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检索条件"任意字段=Conference on Visualization in Biomedical Computing"

共 894 条记录，以下是121-130 订阅

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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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From Image to Insight: Transformer-Based Multimodal Feature Fusion for Enhanced Chest X-Ray Report Generation

From Image to Insight: Transformer-Based Multimodal Feature ...

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International Symposium on Advanced computing and Communication (ISACC)

作者： Jayashree Das Partha Garai Department of CSE Kalyani Govt. Engg. College Kalyani India Department of CA Kalyani Govt. Engg. College Kalyani India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Automated chest X-Ray report generation offers a potential solution to reduce radiologists’ workload while improving efficiency and diagnostic accuracy. However, existing methods face challenges such as high visual similarity between normal and abnormal cases, limited descriptive diversity in reports, and biases in both textual and visual data. A novel framework has been proposed in this paper, combining multimodal fusion and contrastive learning to generate more descriptive and clinically relevant reports. The model integrates visual and textual features and employs mechanisms to focus on abnormal regions, enhancing report quality. Evaluated on the IU-X-Ray and MIMIC-CXR datasets, the framework achieves state-of-the-art performance in text generation (BLEU, ROUGE-L, METEOR) and clinical metrics (Precision, Recall, F1-Score). Human evaluation further validates its clinical utility, with fluency (3.62 and 3.61), faithfulness (3.90 and 3.92), and comprehensiveness (3.61 and 3.95) scores rated on a 5-point scale for the IU-X-Ray and MIMIC-CXR datasets, respectively. These results demonstrate the potential of the proposed framework for real-world clinical applications, enabling accurate and timely chest X-Ray report generation.

关键词： visualization Accuracy MIMICs Contrastive learning Radiology Transformers Real-time systems Meteors X-ray imaging biomedical imaging

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Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier System

Comfort Shoulder (Rhomboid Major) Muscles Chair Identifier S...

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International conference on Communications, Information System and Computer Engineering (CISCE)

作者： Ata Jahangir Moshayedi Yuexin Yu Kimia Savoji Mehran Emadi Andani School of Information Engineering Jiangxi University of Science and Technology Ganzhou Jiangxi China Biomedical Data Science and Informatics School of computing Clemson University Clemson South Carolina USA Department of Neurosciences Biomedicine and Movement Sciences University of Verona Verona Italy

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

Nowadays, Sitting is one of the frequent activities of humans. particularly with the rise of office work, which is leading to an increase in the sitting periods for many individuals. Research highlights the importance of studying sitting posture to enhance seat quality, correct sitting methods, and prevent various health issues like neck and shoulder strains, etc. This article focuses on designing an IoT-based system that enables web-based data visualization without specialized software that can enable distance sampling. Through temperature sensors (DS18b20 and SHT11) and an FSR sensor, the system can identify distinct patterns in the main rhomboid muscles (the most important muscles of humans). The results show a significant relationship between individuals' body mass (BMI) and the observed muscle patterns, highlighting the importance of this correlation for researchers and industry designers alike.

关键词： Temperature sensors Pediatrics Shape Ergonomics Muscles Software Neck

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Reversible Steganographic System for the Transmission of Personal Medical Data

Reversible Steganographic System for the Transmission of Per...

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International conference on Computer Science and Engineering (UBMK)

作者： Elmira Daiyrbayeva Ekaterina Merzlyakova Aigerim Yerimbetova Aigul Mukhitova Institute of Information and Computational Technologies of the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan Satbayev University Almaty Kazakhstan Siberian State University of Telecommunications and Information Science Novosibirsk Russia al-Farabi Kazakh National University Institute of Information and Computing Technologies of the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan Almaty Kazakhstan

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

This paper discusses the development and analysis of a steganography method for reversible data hiding in raster images. This method is intended for use in medical images for the purpose of storing personal data of patients. The stego-containers created with its help preserve some statistical characteristics of the image by dividing it into coherent regions by the “forest fire” method and collecting statistics of low-order bits, which allows to form a sequence with a given probability distribution. To ensure the reversibility of the steganographic system, the INP interpolation method is used. The developed system has an embedding capacity of 0.6 bits/pixel. The paper also presents the results of stegoanalysis on the basis of BOSS _ vl.01 images processed by this method, as well as the data of visual analysis of stego-containers and evaluation of their properties by visual distortion criteria.

关键词： Image quality visualization Interpolation Steganography Memory management Distortion Real-time systems Probability distribution biomedical imaging Immune system

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Vocal Cords Detection Using Edge Detection and Machine Learning Filtration

Vocal Cords Detection Using Edge Detection and Machine Learn...

