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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

共 40714 条记录，以下是4691-4700 订阅

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SAR-Optical Image Matching Model Based on Contrastive learning 20

SAR-Optical Image Matching Model Based on Contrastive Learni...

引用

20th international computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Yaoting, Xiong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Due to the strong complementary nature of synthetic aperture radar (SAR) and optical imagery in remote sensing, matching SAR images with optical images enables the complementary extraction of ground surface feature information. This approach is beneficial for addressing tasks such as change detection, target recognition, and land cover classification. With the development of deep learning, most current models for SAR-optical image matching utilize siamese neural networks and have achieved good matching performance. However, in practical applications, test data is often unknown, and there may be differences between training and test data. A matching model that performs well on the training dataset may not generalize well to the test data. Domain generalization research for heterogeneous image matching aims to improve the generalization performance of matching models on unknown test domains, and this task has practical significance and application prospects. In this paper, we propose a contrastive learning-based SAR-optical image matching model specifically designed to enhance the generalization performance of matching models trained on multiple datasets. When dealing with cross-domain scenarios, enforcing feature consistency of matching points preserves more information conducive to matching. We propose a contrastive learning method to limit the feature consistency of matching points, thereby improving the generalization performance of cross-source image matching models. © 2023 IEEE.

关键词： Image matching

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IoT based monitoring model to identifying and classifying heart disease through machine learning

IoT based monitoring model to identifying and classifying he...

引用

international conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Parisha Kumar, Santosh Shrivastava, Gaurav Kumar Department of Computer Science & Engineering Babu Banarasi Das University Lucknow India School of Computing & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

Healthcare research has advanced thanks to artificial intelligence. Because there is free access to healthcare information, researchers have developed methods to help with the heart disease diagnosis and prognosis. For such difficult illnesses, deep learning and machine learning models offer a reliable, quick, and efficient solution. The articles from Web of Science, EBSCO, and EMBASE that were released between 2009 and 2021 were chosen using PRISMA criteria. A successful search method was used to discover the research papers for this study that used AI-based learning techniques for heart disease prediction. There are 185 studies in all that conventional machine learning-based classifications and deep learning-based classifiers rank as having a significant impact on heart disease prediction. The survey also analysed the research done by various researchers and drew attention to the flaws in the earlier literature. Several metrics, including prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area covered, precision, recall, and F1-score, were utilized to compare the findings. The solutions to the five scheduled investigations have been examined. Even though several of the methods suggested in the literature have excellent predicative accuracy, heart disease mortality has not decreased. As a result, further study is needed to address the problems with heart disease prediction. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Forecasting

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Underwater Image Color Analysis and Indicators Based on Statistical Characteristics 4

Underwater Image Color Analysis and Indicators Based on Stat...

引用

4th international conference on computer Vision, Image and Deep learning, CVIDL 2023

作者： Peng, Jin Yu, Haiyang Wang, Zhe School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

ISBN: (纸本)9798350326444

When light propagates in water, different wavelengths of light have different degrees of attenuation, resulting in the color cast of underwater images. Color characteristics are important to solve the problem of color cast. Therefore qualitative and quantitative analysis of underwater image color characteristics and color indicators studies in RGB space are carried out in this paper. A qualitative comparative analysis between underwater images and land color cast-free images is carried out. It is concluded that there are differences in histogram and statistical characteristics between RGB channels of underwater images. Then a set of color characteristics indicators is established that can measure the degree of color cast and the differences between color channels of the underwater images. Finally, experiments on quantitative analysis of color characteristics are carried out. Different turbidity levels and colors of the water environment are set up to analyze the changes and differences of color characteristics. Underwater image color characteristics are obtained by comprehensive analysis. © 2023 IEEE.

关键词： Color

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An Attention-Based Deep learning Approach to Knee Injury Classification from MRI Images 26

An Attention-Based Deep Learning Approach to Knee Injury Cla...

