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检索条件"任意字段=22nd International Conference on Intelligent Data Engineering and Automated Learning"

共 2238 条记录，以下是181-190 订阅

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Enhancing Time Series Representation learning with Spatiotemporal Dynamic Encoding 22

Enhancing Time Series Representation Learning with Spatiotem...

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22nd IEEE/WIC international conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Ji, Jinquan Yan, Jianzhuo Cao, Yu Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9798350309188

With the proliferation of time series data across diverse domains, achieving accurate and high-performance time series representation has become paramount. The intricacies of large-scale datasets characterized by nonlinear and heterogeneous distributions have prompted the exploration of attention models, such as the Transformer architecture, in time series analysis. However, these models often neglect temporal positional relationships among sequences, leading to suboptimal capture of long-term dependencies. This paper introduces an innovative approach that integrates temporal decay positional encoding with ordinary differential equations (ODEs) to formulate decaying position encodings, thereby enhancing time series representation. Additionally, a Spatial Dynamic Correlation Subgraph Module (SDCSM) is proposed to dynamically capture evolving interactions among observations. The presented research significantly boosts representation performance, while also addressing training time and memory space constraints, making it applicable across domains reliant on precise time series representation.

关键词： Time Series Representation learning Temporal Position Encoding Temporal Decay Multi-Head Attention Mechanism Dynamic Correlation Subgraphs

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Predicting the Acceptability of Atomic Candidate OWL Class Axioms 22

Predicting the Acceptability of Atomic Candidate OWL Class A...

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22nd IEEE/WIC international conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Ballout, Ali Pereira, Celia da Costa Tettamanzi, Andrea G. B. Univ Cote Azur Inria I3S Sophia Antipolis Nice France

ISBN: (纸本)9798350309188

The task of evaluating the fitness of a candidate axiom against known facts or data is known as candidate axiom scoring. Being able to accurately score candidate axioms is a prerequisite for automatic schema or ontology induction, but can also be useful for ontology and/or knowledge graph validation. Accurate axiom scoring heuristics are often heavy to compute, which is a big problem if one wants to exploit them in iterative search methods like level-wise generate-and-test or evolutionary algorithms, where large numbers of candidate axioms need to be scored. We tackle the challenge of learning a predictive model as a surrogate to reasoning, that predicts the acceptability of candidate class axioms, that is fast to execute yet accurate enough to be used in such settings. For this purpose, we leverage a semantic similarity measure extracted from the subsumption hierarchy of an ontology. We prove that the method proposed in this paper is able to learn the acceptability labels of candidate OWL class axioms with high accuracy and that it can do so for multiple types of OWL class axioms.

关键词： Ontology learning OWL Class Axioms Concept Similarity Reasoning

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learning Discriminative Feature Representations via Metric learning for Early Operation of Wind Turbine Anomaly Detection Systems 22

Learning Discriminative Feature Representations via Metric L...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Inoue, Taiki Ogata, Jun Iida, Makoto Ogawa, Tetsuji Waseda University Department of Communications and Computer Engineering Japan Japan Research Center for Advanced Science and Technology The University of Tokyo Japan

ISBN: (纸本)9798350345346

To achieve robust wind turbine anomaly detection, we attempted to incorporate metric learning into the process of learning discriminative feature representations. In the context of anomaly detection based on inlier modeling, where anomalies are identified as inputs deviating from the normal state distribution, the key to building a high-performance and robust system, even with limited training data, lies in eliminating the influence of environmental differences from the inputs and obtaining a compact normal state distribution. To address this, we designed a deep neural network-based feature extractor to mitigate the impact of environmental changes. Additionally, we integrated metric learning into its learning process to obtain a more compact distribution by embedding inputs with similar properties close to one another. We validated the effectiveness of the developed network as a feature extractor for constructing a normal model with limited data through experimental comparisons using vibration data collected from sensors. Specifically, we utilized MobileNet as a discriminative feature extractor to identify 37 wind turbines. By introducing metric learning based on t-SNE into its training process, we achieved a notable 50 % improvement in the AUC. Remarkably, this significant improvement was accomplished using only a small amount of data, approximately 13 minutes, obtained from the monitored turbine. © 2023 IEEE.

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RLGNN: Robust Graph Neural Networks with Noisy Label learning 22

RLGNN: Robust Graph Neural Networks with Noisy Label Learnin...

