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检索条件"任意字段=21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020"

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21th international conference on intelligent data engineering and automated learning, ideal 2020

21th International Conference on Intelligent Data Engineerin...

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21th international conference on intelligent data engineering and automated learning, ideal 2020

ISBN: (纸本)9783030623647

the proceedings contain 34 papers. the special focus in this conference is on intelligent data engineering and automated learning. the topics include: Network Analysis for Fraud Detection in Portuguese Public Procurement;biased Language Detection in Court Decisions;a One-by-One Method for Community Detection in Attributed Networks;data Pre-processing and data Generation in the Student Flow Case Study;a Hybrid Approach to the Analysis of a Collection of Research Papers;Sequential Self-tuning Clustering for Automatic Delimitation of Coastal Upwelling on SST Images;time Series Clustering for Knowledge Discovery on Metal Additive Manufacturing;quaternion Neural Networks: State-of-the-Art and Research Challenges;a Solar thermal System Temperature Prediction of a Smart Building for data Recovery and Security Purposes;a Fault Detection System for Power Cells During Capacity Confirmation Test through a Global One-Class Classifier;a Deep Metric Neural Network with Disentangled Representation for Detecting Smartphone Glass Defects;improving Performance of Recommendation System Architecture;automated learning of In-vehicle Noise Representation with Triplet-Loss Embedded Convolutional Beamforming Network;Sequence Mining for Automatic Generation of Software Tests from GUI Event Traces;enhanced Credit Prediction Using Artificial data;deep learning Based Algorithms for Welding Edge Points Detection;detecting Performance Anomalies in the Multi-component Software a Collaborative Robot;prediction of Small-Wind Turbine Performance from Time Series Modelling Using intelligent Techniques;review of Trends in Automatic Human Activity Recognition Using Synthetic Audio-Visual data;atmospheric Tomography Using Convolutional Neural Networks;workshop on Machine learning in Smart Mobility;Driver Monitoring System Based on CNN Models: An Approach for Attention Level Detection;preface.

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21th international conference on intelligent data engineering and automated learning, ideal 2020

21th International Conference on Intelligent Data Engineerin...

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21th international conference on intelligent data engineering and automated learning, ideal 2020

ISBN: (纸本)9783030623616

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Towards Reliable Drift Detection and Explanation in Text data 25th

Towards Reliable Drift Detection and Explanation in Text Dat...

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25th international conference on intelligent data engineering and automated learning

作者： Feldhans, Robert Hammer, Barbara Bielefeld Univ Bielefeld Germany

ISBN: (纸本)9783031777301;9783031777318

When delivered to the market, machine learning models face new data which are possibly subject to novel characteristics - a phenomenon known as concept drift. As this might lead to performance degradation, it is necessary to detect such drift and, if required, adapt the model accordingly. While a variety of drift detection and adaptation methods exists for standard vectorial data, a suitable treatment of text data is less researched. In this work we present a novel approach which detects and explains drift in text data based on their representation via transformer embeddings. In a nutshell, the method generates suitable statistical features from the original distribution and the possibly shifted variation. Based on these representations, drift scores can be assigned to individual data points, allowing a visualization and human-readable characterization of the type of drift. We demonstrate the approach's effectiveness in reliably detecting drift in several experiments.

关键词： Drift Explanation Text data Transformer Visualization

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An Ontology-Lexicon-Driven Approach for Refining Sentiment Analysis Processes 25th

An Ontology-Lexicon-Driven Approach for Refining Sentiment A...

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25th international conference on intelligent data engineering and automated learning

作者： Toldy, Francisco Belo, Orlando Univ Minho ALGORITMI Res Ctr LASI P-4710059 Braga Portugal

ISBN: (纸本)9783031777370;9783031777387

the high number of sentiment analysis systems and applications developed over the last few years provided companies with very sophisticated analysis tools, allowing them to establish preferences, trends and patterns of customer behavior. this is quite important for companies intending to change their way of being, promoting work actions aimed at specific customer segments, to obtain business advantages and improve their image and performance in the market in which they work. In this paper, we present and describe a sentiment analysis system that combine techniques based on ontologies and domain lexicons, to provide relevant indicators to support the evaluation of the degree of user satisfaction and know the influence of each ontological element incorporated in opinion texts in sentiment classification.

