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检索条件"任意字段=23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022"

共 102 条记录，以下是11-20 订阅

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An intelligent Decision Support System for Road Freight Transport 23rd

An Intelligent Decision Support System for Road Freight Tran...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Carvalho, Hugo Silva Pilastri, Andre Matta, Arthur Matos, Luis Miguel Novais, Rui Cortez, Paulo CCG ZGDV Inst EPMQ Guimaraes Portugal ABMN Business Solut Azurem Guimaraes Portugal Univ Minho Dept Informat Syst ALGORITMI Res Ctr LASI Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

This paper presents an intelligent Decision Support System (IDSS) to optimize transport and logistics activities in a set of Portuguese companies currently operating in the freight transport sector. This IDSS comprises three main modules that can be used individually or chained together, dedicated to: a geographic clustering detection of transport services;a transport driver suggestion;and a route and truck-load optimization. The IDSS was entirely designed and developed to support real-time data and it consists of an end-to-end solution (E2ES), given that it covers all the main transport and logistics processes since the registration in the database to the optimized transport plan. The entire set of functionalities inserted in the IDSS was designed and validated by freight transport sector experts from the different companies that will use the proposed system.

关键词： intelligent decision support system Transport and logistics Geospatial clustering Resource optimization Route optimization TAM 3

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Benchmarking data Augmentation Techniques for Tabular data 23rd

Benchmarking Data Augmentation Techniques for Tabular Data

引用

23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Machado, Pedro Fernandes, Bruno Novais, Paulo Univ Minho ALGORITMI Ctr Braga Portugal

ISBN: (纸本)9783031217524;9783031217531

Imbalanced learning and small-sized datasets are usual in machine learning problems, even with the increased data availability provided by recent developments. The performance of learning algorithms in the presence of unbalanced data and significant class distribution skews is known as the "imbalanced learning problem". The models' performance on such problems can drastically decrease for certain classes with an uneven distribution because the models do not learn the distributive features of the data and present accuracy too favorable for a specific set of classes of data. As an example, this can have negative consequences when talking about cancer detection since the model may poorly identify unhealthy patients. Hence, data augmentation techniques are usually conceived to evaluate how models would behave in non-data-scarce environments, generating synthetic data that mimics the characteristics of real data. By applying those techniques, the amount of available data can be increased, balancing the class distributions. However, there are no standardized data augmentation processes that can be applied to every domain of tabular data. Therefore, this study aims to identify which characteristics of a dataset provide a better performance when synthesizing samples by a data augmentation technique in a tabular data environment.

关键词： data augmentation Imbalanced data Machine learning

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Solving Multi-class Imbalance Problems Using Improved Tabular GANs 23rd

Solving Multi-class Imbalance Problems Using Improved Tabula...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Farou, Zakarya Kopeikina, Liudmila Horvath, Tomas Eotvos Lorand Univ Inst Ind Acad Innovat Dept Data Sci & Engn Pazmany Peter Setany 1-C H-1117 Budapest Hungary Pavol Jozef Safarik Univ Inst Comp Sci Jesenna 6 Kosice 04001 Slovakia

ISBN: (纸本)9783031217524;9783031217531

Multi-class imbalance problems are non-standard derivative data science problems. These problems are associated with the skewness in the data underlying distribution, which, in turn, raises numerous issues for conventional machine learning techniques. To address the lack of data in imbalance problems, we can either collect new data or oversample the underrepresented classes by synthesizing artificial data from original instances. This paper focuses on the latter and introduces two novel tabular GAN variants to handle multi-class imbalance problems. Empirical results on three datasets from the UCI repository demonstrated that the suggested approaches that use our proposed filtering algorithm based on neighboring rules improved the ability of the decision tree classification model to recognize underrepresented class instances, decreased the bias toward the majority class, and enhanced its generalization ability.

关键词： Imbalanced learning Generative adversarial networks data augmentation data filtering Multi-class classification

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Detection of False Information in Spanish Using Machine learning Techniques 23rd

Detection of False Information in Spanish Using Machine Lear...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Tretiakov, Arsenii Martin, Alejandro Camacho, David Univ Politecn Madrid Dept Comp Syst Engn Calle Alan Turing Madrid 28031 Spain

ISBN: (纸本)9783031217524;9783031217531

This research focuses on the detection of false claims in Spanish through the use of machine learning techniques. Most of the current work related to automated, or semi-automated, fake news detections are carried out for the English language, however, there is still a large room for improvement in other languages such as Spanish. The detection of fake news content and its dissemination (spread) in online platforms is an open and hard problem, this work is focused on the detection of false and misleading information spreading during the election campaign in the Spanish Parliament and Catalonia crisis in 2019, migration crisis, COVID-19 pandemic, and hate speech against minorities. We propose the use of a machine learning model adapted for dealing with human language understanding tasks, called BERT, which has been trained and experimentally tested. We have collected a corpus of different types of false information and claims such as articles, posts on Facebook, WhatsApp's messages, tweets, and others. The results evidence how usage of machine learning techniques can help in the identification of false statements with more than 88% accuracy, and in collecting samples of false information. The experiments, with a comparison between different machine learning methods, have also been carried out using previous datasets, providing a comparison between different approaches.

