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

共 3596 条记录，以下是31-40 订阅

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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...

引用

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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A Note on Even-Sized Spectral Clustering 13

A Note on Even-Sized Spectral Clustering

引用

Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Miyakawa, Hiroki Endo, Yasunori Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master's Program in Risk and Resilience Engineering University of Tsukuba Ibaraki Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Ibaraki Tsukuba Japan

ISBN: (纸本)9798350373332

Clustering is a typical unsupervised learning method for classifying unsupervised data. One of the clustering meth-ods, even-sized clustering based on optimization (ECBO), is a clustering algorithm that imposes a constraint to equalize the size of each cluster. ECBO has been suggested to be effective in delivery and other problems. However, it is limited to Euclidean space. On the other hand, spectral clustering with a wide range of applicability for partitioning graph data has been proposed. In this paper, we propose even-sized spectral clustering, which imposes a size-equal constraint on spectral clustering, and show that it is an extension of the graph partitioning problem. We also verify the validity of the results through numerical examples. © 2024 IEEE.

关键词： Unsupervised learning

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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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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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Low Consumption Models for Disease Diagnosis in Isolated Farms 25th

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

引用

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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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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Cooperative-Competitive Decision-Making in Resource Management: A Reinforcement learning Perspective 25th

Cooperative-Competitive Decision-Making in Resource Manageme...

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25th international conference on intelligent data engineering and automated learning

作者： Isakov, Artem Peregorodiev, Danil Brunko, Pavel Tomilov, Ivan Gusarova, Natalia Vatian, Alexandra ITMO Univ St Petersburg 197101 Russia Almazov Natl Med Res Ctr World Class Res Ctr Personalized Med St Petersburg 194156 Russia

ISBN: (纸本)9783031777301;9783031777318

this study presents a multi-agent approach to modeling deci-sion-making processes for cooperative-competitive game scenarios generated by long-tailed distributions. We introduce a novel simulation environment that aims to replicate real-world resource allocation challenges, particularly in healthcare settings. the environment incorporates dynamic changes in agents' perceptions and utilizes heavy-tailed distributions to model unpredictable events. We adapt the Multi-Agent Deep Deterministic Policy Gradient algorithm to this environment, demonstrating its effectiveness in learning and strategy development. Our approach includes a role-based system and a two-component reward function that guides agents through the learning process. the results, evaluated using metrics such as cumulative reward, average episodic reward, and approximate Kullback-Leibler divergence, indicate successful learning and sufficent agent strategies. this research contributes to the field of multi-agent systems and resource management by providing a framework that can generalize learned policies to varying numbers of agents and resources, making it applicable to real-world scenarios with changing conditions.

关键词： Multi-agent systems Human-in-the-loop Reinforcement learning Resource allocation Cooperative-competitive games Heavy-tailed distribution

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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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Dissecting data Practices in Android Apps: A Comparative Study of data Collection and Sharing Behaviors 25th

Dissecting Data Practices in Android Apps: A Comparative Stu...

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25th international conference on intelligent data engineering and automated learning

作者： Nguyen, Triet M. Pham, Nghiem T. Doan, Hieu M. Tran, Khoa D. Tran, Bao Q. Huynh, Khiem G. Tran, Nam B. Vo, Khanh H. FPT Univ Can Tho Vietnam

ISBN: (纸本)9783031777301;9783031777318

the exponential growth of mobile apps within the Android ecosystem has underscored the critical need for robust user privacy and data protection measures. Central to these concerns are the privacy policies that serve as the primary channel of communication between organizations and users, detailing data collection, utilization, and sharing practices. However, the efficacy of these policies is often undermined by their inaccessibility and the legalese that obfuscates their intent, presenting a barrier to informed user consent. this study addresses these challenges by harnessing the capabilities of Generative AI (GenAI) to perform a detailed analysis of data practices in Android apps. Our methodology extends beyond the traditional scope of AI-assisted analysis by not only identifying third-party entities but also by elucidating their data handling purposes. We introduce a classification system that distinguishes between 'Regular' and 'Irregular' app behaviors, offering a benchmark for app evaluation and compliance assessment. Our comparative analysis across various apps reveals patterns and anomalies in data management, providing actionable insights for developers, regulators, and users.

关键词： Android Apps data Collection data Sharing Privacy Policy data Usage Behavior Mobile Privacy

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