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

共 893 条记录，以下是111-120 订阅

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Overdrive Pacing of 2D Coupled Spiral Waves

Overdrive Pacing of 2D Coupled Spiral Waves

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Ural Symposium on biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

作者： Evgeniy Kuklin Sergei Pravdin HPC Department Institute of Natural Sciences and Mathematics Ural Federal University Ekaterinburg Russia Computing Systems Department Mathematical Modelling in Cardiology Sector Krasovskii Institute of Mathematics and Mechanics Ekaterinburg Russia Mathematical Modelling in Cardiology Sector Krasovskii Institute of Mathematics and Mechanics Ekaterinburg Russia

Spiral waves can occur in excitable media, including cardiac tissue, and can be single or coupled; the latter have not been studied enough so far. The present paper explores the interaction between coupled spiral waves and plane wave trains emitted from electrodes with a constant period in a two-variable model of an excitable medium. The emission is aimed to terminate the spiral-wave activity in the medium by making the spiral waves drift. Our results show that the pacing can terminate coupled spirals in two ways, by separate annihilation at the domain boundary, or by mutual collision. The effective pacing periods were found to be the same for both single spirals and coupled spirals. We measure the drift velocities and propose a new visualization method using polygon to show the oblique angles of the induced drift trajectories and the drift speed depending on the pacing period. Future research may include simulations with ionic cell-level models and whole heart models.

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A Efficient Approach of Image Stegnorgrapy Using Advanced Encryption System and Deep Neural Network

A Efficient Approach of Image Stegnorgrapy Using Advanced En...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： S. Jim Hawkinson A. Sundar Raj S. Senthilkumar R. Muthaiyan K. Balasubramaniyan M. Chinnadurai Department of Biomedical Engineering E.G.S. Pillay Engineering College Nagapattinam Tamilnadu India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam Tamilnadu India Department of Electronics and Communication Engineering University College of Engineering Thirukkuvalai Tamilnadu India Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam Tamilnadu India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Background: Image steganography is a technique that conceals confidential data within an image. The conventional technique of image steganography aims to effectively conceal confidential data within the host image, prioritising the secrecy of the payload over its capacity. However, it is necessary to enhance the quality of the steganography image to ensure optimal perception by the human visual system. Methods: The hidden image is subjected to the Discrete Wavelet transform for transformation, and subsequently encrypted using an advanced encryption technique (AES) to enhance the image's resistance to detection. The Deep Neural Network, consisting of Hiding and Extraction networks, enhances the steganography capacity by facilitating the concealment and retrieval of full-size images. Results: Some of the relevant metrics are evaluated and the outcome proves that the proposed method exhibits increased peak signal to noise ratio (PSNR), Structural similarity (SSIM) index, entropy, and reduced mean square error values than existing methods.

关键词： Steganography visualization PSNR Random access memory Artificial neural networks Transforms Visual systems

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MedMM: A Multimodal Fusion Framework for 3D Medical Image Classification with Multigranular Text Guidance

MedMM: A Multimodal Fusion Framework for 3D Medical Image Cl...

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International conference on Big Data computing and Communications (BIGCOM)

作者： Shanbo Zhao Meihui Zhang Xiaoqin Zhu Junjie Li Yunyun Duan Zhizheng Zhuo Yaou Liu Chuyang Ye School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Radiology Beijing Tiantan Hospital Capital Medical University Beijing China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331509538

ISBN: (纸本)9798331509545

Deep learning approaches are widely used in medical image analysis and have shown impressive results on many analytical tasks. However, textual information related to medical images are often underutilized in existing methods, despite the great semantic value and potential multigranular guidance in medical image analysis. Meanwhile, many medical images, like magnetic resonance (MR) images are usually in 3D format consisting of multiple slices which contain more complex and redundant information, making them especially hard to be represented. In this paper, we propose a multimodal funsion framework for 3D medical image classification, which utilizes the medical text paired with the 3D medical image to guide the generation and aggregation of image features. Results show that our method significantly outperforms uni-modal and multimodal baseline methods. Ablation studies validate the effectiveness of each component, and visualization results also reveal the strong ability of our model on capturing fine-grained and coarse-grained information.

关键词： Deep learning Three-dimensional displays Computational modeling Semantics Magnetic resonance Brain modeling Data models Reliability biomedical imaging Image classification

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Utilizing IoT for Animal Farming Waste Detection: YOLO-Based Plastic Component Tracking with Buzzer Alert System

Utilizing IoT for Animal Farming Waste Detection: YOLO-Based...

