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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

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. © 2025 IEEE.

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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

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.

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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A Machine Learning Approaches with Special Emphasis for Identification of Plant Leaf Diseases in Crop Yield Prediction 1st

A Machine Learning Approaches with Special Emphasis for Iden...

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1st International conference on Intelligent Systems in Computing and communications, ISCComm 2023

作者： Babu, Ch Ravindra Jyothi, E.V.N. Prassana, K. Naga Jayanth, T. Siva Sandani, Sk. Chand, S. Veera Venkata Gopi Shwetha, R.J. Pace Institute of Technology and Sciences AP Ongole India IV B. Tech CSIT Pace Institute of Technology and Sciences AP Ongole India Global Payex Technologies Pvt. Ltd. Mumbai India

ISBN: (纸本)9783031756047

Productivity in farming is very essential to the Indian economic system. When a plant gets any disease it can significantly reduce production, cost money and reduce the quality and quantity of farm produce. To prevent agriculture from losing quantity and quality, it is essential to recognise diseases of plants. Plant disease identification is receiving more and more attention these days since large crops area is being monitored. Monitoring plant health and identifying diseases is critical for sustainable agriculture. This requires a lot of work, knowledge of plant diseases, and a lot of processing time. Utilizing Machine Learning algorithms, To predict increased agricultural yields, crop yield projections are made. One of the difficult problems in the agricultural industry is this. Given the growing significance of agricultural yield prediction, with a concentrate on agricultural yield prediction, it is important to apply machine learning techniques to crop yield predictions. Techniques like decision tree, support vector machine, Bayesian Network, and random forest, among others, are used in Machine Learning support in the automatic identification of plant disease based on the plant’s visual symptoms. This research presents an analysis of several machine learning approaches for plant disease prediction. The outcomes demonstrate that this technique conducted illness diagnosis with success and enhanced diffusion pattern analysis and disease progression level detection accuracy. Thus machine learning approaches with special emphasis for identification of plant leaf diseases in crop yield prediction results show that plant diseases can be accurately classified. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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A Proposed Model to Transliterate Digitalized Braille Text into Gujarati: An Aid for visually Challenged People

A Proposed Model to Transliterate Digitalized Braille Text i...

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3rd International conference on Advancements in Smart Computing and information Security, ASCIS 2024

作者： Jariwala, Nikisha Popat, Kalpesh Smt. Tanuben & Dr. Manubhai Trivedi College of Information Science Affiliated to Veer Narmad South Gujarat University Gujarat Surat India Faculty of Computer Applications Marwadi University Gujarat Rajkot India

ISBN: (纸本)9783031863011

information and communication Technology (ICT), in the modern digital era has become so cutting-edge and touched to each and every field but there is still a disconnect between visually impaired persons and communication technologies because of ignorance and inattention. Due to the limited availability of resources and tools;people with visual impairments are unable to communicate in writing. In this paper, researcher has tried to draw the attention towards the challenges that are faced by visually impaired individuals in written communication and also discussed the benefits of having a tool that eradicate the communication gap. To overcome the challenges faced by blind people;author has proposed a model based on Natural Language processing (NLP) and character mapping technique that act as an aid to transliterate the Braille text written by blind people to our regional language Gujarati. The technique used in the model uses standard Braille syntax. Rules are formed, lookup table is created, Braille characters are identified and they are transliterated to Gujarati language text. The model shows satisfactory result in the transliteration of Braille characters to Gujarati. It will also help visually impaired students in their education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Natural language processing systems

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Proceedings of the IADIS International conferences - Computer Graphics, visualization, Computer Vision and Image processing 2010, CGVCVIP 2010, visual communication 2010: Creative Industries, Photography and Culture, VC 2010, Web Virtual Reality and Three-Dimensional Worlds 2010, Web3DW 2010, Part of the IADIS Multi conference on Computer Science and information Systems 2010, MCCSIS 2010

Proceedings of the IADIS International Conferences - Compute...

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IADIS Int. Conf. - Computer Graphics, visualization, Computer Vision and Image processing, CGVCVIP 2010, visual Commun., VC 2010, Web Virtual Reality and Three-Dimensional Worlds, Web3DW 2010, Part of the MCCSIS 2010

ISBN: (纸本)9789728939229

The proceedings contain 82 papers. The topics discussed include: GPU-aware hybrid terrain rendering;time-curvature and time-torsion of virtual bubbles as fluid mixing indicators;a repartition structure for collision detection and deformation of discrete objects based on 3D wavelets;volume-based diffuse global illumination;contraction theory as method for the analysis and design of the stability of collective behavior in crowds;balloon cut layout based on a 3D balloon model;efficient virtual plant data structure for visualization and animation;comparison of bilateral filtering strategies for video processing in color and depth;marker-less computer vision tracking for augmented reality;a novel edge detection method based on image energy and skewness with application to intramuscular fat recognition;and texture feature representation on a coordinate system for efficient pattern classification.

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Human-Centered Interface Design and 'Must-Attractiveness' in the US Art and Design School Websites 28

Human-Centered Interface Design and 'Must-Attractiveness' in...

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28th International conference on information visualisation (iv)

作者： Zarina, Solvita Univ Latvia Fac Sci & Technol Riga Latvia

ISBN: (纸本)9798350380170;9798350380163

The online presence of art and design schools needs to be attractive, surprising and different to meet the expectations of potential students in an era of clarity of visual communication and rational choices about how the visual language of educational institutions should be accessible to all users. The common principles in human-centered interface design practices and the necessary appeal or must-attractiveness from the perspective of visual communication design and visual accessibility is explored. Three U.S. art and design schools were selected whose home page designs exhibit a contrast or harmony of design must-attractiveness and visual accessibility in varying proportions. A comparison of the design solutions reveals the strategies how to communicate in a way that is both attractive and visually accessible.

关键词： human-centered design must-attractiveness visual accessibility visual communication art and design schools

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