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检索条件"任意字段=Conference on Visual Information Processing and Communication"

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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...

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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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IoT-Enhanced Airborne Pathogen Surveillance in Healthcare Environments using Deep Generative Networks

IoT-Enhanced Airborne Pathogen Surveillance in Healthcare En...

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visual Analytics and Data visualization (ICVADV), International conference on

作者： Prabhu V S Satheeshkumar Sekar S. Dhanalakshmi N. Mohankumar B. Gopi S. Murugan Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India Mphasis Corporation Project Manager Chandler Arizona USA Department of Information Technology Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Biomedical Engineering Innovation Center Sona College of Technology Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

In hospital settings, airborne pathogens present significant risks to the health and safety of workers and patients. This research proposes an enhanced method for airborne pathogen detection by integrating deep generative networks (DGNs) with Internet of Things (IoT) technologies. The system uses a network of IoT sensors placed in hospitals to continuously monitor environmental factors, including humidity, temperature, and particulate matter, to identify pathogens. By analyzing the data collected by the sensors, DGNs can accurately detect the amount and kind of airborne pathogens by modeling complicated patterns. The system's deep generative method enhances the model's capacity to identify minute variations in pathogen levels by producing artificial data that complements sensor data. The possibility of spreading infection may be greatly reduced with real-time data processing and analysis, which enables quick alerts and responses. The proposed system's capacity to enhance infection control measures and sustain a safer hospital environment is shown via simulations and real implementations, proving its efficiency. Integrating IoT real-time monitoring capabilities with DGNs' complex analytical capability signifies a notable leap forward in pathogen surveillance.

关键词： Temperature sensors Temperature measurement Pathogens Hospitals Atmospheric modeling Surveillance Real-time systems Data models Sensor systems Internet of Things

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IoT-Enhanced Airborne Pathogen Surveillance in Healthcare Environments using Deep Generative Networks

IoT-Enhanced Airborne Pathogen Surveillance in Healthcare En...

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2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Prabhu, V.S. Sekar, Satheeshkumar Dhanalakshmi, S. Mohankumar, N. Gopi, B. Murugan, S. R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Mphasis Corporation Project Manager ChandlerAZ United States Sri Muthukumaran Institute of Technology Department of Information Technology Tamil Nadu Chennai India Nagpur Campus Pune India Innovation Center Sona College of Technology Department of Biomedical Engineering Tamil Nadu Salem 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

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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...

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visual Analytics and Data visualization (ICVADV), International conference on

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

关键词： Analytical models Accuracy Decision making Predictive models Big Data Real-time systems Data models Resource management Long short term memory Load modeling

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Enhancing Industrial Automation Flexibility Through Neural Network-Empowered Machine Vision Applications 1st

Enhancing Industrial Automation Flexibility Through Neural N...

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1st International conference on Artificial Intelligence and its Applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (纸本)9783031843969

Vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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A Hybrid Deep Learning Model for Optimizing Electric Vehicle Battery and Navigation Systems

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

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Machine Learning and Autonomous Systems (ICMLAS), International conference on

作者： R. Arul Jose P. Ebby Darney R. Santhana Krishnan J. Relin Francis Raj D. Jansi Rani P. Sundaravadivel Department of Electrical and Electronics Engineering Rajarajeswari College of Engineering Bengaluru India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmhadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. Data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency.

关键词： Deep learning Training Computational modeling Battery management systems Real-time systems Data models Batteries State of charge Optimization Long short term memory

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Demonstration of Millimeter Wave Radar Sensing in Fire and Smoky Environments

Demonstration of Millimeter Wave Radar Sensing in Fire and S...

