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检索条件"任意字段=1st International Workshop on Deterministic and Statistical Methods in Machine Learning"

共 72 条记录，以下是1-10 订阅

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End-to-end Fault Diagnosis of High-Voltage GIS Devices Leveraging Masked Auto Encoder 1

End-to-end Fault Diagnosis of High-Voltage GIS Devices Lever...

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1st international workshop on Efficient Multimedia Computing under Limited Resources (EMCLR)

作者： He, Yuhang Wang, Jiayi Zhou, Dianbo Zhang, Zongxi Zhang, Jin Fang, Yuan Sichuan Elect Power Res Inst Chengdu Peoples R China

ISBN: (纸本)9798400711909

The runtime fault diagnosis of High Voltage Gas Insulated Switches (HV-GIS) is essential for ensuring the safety and reliability of electrical systems. Traditional fault diagnostic algorithms predominantly rely on statistical feature extraction combined with machine learning classification methods. However, these approaches often underperform in complex conditions, such as various aging and working environments. To address this limitation, we propose a fault diagnosis framework for HV-GIS based on masked auto encoder (MAE) pretraining. Our framework renders images from sensing data and approaches the diagnosis task as an image classification problem and utilizes MAE pretraining to achieve generalized fault diagnosis. Experimental results indicate that the diagnosis model trained with our framework outperforms baseline models, offering a more effective and generalized solution for HV-GIS fault detection.

关键词： Fault detection Deep learning Self-supervised learning

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machine learning methods’ comparison for land surface temperatures forecasting due to climate classification 1

Machine learning methods’ comparison for land surface tempe...

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1st international workshop on Advanced Applied Information Technologies, AdvAIT 2024

作者： Hovorushchenko, Tetiana Alekseiko, Vitalii Levashenko, Vitaly Khmelnytskyi National University Institutska str. 11 Khmelnytskyi29016 Ukraine Zilina University Univerzitná 8215 Žilina010 26 Slovakia

The application of machine learning methods for short- and medium-term forecasting of the average monthly temperature of the Earth’s surface, taking into account climatic zoning, is considered. The peculiarities of predicting the temperature of a moving surface in the context of the regression problem using machine learning methods are described. A comparison of the forecasting accuracy of methods based on metrics was made. Peculiarities of calculating the metrics, according to the values of the investigated parameters, are considered. The speed of operation of the methods was analyzed and statistical indicators were calculated. To visualize the effectiveness of the methods, Taylor diagrams were constructed. The most effective methods for forecasting the temperature of the Earth’s surface have been determined. © 2024 Copyright for this paper by its authors.

关键词： Surface temperature

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Using machine learning to Predict the Number of Latent Skills in Online learning Environments 1

Using Machine Learning to Predict the Number of Latent Skill...

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1st workshop on Adaptive Lifelong learning, ALL 2024

作者： Chen, Changsheng D’hondt, Robbe Vens, Celine Van Den Noortgate, Wim Faculty of Psychology and Educational Sciences KU Leuven Campus KULAK Kortrijk Belgium imec research group itec KU Leuven Kortrijk Belgium Department of Public Health and Primary Care KU Leuven Campus KULAK Kortrijk Belgium

Extracting skill information for students in online learning environments has been a challenging topic across different domains. Predicting the number of skills is the first step towards estimating students’ skills. In this paper, we propose prediction methods based on machine learning (ML) models, where we used the analysis model to generate simulation data reflecting the data features of our target scenarios and took the features from simulation data to train and test ML models. We illustrated this approach in tandem with Multidimensional Item Response Theory (MIRT) for the simple and complex structure, and further compared the trained ML models with a selection of statistical methods based on the test data. Our preliminary results show that, compared to statistical methods, ML models generally reach a noticeably higher proportion of correct estimations for both structures. Additionally, we find that an increase in the percentage of missing values and sample size leads to negative and positive effects on the methods’ performance respectively. Using simulation data from the analysis model to train ML models and doing prediction can extend the current operation of skill extraction, which provides extra options for the practitioners. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Prediction models

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An Interpretable machine learning Model with Deep learning-Based Imaging Biomarkers for Diagnosis of Alzheimer's Disease 26th

An Interpretable Machine Learning Model with Deep Learning-B...

