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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

共 3589 条记录，以下是51-60 订阅

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Assessing the Impact of Temporal data Aggregation on the Reliability of Predictive Machine learning Models 25th

Assessing the Impact of Temporal Data Aggregation on the Rel...

引用

25th international conference on intelligent data engineering and automated learning

作者： Barhrhouj, Ayah Ananou, Bouchra Ouladsine, Mustapha Aix Marseille Univ Univ Toulon CNRS LIS UMR 7020 Marseille France

ISBN: (纸本)9783031777301;9783031777318

In time series analysis, data aggregation is an essential preprocessing step that consolidates data points over specified time intervals, simplifying the data structure and reducing noise. this process is vital for enhancing the manageability of large, complex datasets, commonly encountered in various domains such as energy consumption forecasting. However, the choice of the most appropriate temporal aggregation (TA) frequency can significantly impact the performance and the reliability of predictive models. Different TA frequencies may perform better in different scenarios, and there is a lack of evidence on what indicators may determine the superiority of each one. In this study, we design and execute an empirical experiment framework to first explore the performance of various machine learning (ML) models using different TA frequencies on 04 multivariate time series datasets of ship fuel consumption. We further investigate the reliability of those models by studying the marginal contribution of the time series features in each aggregation period. Using the Random Forest Regressor, XGBoost Regressor, LightGBM, and Extra Trees predictive models along with the Shapley Additive Explanations to analyze feature contributions, we found that time series aggregation consistently produces accurate results regardless of the TA period. However, the models may exhibit significant biases, highlighting the need to examine their inner workings to select the appropriate TA period.

关键词： Machine learning Explainable AI Time-series Temporal aggregation

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

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

引用

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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Using data Augmentation for Improving Text Summarization 25th

Using Data Augmentation for Improving Text Summarization

引用

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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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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Drowsiness Detection Using Vital Sign Sensors and Deep learning on Smartwatches 25th

Drowsiness Detection Using Vital Sign Sensors and Deep Learn...

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25th international conference on intelligent data engineering and automated learning

作者： da Rosa Pereira, Vitor Augusto Berri, Rafael Alceste Osorio, Fernando Santos Univ Sao Paulo BR-13566590 Sao Carlos Brazil Fed Univ Rio Grande BR-96203900 Rio Grande RS Brazil

ISBN: (纸本)9783031777301;9783031777318

Drowsy drivers contribute to high rates of road accidents, leading to numerous fatalities and injuries. the dangers of drowsiness extend beyond the roads, affecting workplaces that require continuous attention and focus. this work aims to develop a non-intrusive, real-time system capable of assessing the user's drowsiness level by processing physiological signals collected by smartwatch sensors. the study utilized photoplethysmography (PPG) and heart rate data from a smartwatch, labeled with an adapted Karolinska Sleepiness Scale (KSS). Deep learning techniques were used to fine-tune hyperparameters and train a 1-dimensional Convolutional Neural Network to reach better prediction performance on drowsiness data. Besides, a 5-fold cross-validation technique was used to evaluate performance and create an ensemble, enhancing prediction robustness through election method. the results show significant prediction accuracy using the ensemble on test data (91.41%). the final pre-trained model was integrated into the smartwatch, creating a real-time drowsiness detection system. this system alerts the user through sound and vibration feedback, preventing them from falling asleep and reducing the risk of accidents.

关键词： Drowsiness Smartwatch Photoplethysmography (PPG) Artificial neural networks Deep learning Ensemble model

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Counterfactual Explanations for Sustainable Tourism Indicators 25th

Counterfactual Explanations for Sustainable Tourism Indicato...

引用

25th international conference on intelligent data engineering and automated learning

作者： Saugar, Javier Lancho, Carmen Cuesta, Marina Cano, Emilio L. Martin de Diego, Isaac Amado, Antonio Rey Juan Carlos Univ Data Sci Lab C Tulipan S-N Mostoles 28933 Spain Dephimatica SL C Rios Rosas 44 3 C Madrid 28002 Spain

ISBN: (纸本)9783031777301;9783031777318

Counterfactual explanations are a well-known technique in Explainable Machine learning (XML) to provide simple explanations on complex Machine learning (ML) models. through understandable "what if" scenarios, counterfactuals explore how changes in the input data affect the results of a model. this article leverages counterfactual explanations for sustainable tourism, an emerging approach within the tourism industry to mitigate the negative impacts of mass tourism on ecological systems and local communities. the proposed method analyzes the relationships between several Sustainable Tourism Indicators (STIs) defined for a specific tourist destination and its general sustainability assessment. It identifies the key changes needed in the STIs to achieve an improved global sustainability score. As a result, a decision-making system is offered for sustainable tourism management, which domain experts can use to make more informed decisions. the effectiveness of the proposed method is illustrated through its application to Mallorca, a popular Spanish tourist destination.

关键词： Counterfactuals Explainable Machine learning Explainable Artificial Intelligence Sustainable Tourism

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Low Consumption Models for Disease Diagnosis in Isolated Farms 25th

Low Consumption Models for Disease Diagnosis in Isolated Far...

