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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33664 条记录，以下是211-220 订阅

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Remote Heart Rate Estimation from Facial Videos with Balanced Contrastive learning

Remote Heart Rate Estimation from Facial Videos with Balance...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Dong-Hyuk Kim, Dae Hyeon Choi, Young-Seok Kwangwoon University Dept. Electronics and Communications Engineering Seoul Korea Republic of

ISBN: (纸本)9798331510756

Measuring remote photoplethysmography (rPPG), a contactless facial video-based PPG estimation, requires a large amount of labeled data via supervised methods, leading to significantly increased labor and costs. Existing rPPG studies based on self-supervised methods have utilized temporal and spatial similarities between rPPG data, which help reduce manual labeling costs. Here, we propose a novel self-supervised rPPG estimation method with contrastive learning. The proposed method not only leverages temporal and spatial similarities as representations but also maintains a well-contained representation. A well-contained representation reduces the computational cost associated with calculating negative loss. We use a 3D-CNN model to capture well-contained representation and multiple rPPG signals, which perform on different spaces but are temporally similar. We show that the proposed method outperforms current rPPG methods in terms of heart rate estimation accuracy on two public datasets, i.e., PURE and UBFC. © 2025 IEEE.

关键词： Contrastive learning

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Modality Modulation and Dual Consistency for Multi-Modality Semi-Supervised Medical Image Segmentation

Modality Modulation and Dual Consistency for Multi-Modality ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Yingyu Yang, Ziyuan Xiong, Deng Zhang, Yi College of Computer Science Sichuan University Chengdu China Computer Science Department Stevens Institute of Technology HobokenNJ United States School of Cyber Science and Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350368741

Multi-modality (MM) semi-supervised learning (SSL) based medical image segmentation has recently gained increasing attention due to its ability to utilize MM data and low dependency on labeled images. However, current MM-SSL methods face two major challenges: (1) Complex network designs make it difficult to apply these methods to scenarios involving more than two modalities. (2) The use of generative methods to leverage unlabeled data may not be reliable for SSL learning. To address these challenges, we propose Modality Modulation Dual Consistency, dubbed MM-DC. Specifically, we design a modality all-in-one network to process data from all modalities, with learnable plug-in Modality Modulation Layers (MML) to gradually modulate features from different modalities into a modality-invariant feature space, enabling unified segmentation. Additionally, we propose a dual-consistency strategy that enforces consistency at both the image and feature levels, which eliminates the requirements for generative methods. Extensive experiments demonstrate that MM-DC outperforms other state-of-the-art methods on open-source datasets with 2- and 4-modalities. The code is available. © 2025 IEEE.

关键词： Medical image segmentation multi-modality learning semi-supervised learning

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The international conference on Deep learning, Big data and Blockchain (DBB 2022) 1

引用

丛书名： Lecture Notes in Networks and Systems

1000年

作者： Irfan Awan Muhammad Younas Jamal Bentahar Salima Benbernou

ISBN: (数字)9783031160356

ISBN: (纸本)9783031160349

This event aims to enable synergy between these areas and provide a leading forum for researchers, developers, practitioners, and professionals from public sectors and industries to meet and share the latest solutions and ideas in solving cutting-edge problems in the modern information society and the economy. The conference focuses on specific challenges in deep (and machine) learning, big data and blockchain. Some of the key topics of interest include (but are not limited to):;Blockchain security and trust

关键词： Computational Intelligence data engineering

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international conference on machine learning and Big data Analytics, ICMLBDA 2022

International Conference on Machine Learning and Big Data An...

引用

international conference on machine learning and Big data Analytics, ICMLBDA 2022

ISBN: (纸本)9783031151743

The proceedings contain 44 papers. The special focus in this conference is on machine learning and Big data Analytics. The topics include: Bilingual Documents Text Lines Extraction Using Conditional GANs;Performance Comparison of YOLO Variants for Object Detection in Drone-Based Imagery;a Microservice Architecture with Load Balancing Mechanism in Cloud Environment;an IoT Application for Detection and Monitoring of Manhole;resource Allocation in 5G and Beyond Edge-Slice Networking Using Deep Reinforcement learning;preface;a Comprehensive Analysis on Mobile Edge Computing: Joint Offloading and Resource Allocation Perspective;the Important Influencing Factors in machine Translation;damaged Units Return Investigation in Printer-Producing Industry Utilizing Big data;colorization of Grayscale Images: An Overview;evolutionary Approaches Toward Traditional to Deep learning-Based Chatbot;analysis of machine learning Algorithms for Detection of Cyberbullying on Social Networks;sentiment Analysis of Political Tweets for Israel Using machine learning;a Novel Approach for Real-Time Vehicle Re-identification Using Content-Based Image Retrieval with Relevance Feedback;Extractive and Abstractive Text Summarization Model Fine-Tuned Based on BERTSUM and Bio-BERT on COVID-19 Open Research Articles;RevCode for NLP in Indian Languages;Application for Mood Detection of Students Using TensorFlow and Electron JS;finding Significant Project Issues with machine learning;Using CNN Technique and Webcam to Identify Face Mask Violation;a Review on Internet of Things-Based Cloud Architecture and Its Application;prediction of Maneuvering Status for Aerial Vehicles Using Supervised learning Methods;HRescue: A Modern ML Approach for Employee Attrition Prediction;using machine learning to Detect Botnets in Network Traffic.