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Global Digital Health Knowledge Exchange & Empowerment conference (gDigiHealth.KEE)

作者： Mohammad Alshoweky Abbes Amira Fatih Kurugollu Bassel Soudan Adel Oulefki College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331531331

ISBN: (纸本)9798331531348

Automating endotracheal intubation (EI), a critical medical procedure required for lung ventilation, has gained significant attention in the realm of medical imaging and robotics. Central to the success of automated EI is the reliable detection and visualization of vocal cords, a step traditionally performed using laryngoscopy. This paper introduces a novel method for detecting and segmenting vocal cords in endoscopic images by leveraging advanced edge detection techniques combined with machine learning (ML) filters. This approach uniquely integrates geometric and textural features derived from contours identified in the image, distinguishing vocal cords with high precision and speed. Beyond automating EI, this methodology has broader applications, including the removal of foreign objects and facilitating tracheal endoscopy. This paper details the development of a specialized dataset and the implementation of the solution, emphasizing its algorithmic innovations. The results demonstrate the efficacy of this approach in improving the accuracy and efficiency of vocal cords detection, offering a repeatable strategy for similar medical imaging challenges.

关键词： Image segmentation Technological innovation Machine learning algorithms Accuracy Image edge detection Machine learning Ventilation Real-time systems Intubation biomedical imaging

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Integrating Cloud computing with IoT for Battery Monitoring in Electric Vehicles

Integrating Cloud Computing with IoT for Battery Monitoring ...

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International conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： J. Selvi T. S. Balaji Damodhar P. Sathyanathan S. Sujatha S. Priya Department of Civil Engineering Shree Sathyam College of Engineering and Technology Sankari Salem Tamil Nadu India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electrical and Electronics Engineering VelTech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Academy of Maritime Education and Training (AMET) Deemed to be University Chennai Tamil Nadu India

Cloud computing and the Internet of Things (IoT) for battery monitoring in electric vehicles (EVs) can improve battery performance and efficiency. EV batteries, IoT devices, cloud infrastructure, data transmission, storage, processing, analysis, visualization, user interface, and integration with EV management systems are used in this integration. These components are integrated to provide a complete system for real-time monitoring, predictive maintenance, and performance improvement of EV batteries. Sensors are essential to this system. The battery's electrical characteristics, temperature, environmental conditions, and potential concerns are collected via voltage, current, and temperature sensors. The technology improves battery performance, lifespan, real-time monitoring and alarms, predictive maintenance insights, and charging and discharging techniques. Stakeholders may make informed decisions and take proactive steps to enhance EV battery efficiency and lifetime using cloud infrastructure, data analysis and visualization tools, and IoT. Advancing electric mobility and sustainability, the system monitors, manages, and improves battery performance.

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Advanced Endoscopic Precision:Focal Modulation Guided CNNs for Targeted Diagnosis of GI Anomalies

Advanced Endoscopic Precision:Focal Modulation Guided CNNs f...

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International conference on Circuit, Power and computing Technologies (ICCPCT)

作者： Anlon J Saji Gopika Pushpan Riny Rajan Nourin Fathima N Manjima Anoop Biomedical Engineering TKM Institute of Technology Kollam India

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Endoscopy is a diagnostic technique used to identify diseases in the gastrointestinal (GI) tract. However, the accurate and timely identification of subtle abnormalities within endoscopic images remains a challenge. This project introduces a groundbreaking approach for innovating endoscopic diagnosis through the implementation of a Focal Modulation-Driven Convolutional Neural Network (CNN). The proposed system leverages the power of deep learning and focuses on integrating focal modulation techniques to enhance the sensitivity and specificity of disease detection in the GI tract. By dynamically adjusting the attention and resolution of the CNN to specific regions of interest within endoscopic images, the model aims to improve diagnostic precision, reducing false positives and negatives. The project involves a comprehensive training dataset comprising diverse endoscopic images representing various GI tract diseases. The CNN will be trained using state-of-the-art deep learning architectures, optimizing its ability to discern subtle visual cues indicative of pathology. Focal modulation will be employed to adaptively allocate computational resources to areas of interest, facilitating more accurate detection of abnormalities and ensuring efficient resource utilization. The anticipated outcomes of this project include a highly specialized endoscopic diagnostic tool capable of providing precise and reliable detection of GI tract diseases. The integration of focal modulation-driven CNN technology is expected to improve the overall diagnostic accuracy, enabling early intervention and personalized treatment strategies. The potential impact of this research extends to the medical community, contributing to advancements in endoscopic diagnostic capabilities and ultimately enhancing patient outcomes in the field of gastroenterology.