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26th international conference on computer and Information Technology, ICCIT 2023

作者： Nath, Kowshik Deb Rahman, A. F. M. Minhazur Hossain, Md. Ali Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Knee injuries, prevalent in athletic and aging populations, pose significant challenges to healthcare professionals due to their complex nature and the critical function of the knee joint. Early and accurate diagnosis is paramount to ensure effective treatment and minimize long-term complications. Traditional diagnostic methods, including physical examinations and imaging techniques like MRI, require expert interpretation and can sometimes be inconclusive. This study introduces an approach to knee injury classification using deep learning techniques by leveraging convolutional neural networks (CNNs) with Attention Mechanism. This research work integrates powerful feature extraction capabilities of CNN and feature refinement of attention mechanism for the binary and multi-class classification of knee MRI images, with the aim of accurately identifying specific knee injury types. Based on our experiment on two comprehensive knee MRI datasets, our custom CNN model achieved 88% testing accuracy on Dataset-1 (Binary classification) and 77% accuracy on Dataset-2 (Multi-class classification). Meanwhile, the Attention-based CNN model achieved 100% accuracy on Dataset- 1 (Binary Classification) and 91% accuracy on Dataset-2 (MultiClass Classification). This approach not only holds promise for enhancing diagnostic accuracy but also for reducing the time to diagnosis. © 2023 IEEE.

关键词： Attention Mechanism Binary Classification Convolutional Neural Networks(CNNs) Data augmentation Deep learning Algorithms Knee Injuries MRI Knee Images Multi-class classification Radiologists

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Proceedings - 2023 IEEE 16th international conference on Cloud Computing, CLOUD 2023

Proceedings - 2023 IEEE 16th International Conference on Clo...

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16th IEEE international conference on Cloud Computing, CLOUD 2023

ISBN: (纸本)9798350304817

The proceedings contain 72 papers. The topics discussed include: InsightsSumm - summarization of ITOps incidents through in-context prompt engineering;fine-grained heterogeneous execution framework with energy aware scheduling;learning representations on logs for AIOps;EN-Beats: a novel ensemble learning-based method for multiple resource predictions in cloud;the case for the anonymization of offloaded computation;deep reinforcement learning in cloud elasticity through offline learning and return based scaling;demystifying deep learning in predictive monitoring for cloud-native SLOs;fine-grained heterogeneous execution framework with energy aware scheduling;Storm-RTS: stream processing with stable performance for multi-cloud and cloud-edge;blaze: a high-performance, scalable, and efficient data transfer framework with configurable and extensible features;and Kepler: a framework to calculate the energy consumption of containerized applications.

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Segmentation Based on 1-Shot learning 6

Segmentation Based on 1-Shot Learning

引用

6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Senthil Kumar, A. Mehra, Ritika Kumar, Prashant Hemelatha, S. Antran Vijay, S. Arul Rege, P.R. Sri Vishnu Engineering College for Women Andhra Pradesh India School of Engineering and Computer Science Dev Bhoomi Uttarakhand University Uttarakhand India TMU Teerthankar Mahaveer University Department of ECE Moradabad India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Vishwakarma Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350304480

Low-risk learning approaches for classifying images encourage learning via little data. In order to facilitate packed semantics segmentation of pictures, we expand those methods. We particular instruct a neural network which generates factors for the completely Convolutional The network (FCN) from just a few sets of analyzed with annotations. From an experiment picture to known as newly introduced semantics the classroom, we execute packed sensor-level prediction using that FCN. Our system is a minimum of three times quicker and exhibits an approximate mean IoU enhancement of 25% over the top-performing baseline techniques for person-shot division on courses that have not yet been observed within the PASCAL volatile organic compounds 2012 information set. © 2023 IEEE.

关键词： CNN FCN Feature extraction FSL

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Transfer learning Based Approaches for Breast Cancer Classification using Mammogram Images 26

Transfer Learning Based Approaches for Breast Cancer Classif...

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26th international conference on computer and Information Technology, ICCIT 2023

作者： Sharifujjaman, Md. Ferdousee, Zannatul Sadad, Nahin Ul Ahmed, Boshir Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Breast cancer is a growing epidemic and a leading cause of death among women worldwide. Mammographic imaging has been found to be highly effective in detecting breast cancer at an early stage which leads to reduce mortality rates through prompt and appropriate treatment. In this research work, the proposed models have used two convolutional neural network (CNN) architecture known as VGG19 and ResNet50, which had been pre-trained with data from imageNet and then Mammographic Image Analysis Society (MIAS) database has been used to train and test. To identify potential cancer hotspots, the mammogram images from MIAS database have went through some image preprocessing steps such as image resizing and augmentation by rotation. The extracted features from the pretrained architecture have been flattened into one dimension and then used as inputs to the trainable dense layers. The images have been classified either into benign or malignant type by using the sigmoid activation function. Measures of performance such as accuracy, recall, precision and F1-score have been calculated to evaluate the proposed models' efficacy. The experimental result depicts that pretrained VGG19 architecture performed 98.46% outperforming ResNet50 achieving a test accuracy of 97.94%. © 2023 IEEE.

