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

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Text data Security Through Hybrid Method Using visual Cryptography and Image Steganography Algorithms 6th

Text Data Security Through Hybrid Method Using Visual Crypto...

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6th international conference on Computational Intelligence in Communications and Business analytics, CICBA 2024

作者： Bhawna Malik, Sanjay Kumar Department of Computer Science and Engineering S R M University Haryana Sonipat India

ISBN: (纸本)9783031813351

Modern society puts a great deal of importance on digital communication, thus keeping it hidden is crucial. Many modern softwares are web-based, and users increasingly rely on social networking sites like Facebook, Twitter, and others. The data must be secured against unauthorized access and usage since it is distributed over unsecured channels. Since the information is sent across unsecured channels, it is crucial to prevent unauthorized access to and use of the data. Although there has been a significant amount of research done in the past, most of it has not taken into account the need for strong hybrid security concerning secret text. The two techniques most commonly used to prevent unwanted access to sensitive text data are visual cryptography and image steganography. These two techniques’ unique merits, nevertheless, are not secure enough for the contemporary era. There may be some vulnerability as a result. If visual cryptography is combined with image steganography, it is feasible to use both algorithms together to obtain high levels of information security. This study presented a hybrid security method for hiding secret text that combines visual cryptography and LSB image steganography. Colour image steganography is used to hide shares in other cover images and cover up secret text in cover images. For splitting the cover image into two shares, use visual cryptography. The secrecy, trustworthiness, and effectiveness of covert communications are considerably improved by these security protocols. Even though there isn’t a parameter to represent the level of security reached by employing cutting-edge technologies, the accuracy of the received text data is calculated in terms of MSE and correlation coefficient by comparing the transmitted and received text data. The time required at the transmitter and receiver ends is also assessed in order to gauge how effective the recommended methodology is. The implementation demonstrates the enhanced resilience of the sug

关键词： Cryptography Decryption Encryption Steganography

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Classification of Tuberculosis Bacilli from Microscopic Sputum Smear Images Using Swin Transformer

Classification of Tuberculosis Bacilli from Microscopic Sput...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), international conference on

作者： Praneeth Vallabhaneni Ashutosh Satapathy Manisha Indugula Department of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Tuberculosis (TB) has been a great challenge in the health world, and proper treatment requires proper diagnosis at the right time. This paper has classified the bacilli in sputum samples into single/simple and clump formations using deep-learning transformer techniques. The method starts with semantic segmentation, where TransUNet model has been used on sputum images to detect regions containing the bacilli. The superior visual task performer, the Swin Transformer, further classifies the regions identified. Accuracy, precision, recall, F1-score, and AUC-ROC are the metrics used to evaluate the model's performance. This classification contributes to TB diagnosis by quantifying bacterial load and categorizing infection severity. This type of differentiation may guide clinicians in developing more precise treatment strategies as the variability of infection severity may be observed in patients suffering from TB. Integrating the recent transformer models, such as Swin Transformer and TransUNet, into the microbiological diagnosis process will improve the speed of TB diagnosis, and automated bacterial classification will occur through an organized process. This study will help diagnose TB by improving the capacity of diagnosis, which subsequently helps in better decision-making regarding treatment and management, independent of human interpretation. The successful classification of TB bacilli resulted in an average accuracy of 93%, average precision of 93%, average recall of 92.5%, and average F1-score of 92.5%.

关键词： Training visualization Accuracy Microorganisms Tuberculosis Semantic segmentation Microscopy Transformers Load modeling Testing

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Interactive Physiotherapy Game Using React Three Fiber for Hand Motion Rehabilitation

Interactive Physiotherapy Game Using React Three Fiber for H...

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IEEE Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Ram Kumar Yadav Ashish Kumar Soni Apeksha Sakare Department of Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering G H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This article describes an interactive physical activity system designed to use React Three Fiber to assist patients with hand rehabilitation. The game simulates a virtual environment where rods controlled by motion-sensing devices represent the patient’s hand. Through the "Create" function, a sphere can be created from the previous position and the patient must hold their hand firmly within the sphere’s boundaries. Adjustable sliders allow optimal control of hold time and sensitivity to adapt to the patient’s growth and treatment needs. The game provides immediate visual feedback, encouraging patients to practice stability and mobility, which are essential elements of good physical therapy. The app simulates real-world movements in a virtual environment, creating a controlled environment that provides immediate visual feedback to encourage patients to take action. This technology uses React Three Fiber to create a 3D environment, including writing information for precise control of the stick. This interactive model is designed to not only develop motor skills through consistent, guided exercises, but also to improve overall patient compliance. Through this application, we explore the intersection of play and physical therapy by examining the potential for digital therapy tools to complement and enhance traditional practices This approach uses the appeal of interactive games to potentially increase treatment compliance, making it an important tool in digital therapy. This article discusses the use of React Three Fiber’s techniques, methods, and therapeutic potential for physical therapy.

