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检索条件"任意字段=5th International Conference on Machine Learning and Computer Application, ICMLCA 2024"

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2024 5th international conference on machine learning and computer application, icmlca 2024

2024 5th International Conference on Machine Learning and Co...

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5th international conference on machine learning and computer application, icmlca 2024

ISBN: (纸本)9798331530334

the proceedings contain 131 papers. the topics discussed include: progressive semantic consistency towards unsupervised cross-modality medical image segmentation;innovative research on image dehazing by integrating multi-dimensional attention and multi-model fusion MixdehazeNet;a hybrid CNN-LSTM architecture for enhanced music genre classification;predicting liquidity coverage ratio with gated recurrent units: a deep learning model for risk management;research on a sentiment analysis model based on RoBERTa integrating bidirectional gated recurrent networks and multi-head attention;improving aircraft engine remaining useful life prediction scheme based on handcrafted features and neural network integration;and rolling bearing fault diagnosis algorithm based on RCMSDE fusion transfer learning.

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2024 5th international conference on machine learning and computer application (icmlca)

2024 5th International Conference on Machine Learning and Co...

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machine learning and computer application (icmlca), international conference on

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9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of machine Intelligence in Medical Image Computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated learning, DeCaF 2024 held at 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

9th International Skin Imaging Collaboration Workshop, ISIC ...

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9th international Skin Imaging Collaboration Workshop, ISIC 2024, 7th international Workshop on Interpretability of machine Intelligence in Medical Image Computing, iMIMIC 2024, Embodied AI and Robotics for Healthcare Workshop, EARth 2024 and 5th MICCAI Workshop on Distributed, Collaborative and Federated learning, DeCaF 2024 held at 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031776090

the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of machine Intelligence in Medical Image Computing, Embodied AI and Robotics for Healthcare Workshop and MICCAI Workshop on Distributed, Collaborative and Federated learning. the topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: application to Skin Lesion Images;a Vision Transformer with Adaptive Cross-Image and Cross-Resolution Attention;lesion Elevation Prediction from Skin Images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings Using Spine MRI Data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated learning with Differential Privacy and MRI applications;enhancing Privacy in Federated learning: Secure Aggregation for Real-World Healthcare applications;federated Impression for learning with Distributed Heterogeneous Data;A Federated learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic

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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

Workshop on Longitudinal Disease Tracking and Modeling with ...

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

ISBN: (纸本)9783031845246

the proceedings contain 21 papers. the special focus in this conference is on Longitudinal Disease Tracking and Modeling with Medical Images and Data. the topics include: Disease Progression Modelling and Stratification for Detecting Sub-trajectories in the Natural History of Pathologies: application to Parkinson’s Disease Trajectory Modelling;back to the Future: Challenges of Sparse and Irregular Medical Image Time Series;Individualized Multi-horizon MRI Trajectory Prediction for Alzheimer’s Disease;towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling;segHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints;Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting;registration of Longitudinal Liver Examinations for Tumor Progress Assessment;Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS);a Radiological-Based Coordinate System for the Human Body: A Proof-of-Concept;language Models Meet Anomaly Detection for Better Interpretability and Generalizability;A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation;predicting Human Brain States with Transformer;Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals;RATNUS: Rapid, Automatic thalamic Nuclei Segmentation Using Multimodal MRI Inputs;HyperMM: Robust Multimodal learning with Varying-Sized Inputs;HEMIT: H&E to Multiplex-Immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator;Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis;medPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis;predicting Stroke through Retinal Graphs and Multimodal Self-supervised learning;multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel Dataset and Deep-learning Experiments;multimodality Frequency Feature Customized learning for Pediatric Ventricu

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How Digital Transformation Affects Enterprise Performance: Evidence from machine Algorithms 24

How Digital Transformation Affects Enterprise Performance: E...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Zhang, Ying Fu, Rui Chen, Feng Xihua University Sichuan Yibin China

ISBN: (纸本)9798400709999

Digital transformation of enterprises, as one of the core issues in modern enterprise management, has become an important scenario for computer applications. this article empirically analyzes how digital transformation affects enterprise performance based on data from Chinese A-share listed companies from 2011 to 2022, using machine learning methods from artificial intelligence technology to explore the predictive capability of digital transformation on enterprise performance. the research results indicate a significant positive correlation between digital transformation and enterprise performance in Chinese companies, and that machine learning methods can effectively handle the relationships between variables, improving the accuracy of enterprise performance predictions. this study provides theoretical and practical support for how enterprises can improve their performance through the application of computer technology during the process of digital transformation. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Research on the evaluation and pricing of data assets under the circulation value of highway data assets 24