关键词： Sentiment Analysis Opinion Analysis Ontologies Lexical Classification data Preparation Tagging Texts Marketing Applications

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Predicting Employee Attrition in a Multi-company Setting 25th

Predicting Employee Attrition in a Multi-company Setting

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25th international conference on intelligent data engineering and automated learning

作者： Gomes, Adriano Silva, Luis M. Cruz, Joao Pedro Univ Aveiro Dept Econ Management Ind Engn & Tourism P-3810193 Aveiro Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Dept Math P-3810193 Aveiro Portugal

ISBN: (纸本)9783031777301;9783031777318

this paper describes the creation of a database and a machine learning model to predict employee attrition. Our proposal deals with attrition by considering 3 classes (voluntary, involuntary and no attritors) giving a more complete view of the loss of qualified personnel to the Human Resources Management. Of the several machine learning models tested to solve the problem, XGBoost stood out as the best performing one on a dataset with more than four thousand employees and twenty-one features collected from three independent companies from different industrial sectors. the model, evaluated on a 20-run experiment, achieved an overall mean accuracy of 78.5%, corresponding to the correct classification of 52.6% of the voluntary attritors, 78.9% of the involuntary attritors and 81.6% of the non-attritors, showing that voluntary attritors are harder to discriminate.

关键词： Employee attrition Machine learning Real data Human resources management Industry 4.0

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data Analysis and Anomaly Detection in a Wind Farm with k-Nearest Neighbors 25th

Data Analysis and Anomaly Detection in a Wind Farm with k-Ne...

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25th international conference on intelligent data engineering and automated learning

作者： Weiss, Bassel Esteban, Segundo Santos, Matilde Univ Complutense Madrid Dpto Arquitectura Computadores Automat Madrid 28040 Spain UCM Inst Knowledge Technol Madrid 28040 Spain

ISBN: (纸本)9783031777370;9783031777387

this paper presents an in-depth analysis of data from the Alpha Ventus offshore wind farm, emphasizing the identification and detection of anomalies in wind turbine performance. Utilizing real-world data from the RAVE (Research at Alpha Ventus) project, we explore the complexities of offshore wind energy generation, including the effects of wind speed, nacelle position, and environmental factors on turbine behaviour. In this paper, among the various machine learning techniques, we have selected k-nearest neighbours (k-NN), to identify patterns and detect anomalies indicative of potential issues. Our findings demonstrate that some turbines of the wind farm, centrally located, are subject to significant wake effects and operational irregularities. By adjusting the parameters of the k-NN model, we achieved an anomaly detection framework, enhancing the reliability of turbine operation and maintenance.

关键词： Offshore wind energy Anomaly detection Wind turbine data analytics Machine learning K-nearest neighbours Predictive maintenance

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Automatic PDF Document Classification with Machine learning 25th

Automatic PDF Document Classification with Machine Learning

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25th international conference on intelligent data engineering and automated learning

作者： Llacer Luna, Socrates Garigliotti, Dario Martinez Plumed, Fernando Ferri Ramirez, Cesar Univ Politecn Valencia Valencia Spain Univ Bergen Bergen Norway

ISBN: (纸本)9783031777301;9783031777318

Universitat Polit`ecnica de Val`encia (UPV) faces challenges in managing its Alfresco document repository, which contains 600,000 PDF files, of which only 100,000 are correctly categorised. Manual classification is laborious and error-prone, hindering information retrieval and advanced search capabilities. this project presents an automated pipeline that integrates optical character recognition (OCR) and machine learning to efficiently classify documents. Our approach distinguishes between scanned and digital documents, accurately extracts text and categorises it into 51 predefined categories using models such as BERT and RF. By improving document organisation and accessibility, this work optimises UPV's document management and paves the way for advanced search technologies and real-time classification systems.