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22nd IEEE international Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Qian, Haodong Li, De Li, Qiyu Wang, Jinyan Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Guangxi Normal University School of Computer Science and Engineering Guilin541004 China

ISBN: (纸本)9798331509712

Graph Neural Networks (GNNs) have become a powerful tool for processing graph-structured data. However, real-world data often faces the problem of label noise and sparsity, which will greatly affect the application of graph neural networks in the real world. Hence, we introduce Robust Graph Neural Networks with Noisy Label learning (RLGNN) to tackle the challenges posed by label noise and sparsity in graph-structured data. Its key components include unsupervised contrastive learning for deriving robust node representations, a two-stage clean sample selection mechanism, and a semi-supervised loss function to harness information from unlabeled nodes. RLGNN ingeniously merges contrastive learning with the prediction space, refining the label selection process and boosting the model's robustness and generalizability. Comprehensive experiments indicate that RLGNN significantly surpasses existing models in managing label noise in semi-supervised node classification scenarios. © 2024 IEEE.

关键词： Contrastive learning

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data Reduction to Improve the Performance of One-Class Classifiers on Highly Imbalanced Big data 22

Data Reduction to Improve the Performance of One-Class Class...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Hancock, John Khoshgoftaar, Taghi M. College of Engineering and Computer Science Florida Atlantic University Boca RatonFL33431 United States

ISBN: (纸本)9798350345346

We propose and demonstrate a data reduction technique to improve the performance of one-class classifiers for highly imbalanced Big data. We focus on insurance fraud detection in Medicare Part D prescription drug claims data. An initial comparison of the performance One-Class Support Vector Machines (SVMs) and One-Class Gaussian Mixture Models (GMMs) showed One-Class GMMs significantly out-performing One-Class SVMs. Therefore, we utilize One-Class Gaussian Mixture Models (GMMs). To enable faster training, we experiment with different levels of reduction in the size of the training data. Our novel application of a data reduction technique to one-class classification maintains model performance in terms of Area Under the Receiver Operating Characteristic Curve (AUC) and Area Under the Precision Recall Curve (AUPRC) scores, while simultaneously lowering model training time and computational resource consumption. Statistical tests confirm models trained on 20 percent of the training data perform equally well in terms of AUC and AUPRC. Furthermore, models trained on five percent of the training data yield AUC scores equivalent to models trained with all available training data. To the best of our knowledge, we are the first to demonstrate a viable data reduction technique for one-class classifiers applied to severely imbalanced Big data. © 2023 IEEE.

关键词： Big data data Reduction Highly Imbalanced data Medicare Fraud Detection One Class Classifier

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A Method for Recommending Resources Across Virtual Academic Communities Based on Knowledge Graph and Prompt learning 22nd

A Method for Recommending Resources Across Virtual Academic ...

引用

22nd Wuhan international conference on E-business (WHICEB)

作者： Chen, Zhihao Yin, Jun Ge, Shilun Wang, Nianxin Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang 212003 Jiangsu Peoples R China

ISBN: (纸本)9783031322983;9783031322990

In the era of big data, virtual academic communities are flourishing and resources are growing explosively. As a result, heterogeneous fragmentation of resources and massive disorder have created constraining problems, which exacerbate the "knowledge island" effect among academic communities and challenge researchers to acquire knowledge effectively. To solve these problems, we propose a method for recommending resources across virtual academic communities (MRRVAC) based on knowledge graph and prompt learning. Firstly, we use the knowledge graph to link resources in different communities, which enables resources to be transferred between communities. Secondly, prompt learning is used to acquire the potential knowledge of knowledge graph. The final recommendation list of academic resources is obtained by training the prompt template with the improved P-tuning method and using it to mine the injected knowledge in the model. Finally, data experiments were conducted on the datasets of two virtual academic communities, Zhihu and ScienceNet. The results show that the average improvement over the original method in HR and ndCG is 0.296% and 0.271%, which validates the effectiveness of the method.

关键词： Virtual Academic Community Knowledge Association Knowledge Recommendation Knowledge Graph Prompted learning

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Investigating the Peacefulness of Brain Connectivity 22

Investigating the Peacefulness of Brain Connectivity

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22nd international conference on ICT and Knowledge engineering, ICT and KE 2024

作者： Leong, Wai Yie Leong, Wai San Leong, Yuan Zhi INTI International University Persiaran Perdana BBN Putra Nilai Nilai71800 Malaysia Schneider Electric Singapore 50 Kallang Avenue Kallang Singapore

ISBN: (纸本)9798350391411

The concept of peace is central to human wellbeing, yet its neural substrates remain largely unexplored. This paper investigates the peacefulness of brain connectivity, aiming to elucidate the neural mechanisms underlying subjective feelings of tranquility and serenity. Through a review of existing literature and neuroscientific findings, this study explores the role of various brain regions and networks in promoting states of peace. Additionally, it discusses methodological approaches, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), that have been utilized to investigate brain connectivity during peaceful states. The paper proposed directions for research in this emerging field, including the integration of bioinformatics and machine learning techniques to unravel the complex dynamics of peaceful brain connectivity. Our analysis focused on identifying specific patterns of brain connectivity that are characteristic of peaceful states. We employed machine learning algorithms, network analysis techniques, and statistical modeling to analyze the EEG and fMRI data and extract meaningful insights. The results reveal distinct patterns of functional connectivity within and between brain regions that are associated with feelings of peace. These findings contribute to our understanding of the neural basis of peace and have implications for mental health research and interventions aimed at promoting well-being. © 2024 IEEE.

