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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是51-60 订阅

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Back to the Future: Challenges of Sparse and Irregular Medical Image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David Stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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An Intrusion Detection System for Multiclass Classification Across Multiple datasets in Industrial IoT Using machine learning and Neural Networks Integrated with Edge computing 2

An Intrusion Detection System for Multiclass Classification ...

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2nd international conference on data, Information and computing science, CDICS 2024

作者： Zhukabayeva, Tamara Ahmad, Zulfiqar Karabayev, Nurdaulet Baumuratova, Dilaram Ali, Mushtaq Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Computer Science and Information Technology Hazara University Mansehra21300 Pakistan

ISBN: (纸本)9781643685762

With the rapid expansion of industrial IoT (IIoT), maintaining robust cybersecurity is essential for the smooth operation of industrial processes. Industrial environments require adaptive solutions to effectively mitigate evolving cyber threats and protect sensitive operations. This research aims to improve the cybersecurity of industrial IoT environments. The research intends to design and implement an adaptive and real-time intrusion detection system with edge computing integration that improves the reliability of the operations in industrial IoT. We incorporated machine learning approaches to classify cyber threats using XGBoost and Deep Neural Networks (DNN). A comparative analysis of results obtained from two datasets shows that the XGBoost model was slightly more accurate than the DNN model, with an accuracy of 79% for dataset D1 and approximately 99.42% for data set D2. This analysis also clearly demonstrates the usefulness of these machine learning approaches and the need to select a model depending on the requirements for detecting particular attacks. Confusion matrix analysis shows that both models have several advantages in terms of recognizing different types of cyber threats. © 2025 The Authors.

关键词： Network intrusion

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SegHeD: Segmentation of Heterogeneous data for Multiple Sclerosis Lesions with Anatomical Constraints

SegHeD: Segmentation of Heterogeneous Data for Multiple Sc...

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作者： Basaran, Berke Doga Zhang, Xinru Matthews, Paul M. Bai, Wenjia Department of Computing Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom School of Integrated Circuits and Electronics Beijing Institute of Technology CN Beijing China UK Dementia Research Institute Imperial College London London United Kingdom Rosalind Franklin Institute Didcot United Kingdom

ISBN: (纸本)9783031845246

Assessment of lesions and their longitudinal progression from brain magnetic resonance (MR) images plays a crucial role in diagnosing and monitoring multiple sclerosis (MS). machine learning models have demonstrated a great potential for automated MS lesion segmentation. Training such models typically requires large-scale high-quality datasets that are consistently annotated. However, MS imaging datasets are often small, segregated across multiple sites, with different formats (cross-sectional or longitudinal), and diverse annotation styles. This poses a significant challenge to train a unified MS lesion segmentation model. To tackle this challenge, we present SegHeD, a novel multi-dataset multi-task segmentation model that can incorporate heterogeneous data as input and perform all-lesion, new-lesion, as well as vanishing-lesion segmentation. Furthermore, we account for domain knowledge about MS lesions, incorporating longitudinal, spatial, and volumetric constraints into the segmentation model. SegHeD is assessed on five MS datasets and achieves a high performance in all, new, and vanishing-lesion segmentation, outperforming several state-of-the-art methods in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Research on machine learning Prediction Algorithm in Stability of Mountain Rock Slope 14th

Research on Machine Learning Prediction Algorithm in Stabili...

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14th international conference on Frontier computing, FC 2024

作者： Xiang, Juan Zhang, Xuguang Li, Yongde School of Architecture and Civil Engineering Chongqing Metropolitan College of Science and Technology Chongqing China Architectural Design Institute China Construction Fifth Engineering Division Corp Ltd. Changsha China

ISBN: (纸本)9789819627974

In order to predict the slope stability, a method based on machine learning is put forward. By analyzing the slope instability characteristics, six typical slope parameters (pore water pressure, slope height) are selected and analyzed, and the corresponding evaluation data set is established. The slope stability model is established by using machine learning methods such as gradient hoist (GBM) and support vector machine (SVM), and verified by 50% cross-validation. By analyzing the characteristics of slope instability and combining with domestic slope cases, according to the sensitivity of characteristic parameters, the slope protection measures for different sensitive factors are put forward. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Slope stability

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FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation 9th

FedGS: Federated Gradient Scaling for Heterogeneous Medical ...