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26th international Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) / 8th ISIC workshop / 1st Care-AI workshop / 1st MedAGI workshop / 4th DeCaF workshop

作者： Kang, Wenjie Li, Bo Papma, Janne M. Jiskoot, Lize C. De Deyn, Peter Paul Biessels, Geert Jan Claassen, Jurgen A. H. R. Middelkoop, Huub A. M. van der Flier, Wiesje M. Ramakers, Inez H. G. B. Klein, stefan Bron, Esther E. Erasmus MC Dept Radiol & Nucl Med Rotterdam Netherlands Erasmus MC Dept Neurol Rotterdam Netherlands Univ Med Ctr Groningen Dept Neurol Groningen Netherlands Univ Med Ctr Groningen Alzheimer Ctr Groningen Netherlands Univ Med Ctr Utrecht UMC Utrecht Brain Ctr Dept Neurol Utrecht Netherlands Radboud Univ Nijmegen Med Ctr Nijmegen Netherlands Leiden Univ Med Ctr Dept Neurol & Neuropsychol Leiden Netherlands Leiden Univ Inst Psychol Hlth Med & Neuropsychol Unit Leiden Netherlands Univ Amsterdam Med Ctr VUmc Amsterdam Netherlands Maastricht Univ Med Ctr Alzheimer Ctr Limburg Sch Mental Hlth & Neurosci MHeNS Maastricht Netherlands

ISBN: (纸本)9783031474002;9783031474019

machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.

关键词： Alzheimer's disease MRI Convolutional neural network Explainable boosting machine Interpretable AI

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SCOPE: structural Continuity Preservation for Retinal Vessel Segmentation 5th

SCOPE: Structural Continuity Preservation for Retinal Vessel...

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5th international workshop on Graphs in Biomedical Image Analysis (GRAIL) / workshop on Overlapped Celi on Tissue - Cell Detection from Cell-Tissue Interaction Challenge (OCELOT) / 5th MICCAI workshop / 1st MICCAI Challenge workshopinternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Yeganeh, Yousef Guevercin, Goektug Xiao, Rui Abuzer, Amr Adeli, Ehsan Farshad, Azade Navab, Nassir Tech Univ Munich Munich Germany Munich Ctr Machine Learning Munich Germany Stanford Univ Stanford CA 94305 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9783031550874;9783031550881

Although the preservation of shape continuity and physiological anatomy is a natural assumption in the segmentation of medical images, it is often neglected by deep learning methods that mostly aim for the statistical modeling of input data as pixels rather than interconnected structures. In biological structures, however, organs are not separate entities;for example, in reality, a severed vessel is an indication of an underlying problem, but traditional segmentation models are not designed to strictly enforce the continuity of anatomy, potentially leading to inaccurate medical diagnoses. To address this issue, we propose a graph-based approach that enforces the continuity and connectivity of anatomical topology in medical images. Our method encodes the continuity of shapes as a graph constraint, ensuring that the network's predictions maintain this continuity. We evaluate our method on three public benchmarks of retinal vessel segmentation and one neuronal structure segmentation benchmark, showing significant improvements in connectivity metrics compared to previous works while getting better or on-par performance on segmentation metrics.

关键词： Topology

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Auto-CIDS: An Autonomous Intrusion Detection System for Vehicular Networks 1

Auto-CIDS: An Autonomous Intrusion Detection System for Vehi...

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1st international workshop on Autonomous Cybersecurity

作者： Sorkhpour, Mohsen Yazdinejad, Abbas Dehghantanha, Ali Cyber Sci Lab CSL Guelph ON Canada Univ Guelph Cyber Foundry CCF Guelph ON Canada

ISBN: (纸本)9798400712296

Control Area Network (CAN), despite facilitating electronic control unit (ECU) communications, lacks built-in mechanisms for secure transmission, exposing its messages to cyber-attacks due to unsecured broadcasting. Current Intrusion Detection Systems (IDSs) for CAN rely predominantly on rule-based, statistical, or supervised machine learning (ML) models, which require significant human intervention for tasks such as reconfiguration, gathering labeled data samples, and retraining with newly released vehicle models. These manual dependencies highlight the critical need for autonomous capability in IDS that can adapt independently, thus mitigating practical deployment challenges in real-world scenarios. In this paper, we propose an autonomous cybersecurity IDS named Auto-CIDS, designed to minimize human intervention and enable active learning utilizing past experiences. By applying Deep Reinforcement learning (DRL) with the advantages of unsupervised algorithms, we train Deep Q-network (DQN) agents in a self-supervised manner using their own past experiences. We develop three standalone autonomous methods. The first method, Single-Task Self-Supervised, uses an autoencoder to supervise DQN agents in each environment, which includes both normal and specific attack data without needing labeled datasets. The second method, Multi-Environment Self-Supervised, enhances the generalization ability of the first by training a DQN agent across multiple environments, allowing knowledge transfer from varied settings into a single agent. The third method, Multi-Task Multi-Agent, increases the robustness of our proposed Auto-CIDS by employing a combination of modified unsupervised methods, including autoencoder, k-means, and isolation forest algorithms, each tailored for a specific type of attack. This approach builds attack-specific DQNs that periodically and cooperatively train a global DQN agent based on their predictions, facilitating ongoing active learning. We conducted experim

关键词： Deep Reinforcement learning RL DQN Autonomous Cybersecurity IDS CAN.