关键词： Hands visualization Three-dimensional displays Sensitivity Medical treatment Virtual environments Games Writing Stability analysis Optical fiber devices

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DeepFakeGuard: Safeguarding Digital Platforms Against Fake Profiles Using AI

DeepFakeGuard: Safeguarding Digital Platforms Against Fake P...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： M. Swarna Sudha S. Manjula Indira Bharathi Valarmathi Krishnasamy K. Vijayalakshmi Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam India School of Computer science and Engineering Vellore Institute of Technology Chennai India Department of Electronics and Communication Engineering PSR Engineering College Sivakas India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This research introduces DeepFakeGuard, a hybrid deep learning framework designed to detect fake profiles on social media platforms, addressing the growing threat of fraudulent accounts online. DeepFakeGuard integrates multiple data modalities, including textual, visual, behavioral, and network features, to accurately classify profiles as real or fake. Leveraging advanced deep learning models such as BERT for textual analysis, ResNet for image processing, and LSTM for modeling behavioral patterns, DeepFakeGuard provides a comprehensive and robust solution for identifying fake profiles across diverse platforms. The framework processes a rich set of profile characteristics, including account information, posting behavior, post sentiment, and network relationships, ensuring a multi-dimensional analysis of social media accounts. The hybrid architecture of DeepFakeGuard merges outputs from individual sub-models, which are then processed through dense layers for final classification using a sigmoid activation function. This multi-faceted approach significantly enhances detection accuracy, precision, and recall compared to traditional methods. The framework is highly scalable and adaptable to various social media platforms, offering a versatile tool for strengthening online security. With strong performance metrics, including high classification accuracy, DeepFakeGuard demonstrates its potential for real-world applications in mitigating the risks associated with fake profiles. By fostering secure and trustworthy social media environments, this research contributes to the ongoing fight against fraudulent accounts, paving the way for safer online interactions.

关键词： Deep learning Analytical models visualization Accuracy Social networking (online) Soft sensors Feature extraction data models Security Long short term memory

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An Improved Dual-Path Attention Mechanism With Enhanced Discriminative Power for Asset Comparison Based on ResNet50

An Improved Dual-Path Attention Mechanism With Enhanced Disc...

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Advances in Electrical, Electronics and Computing Technology (EECT), international conference on

作者： Ying Jing Li Zhang School of Computer Science Beijing Information Science and Technology University Beijing China School of Management Science and Engineering Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

With the wide application of deep convolutional neural networks (CNNs), higher requirements are put forward for feature extraction and discriminant ability in asset comparison tasks. The rapid development of CNNs has significantly promoted the performance improvement of computer vision tasks, in which ResNet50, as the mainstream feature extraction network, effectively alleviates the problem of gradient disappearance in deep networks by means of residual learning. However, despite ResNet50's excellent performance in many visual tasks, there is still room for improvement in the effectiveness of feature expression and the mechanism of attention to key areas in response to changes in complex scenes. Therefore, this paper puts forth a new network architecture with improved dual-path attention mechanism and discriminative feature enhancement module based on ResNet50 (DADF-ResNet50). This method strengthens important features in channel and spatial dimensions respectively through the improved dual-path attention mechanism to improve the model's focusing ability on key asset areas. At the same time, the discriminating power enhancement module further optimizes the distinguishing ability of features and effectively improves the feature comparison performance between different assets. We design and implement the network architecture of this approach and evaluate it comprehensively in the asset comparison task. The experimental results show that the model has better distinguishing ability and feature extraction efficiency in diverse asset comparison scenarios, which is helpful to solve the asset identification problem in complex scenarios.

关键词： Training visualization Attention mechanisms Focusing Network architecture Feature extraction data models Convolutional neural networks Residual neural networks Image classification

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Advanced Block Chain of Electronics Health Care Medical Record Systems Wearable Device Industry 5.0

Advanced Block Chain of Electronics Health Care Medical Reco...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Thiagarajan Kittappa L. Manjunath G.B. Suresh K. Selvi P.M Murali Revathi V Department of Mathematics Rajalakshmi Institute of Technology Poonamalle Chennai Dept of ECE CVR College of Engineering Hyderabad Telangana Dhanalakshmi Srinivasan College of Engineering and Technology Chennai Department of Management Studies S.A. Engineering College (Autonomous) Chennai ECE Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation Deemed to be University Salem India New Horizon College of Engineering Bangalore

