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24th international conference on intelligent data engineering and automated learning, ideal 2023

24th International Conference on Intelligent Data Engineerin...

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24th international conference on intelligent data engineering and automated learning, ideal 2023

ISBN: (纸本)9783031482311

the proceedings contain 49 papers. the special focus in this conference is on intelligent data engineering and automated learning. the topics include: GPU-Based Acceleration of the Rao Optimization Algorithms: Application to the Solution of Large Systems of Nonlinear Equations;direct Determination of Operational Value-at-Risk Using Descriptive Statistics;using Deep learning Models to Predict the Electrical Conductivity of the Influent in a Wastewater Treatment Plant;unsupervised Defect Detection for Infrastructure Inspection;Generating Adversarial Examples Using LAD;emotion Extraction from Likert-Scale Questionnaires: – An Additional Dimension to Psychology Instruments –;recent Applications of Pre-aggregation Functions;a Probabilistic Approach: Querying Web Resources in the Presence of Uncertainty;domain Adaptation in Transformer Models: Question Answering of Dutch Government Policies;complexity-Driven Sampling for Bagging;sustainable On-Street Parking Mapping with Deep learning and Airborne Imagery;hebbian learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks;extracting Automatically a Domain Ontology from the "Book of Properties" of the Archbishop’s Table of Braga;language Models for Automatic Distribution of Review Notes in Movie Production;extracting Knowledge from Incompletely Known Models;threshold-Based Classification to Enhance Confidence in Open Set of Legal Texts;comparing Ranking learning Algorithms for Information Retrieval Systems;analyzing the Influence of Market Event Correction for Forecasting Stock Prices Using Recurrent Neural Networks;measuring the Relationship Between the Use of Typical Manosphere Discourse and the Engagement of a User with the Pick-Up Artist Community;uniform Design of Experiments for Equality Constraints;a Pseudo-Label Guided Hybrid Approach for Unsupervised Domain Adaptation;globular Cluster Detection in M33 Using Multiple Views Representation learning;segmentation of Brachial Plexus

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A New dataset for Analyzing Battery Failures in Wheelchairs 25th

A New Dataset for Analyzing Battery Failures in Wheelchairs

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25th international conference on intelligent data engineering and automated learning

作者： Manzolli, William M. Rickes, Tiago B. Lucca, Giancarlo Oliveira, Lizandro de Souza Yamin, Adenauer Correa Univ Catolica Pelotas Ctr Ci encias Sociais Tecnol Mestrado Engenharia Eletron & Comp Pelotas RS Brazil Univ Fed Pelotas Pelotas RS Brazil

ISBN: (纸本)9783031777370;9783031777387

this study addresses battery failure in motorized wheel chairs, which are essential for the mobility of individuals with disabilities. the main objective was to concept a comprehensive dataset comprising six attributes that directly impact battery life, consisting of 498 instances. Using the Random Forest algorithm, we demonstrate the ability to accurately predict battery failures. the results highlight the necessity for proactive measures to prevent battery degradation and extend its lifespan.

关键词： data Repository Machine learning Deep learning Model sharing data Symbion EI

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Federated learning for Vietnamese SMS Spam Detection Using Pre-trained PhoBERT 25th

Federated Learning for Vietnamese SMS Spam Detection Using P...

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25th international conference on intelligent data engineering and automated learning

作者： Hoang Quang Anh Pham Tuan Anh Pham Son Nguyen Phan Duy Hung FPT Univ Hanoi Vietnam

ISBN: (纸本)9783031777301;9783031777318

SMS Spam Detection has increasingly garnered attention due to the widespread use of mobile devices. Currently, most SMS spam detection model training methods rely on centralized data collection, which poses numerous privacy threats and creates security vulnerabilities that expose sensitive information. this study aims to propose a training method that does not require data sharing between parties, based on a federated learning system. In this paper, we experiment with FedAvg, FedAvgM, and FedAdam algorithms using a fine-tuned PhoBERT model tailored for the SMS spam classification task. the results show that the FedAvg algorithm achieves high performance with an accuracy of 99.38% in the IID setting, while the FedAdam algorithm proves more effective in the Non-IID setting, yielding a model with an accuracy of up to 98.5%. this study demonstrates that models like PhoBERT trained with FL algorithms can achieve classification capabilities comparable to centralized data training methods, highlighting the significant potential of FL for natural language processing models without the need for centralized data collection.