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Pervasive computing and Social Networking (ICPCSN), International conference on

作者： Nithisha A E. Bijolin Edwin M. Roshni Thanka V. Ebenezer Stewart Kirubakaran T. Mathu Department of CSE Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of DSCS Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

Smart agricultural technologies are quickly evolving due to their efficacy and efficiency in animal health monitoring. The increasing prevalence of plastic waste poses a significant threat to animal health in farming environments. This paper presents a novel approach for tracking plastic components, specifically paper and plastic bottles, within the felids of animal farming using Internet of Things (IoT) technology. The proposed system employs the YOLO (You Only Look Once) YOLO model for real-time object detection, which identifies waste paper and bottles in the fields. Upon detection, the system sends the information to a NODEMCU, which triggers a buzzer alert to discourage animals from consuming the identified hazardous items. The implementation involves deploying IoT sensors strategically across the animal farming facility to capture visual data. The YOLO model is then employed to process the collected data, accurately identifying, and categorizing plastic components. Upon successful detection, the system sends serial data to a NODEMCU, triggering a predefined response. In this case, an alarm is activated to alert farm personnel about plastic waste in the fields.

关键词： YOLO visualization Animals Social networking (online) Sensors Internet of Things biomedical monitoring

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An Explainable Approach for Brain Tumor Segmentation using Grad-CAM based U-Net Models

An Explainable Approach for Brain Tumor Segmentation using G...

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International conference on Computer Theory and Applications (ICCTA)

作者： Dina Essam Abd-Elhafeez Yasser Ahmed Dahab Nashwa El-Bendary Ossama Mohamed Badawy College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport (AASTMT) Aswan Egypt College of Computing and Information Technology Arab Academy for Science Technology and Maritime Transport (AASTMT) Alexandria Egypt

ISBN: (数字)9798331529680

ISBN: (纸本)9798331529697

Due to the complexity of brain anatomy and the prevalence of diverse tumor forms, interpretation of brain tumors segmentation using deep learning models in medical images presents a challenging task. This paper proposes several explainable deep learning based segmentation models for medical images using Explainable Artificial Intelligence (XAI). This research implements five segmentation models: U-Net, Attention U-Net, U-Net 3+, ResUNet++, and EfficientNet-B7 on the TCGA-Lgg dataset, which includes Magnetic Resonance Imaging (MRI) scans. In addition, Gradient Weighted Class Activation (Grad- CAM) XAI technique is applied to make brain tumor segmentation models understandable through providing more accurate visual explanations. The proposed segmentation models applied training and testing procedures, where the EfficientNet-B7 model achieved the highest efficiency in brain tumor segmentation, followed by the Attention U-Net model. The EfficientNet-B7 achieves a Dice Similarity Coefficient (DSC) of 91.21% and Intersection Over Union (IOU) of 84.08%.

关键词： Deep learning Training Image segmentation visualization Explainable AI Computational modeling Magnetic resonance imaging Brain tumors Brain modeling biomedical imaging

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Novel Signal Processing Techniques for High Angular Resolution Diffusion-Weighted Imaging

Novel Signal Processing Techniques for High Angular Resoluti...

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International conference on computing and Networking Technology (ICCNT)

作者： Dushyanth V Babu R Praveen Kumar Thakur Shiv Shankar Shankar Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India ARKA JAIN University Jamshedpur Jharkhand India Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Excessive angular decision diffusion-weighted imaging (HARDWI) is an effective approach for visualizing tissue microstructures, which are otherwise hard to look at the usage of conventional MRI technology. To attain this, HARDWI relies on sign-processing techniques to reconstruct images from tremendously anisotropic diffusion-weighted alerts. Novel signal processing strategies advanced in current years have further advanced the abilities of HARDWI. These strategies leverage the sparsity of diffusion signal in more than one angular guideline, allowing the reconstruction of man or woman diffusion occasions within the specimen’s microstructure. Wavelet rework-primarily based strategies allow for the advanced decision of the reconstructed images, extended evaluation, and stepped forward signal-to-noise ratio in the resulting photographs. Other strategies, inclusive of diffusion orientation transform, have been advanced to estimate the orientation of fiber tracts better. Furthermore, spatially-variable regularization techniques have been advanced for advanced estimation of the diffusion parameters at a voxel stage. The combination of these signal processing techniques has advanced the application of HARDWI to a spread of biomedical applications, such as neuroanatomy, tumor imaging, and small animal studies.