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27th international conference on Medical Image computing and Computer Assisted Intervention (MICCAI)

作者： Schutte, Philip Corbetta, Valentina Beets-Tan, Regina Silva, Wilson Univ Amsterdam Amsterdam Netherlands Netherlands Canc Inst Dept Radiol Amsterdam Netherlands Univ Utrecht Dept Informat & Comp Sci Dept Biol AI Technol Life Utrecht Netherlands Maastricht Univ GROW Sch Oncol & Dev Biol Med Ctr Maastricht Netherlands

ISBN: (纸本)9783031776090;9783031776106

Federated learning (FL) in Deep learning (DL)-automated medical image segmentation helps preserving privacy by enabling collaborative model training without sharing patient data. However, FL faces challenges with data heterogeneity among institutions, leading to suboptimal global models. Integrating Disentangled Representation learning (DRL) in FL can enhance robustness by separating data into distinct representations. Existing DRL methods assume heterogeneity lies solely in style features, overlooking content-based variability like lesion size and shape. We propose FedGS, a novel FL aggregation method, to improve segmentation performance on small, under-represented targets while maintaining overall efficacy. FedGS demonstrates superior performance over FedAvg, particularly for small lesions, across PolypGen and LiTS datasets. The code and pre-trained checkpoints are available at the following link: https://***/Trustworthy-AI-UU-NKI/Federated-learning-Disentanglement.

关键词： Federated learning

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Adaptive Local Neighborhood Window Laplacian Eigenmaps 24

Adaptive Local Neighborhood Window Laplacian Eigenmaps

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Xu, Jiayi Huang, Qirui Zhao, Yang Pei, Jihong Yang, Xuan College of Electronics and Information Engineering Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China

ISBN: (纸本)9798400717482

Manifold learning method is a dimensionality reduction method that treats the data in non-Euclidean space as a Euclidean space in a local scope. However, most existing manifold learning methods cannot obtain the true neighborbood relationship of each sample point well. Therefore, we propose a manifold learning method based on adaptive local neighborhood. This method can adaptively select a neighborhood window. It can be approximated as a local Euclidean space based on the sample characteristics. To implement this method, a specific manifold learning method, named Laplacian Eigenmap of Adaptive Local Neighborhood Window (ALNWLE) is proposed. ALNWLE mainly contains two modules, namely the manifold structure learning module (MSLM) and the manifold structure pattern recognition module (MSPR). MSLM constructs an adaptive local neighborhood adjacency matrix to constrain the learning of the manifold structure. MSPR uses the interpolation method to recognize new sample patterns. The adaptive local neighborhood method proposed in this paper is a general method which is applicable to any manifold learning method. © 2024 Copyright held by the owner/author(s).

关键词： Dimensionality reduction

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Ensemble learning with Feature Fusion for Well-Overflow Detection 5th

Ensemble Learning with Feature Fusion for Well-Overflow Dete...

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5th international conference on Neural computing for Advanced Applications (NCAA)

作者： Cui, Ziliang Liu, Li Xiong, Yinzhou Liu, Yinguo Su, Yu Man, Zhimin Wang, Ye Chongqing Univ Posts & Telecommun Chongqing Peoples R China Chongqing Energy Big Data Ctr Co Ltd Chongqing Peoples R China Shandong Zibo Polytech Vocat Sch Chongqing Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Traditional methods for kick prediction rely on experience and basic physical models, but they struggle to maintain high accuracy under complex well conditions and varying geological environments. Moreover, conventional approaches often fail to fully leverage big data resources. Therefore, this study aims to improve the accuracy of kick prediction through machine learning models, especially by adopting an innovative feature fusion strategy to optimize the prediction model. We evaluated the comprehensive performance of six different machine learning models: GBM, LightGBM, CatBoost, XGBoost, Random Forest, and AdaBoost. Based on these evaluations, we proposed a feature score weighted fusion method. This method weights the importance scores of features based on the accuracy of each model, selecting the features that have the most significant impact on the prediction results. Experimental results show that the model utilizing the feature fusion strategy performs better on the test set than any single base model. Specifically, the GBM model, optimized through feature fusion, achieved better values in accuracy, F1 score, and F2 score, proving the effectiveness of this feature fusion strategy in enhancing the accuracy of kick predictions during well control operations. Furthermore, this study compared the performance of different models, providing valuable insights for kick prediction in well control operations. This research not only demonstrates the potential of feature fusion strategies in improving the accuracy of kick prediction but also offers new insights for the future application of machine learning technologies in the field of oilfield data analysis.