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25th international conference on intelligent data engineering and automated learning

作者： Vazquez, I. X. Garcia-Vico, A. M. Seker, H. Sedano, J. ITCL Technol Ctr Burgos 09001 Spain Univ Jaen Andalusian Res Inst Data Sci & Computat Intellige Jaen 23071 Spain Univ Sharjah Coll Comp & Informat Dept Informat Syst Sharjah U Arab Emirates

ISBN: (纸本)9783031777301;9783031777318

the detection of bacterial and viral microbes is pivotal for both human and animal well-being in public health services and veterinary care, but it traditionally requires time-consuming procedures and expert technicians. However, the rise of Machine learning and Deep learning has led to a surge in the application of new techniques that can perform bacterial and viral detection faster and at a lower cost. Yet, despite that success, Deep learning approaches tend to have high energy demands, which can in some contexts limit their application, increasing both costs and environmental concerns. In this study, a new hybrid methodology, in which an Artificial Neural Network was combined with a more energy efficient Spiking Neural Network, was employed to develop a model able to classify 18 species of Eimeria parasites, affecting both rabbits and chickens, from microscope images. We show how significant energy savings can be obtained from SNN layers, while their use in the model can improve its performance.

关键词： Eimeria Microorganisms Detection Green AI Spiking Neural Network

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CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care data Interoperability 25th

CSSDH: An Ontology for Social Determinants of Health to Oper...

引用

25th international conference on intelligent data engineering and automated learning

作者： Das, Subhashis Naskar, Debashis Rodriguez Gonzalez, Sara Univ Salamanca Dept Comp Sci & Automat BISITE Res Grp Salamanca 37007 Spain

ISBN: (纸本)9783031777301;9783031777318

the rise of digital platforms has led to an increasing reliance on technology-driven, home-based healthcare solutions, enabling individuals to monitor their health and share information with healthcare professionals as needed. However, creating an efficient care plan management system requires more than just analyzing hospital summaries and Electronic Health Records (EHRs). Factors such as individual user needs and social determinants of health, including living conditions and the flow of healthcare information between different settings, must also be considered. Challenges in this complex healthcare network involve schema diversity (in EHRs, personal health records, etc.) and terminology diversity (e.g., ICD, SNOMED-CT) across ancillary healthcare operations. Establishing interoperability among various systems and applications is crucial, with the European Interoperability Framework (EIF) emphasizing the need for patient-centric access and control of healthcare data. In this paper, we propose an integrated ontological model, the Common Semantic data Model for Social Determinants of Health (CSSDH), by combining ISO/DIS 13940:2024 ContSys with WHO Social Determinants of Health. CSSDH aims to achieve interoperability within the Continuity of Care Network.

关键词： Continuity of care EHR OWL Social Determinants of Health

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Towards Sustainable Precision: Machine learning for Laser Micromachining Optimization 25th

Towards Sustainable Precision: Machine Learning for Laser Mi...

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25th international conference on intelligent data engineering and automated learning

作者： Correas-Naranjo, Luis Camacho-Sanchez, Miguel Launet, Laetitia Zuric, Milena Naranjo, Valery Univ Politecn Valencia Inst Univ Invest Tecnol Ctr Ser Humano HUMAN Tech Valencia Spain Fraunhofer Inst Laser Technol ILT Steinbachstr 15 D-52074 Aachen Germany

ISBN: (纸本)9783031777301;9783031777318

In the pursuit of sustainable manufacturing, ultra-short pulse laser micromachining stands out as a promising solution while also offering high-precision and qualitative laser processing. However, unlocking the full potential of ultra-short pulse lasers requires an optimized monitoring system capable of early detection of defective workpieces, regardless of the preprocessing technique employed. While advances in machine learning can help predict process quality features, the complexity of monitoring data necessitates reducing both model size and data dimensionality to enable real-time analysis. To address these challenges, this paper introduces a machine learning framework designed to enhance surface quality assessment across diverse preprocessing techniques. To facilitate real-time laser processing monitoring, our solution aims to optimize the computational requirements of the machine learning model. Experimental results show that the proposed model not only outperforms the generalizability achieved by previous works across diverse preprocessing techniques but also significantly reduces the computational requirements for training. through these advancements, we aim to establish the baseline for a more sustainable manufacturing process.

关键词： Laser Processing Laser Structuring Process Monitoring Machine learning Sustainable Manufacturing

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Automatic Classification of Signal and Noise in Functional Magnetic Resonance Imaging Scans Using Convolutional Neural Networks 25th

Automatic Classification of Signal and Noise in Functional M...

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25th international conference on intelligent data engineering and automated learning

作者： Arighelescu, Georgian Chira, Camelia Mansson, Kristoffer N. T. Babes Bolyai Univ Fac Math & Comp Sci Cluj Napoca Romania Babes Bolyai Univ Dept Clin Psychol & Psychotherapy Cluj Napoca Romania Karolinska Inst Ctr Psychiat Res Dept Clin Neurosci Stockholm Sweden

ISBN: (纸本)9783031777301;9783031777318

the integration of Artificial Intelligence (AI), particularly deep learning models like VGG16 and ResNet50, in the analysis of functional magnetic resonance imaging (fMRI) data has significantly advanced our understanding of brain functionality and the diagnosis of neurological disorders. this paper explores the application of Convolutional Neural Networks (CNNs) to enhance the accuracy and efficiency of fMRI data analysis, addressing challenges such as high dimensionality, noise, and the need for complex data preprocessing. Our study evaluates the performance of VGG16 and ResNet50 models and one 3D CNN on 2D and 3D fMRI datasets, highlighting the limitations of VGG16 in handling 2D data and demonstrating the superior performance of ResNet50 on balanced and unbalanced datasets. Additionally, we investigate the impact of using 3D data from the Human Connectome Project (HCP), achieving up to 98% accuracy on the validation set. the results indicate that CNNs can effectively replace traditional Independent Component Analysis (ICA) methods by leveraging their capability for automatic feature extraction and end-to-end learning.

关键词： fMRI Artificial Intelligence Deep learning CNN VGG16 ResNet50

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