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The creation and deployment of the biometric-based medical record system the research paper suggests a novel way to use biometric technology to expedite the management of medical records: the “Med Vault - Biometric Based Medical Record System.” Although the idea seems intriguing, there are a few things that could be done better. First of all, there are privacy and security issues with the use of biometric authentication. Biometrics offer a practical means of identification verification, but they are not infallible. By evading or altering the biometric authentication procedure, hackers may be able to obtain private medical data. To safeguard patient data, Med Vault's developers must thus make significant investments in strong encryption and security procedures. Furthermore, broad acceptance by healthcare practitioners is crucial to the system's efficacy. Patients may still encounter difficulties accessing their medical records across various healthcare facilities if universal implementation is not achieved. It would be advantageous for Med Vault to collaborate with significant healthcare institutions and provide them with incentives to incorporate this technology into their current infrastructure. Additionally, Med Vault ignores any problems with data input errors or missing records, despite its claims to increase productivity by lowering paperwork and administrative duties. Providing high-quality treatment depends on accurate and comprehensive medical records, therefore any system that seeks to transform record-keeping should give priority to these features. In conclusion, even if the Biometric Based Medical Record System offers a promising chance to enhance medical record administration, important issues need to be resolved before its broad adoption can be deemed effective. Its long-term survival as a solution for effective medical record administration will depend in large part on how well privacy concerns are addressed, how widely healthcare providers adopt it, a

关键词： Privacy visual analytics Medical treatment Transformers Blockchains Security Servers Internet of Things History Electronic medical records

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Electric Vehicle Charging System Using RFID and IoT for Enhanced User Experience

Electric Vehicle Charging System Using RFID and IoT for Enha...

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IEEE Middle East and North Africa Communications conference (MENACOMM)

作者： Md Mehedi Hassain Injamul Haque Jamil Md Mahmudul Hoque Md. Jahid Hasan Dept. of Electrical & Electronics Engineering International Islamic University Chittagong Chittagong Bangladesh Dept. of Computing & Mathematical Sciences University of Greenwich London United Kingdom Dept. of Computer Science & Engineering CCN University of Science and Technology Cumilla Bangladesh Dept. of Information & Communication Technology Comilla University Cumilla Bangladesh

ISBN: (数字)9798331519957

ISBN: (纸本)9798331519964

Electric vehicles (EVs) are surging in popularity globally, offering a greener and more energy-efficient alternative to traditional cars. Our research aims to develop an RFID based system designed to automate EV charging with minimal user input. This system integrates RFID readers, tags, a microcontroller, and a relay module to manage and monitor charging sessions efficiently. An IoT platform supports real time monitoring and remote control, while a secure payment mechanism handles transactions seamlessly. RFID tags identify vehicles for automatic charging initiation, and the relay module prevents overcharging by disconnecting power after a set time. The system uses MQTT for data transmission and offers real-time insights into energy use and account balances. All information is updated and displayed in real-time on the server. MySQL ensures scalability, enabling effective handling of large volumes of data. The mobile application dashboard provides a view of the charging and discharging processes for user monitoring, and an email notification is sent after the recharge. EV battery charging status is monitored through visual graphs. Key benefits include a streamlined user experience, improved charging management, and support for EV adoption. This innovation advances sustainable mobility with a user friendly, efficient charging solution.

关键词： visualization Technological innovation Transportation Real-time systems Electric vehicle charging User experience Energy efficiency Relays Monitoring Radiofrequency identification

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Using Deep Learning to Spot False Product Photos in Online Sales

Using Deep Learning to Spot False Product Photos in Online S...

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Intelligent Control, Computing and Communications (IC3), international conference on

作者： Manjulata Bhoi Patil Manisha Prashant Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Ways to shop for various items without stepping outside became totally feasible with the e-commerce site boost mod, and it helped change the dynamic within the retail industry. However, this online facility has also led to the penetration of counterfeit and fake goods in the market, which have posed a significant threat to consumers and manufacturers. Counterfeit product images detection on e-commerce web sites is a challenging task as counterfeit products are visually highly similar to the genuine products. Due to the large volume of products in the market and a wide spectrum of means of differentiating authentic from inauthentic products, traditional means to identify them (mainly requiring human inspection or rule-based systems) are also changing, especially for counterfeit products where these means are rapidly losing effectiveness. A lot of deep learning image classification techniques is lately proposed as a good starting point for automating the detection of fake product images. In this paper, we propose to use deep learning models to address detection of counterfeit images of products on e-commerce websites. ABSTRACT. Specifically, we look at Convolutional Neural Networks (CNNs) for image feature extraction and Generative Adversarial Networks (GANs) for image verification. A CNN which has been widely studied for its power to learn spatial hierarchies in images would work in detecting the visual discrepancies and irregularities between images of genuine and counterfeited products. On the other hand, GAN s can generate fake product images for the defined model to learn the small details differentiating the real items from the counterfeits, through comparing the input real-world images with adversarially generated ones. An important element of the solution implementation is the creation of a bulk dataset, which includes product photos from different categories, specifically electronics, clothing, and accessories. The models for this approach are trained on a se