关键词： machine learning feature fusion kill well overflow

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IoT-Based Convolutional Neural Networks in a Farm Pest Detection Using Transfer learning 5th

IoT-Based Convolutional Neural Networks in a Farm Pest Detec...

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5th international conference on computing science, Communication and Security

作者： Jani, Keyurbhai A. Chaubey, Nirbhay Kumar Panchal, Esan Tripathi, Pramod Yagnik, Shruti Gujarat Technol Univ Comp IT Engn Ahmadabad 382424 Gujarat India Ganpat Univ Comp Sci Mahesana 384012 Gujarat India Govt Polytech Informat Technol Gandhinagar 382027 Gujarat India Indus Univ Comp Engn Ahmadabad 382115 Gujarat India

ISBN: (纸本)9783031751691;9783031751707

In this study explores agriculture pest detection using transfer learning with IoT devices, evaluating VGG16, VGG19, Inception, and Xception CNN architectures with the agripest dataset. VGG16 and VGG19 show effective learning with consistent accuracy improvements. Inception V3 exhibits strong training but with variability in validation metrics, while Xception demonstrates robust performance and strong generalization to new data. The integrated system utilizes cameras, sensors, and drones for real-time image processing through a gateway and cloud server with a customized agripest dataset. Transfer learning generates a deployable.h5 file for pest identification. The generated custom model deployed on a gateway or server to classifies pests, alerting farmers through SMS, dashboard, or mobile app notifications. This synergy between machine learning and IoT offers rapid and precise pest detection in agriculture.

关键词： IoT deep learning CNN pest detection cloud farm gateway

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Modal-Centric Insights Into Multimodal Federated learning for Smart Healthcare: A Survey 24th

Modal-Centric Insights Into Multimodal Federated Learning fo...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Di Liu, Wenjian Gao, Longxiang Qu, Y. Neil. Zhang, Hu Shi, Jihong The Faculty of Data Science City University of Macau Macau China Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Medical Integration and Practice Center Shandong University Jinan China

ISBN: (纸本)9789819615476

Federated learning (FL) has progressed, providing a distributed mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Multimodal federated learning (MFL) combines federated learning’s distributed nature with the flexibility to work with various data types. This capability is particularly relevant in medical contexts, where healthcare data can range from electronic health records (EHR) to radiology imaging, genomic data, and wearable device information. The key advantage of MFL in medical settings is the ability to create robust models without centralizing sensitive patient information. However, there are no existent research to comprehensively review the application status of MFL in smart healthcare systems from the modal-centric aspect. Motivated by this, we explore the systematization of knowledge about multimodal federated learning on medical data, find the challenges of this area and related solutions. With this research, we hope to provide a systematical review of this under-explored domain and provide insights for forthcoming researchers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Enhancing Cross-Lingual Named Entity Recognition via Dual Contrastive learning Based on MRC Framework 13th

Enhancing Cross-Lingual Named Entity Recognition via Dual Co...

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13th international conference on Natural Language Processing and Chinese computing

作者： Zhuo, Aiqing Shi, Kunli Gu, Jinghang Qian, Longhua Zhoul, Guodong Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Hong Kong Polytech Univ Dept Chinese & Bilingual Studies Hong Kong 999077 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Cross-lingual Named Entity Recognition (NER) has recently become a research hotspot because it can transfer knowledge from high-resource languages to low-resource languages, thus meeting the challenge of data scarcity in low-resource languages. Most of the current model-transfer methods rely on directly using multilingual models to represent text yet ignoring the cross-lingual word alignment information in multi-lingual models, and also neglecting the utilization of prior knowledge. We propose a dual contrastive learning method based on machine reading comprehension (MRC) framework, by combining Query Contrastive learning (QCL) and Translation Word Contrastive learning (TWCL), to mitigate above problems. Specifically, we utilize QCL to design contrastive objectives for different query templates to enhance the representation ability of prior knowledge. In addition, we utilize TWCL to help the model capture the word alignment relationship between the source language and target language via a pseudo-parallel corpus. We conducted extensive experiments on 4 different datasets and the experimental results demonstrate the effectiveness of our method.

关键词： Cross-lingual Named Entity Recognition Contrastive learning machine Reading Comprehension

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