关键词： Adversarial machine learning

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Predicting Academic Performance of University Students Using Machine learning: A Case Study in the UK 22

Predicting Academic Performance of University Students Using...

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22nd IEEE/WIC international conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Soyoye, Titilayo Olabisi Chen, Tianhua Hill, Richard Mccabe, Keith Univ Huddersfield Sch Comp & Engn Huddersfield W Yorkshire England Univ Huddersfield Planning & Business Intelligence Huddersfield W Yorkshire England

ISBN: (纸本)9798350309188

Students' performance is one of the key success factors in educational institutions. Understanding the performance of students can help identify issues, enabling real-time action where and when necessary. Further, early identification and improvement of students' academic performance at all levels has been a major challenge in these institutions. Students may experience some difficulties which can impair their study and can negatively impact their academic performance. These issues can be efficiently addressed if students' data is pre-analysed, and students' performance predicted early to allow immediate decisions on support. Early prediction by educators and policy makers can assist in improving student and class performance. This work applied machine learning algorithms to analyse significant factors that influence students' academic performance, which could be used to inform decisions on support, or to identify and notify the students who require assistance;thus, taking effective steps to improving their performance. We used the academic records of computer-science students at the University of Huddersfield from 2017-2022, which provided several features that useful in predicting the students' performance. Evaluation of the results showed that decision tree and Ada Boost regression have higher accuracy score.

关键词： data mining machine learning techniques students' academic performance systematic review

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Detecting High Fuel Consumption in HDVs with Ensemble of Anomaly Detection Models 22

Detecting High Fuel Consumption in HDVs with Ensemble of Ano...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Turan, Berkay Baris Genc, Emre Akcig, Inci Nil Goztepe, Neslihan Mumcuoglu, Mehmet Emin Unel, Mustafa Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey

ISBN: (纸本)9798331527471

In this paper, a machine learning (ML) system is introduced to detect high fuel consumption in heavy-duty vehicles (HDVs) using operational data. The system addresses environmental and efficiency challenges in the transportation industry by precisely monitoring fuel consumption to curb CO2 emissions. An ensemble learning method that integrates unsupervised anomaly detection techniques, including Isolation Forest, Autoencoder, and k-NN Regressor models, is proposed. The anomaly detection results from these models are combined using a weighted majority voting (WMV) approach. This method was tested on a dataset comprising 459 driving records and 14 signals collected from 187 HDVs. Additionally, the Local Outlier Factor (LOF) model was employed to validate the ensemble learning method and investigate the factors contributing to the anomalies. This work enhances transportation efficiency by offering a novel approach for analyzing fuel consumption in HDVs, thereby paving the way for future advancements in sustainable transportation practices. © 2024 IEEE.

关键词： Anomaly Detection Environmental Sustainability Fuel Consumption Heavy-Duty Vehicles Machine learning

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Multi-Sensor Fusion Approach for Calibrating 2D Lidar Distance Measurement via Fastsam Image Segmentation 22

Multi-Sensor Fusion Approach for Calibrating 2D Lidar Distan...

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22nd international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2024

作者： Chu, Shao-Wei Wang, Chuin-Mu Tsai, Ming-Yu Wu, Yen-Ching Wang, Yong-Hong National Chin-Yi University of Technology Department of Artificial Intelligence and Computer Engineering Taichung Taiwan National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798331528072

This paper introduces a method that integrates LiDAR and FastSAM for precise distance measurement, object identification, and segmentation. In the fields of computer vision and intelligent robotics, accurate identification and localization of objects of interest are crucial. LiDAR provides the necessary distance information;however, the signals emitted by LiDAR diverge as the distance increases, potentially leading to inaccurate distance information. Therefore, this paper employs FastSAM image segmentation to assist LiDAR in distance measurement by segmenting target objects from images, thereby obtaining accurate distance information. Experimental results demonstrate that this method effectively fuses distance information with image data. By segmenting images to obtain object information and complementing LiDAR's depth information, the method accurately performs distance measurement and object identification. This technology has broad application prospects in areas such as robot navigation and autonomous driving, laying the foundation for intelligent systems to perceive the environment and understand scenes. Future work will focus on further improving the accuracy of distance measurement and exploring the potential of integrating additional sensors. © 2024 IEEE.

关键词： intelligent systems

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