关键词： Training visualization Accuracy Image resolution Modulation Sensitivity and specificity Gastrointestinal tract

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Overview of the Methods and Applications of Electrodermal Activity Assessment and Its Relation with Electrotactile Feedback and Potential Use in Automated Calibration

Overview of the Methods and Applications of Electrodermal Ac...

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Electrical, Electronic and computing Engineering (IcETRAN), International conference on

作者： Tanja Boljanić Matija Štrbac Miloš Kostić Jovana Malešević Tecnalia Serbia Ltd. Belgrade Serbia University of Belgrade - School of Electrical Engineering Belgrade Serbia

ISBN: (数字)9798350386998

ISBN: (纸本)9798350387001

This paper presents an overview of the Electrodermal Activity (EDA) applications and methods for signal analysis, as it is a tool that has a wide range of potential uses in monitoring of user's wellbeing. Significant correlations have been shown between EDA and stress, both when induced through external stimulation (auditory, visual, pain, etc.), cognitive load or physiological strain (heat, dehydration, intense physical activity). Also, along with the heartrate it is one of the least intrusive parameters to be measured and requires relatively simple signal processing. All of this makes it a highly promising tool in a number of applications. However, naturally occurring variability between subjects and between different measurement sessions of the same subjects require significant effort in the experimental setup, as well as meticulous calibration procedures, in order to obtain reliable results. This largely limits the use of EDA to highly controlled laboratory or clinical conditions, significantly limiting its real-world impact. There is an observed connection between the EDA responses and electrotactile stimulation which can be highly controlled. This may potentially be used in an automated calibration procedure that can be rapidly performed before each monitoring session and would not require any expertise of the end user. Here we introduce this hypothesis to lay the foundation for the future experimental work.

关键词： Electric potential visualization Pain Calibration Reliability biomedical monitoring Signal analysis

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The performance analysis of image resolution model using deep residual algorithm

The performance analysis of image resolution model using dee...

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International conference on computing and Networking Technology (ICCNT)

作者： K.G Chandi Priya P Savitha V. Sujatha A. Sabarivani M Dinesh Department of General Engineering R.M.K College of Engineering and technology Chennai Department of CSE Nandha Engineering College Perundurai Erode Master of Computer Applications S.A. Engineering College Chennai Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep residual algorithms succeeded wildly in different computer vision problems (e.g., Image Super resolution). Deep neuraenablersraredesign and residual learning are great enablers that define how these architectures overcome limitations in traditional image resolution models. This article implements a deep residual algorithm representing the image resolution model performance research. This paper aims to synthesize HR images from their low-resolution (LR) counterparts by training the proposed model using large-scale image pairs across different resolutions. The model is tested using multiple evaluation criteria and compared with classical resolution models. Experimental results show that the deep residual model performs better in peak signal-to-noise ratio(PSNR), structural similarity index(SSIM), and visual quality metrics. It examines the influence of various network architectures, training strategies, and datasets on how well this algorithm can recognize dialog acts. Experiments demonstrate the superiority of the proposed model with a deep residual algorithm in terms of improving Resolution compared to traditional models.

关键词： Training Measurement visualization Computational modeling Superresolution Network architecture Performance analysis Signal resolution Image reconstruction Signal to noise ratio

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A Hybrid Multi-Modal Medical Image Fusion Technique using Optimal Sub-band Tree Structuring and Principal Component Analysis

A Hybrid Multi-Modal Medical Image Fusion Technique using Op...

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International conference on Parallel Distributed and Grid computing (PDGC)

作者： Shailesh Bhosekar Prabhishek Singh Deepak Garg School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In image processing, "image fusion" is the amalgamation of attributes and requirements from many images into a singular, more comprehensive representation. Multi-modal medical image fusion is a significant category of image fusion. It entails the integration of medical images acquired from several modalities. This work utilizes computed tomography (CT) scans, Positron Emission Tomography (PET) and magnetic resonance imaging (MRI) as modalities. This study (M3IF-SBTS) aims to construct a multi modal medical image approach that combines Optimal Sub-band Tree Structuring (SBTS) and Principal Component Analysis (PCA) with MRI and CT images in a manner that maximizes the information content in the fused image. The SBTS is an advanced wavelet transform version of Discrete Wavelet Transform (DWT), where signal is filtered more times. The PCA provides dimensionality reduction and retains the relevant features. The wavelet coefficients are fused using PCA-based fusion method. This combination of SBTS and PCA provides superior results compared to using SBTS, DWT, or PCA as individual methods. This improves overall visual and parametric quality of fusion results than many compared methods.

关键词： visualization Magnetic resonance imaging Computed tomography Information filters Discrete wavelet transforms Positron emission tomography Image fusion Principal component analysis biomedical imaging Wavelet coefficients

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