M. Sairam J. Tamilarasan K.C. Mohanraj Department of Information Technology K.S.Rangasamy College of Technology Tiruchengode India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Using the multivariate correlation analysis technique, Frothy Disturbance Intrusion Detection Systems (FIDSs) can be crucial in identifying and stopping security threats. Understanding the importance of Frothy Disturbance Intrusion Detection Systems (FIDSs) in protecting various internet-connected domains, we concentrate on modifying traditional intrusion detection techniques for the environment, which has limitations like memory and battery life. In this study, we create a lightweight attack detection technique using the Multivariate Correlation Analysis algorithm and a supervised machine learning-based FIDS. Using a combination of two or three complex characteristics, we illustrate through simulations how effective the suggested Multivariate Correlation Analysis-based FIDS classifier is, exhibiting good performance in terms of detection time and classification accuracy. This strategy has the potential to improve application security by effectively meeting the unique limitations and specifications of intrusion detection in situations with limited resources.

关键词： Training Correlation Machine learning algorithms Accuracy visual analytics Simulation Intrusion detection Telecommunication traffic Batteries Security

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Lightweight Deep Learning Model for Wireless Capsule Endoscopy Image Classification Using SqueezeNet

Lightweight Deep Learning Model for Wireless Capsule Endosco...

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

作者： S. Gandhimathi Alias Usha P. Uma Maheswari M.Jeya Priya V. Jeevalakshmi D. Harsha Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Wireless Capsule Endoscopy has revolutionized gastrointestinal imaging by furnishing a non-invasive system to capture images of the entire digestive tract. This paper proposes a lightweight deep learning model for WCE image using the SqueezeNet enhanced with an attention mechanism. SqueezeNet, known for its compact armature, significantly reduces the number of parameters without compromising performance, making it suitable for resource - constrained surroundings. By integrating an attention medium, the model widely focuses on the most appropriate features in the WCE images. Experimentation has been carried out over a dataset of gastrointestinal images, and the proposed model achieved greater classification accuracy compared to traditional deep learning models, with significantly reduced computational cost. The lightweight nature of this model makes it ideal for deployment in real- time clinical operations.

关键词： Deep learning Wireless communication Attention mechanisms Accuracy Endoscopes Computational modeling visual analytics Gastrointestinal tract Real-time systems Personnel

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Optimal Placement of Solar Power Plant by Using AVOA Algorithm

Optimal Placement of Solar Power Plant by Using AVOA Algorit...

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

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Infrastructure development and its design are more complex because of its constraint on budget and efficiency. By remembering these difficulties, a modified electrical structure is necessary for the nation. The power system structure should not depend on the conventional system. This main criterion is being in our engineer's mind. But, strengthening loads and their necessaries do not accept them to sleep. To face complex structures and constraints-based infrastructure facilities, optimal places for renewable systems are necessary. Here, the optimal placement of solar power plant is taken as the primary concern and its related analysis is made. Even though many algorithms are coming from all over the world. The part of those algorithms is not analyzed in many environments. In this paper, an AVOA algorithm is used for our analysis. This part of our research makes us excited with its results. The IEEE 57 system is used for the analysis.

关键词： Renewable energy sources Heuristic algorithms visual analytics Power system dynamics Stability criteria Voltage System integration Power system stability Robustness Optimization

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