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C2F-CHART: A Curriculum learning Approach to Chart Classification 27th

C2F-CHART: A Curriculum Learning Approach to Chart Classifi...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Shaheen, Nour Elsharnouby, Tamer Torki, Marwan Faculty of Engineering Alexandria University Alexandria Egypt Applied Innovation Center MCIT Giza Egypt

ISBN: (纸本)9783031781063

In scientific research, charts are usually the primary method for visually representing data. However, the accessibility of charts remains a significant concern. In an effort to improve chart understanding pipelines, we focus on optimizing the chart classification component. We leverage curriculum learning, which is inspired by the human learning process. In this paper, we introduce a novel training approach for chart classification that utilizes coarse-to-fine curriculum learning. Our approach, which we name C2F-CHART (for coarse-to-fine) exploits inter-class similarities to create learning tasks of varying difficulty levels. We benchmark our method on the ICPR 2022 CHART-Infographics UB UNITEC PMC dataset, outperforming the state-of-the-art results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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Few-Shot Arbitrary Super-Resolution Considering Consistency and Realness

Few-Shot Arbitrary Super-Resolution Considering Consistency ...

引用

2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Joonhee Bang, Junseo Chun, Se Young Department of Electrical and Computer Engineering Korea Republic of INMC & IPAI Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798331510756

Super-resolution (SR) is the task of reconstructing visually natural high-resolution (HR) images from low-resolution (LR) images. This field has made significant advancements with the development of deep learning, and recent research has focused on arbitrary-scale super-resolution, where a single model performs upsampling across continuous scales. However, most existing studies on arbitrary-scale super-resolution have relied on supervised learning models, which require large amounts of data and annotations. We propose a novel approach that demonstrates performance equal to or better than existing methods while utilizing fewer data, thus opening up new possibilities in this domain. This method also presents the potential for personalization, allowing the model to be adjusted according to individual user preferences or requirements. Specifically, we propose a method that integrates a few-shot arbitrary super-resolution upsampler trained with limited data into a zero-shot learning framework for super-resolution based on existing Diffusion Posterior Sampling (DPS) techniques. Additionally, this approach addresses the issues of insufficient detail representation in traditional regression-based SR methods and the lack of fidelity to the original images observed in generative-based SR methods. To achieve this, we employ a parallel diffusion model that uses DPS in both spatial and Fourier domains to simultaneously generate two types of images while maintaining consistency and realness. As a result, our method achieves performance in both PSNR and LPIPS that is competitive with supervised learning-based regression and generative SR methods. © 2025 IEEE.

关键词： Self-supervised learning

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5th international conference on Applied machine learning and data Analytics, AMLDA 2022

5th International Conference on Applied Machine Learning and...

引用

5th international conference on Applied machine learning and data Analytics, AMLDA 2022

ISBN: (纸本)9783031342219

The proceedings contain 20 papers. The special focus in this conference is on Applied machine learning and data Analytics. The topics include: RU-Net: A Novel Approach for Gastro-Intestinal Tract Image Segmentation Using Convolutional Neural Network;a Credit Card Fraud Detection Model Using machine learning Methods with a Hybrid of Undersampling and Oversampling for Handling Imbalanced datasets for High Scores;Implementation of YOLOv7 for Pest Detection;online Grocery Shopping: - Key Factors to Understand Shopping Behavior from data Analytics Perspective;a Novel Approach: Semantic Web Enabled Subject Searching for Union Catalogue;how People in South America is Facing Monkeypox Outbreak?;multilevel Classification of Satellite Images Using Pretrained AlexNet Architecture;handwriting Recognition for Predicting Gender and Handedness Using Deep learning;retrieval of Weighted Lexicons Based on Supervised learning Method;univariate Feature Fitness Measures for Classification Problems: An Empirical Assessment;performance Evaluation of Smart Flower Optimization Algorithm Over Industrial Non-convex Constrained Optimization Problems;securing Advanced Metering Infrastructure Using Blockchain for Effective Energy Trading;keratoconus Classification Using Feature Selection and machine learning Approach;Semantic Segmentation of the Lung to Examine the Effect of COVID-19 Using UNET Model;SMDKGG: A Socially Aware Metadata Driven Knowledge Graph Generation for Disaster Tweets;Brain MRI Image Classification Using Deep learning;a Real-Time Face Recognition Attendance Using machine learning;towards Abalone Differentiation Through machine learning.