关键词： Breast Cancer Convolutional Neural Network Deep learning Mammographic Images MIAS database ResNet50 Transfer learning VGG19

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Multimodal contrastive learning with neuroimaging and cognitive tests for Alzheimer's disease diagnosis

Multimodal contrastive learning with neuroimaging and cognit...

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2024 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Liang Zhang, Jian Xu, Bo Yang, Yi Zhang, Yangqianhui Ma, Ruixin Dalian University of Technology School of Software Technology Dalian China Beihang University School of Reliability and Systems Engineering Beijing China Zhejiang University School of Mechanical Engineering Hangzhou China

ISBN: (纸本)9798350386226

Alzheimer's disease (AD) is a neurological illness that causes cognitive impairment. computer-aided diagnosis can help diagnose Alzheimer's disease early before clinical symptoms appear. Currently, many deep learning methods show good performance in AD diagnosis. Still, most of these methods are based on single/multimodal neuroimaging, leading to a one-sided approach to disease modelling. Combining neuroimaging, cognitive tests, and demographics can significantly improve model performance and reduce the negative impact of noise. This study proposes a multimodal model that introduces contrastive learning, extracting and fusing feature representations separately from cognitive tests and neuroimaging data. After that, contrastive learning based on similarity is employed for both modalities' features, assisting the network in learning cross-modal features. Moreover, the hybrid attention mechanism of the Transformer encoder is explored for feature fusion. Experimental results on 2082 cases from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset validate the effectiveness of our proposed multimodal model. © 2024 IEEE.

关键词： Contrastive learning

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Advanced Traffic Sign and Lane Discernment 4

Advanced Traffic Sign and Lane Discernment

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4th international conference for Emerging Technology, INCET 2023

作者： Naidu, Roshan Vedhapriyavadhana, R. Vellore Institute of Technology Chennai School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335750

Frameworks based on computer vision can be deployed for innumerable tasks assisting the vehicle driver, as such the ADAS has its roots emerging from such deep learning algorithms and CNN architectures. In this paper, a machine learning model has been deployed with the aim of assisting the human driver in analysing the road lanes and the approaching traffic signs in much efficient manner taking into account the adverse weather conditions. The work of analysing the road has been bifurcated into 2 separate tasks which have been carried out in parts. In the first part, the algorithm devised will detect the approaching traffic signs and in the second part, the algorithm would carry out the lane assisting task both of which would be done taking bad weather conditions in consideration. With each iteration of detecting the road signs and classifying the lanes, an additional algorithm would be implemented simultaneously to retrieve clear images removing the fog and other such adverse weather obstructions. Since it is not possible for human drivers to perform the operation required all at once as efficiently as machines, the algorithm devised would ensure the automation of the driving system giving the human driver a sense of relief. © 2023 IEEE.

关键词： computer vision

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Editorial to the Second IFIP WG 2.7/13.4 Workshop on HCI engineering Education 19th

Editorial to the Second IFIP WG 2.7/13.4 Workshop on HCI Eng...

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19th international-Federation-for-Information-Processing-Technical-Committee-13 (IFIP TC13) international conference on Human-computer Interaction (INTERACT)

作者： Spano, Lucio Davide Campos, Jose Creissac Dittmar, Anke Univ Cagliari Cagliari Italy Univ Minho Braga Portugal HASLab INESC TEC Braga Portugal Braga Portugal Univ Rostock Rostock Germany

ISBN: (纸本)9783031616877;9783031616884

The second workshop on HCI engineering Education continued the effort of the IFIP Working Group 2.7/13.4 on User Interface engineering by discussing the issues and identifying the opportunities in teaching and learning Human-computer Interaction (HCI) engineering. The workshop attracted eight papers covering different teaching contexts, ranging from massive university courses, passing through different teaching experiences in specific academic curricula, and even teaching engineering concepts to children. In addition, the workshop received input for improving and adapting the repository material to the dynamic nature of this field. The discussion after the presentation of the contributions focused on how to model competencies, the support to interdisciplinary work, the overall course design, the recruitment of the students and the provision of educational resources, paving the way for further editions of the workshop.

关键词： HCI engineering education teaching case study repository Git end-user development model-based design of user interfaces

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