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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91-100 订阅

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601424

The proceedings contain 49 papers. The special focus in this conference is on Frontiers of Intelligent computing: Theory and Applications. The topics include: Nepali Word Spelling Correction Using Ensemble learning Technique;the Impact of Information Security Policies and Innovations in Digital Technology on the Transformation of Healthcare in Developing Nations: A Literature Review;integrating Multi-omics and Clinical Narratives for Predictive Modeling: Genomics, Transcriptomics, Proteomics, and Medical Texts in Disease Analysis;a Comprehensive Survey of Fake Review Detection Technology;a Hybrid Intelligent Decision Support System for Automating Financial Credit Evaluation;Advancing Taxation in the New Era: Enhancing Tax Ratios with the Core Tax Administration System (CTAS);Enabling Grid Stability: Harnessing μPMU data for data-Driven Analysis of Grid Frequency Events;machine learning Techniques to Detect Fake News on Social Media: A Systematic Review;cutting-Edge Technology-Based Social Enterprises in India for Sustainable, Inclusive Healthcare;enhancing Telugu Sarcasm Classification Models with Word Embeddings in Imbalanced datasets;capital Punishment: Analyzing Trends in the United States (1976–2016);exploring the Technostress Issue Among Indonesian Young Entrepreneurs;Teaching Computer science Using Cloud-Based IDE with Perspectives in Inclusivity;Comparison of data Encryption Standard (DES) and Advanced Encryption Standard (AES) in Security Issues of Cloud computing: A Literature Review;enhancing Corporate and Factory Training Through Game Development: A Comparative Review;usability of Intelligent System in Implementing Tutoring Lesson in Education: A Literature Review;performance Efficiency of Cloud computing—A Literature Review;decentralized Finance (DeFi) Wallets: A Review of its Efficiency, Usability, and Effectiveness;Social Networks on WEB 3.0.

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Federated Impression for learning with Distributed Heterogeneous data 9th

Federated Impression for Learning with Distributed Heterogen...

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

作者： Arya, Atrin Ayromlou, Sana Saadat, Armin Abolmaesumi, Purang Li, Xiaoxiao Univ British Columbia Elect & Comp Engn Dept Vancouver BC V6T IZ4 Canada Vector Inst Toronto ON M5G 0C6 Canada

ISBN: (纸本)9783031776090;9783031776106

Standard deep learning-based classification approaches may not always be practical in real-world clinical applications, as they require a centralized collection of all samples. Federated learning (FL) provides a paradigm that can learn from distributed datasets across clients without requiring them to share data, which can help mitigate privacy and data ownership issues. In FL, sub-optimal convergence caused by data heterogeneity is common among data from different health centers due to the variety in data collection protocols and patient demographics across centers. Through experimentation in this study, we show that data heterogeneity leads to the phenomenon of catastrophic forgetting during local training. We propose FedImpres which alleviates catastrophic forgetting by restoring synthetic data that represents the global information as federated impression. To achieve this, we distill the global model resulting from each communication round. Subsequently, we use the synthetic data alongside the local data to enhance the generalization of local training. Extensive experiments show that the proposed method achieves state-of-the-art performance on both the BloodMNIST and Retina datasets, which contain label imbalance and domain shift, with an improvement in classification accuracy of up to 20%. The code is available at https://***/Atrin78/FedImpress.

关键词： Federated learning Catastrophic Forgetting data Synthesis data Heterogeneity

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

12th International Conference on Frontiers of Intelligent Co...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

ISBN: (纸本)9789819601462

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Fine-Grained Zero-Shot Object Detection via Semantic Decoupling 24

Fine-Grained Zero-Shot Object Detection via Semantic Decoupl...

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8th international conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Duan, Lijuan Zhang, Zichen Gong, Zhi Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing China School of Computer Science Beijing University of Technology Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing China

ISBN: (纸本)9798400718182

Recent deep learning models have advanced object detection capabilities, but their reliance on labeled data limits scalability. Fine-grained zero-shot detection remains challenging due to the need to generalize to unseen classes and distinguish subtle features. To address this, we propose Fine-Grained Zero-Shot Object Detection via Semantic Decoupling (ZSD-SD). Our approach introduces a semantic decoupling module to separate visual features into semantic-related and semantic-unrelated components, facilitating alignment between visual and semantic spaces. A semantic-visual learning module further decouples these features, enhancing visual representations relevant to external semantic knowledge. This semantic-related representation significantly improves zero-shot performance. Experiments on two fine-grained object detection datasets demonstrate that ZSD-SD achieves state-of-the-art results. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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The Applications and Research of NLP and Deep learning Technologies in Public Security data 24

The Applications and Research of NLP and Deep Learning Techn...