关键词： Vietnamese SMS spam detection PhoBERT Federated learning

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Using Contrastive learning to Map Stylistic Similarities in Narrative Writers 25th

Using Contrastive Learning to Map Stylistic Similarities in ...

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25th international conference on intelligent data engineering and automated learning

作者： Valero-Redondo, Maria Huertas-Tato, Javier D'Antonio Maceiras, Sergio Martin, Alejandro Camacho, David Univ Cordoba Cordoba Spain Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Exploring how literary authors influence each other and how intertextual connections can be found among them is a complex problem that is handled manually by experts. this research field, intertextuality, seeks to understand how writers relate to each other and how the works of one writer have reflected on the work of another. this analysis usually provides insight into the historical, cultural, biographical, and literary contexts that shape literary works. From a computational point of view, the approaches related to this problem have mainly dealt with problems of style classification, known as authorship attribution, but none have so far dealt with the problem of intertextuality. this paper proposes a novel approach based on contrastive learning to generate authorship embeddings that encapsulate the stylistic signatures of narrative writers and that can be projected into graphical representations to discern similarities between a dataset of literary writers. the embeddings generated with this approach aim to represent authorship styles are created and evaluated using a dataset of books from Project Gutemberg. During the evaluation, we perform a book and chunk-level evaluation, showing good performance in both cases. Finally, we present different graphical representations and provide a deep analysis of the relations that arise between the writers.

关键词： Contrastive learning Intertextual analysis Language Models Transformer

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Exploring data Symbion EI Deep learning and Model Sharing Modules 25th

Exploring Data Symbion EI Deep Learning and Model Sharing Mo...

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25th international conference on intelligent data engineering and automated learning

作者： Huszcza, Rafael Mendes, Amanda Lopes, Jeferson Borges, Eduardo N. Lucca, Giancarlo B. Guilherme, Pablo D. B. Pereira, Leandro A. Univ Fed Rio Grande FURG Ctr Ciencias Computac C3 Rio Grande Brazil Univ Catolica Pelotas Ctr Ciencias Sociais & Tecnol Mestrado Engenharia Eletron & Computacao Pelotas RS Brazil Univ Estadual Parana UNESPAR Ctr Educ Humanidades Ciencias Biolog Paranagua Brazil Inst Fed Parana IFPR Eixo Tecnolog Meio Ambiente Paranagua Brazil

ISBN: (纸本)9783031777370;9783031777387

Considering the growing volume of available information, storing and managing data is an increasing challenge. Leveraging these raw datasets to extract meaningful knowledge and make informed decisions requires data mining methods, especially when combined with Machine learning algorithms to uncover hidden patterns in data. Considering this context, this work describes a project to contribute to datadriven initiatives by seamlessly integrating a data repository system with Machine learning (ML) tools. this integration allows users to implement pipelines within a single system-from storing and managing datasets to training and deploying ML models, making easier, more efficient, and intuitive data science workflow accessible to all, not just computer scientists. Furthermore, the system aims to enhance knowledge sharing among researchers and practitioners by enabling users to share datasets and trained machine learning models within the platform. this work highlights the system's key features, particularly the Machine learning module, which includes classic ML algorithms for tabular data and the newly introduced Deep learning algorithms for image classification. the new Model Sharing module also emphasizes promoting collaboration by allowing users to share and manage trained models effectively.

关键词： data Repository Machine learning Deep learning Model sharing data Symbion EI

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An Encoder-Based Framework for Privacy-Preserving Machine learning 24th

An Encoder-Based Framework for Privacy-Preserving Machine L...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Jiayun Ren, Wei Zhang, Xianchao Zheng, Xianghan School of Computer Science China University of Geosciences Wuhan430074 China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China Ministry of Education Chengdu China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China College of Computer and Big Data Fuzhou University Fujian Fuzhou China School of Information and Intelligent Engineering Sanya College Hainan China