关键词： Wavelet transforms visualization Image resolution Imaging Optical fiber networks Microstructure Image reconstruction Signal resolution Tumors Signal to noise ratio

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MS-Glance: Bio-Inspired Non-Semantic Context Vectors and Their Applications in Supervising Image Reconstruction

MS-Glance: Bio-Inspired Non-Semantic Context Vectors and The...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Ziqi Gao Wendi Yang Yujia Li Lei Xing S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China Department of Radiation Oncology Stanford University Stanford CA USA Key Laboratory of Precision and Intelligent Chemistry USTC CAS Hefei Anhui

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction, non-semantic information, such as global spatial structures, is often overlooked. To bridge the gap, we propose a biologically informed non-semantic context descriptor, MS-Glance, along with the Glance Index Measure for comparing two images. A Global Glance vector is formulated by randomly retrieving pixels based on a perception-driven rule from an image to form a vector representing non-semantic global context, while a local Glance vector is a flattened local image window, mimicking a zoom in observation. The Glance Index is defined as the inner product of two standardized sets of Glance vectors. We evaluate the effectiveness of incorporating Glance supervision in two reconstruction tasks: image fitting with implicit neural representation (INR) and undersampled MRI reconstruction. Extensive experimental results show that MS-Glance outperforms existing image restoration losses across both natural and medical images. The code is available at https://***/Z7Gao/MSGlance.

关键词： Computer vision visualization Magnetic resonance imaging Fitting Vectors Image restoration Indexes Image reconstruction biomedical imaging Visual perception

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Application of Deep Learning for Mobile-Based Herbal Leaf Diseases Classification Using CNN-Based Ensemble Learning

Application of Deep Learning for Mobile-Based Herbal Leaf Di...

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International conference on Informatics and computing (ICIC)

作者： Ketut Dionanda Sutrisna Ni Luh Wiwik Sri Rahayu Ginantra I Putu Agus Eka Darma Udayana Christina Purnama Yanti Technology and Informatics Informatics Engineering Institut Bisnis dan Teknologi Indonesia (INSTIKI) Denpasar Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Herbal leaves are an integral part of traditional Balinese medicine, known for their natural healing properties with minimal side effects. However, limited knowledge of the diseases that attack these plants may lead to decreased use and conservation. This research aims to develop a disease classification model on herbal plants using Convolutional Neural Network (CNN) as well as ensemble learning methods. The CNN method used to automatically extract features from leaf images, while ensemble learning using the ensemble averaging method improves the accuracy of classification results. This research identifies various leaf diseases, such as leaf spot, bacterial spot, mosaic virus, powdery mildew, and target spot, as significant threats to these crops. The proposed model was implemented in a mobile application, allowing users to detect diseases directly through their devices, thus supporting the preservation of the Usada Bali treatment tradition. The ensemble model, which incorporates architectures such as ResNet50, DenseNet121, MobileNetV2, and VGG16, achieved high accuracy in validation, with results up to $96.7 \%$. The findings show that this model offers a practical solution for the early classification of diseases in herbal plants, contributing to the conservation of traditional Balinese medicine.

关键词： visualization Accuracy Computer architecture Feature extraction Convolutional neural networks Ensemble learning Viruses (medical) Diseases biomedical imaging Residual neural networks

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A Novel Approach of Human Stress Rate Monitoring System in Smart Society 5.0

A Novel Approach of Human Stress Rate Monitoring System in S...

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computing, Sciences and Communications (ICCSC), International conference on

作者： Ambika Aggarwal Pooja Joshi Ashutosh Bhatt Kapil Joshi Mayurika Joshi Mansi Mahur School of Computer Science UPES Uttarakhand India Department of Computer Science and Engineering HSST Swami Rama Himalayan University Uttarakhand India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of computer science and engineering Graphic Era hill university Haldwani India Infosys Hyderabad India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Over the course of the present years, the Internet of Things (IoT) made ways for developers which work with communications among people and things. Zeroing in on medical care space, devices like clinical detectors, visual detectors, cameras, and the remote detector network were driving that developmental pattern. Objective: Toward this path, these studies proposed a unique IoT-mindful understudy-driven pressure, observing structure to foresee an understudy pressure list in a specific setting. Method: Bayesian Network (BN) was utilized the characterize pressure occasion in a typical or unusual utilizing physiological readings gathered among the clinical detectors in the haze layer. Strange transient primary data that was time-advanced data file grouping was broken down at various pressure-related boundaries in the cloud layer. From the technique's understudy pressure record, a Dynamic Bayesian Network (DBN) of two stage design is framed. Results: This system figures pressure given four boundaries, in particular, leaf node confirmations, responsibility, and setting, also to understudy the wellbeing characteristic. In the wake of processing the pressure file based on the understudy, choices were taken as ready age components with the redemption of time-delicate data to the overseer or responder. Conclusion: Tests were led both at the haze and cloud layer that holds the proof in the usage also the exactness based on the BN classifier and the DBN prescient system in our proposed framework.

关键词： Cloud computing visualization Detectors Skin Bayes methods Internet of Things biomedical monitoring Transient analysis Stress Monitoring

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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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