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Multi-perspective Spatiotemporal Context-aware Neural Networks for Human Mobility Prediction 1

Multi-perspective Spatiotemporal Context-aware Neural Networ...

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1st ACM SIGSPATIAL international workshop on the Human Mobility Prediction Challenge, HuMob 2023

作者： Wang, Chenglong Deng, Zhicheng Peking University Shenzhen Graduate School Shenzhen China

ISBN: (纸本)9798400703560

Accurately predicting human mobility is crucial for understanding human dynamics, addressing urban challenges, and improving urban planning. However, it still faces challenges due to the dynamic and complex nature of human mobility. Traditional methods rely on manual feature design and statistical analysis, limiting the accuracy and generalizability of predictions. In recent years, the development of deep learning techniques has provided new solutions. In this study, we propose a Multi-perspective Spatiotemporal Context-aware Neural Network for accurately predicting the next location in human mobility. Our model considers individual and collective behaviors, as well as explicit representation of spatiotemporal characteristics. Experimental results demonstrate competitive performance on the large-scale human mobility dataset. © 2023 ACM.

关键词： Deep learning

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FL-PPELA: Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning

FL-PPELA: Partial Parameter Enhancement and Local Adaptive A...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international Conference on Web Information Systems Engineering, WISE 2024

作者： Pan, Jinkun Liang, Xiaoyan Du, Ruizhong School of Cyber Security and Computer Hebei University Hebei070012 China Key Lab on High Trusted Information System of Hebei Province Hebei070012 China

ISBN: (纸本)9789819614820

A key challenge in federated learning is statistical heterogeneity, which affects the generalization ability of the global model on each client. To address this issue, we propose a method called Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning (FL-PPELA). The key component of FL-PPELA is the adaptive local aggregation module PPELA. In the PPELA, partial parameters in the global model are enhanced in the direction beneficial to the local client. Next, adaptive aggregation of the local model and the global model before local training. Finally, build different models for each client’s local dataset. To evaluate the effectiveness of FL-PPELA, we conducted experiments on four real datasets. FL-PPELA demonstrated superior test accuracy compared to 12 advanced baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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A Comparative study on Physical and Perceptual Features for Deepfake Audio Detection 1

A Comparative Study on Physical and Perceptual Features for ...

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1st international workshop on Deepfake Detection for Audio Multimedia, DDAM 2022

作者： Li, Menglu Ahmadiadli, Yasaman Zhang, Xiao-Ping Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9781450394963

Audio content synthesis has stepped into a new era and brought a great threat to daily life since the development of deep learning techniques. The ASVSpoof Challenge and the ADD Challenge have been launched to motivate the development of Deepfake audio detection algorithms. Currently, the detection models, which consist of front-end feature extractors and back-end classifiers, utilize the physical features mainly, rather than the perceptual features that relate to natural emotions or breathiness. Therefore, we provide a comprehensive study on 16 physical and perceptual features and evaluate their effectiveness in both Track 1 and Track 2 of the ADD Challenge. Based on results, PLP, as a perceptual feature, outperforms the rest of the features in Track 1, while CQCC has the best performance in Track 2. Our experiments demonstrate the significance of perceptual features in detecting Deepfake audios. We also seek to explore the underlying characteristics of features that can distinguish Deepfake audio from a real one. We perform statistical analysis on each feature to show its distribution differences on real and synthesized audios. This paper will provide a potential direction in selecting appropriate feature extraction methods for the future implementation of detection models. © 2022 Association for Computing machinery.

关键词： Deep learning

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Building Disease Prediction Model Using machine learning Algorithms on Electronic Health Records' Logs 7

Building Disease Prediction Model Using Machine Learning Alg...

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7th international Conference on Digital Technologies in Education, Science and Industry, DTESI 2022

作者： Rakhmetulayeva, Sabina Kulbayeva, Aliya International Information Technology University Manas St. 34/1 Almaty Kazakhstan

The number of tasks devoted to predicting the incidence of infectious diseases is growing rapidly due to the availability of statistical data supporting the analysis. This article describes the main solutions currently available for creating short and long-term disease forecasts. Their limitations and practical applications are shown. Much attention is given to the Naïve Bayes classification, logistic regression, artificial neural network algorithm and k-means artificial neural networks as methods of model analysis based on machine learning. This article provides an overview of two popular machine learning algorithms used to predict diseases. The standard datasetis used for a wide range of diseases including fungal infection, allergy, GERD, chronic cholestasis, peptic ulcer disease, diabetes, bronchial asthma, migraine, paralysis (brain hemorrhage) and more. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Neural networks

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