ISBN: (纸本)9789819615506

As a data-driven science, machine learning requires vast amounts of training data and computational resources. However, for highly privacy-sensitive data, it is crucial to protect the privacy of the data during both the training and utilization of machine learning models. In this paper, we propose a privacy-preserving machine learning approach using autoencoders and differential privacy mechanisms to safeguard data privacy while minimizing the impact on data availability. Specifically, we augment logistic regression and ResNet18 models with different architectures of autoencoders to perform data encryption? without compromising the machine learning tasks. Additionally, we employ differential privacy mechanisms to introduce gradient perturbations in the encoding part of the autoencoder, enhancing the algorithm’s security and further protecting data privacy. We also design the cosine similarity between the encoded and original data as a metric for evaluating data privacy, considering model performance, privacy budget, and data privacy collectively to balance data availability and privacy. Extensive experiments conducted on MNIST, CIFAR-10, PathMNIST, and BloodMNIST datasets demonstrate that for simple logistic regression models handling easily classifiable datasets, employing simple autoencoder structures can enhance classification accuracy, with significant performance impact after adding differential privacy. For ResNet18, utilizing convolutional autoencoders for data encryption generally has minimal impact on model classification performance and can even improve accuracy in most cases. Adding differential privacy has minor effects on model classification performance. Selecting appropriate model structures and privacy budgets for different usage scenarios can ensure both data availability and privacy. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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intelligent data engineering and automated learning – ideal 2023 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Paulo Quaresma Teresa Gonçalves David Camacho Hujun Yin Vicente Julian Antonio J. Tallón-Ballesteros

ISBN: (数字)9783031482328

ISBN: (纸本)9783031482311

this book constitutes the proceedings of the 24th international conference on intelligent data engineering and automated learning, ideal 2023, held in Évora, Portugal, during November 22–24, 2023.;the 45 full papers and 4 short papers presented in this book were carefully reviewed and selected from 77 submissions. ideal 2023 is focusing on big data challenges, machine learning, deep learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models, agents and hybrid intelligent systems, and real-world applications of intelligence techniques and AI.;the papers are organized in the following topical sections: main track; special session on federated learning and (pre) aggregation in machine learning; special session on intelligent techniques for real-world applications of renewable energy and green transport; and special session on data selection in machine learning.

关键词： data Mining and Knowledge Discovery Machine learning Software engineering/Programming and Operating Systems Computers and Education Image Processing and Computer Vision

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intelligent control of micro heat exchanger with locally linear identifier and emotional based controller

Intelligent control of micro heat exchanger with locally lin...

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6th international conference on intelligent data engineering and automated learning (ideal 2005)

作者： Jalili-Kharaajoo, M Islamic Azad Univ Young Researchers Club Tehran Iran

ISBN: (纸本)354026972X

In this paper, an intelligent controller is applied to electrically heated micro heat exchanger. First, the dynamics of the micro heat exchanger, which is a highly nonlinear plant, is identified using Locally Linear Model Tree (LOLIMOT) algorithm. then, an intelligent controller is applied to the identified model. the performance of the proposed intelligent controller is compared with that of classic controllers like PID. Our results demonstrate excellent control action, disturbance handling and system parameter robustness for the intelligent controller.

关键词： Heat exchangers

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Instance Selection Techniques for Large Volumes of data 24th

Instance Selection Techniques for Large Volumes of Data

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24th international conference on intelligent data engineering and automated learning, ideal 2023

作者： Cubillos, Marco Antonio Peña Ballesteros, Antonio Javier Tallón Department of Electronic Computer Systems and Automation Engineering International University of Andalusia Huelva Spain

ISBN: (纸本)9783031482311

Instance selection (IS) serves as a vital preprocessing step, particularly in addressing the complexities associated with high-dimensional problems. Its primary goal is the reduction of data instances, a process that involves eliminating irrelevant and superfluous data while maintaining a high level of classification accuracy. IS, as a strategic filtering mechanism, addresses these challenges by retaining essential instances and discarding hindering elements. this refinement process optimizes classification algorithms, enabling them to excel in handling extensive datasets. In this research, IS offers a promising avenue to strengthen the effectiveness of classification in various real-world applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： data mining

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Computational Ethology: Short Review of Current Sensors and Artificial Intelligence Based Methods 1

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24th international conference on engineering Applications of Neural Networks (EANN)

作者： Aguilar-Moreno, Marina Grana, Manuel Univ Basque Country UPV EHU Computat Intelligence Grp San Sebastian Spain

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Computational Ethology provides automated and precise measurement of animal behavior. Artificial Intelligence (AI) techniques have also introduced the enhanced capabilities to interpret experimental data in order to extract accurate ethograms allowing the comparison of animal models with high discriminative power. In this short review we introduce the most recent software tools that employ AI tools for this endeavor, including the popular deep learning approaches.

关键词： Computational Ethology Animal Behavior Sensors Tracking Machine learning Deep learning

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