Research on the evaluation and pricing of data assets under ...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Wang, Sheng Guangxi Vocational Normal College College of Accounting and Audit Guangxi Nanning China

ISBN: (纸本)9798400709999

With the digital transformation of highway construction, a considerable amount of structured, semi-structured, and unstructured data resources have been accumulated, exerting an extremely high application value in the highway sector. the market demand for highway data transactions is gradually escalating, and it becomes particularly crucial to scientifically assess the value of highway data. Nevertheless, at present, the application of highway data has limitations, the value content is indistinct, and a set of methods or models applicable to the valuation of highway data assets has not yet been established. Hence, based on the characteristics of highway data assets, this paper utilizes data mining and machine learning techniques to construct a dynamic valuation model of highway data asset value, which holds reference value for the assessment of highway data value. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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Stock trend prediction based on multi-factor screening machine 24

Stock trend prediction based on multi-factor screening machi...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Hu, Ruijie Faculty of Science and Technology Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798400709999

With the rapid development of financial markets, stock price prediction, as a highly complex and challenging topic, has attracted more and more attention from academia and industry. this study aims to use multiple machine learning algorithms to predict stock market prices, especially exploring the advantages of deep learning models in capturing market nonlinear and time series characteristics. In this article, a machine learning model is used for contribution analysis, analyzing multiple features of stocks, performing a weighted combination analysis of features, and then using a convolutional neural network to perform feature extraction and analysis of stocks, and combining the attention mechanism for deep feature extraction, and finally use the long short-term memory network (LSTM) model to output the model to analyze and predict future stock trends. the experimental results show that combining deep learning analysis technology and factor screening machine can significantly improve the prediction accuracy of the model. At the same time, this article also compared the performance of different models in different market environments, and concluded that in markets with higher volatility, models based on deep learning are more stable. Ultimately, this research provides a new technical means for risk management and investment decisions in financial markets. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Convolutional neural networks

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machine learning Based Supply Chain Risk Prediction and Management 24

Machine Learning Based Supply Chain Risk Prediction and Mana...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Li, Changxia Chen, Haolin School of Mechanical and Energy Engineering Tongji University Shanghai China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798400709999

Traditional supply chain risk management methods show limitations when facing unexpected events and complex supply chain networks, however, machine learning techniques that can handle unstructured and multi-dimensional data and extract valuable risk information from complex data sets through efficient algorithms have provided new ideas for supply chain management in recent years. this paper proposes a continuous multi-perspective conceptual framework that aims to systematically explore how machine learning can be effectively applied in supply chain risk prediction and management. Meanwhile, this paper will also investigate the potential contribution of utilizing innovative technologies such as blockchain in enhancing supply chain security and competitiveness, with a view to providing more comprehensive and profound insights into modern supply chain management. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adversarial machine learning

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TLDALRLNMT: Low-Resource Language Neural machine Translation Based on Transfer learning and Dynamic Attention Enhancement 24

TLDALRLNMT: Low-Resource Language Neural Machine Translation...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Liu, Zhenqing Zhu, Rong Dai, Lingyun School of Computer Science Qufu Normal University Shandong Rizhao China

ISBN: (纸本)9798400709999

With the increasing pace of globalization, machine translation has become crucial for facilitating cross-cultural ***, translating low-resource languages remains challenging due to the scarcity of large parallel *** address this issue, an innovative approach was introduced, combining transfer learning and a dynamic attention *** method enhances translation quality by utilizing high-resource language models to transfer knowledge and by dynamically concentrating on important elements of the *** experiments with various low-resource language pairs, the proposed model demonstrated notable improvements, with BLEU score gains ranging from 1.30 to 4.44, validating the approach's effectiveness. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Dynamic attention

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"the Enterprise Performance Puzzle- Predictive Modeling Based on Interpretable machine learning" 24

"The Enterprise Performance Puzzle- Predictive Modeling Base...

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5th international conference on computer Science and Management Technology, ICCSMT 2024

作者： Yang, Nan School of Management Shanghai University Shanghai China

ISBN: (纸本)9798400709999

Corporate performance is a mature and popular research topic, and this study focuses on the influence of internal factors on corporate performance, and constructs a systematic corporate performance prediction model through the tree model and SHAP algorithm in machine learning. Unlike previous studies that explored corporate performance in a single dimension, this study covers corporate characteristics and executive characteristics, and explores the contribution of internal factors in corporate performance prediction. We compare and analyze firm-level and executive-level characteristics to discover the importance and mode of their roles in predicting firm performance. the results show that firm-level characteristics have a more direct and significant impact than executive-level characteristics in predicting firm performance. Among them, total corporate assets play the most important role in predicting corporate performance, and this study not only overcomes the limitations of traditional measurement models, but also provides new perspectives and tools for corporate performance prediction and management practices. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Forestry

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