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Prototype-Guided Contrastive Knowledge Graph Representation learning for Diagnosis Prediction 4th

Prototype-Guided Contrastive Knowledge Graph Representation ...

引用

4th international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Park, Wooyeol Mulyadi, Ahmad Wisnu Kang, Eunsong Suk, Heung-Il Department of Artificial Intelligence Korea University Seoul Korea Republic of Department of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9789819787012

The medical knowledge graph (KG) constructed from electronic health records (EHR) offers a comprehensive understanding of patients as it facilitates interconnections among medical codes. While KG has been proven to improve diagnostic accuracy, it inevitably contains noisy and incomplete attributes that could compromise the verdicts. Hence, direct utilization of medical KG for downstream clinical tasks may entail inadequacy, even by resorting to proper machine learning methods. Several prior studies have initiated noise filtering methods upon medical KG to relieve such issues by masking its task-irrelevant nodes and relations. However, they focused solely on a patient-wise perspective that feasibly overlooked essential shared traits from a (sub-)population viewpoint. This study proposes a novel prototype-guided predictive model called ProtoCare to predict diagnoses in EHR by unifying prototype and contrastive learning. ProtoCare leverages a set of prototypes to discover latent shared characteristics among groups of patients, while contrastive learning plays the role of noise filtering and KG refinement accounting for both learned patient and selected prototype features. Experimental results on the real-world EHR cohort exhibited that our proposed ProtoCare outperformed several baselines on the diagnosis prediction task. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Online Network Inference from Graph-Stationary Signals with Hidden Nodes

Online Network Inference from Graph-Stationary Signals with ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Buciulea, Andrei Navarro, Madeline Rey, Samuel Segarra, Santiago Marques, Antonio G. Dept. of Signal Theory and Communications Rey Juan Carlos University Madrid Spain Dept. of Electrical and Computer Engineering Rice University HoustonTX United States

ISBN: (纸本)9798350368741

Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneously but also that all nodes can be observed. However, in many real-world scenarios, data can neither be known completely nor obtained all at once. We present a novel method for online graph estimation that accounts for the presence of hidden nodes. We consider signals that are stationary on the underlying graph, which provides a model for the unknown connections to hidden nodes. We then formulate a convex optimization problem for graph learning from streaming, incomplete graph signals. We solve the proposed problem through an efficient proximal gradient algorithm that can run in real-time as data arrives sequentially. Additionally, we provide theoretical conditions under which our online algorithm is similar to batch-wise solutions. Through experimental results on synthetic and real-world data, we demonstrate the viability of our approach for online graph learning in the presence of missing observations. © 2025 IEEE.

关键词： graph signal processing hidden nodes Online graph learning

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Towards Robust Federated learning: Investigating Poisoning Attacks Under Clients data Heterogeneity 19

Towards Robust Federated Learning: Investigating Poisoning A...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Soubih, Abdenour Lahmer, Seyyid Ahmed Abuhamad, Mohammed Abuhmed, Tamer College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of Information Engineering University of Padova Padua Italy Department of Computer Science Loyola University Chicago Chicago United States

ISBN: (纸本)9798331507817

Federated learning (FL) offers a privacy-preserving solution by enabling multiple clients to train a shared model collaboratively without centralizing data. However, the decentralized nature of FL presents challenges, particularly regarding security and performance under adversarial conditions. This paper investigates the effects of poisoning attacks under data heterogeneity. Our experiments evaluate the impact of varying malicious client fractions and poison concentration levels on the accuracy of the model. We explore the effects of poisoning attacks on FedAvg and FedNova models using medical imaging tasks. Our findings reveal that increasing data heterogeneity exacerbates the effects of poisoning, with FedNova demonstrating greater resilience compared to FedAvg. We found that the number of malicious clients plays a more significant role in degrading performance than the ratio of poisoning samples shared by each malicious client, suggesting that even modest levels of poisoning can be tolerated by most algorithms. The study highlights the importance of developing robust defense mechanisms to maintain model performance under adversarial conditions. © 2025 IEEE.

关键词： Federated learning

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