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8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhu, Yupeng Zhu, Yuhao College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China The Smart City Research Institute of China Electronics Technology Group Corporation Guangdong Shenzhen China

ISBN: (纸本)9798400710094

With the deepening reform and construction of the police system, it is imperative to fully utilize technologies such as artificial intelligence, big data, blockchain, cloud computing, and the Internet of Things to rapidly enhance the intelligence level of the new generation command center. Natural language processing and deep learning technologies can achieve recognition, understanding, and correlation analysis of public security textual information, which are core steps in building intelligence. Due to the diversity of information in the public security field, the accuracy of these technologies still requires further research. In the analysis of public security incidents, the application of NLP technology faces challenges in optimizing technical models and algorithms, which greatly enhances the efficiency and accuracy of public safety management by analyzing, understanding, predicting, and responding to various security challenges in an intelligent manner. With the advent of the big data era, the public security sector has accumulated massive amounts of police incident data, which contain rich information but also bring difficulties in processing and analysis. Traditional NLP models often struggle to cope with the complex and ever-changing police incident texts, leading to compromised accuracy and reliability of the analysis results. This paper will explore the key technological applications of NLP and deep learning in the field of public safety and provide a review of their research progress. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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A data Model of a data Lineage Management System for database Repair and Simulation 26th

A Data Model of a Data Lineage Management System for Data...

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26th international conference on Information Integration and Web Intelligence, iiWAS 2024

作者： Wong, Wei Jun Yasuda, Kyoko Chang, Qiong Miyazaki, Jun Department of Computer Science School of Computing Tokyo Institute of Technology 2 Chome-12-1 Ookayama Tokyo Meguro152-8550 Japan

ISBN: (纸本)9783031780929

In this paper, we propose a new data model that uses data lineage, which represents relationships between data and their histories, for the verification of data. In recent years, although the verifiability of processes in many industries for their data is required, current data management systems are inadequate, as they do not distinguish between data flow and data dependencies, and also offer little support for machine learning models. Our data model for managing data lineage metadata is based on the property graph model, and supports not only machine learning models which must be treated as both operations to data as well as the data derived from training datasets, but also database repairs and database simulations, which are for maintaining the integrity of data and leveraging past data for future use. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data integrity

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An Autoencoder Framework for Few-Shot Human Activity Recognition with Sensor data 3rd

An Autoencoder Framework for Few-Shot Human Activity Recogni...

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3rd international conference on machine learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Han, Zhengxuan He, Mingshu School of International School Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819624676

As the training sample data in deep learning is not always easy to obtain, and the labeling task of sample data is very labor-intensive, weakly supervised learning method as a branch of machine learning has increasingly garnered interest. The scale of training datasets for different actions in sensor-based human activity recognition (HAR) often varies widely. For some action categories with little sample data, it is difficult for conventional learning methods to learn their features, which will lead to poor model performance in practical applications. In weakly supervised learning methods, the model can still achieve excellent performance without the need for a good deal of various label data in the training stage. Therefore, this study will use weakly supervised learning methods to improve low model performance caused by unbalanced samples. Based on autoencoder, which is a weakly supervised learning method, this research proposed a framework for sensor-based HAR to ameliorate the negative effects of the unbalanced distribution of training samples on the model. Results from experiments on open datasets demonstrated that the proposed framework enhances the original model's low accuracy caused by sample imbalance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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MedPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis

MedPromptX: Grounded Multimodal Prompting for Chest X-Ray D...