关键词： Document Classification OCR Machine learning Alfresco Repository

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Federated learning with Discriminative Naive Bayes Classifier 25th

Federated Learning with Discriminative Naive Bayes Classifie...

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25th international conference on intelligent data engineering and automated learning

作者： Torrijos, Pablo Alfaro, Juan C. Gamez, Jose A. Puerta, Jose M. Univ Castilla La Mancha Inst Invest Informat Albacete Albacete 02071 Spain Univ Castilla La Mancha Dept Sistemas Informaticos Albacete 02071 Spain

ISBN: (纸本)9783031777370;9783031777387

Federated learning has emerged as a promising approach to train machine learning models on decentralized data sources while preserving data privacy. this paper proposes a new federated approach for Naive Bayes (NB) classification, assuming discrete variables. Our approach federates a discriminative variant of NB, sharing meaningless parameters instead of conditional probability tables. therefore, this process is more reliable against possible attacks. We conduct extensive experiments on 12 datasets to validate the efficacy of our approach, comparing federated and non-federated settings. Additionally, we benchmark our method against the generative variant of NB, which serves as a baseline for comparison. Our experimental results demonstrate the effectiveness of our method in achieving accurate classification.

关键词： Federated learning Bayesian network classifiers Naive Bayes Discriminative learning

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Using data Augmentation for Improving Text Summarization 25th

Using Data Augmentation for Improving Text Summarization

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25th international conference on intelligent data engineering and automated learning

作者： Constantin, Daniel Mihaescu, Marian Cristian Heras, Stella Jordan, Jaume Palanca, Javier Julian, Vicente Univ Craiova Craiova Romania VRAIN Univ Politecn Valencia Valencian Res Inst Artificial Intelligence Valencia Spain

ISBN: (纸本)9783031777370;9783031777387

In today's society, the amount of information we need to process daily from sources such as news, videos, and literature is relatively high. the primary strategy to decrease the workload is to use effective summarization techniques, either through extractive (where the summary is made up of extracts from the source itself) or abstractive methods. Traditional summarization models often rely on extensive humanannotated data, which is usually quite costly. this research proposes an approach leveraging transformer models to optimize and affordably augment small datasets, enhancing the performance of summarization models. Using sentence clustering and pre-trained models on tasks such as summarization or paraphrasing, we explore whether such an approach can yield better results across various summarization datasets that target different formats, such as video conference transcripts and news articles.

关键词： Text Summarization data Augmentation Transformer Models ROUGE

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Padel Two-Dimensional Tracking Extraction from Monocular Video Recordings 25th

Padel Two-Dimensional Tracking Extraction from Monocular Vid...

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25th international conference on intelligent data engineering and automated learning

作者： Novillo, Alvaro Acena, Victor Lancho, Carmen Cuesta, Marina De Diego, Isaac Martin Rey Juan Carlos Univ Data Sci Lab C Tulipan S-N Mostoles 28933 Spain

ISBN: (纸本)9783031777301;9783031777318

this study introduces a novel framework for the automatic two-dimensional tracking of padel games using monocular recordings. By integrating advanced Computer Vision and Deep learning techniques, our algorithm detects and tracks players, the court, and the ball. through homography, we accurately project detected player positions onto a two-dimensional court, enabling comprehensive tracking throughout the game. We tested the proposed algorithm using amateur video recordings of padel games found in literature. this approach remains user-friendly, cost-effective, and adaptable to various camera angles and lighting conditions. this makes it accessible to both amateur and professional players and coaches, providing a valuable tool for performance analysis. Additionally, the proposed framework holds potential for adaptation to other sports with minimal modifications, further broadening its applicability.

关键词： Computer Vision Padel Deep learning Court Detection Homography Applied Intelligence Tracking data

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