关键词： Deep learning visualization Accuracy Computational modeling data preprocessing Generative adversarial networks Electronic commerce Convolutional neural networks Web sites Image classification

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Detection of Emergency Vehicles in Traffic and Assign Traffic Free Path Using Deep Learning

Detection of Emergency Vehicles in Traffic and Assign Traffi...

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Sentiment Analysis and Deep Learning (ICSADL), international conference on

作者： Sriharsha Vikruthi Tejesh Reddy Singasani V T Ram Pavan Kumar M P V V S D Nagendrudu Ch Raghavendra R. Sahith Dept of CSE B V Raju Institute of Technology Narsapur Medak Telangana India University of Cumberlands Lousiville KY Dept of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhrapradesh India Department of Artificial Intelligence and Machine Learning Aditya Univeristy Suramplaem India Dept of CSE(Data Science) KKR & KSR Institute of Technology and Sciences Guntur Andhra Pradesh Dept of Computer Science and Engineering CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Too much of Traffic jams are in a highly populated country. Sometimes emergency vehicles such as fire-fighter and ambulance get stuck in traffic, putting lives at threat in many cases. Priority should be given to special vehicles and their way should be cleared. However, traffic police find it difficult or sometimes impossible to handle with it. Emergency vehicles have visual and audio warning devices help them to overtake traffic by demanding as little space as possible from other vehicles. Many death happen due to delay in doctors, ambulance and fire engine vehicles while reaching their destinations. The traditional systems didn't provide path free route accurately. Therefore, by using detection of emergency vehicles in traffic and assign traffic free path using deep learning system, emergency vehicles can get free path or fast route by observing the traffic cameras. So, those emergency vehicles can reach the location on time by using RNN system. Therefore, the main objective is to improve this system for better results interms of accuracy, detection rate and precision.

关键词： Deep learning Space vehicles visualization Sentiment analysis Recurrent neural networks Accuracy Law enforcement Roads Medical services Automobiles

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Real-Time Driver Drowsiness Detection Using Mobile Net for Road Safety Enhancement

Real-Time Driver Drowsiness Detection Using Mobile Net for R...

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Disruptive Technologies (ICDT), international conference on

作者： Chengamma Chitteti Mallavarapu Sravika Shaik Ayesha Dungavath Sridhar Naik Vangipuram Anil Kumar Muthyamaina Pavan kumar Dept. of Data Science Mohan Babu University (Erst While Sree Vidyaniketan Engineering College) Tirupati A.P India Dept. of CSSE Sree Vidyanikethan Engineering College Tirupati A.P India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Research shows that fatigue ranks within the three main road accident causes worldwide. This paper develops a driver sleep detection system in real time with MobileNet as the light weight convolutional neural network architecture built specifically for indoor and mobile environments. Real-time system facial scans seek driver indicators of drowsiness which include lengthy eye closures and slow yawn activity leading to sleepiness. MobileNet operates at a low cost which makes it both rapid and functional for vehicle implementation without accuracy compromises. The system helps prevent driver fatigue which leads to better vehicle safety because drivers receive instant feedback through this alert model. This solution presents several advantages such as affordability as well as mobility through mobile devices which makes it ideal for current vehicle safety requirements. Fatigue signs to avoid accidents. When drowsiness accelerates the slowdown of reaction times but increases alertness it causes higher risks of vehicle crashes. System implementations that depend on physical signals need drivers to maintain close positions with the equipment which results in driver distraction. We evaluate the driver eye movement behavior to measure driver behavior through imaging-based dataset measurements in this paper. MobileNet eliminates optical images successfully at a low cost thus speeding up the visual fatigue relief process. MobileNet model demonstrated a performance of 94 % in the evaluation results. The system requires 0.00829 seconds to recognize tiredness. The system delivers exceptional real-time functionality thus making it an extraordinary example. Better performance was achieved through the implementation of hyperparameter optimization in connection with k fold validation. The study matters greatly because its implementation would assist in protecting lives by presenting an effective strategy to stop road fatalities arising from unpredictable seizures.

关键词： visualization Accuracy Road accidents Costs Sleep Vehicle safety Fatigue Real-time systems Road safety Vehicles

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