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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

作者： Shaaban, Mai A. Khan, Adnan Yaqub, Mohammad Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Mathematics and Computer Science Faculty of Science Alexandria University EG Alexandria Egypt School of Computer Science Carleton University OttawaCA Canada

ISBN: (纸本)9783031845246

Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to incomplete electronic health records (EHR). This paper introduces MedPromptX, the first clinical decision support system that integrates multimodal large language models (MLLMs), few-shot prompting (FP) and visual grounding (VG) to combine imagery with EHR data for chest X-ray diagnosis. A pre-trained MLLM is utilized to complement the missing EHR information, providing a comprehensive understanding of patients’ medical history. Additionally, FP reduces the necessity for extensive training of MLLMs while effectively tackling the issue of hallucination. Nevertheless, the process of determining the optimal number of few-shot examples and selecting high-quality candidates can be burdensome, yet it profoundly influences model performance. Hence, we propose a new technique that dynamically refines few-shot data for real-time adjustment to new patient scenarios. Moreover, VG narrows the search area in X-ray images, thereby enhancing the identification of abnormalities. We also release MedPromptX-VQA, a new in-context visual question answering dataset encompassing interleaved images and EHR data derived from MIMIC-IV and MIMIC-CXR-JPG databases. Results demonstrate the SOTA performance of MedPromptX, achieving an 11% improvement in F1-score compared to the baselines. Code and data are publicly available on https://***/BioMedIA-MBZUAI/MedPromptX. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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HyperMM: Robust Multimodal learning with Varying-Sized Inputs

HyperMM: Robust Multimodal Learning with Varying-Sized Inpu...

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作者： Chaptoukaev, Hava Marcianó, Vincenzo Galati, Francesco Zuluaga, Maria A. Data Science Department EURECOM Biot France School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom

ISBN: (纸本)9783031845246

Combining multiple modalities carrying complementary information through multimodal learning (MML) has shown considerable benefits for diagnosing multiple pathologies. However, the robustness of multimodal models to missing modalities is often overlooked. Most works assume modality completeness in the input data, while in clinical practice, it is common to have incomplete modalities. Existing solutions that address this issue rely on modality imputation strategies before using supervised learning models. These strategies, however, are complex, computationally costly and can strongly impact subsequent prediction models. Hence, they should be used with parsimony in sensitive applications such as healthcare. We propose HyperMM, an end-to-end framework designed for learning with varying-sized inputs. Specifically, we focus on the task of supervised MML with missing imaging modalities without using imputation before training. We introduce a novel strategy for training a universal feature extractor using a conditional hypernetwork, and propose a permutation-invariant neural network that can handle inputs of varying dimensions to process the extracted features, in a two-phase task-agnostic framework. We experimentally demonstrate the advantages of our method in two tasks: Alzheimer’s disease detection and breast cancer classification. We demonstrate that our strategy is robust to high rates of missing data and that its flexibility allows it to handle varying-sized datasets beyond the scenario of missing modalities. We make all our code and experiments available at ***/robustml-eurecom/hyperMM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semi-supervised learning

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Bias-Rectified Multi-way learning with data Augmentation for Implicit Discourse Relation Recognition 13th

Bias-Rectified Multi-way Learning with Data Augmentation for...

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13th international conference on Natural Language Processing and Chinese computing

作者： Zheng, Ziwei Liang, Chao Xiang, Wei Wang, Bang Huazhong Univ Sci & Technol Sch Elect Informat & Commun Hubei Key Lab Smart Internet Technol Luoyu Rd 1037 Wuhan 430074 Hubei Peoples R China Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Implicit Discourse Relation Recognition (IDRR) is a challenging but vital task in discourse analysis that focuses on identifying and classifying the relation between two arguments without explicit connectives. Previous research has focused on constructing sophisticated argument representations or utilizing labels' hierarchical information while neglecting the intrinsic prior relational bias, where some relation categories are significantly less frequent than others due to inherent linguistic properties. Furthermore, some works amalgamate Explicit Discourse Relation Recognition (EDRR) data with IDRR data to achieve data augmentation;however, the linguistic discrepancies between EDRR and IDRR data may mislead the relation recognition process. To address the prior relation bias and misleading from linguistic discrepancies, we propose a novel Bias-REctified Multi-way learning (BREM) model, which incorporates prior probability knowledge to assist in understanding the discourse relations and employs a PLM-based method for pre-training on the EDRR dataset to achieve data augmentation without introducing misleading effects. Experiments on the PDTB 3.0 corpus show that BREM outperforms previous models, especially in identifying less frequent relation senses, highlighting the effectiveness of our proposed model. The source code of our proposed model is publicly available at https://***/zzzziwy/NLPCC2024-BREM_for_IDRR.

关键词： Implicit Discourse Relation Recognition prior relation bias data Augmentation

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