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International Workshop on Mobile Terahertz Systems (IWMTS)

作者： Aman Batra Andreas Prokscha Thorsten Schultze Ridha Farhoud Michael Wiemeler Diana Göhringer Jan C. Balzer Thomas Kaiser Institute of Digital Signal Processing University of Duisburg-Essen Duisburg Germany HELLA GmbH & Co. KGaA Lippstadt Germany Chair of Communication Systems University of Duisburg-Essen Duisburg Germany Adaptive Dynamic Systems Technische Universität Dresden Dresden Germany

ISBN: (数字)9798350365832

ISBN: (纸本)9798350365849

In emergency situations involving fire and smoke, optical and infrared sensors offer limited information due to visual obstructions and intense heat, whereas radar sensing seems to be a promising solution. Radar sensing's ability to penetrate smoke and fire is well established at microwave frequencies of sub-24 GHz. However, spatial resolution is limited at these frequencies, prompting the exploration of higher-frequency regions. Therefore, this study investigates radar sensing within the millimeter-wave (mmWave) spectrum, specifically in the 75-110 GHz frequency range. A vector network analyzer (VNA) based testbed in a monostatic configuration is implemented, and test cases involving the standardized generation of various smoke and flame types are examined. Synthetic aperture radar (SAR) technique is employed to generate a 2D map of the investigated environment. The map is explored for estimation of attenuation in signal power due to the fire. Additionally, the imaging results are analyzed for artifacts resulting from phase errors caused by flame and smoke.

关键词： visualization Terahertz wave imaging Laser radar Fires Millimeter wave radar Radar imaging Attenuation Vectors Sensors Synthetic aperture radar

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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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visual and Multimedia information Management: IFIP TC2/WG2.6 Sixth Working conference on visual Database Systems May 29-31, 2012 Brisbane, Australia ... in information and communication Technology)

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2013年

作者： Xiaofang Zhou Pearl Pu

ISBN: (纸本)9781475769357

Current research in visual Database Systems can be characterized by scalability, multi-modality of interaction, and higher semantic levels of data. visual interfaces that allow users to interact with large databases must scale to web and distributed applications. Interaction with databases must employ multiple and more diversified interaction modalities, such as speech and gesture, in addition to visual exploitation. Finally, the basic elements managed in modern databases are rapidly evolving, from text, images, sound, and video, to compositions and now annotations of these media, thus incorporating ever-higher levels and different facets of semantics. In addition to visual interfaces and multimedia databases, visual and Multimedia information Management includes research in the following areas: Speech and aural interfaces to databases; visualization of web applications and database structure; Annotation and retrieval of image databases; visual querying in geographical information systems; Video databases; and Virtual environment and modeling of complex shapes. visual and Multimedia information Management comprises the proceedings of the sixth International conference on visual Database Systems, which was sponsored by the International Federation for information processing (IFIP), and held in Brisbane, Australia, in May 2002. This volume will be essential for researchers in the field of management of visual and multimedia information, as well as for industrial practitioners concerned with building IT products for managing visual and multimedia information.

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Research on Experimental Teaching information Collection Method of visual communication Specialty in Hybrid Universities Under Intelligent Terminal 5th

Research on Experimental Teaching Information Collection Met...

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5th EAI International conference on Advanced Hybrid information processing (ADHIP)

作者： Huang, Zhen-bin Sanda Univ Art Design & Media Coll Shanghai 201209 Peoples R China

ISBN: (纸本)9783030945510;9783030945503

In order to enhance the efficiency of information collection in experimental teaching of visual communication specialty and improve students' art design ability, this paper proposes a method of information collection in experimental teaching of visual communication specialty in hybrid Universities under intelligent terminal. The delay model of experimental teaching information collection is established, and the user delay under different modes is obtained. According to the residual value of all the information in the experimental teaching information processing node, the dynamic weight of each experimental teaching information is calculated, and the residual load capacity of the experimental teaching information processing node is estimated to achieve the balanced distribution of the experimental teaching information of visual communication specialty, This paper constructs the experimental teaching information collection mode of visual communication specialty in compound university to realize the experimental teaching information collection. The experimental results show that the method can meet different reliability requirements, has high acquisition accuracy and the effectiveness of redundant information filtering, and can promote the further improvement of students' art design ability.

关键词： Intelligent terminal Mixed colleges and universities visual communication major Experimental teaching information Collection method Art design

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