关键词： Fake news False information Mass media Social media Machine learning Media literacy

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Gradient Regularization with Multivariate Distribution of Previous Knowledge for Continual learning 23rd

Gradient Regularization with Multivariate Distribution of Pr...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Kim, Tae-Heon Moon, Hyung-Jun Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea Yonsei Univ Dept Artificial Intelligence Seoul 03722 South Korea

ISBN: (纸本)9783031217524;9783031217531

Continual learning is a novel learning setup for an environment where data are introduced sequentially, and a model continually learns new tasks. However, the model forgets the learned knowledge as it learns new classes. There is an approach that keeps a few previous data, but this causes other problems such as overfitting and class imbalance. In this paper, we propose a method that retrains a network with generated representations from an estimated multivariate Gaussian distribution. The representations are the vectors coming from CNN that is trained using a gradient regularization to prevent a distribution shift, allowing the stored means and covariances to create realistic representations. The generated vectors contain every class seen so far, which helps preventing the forgetting. Our 6-fold cross-validation experiment shows that the proposed method outperforms the existing continual learning methods by 1.14%p and 4.60%p in CIFAR10 and CIFAR100, respectively. Moreover, we visualize the generated vectors using t-SNE to confirm the validity of multivariate Gaussian mixture to estimate the distribution of the data representations.

关键词： Continual learning Memory replay Sample generation Multivariate gaussian distribution Expectation-maximization

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EduBot: A Proof-of-Concept for a High School Motivational Agent 23rd

EduBot: A Proof-of-Concept for a High School Motivational Ag...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Faria, Hugo Barbosa, Maria Araujo Veloso, Bruno Marcondes, Francisco S. Lima, Celso Duraes, Dalila Novais, Paulo Univ Minho ALGORITMI Ctr Braga Portugal High Sch Caldas Taipas Caldas Das Taipas Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

Motivation appears to plays a role in student dropout. This paper uses a dataset with high school information (756 entries) for creating a model that relates year failures with the other features on the dataset. The resulting model, based on XGBoost, shows that the top-ranked features are related with motivation issues corroborating with the initial observation. In this sense, a digital assistant embedded with a motivation module may aid on improving motivation and on avoiding dropout. In other words, if the predictor detects year failure possibility it start to act on motivating the student. Considering this paper being direct to underage students, this paper stops on the proof-of-concept level considering the actual dialogues and live tests are expected to performed with the support of educational psychologists.

关键词： Educational data mining Motivational assistant Predict at-risk students Student's performance

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Convolutional Neural Network Approach for Multiple Sclerosis Lesion Segmentation 23rd

Convolutional Neural Network Approach for Multiple Sclerosis...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Messaoud, Nada Haj Mansour, Asma Ayari, Rim Ben Abdallah, Asma Aissi, Mouna Frih, Mahbouba Bedoui, Mohamed Hedi Univ Monastir Lab Technol & Med Imaging Fac Med Monastir Tunisia Fatouma Bouguiba Hosp Dept Neurol Monastir Tunisia Univ Monastir Fac Sci Monastir FSM Monastir Tunisia

ISBN: (纸本)9783031217524;9783031217531

Nowadays Deep learning (DL) based automatic segmentation has outperformed traditionalmethods. In the present paper, we are interested in automatic MS lesion segmentation of 2D images based on DL techniques. The main challenge consists in proposing a new model that takes advantage of referenced CNN models: U-Net, ResNet, and DenseNet with a reduced number of parameters and a shorter execution time. To evaluate the proposed approach named "Concat-U-Net", we compared its performance to those of three implemented models, namely U-Net, U-ResNet, and Dense-U-Net. Furthermore, we employed just one modality (FLAIR) from the public ISBI dataset to segment MS lesions accurately. The best Dice value obtained was 0.73, which outperformed those reported in the literature. Our approach reduced the elapsed execution time from 48 s to 7 s. By reducing the number of parameters, an 85.42% time gain was achieved.

关键词： Deep learning Multiple sclerosis segmentation MRI data augmentation

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Association Rules Mining for Reducing Items from Emotion Regulation Questionnaires 23rd

Association Rules Mining for Reducing Items from Emotion Reg...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Khadimallah, Rihab Kallel, Ilhem Drira, Fadoua REGIM Lab Res Grp Intelligent Machines Sfax Tunisia FSEGS Fac Econ & Management Sfax Sfax Tunisia ISIMS Higher Inst Comp Sci & Multimedia Sfax Sfax Tunisia ENIS Natl Sch Engn Sfax Sfax Tunisia

ISBN: (纸本)9783031217524;9783031217531

Regulating learners' negative emotions during a learning session is an important factor in educational settings that aims at enhancing learner's cognitive performance and achievement outcomes. In this context, many researchers in psychology have proposed emotion regulation questionnaires that help to assess the use of emotion regulation strategies by learners in order to regulate their emotions while learning. However, the number of items in each questionnaire is large which may annoy the learner and prevent him/her from completing all of the items;this may lead to inappropriate emotional regulation. Thus, we propose in this paper a machine learning method for mining items in order to reduce the fully associated ones to one item. First of all, the paper presents a critical overview on statistical methods applied for reducing the large number of items in questionnaires. Then, after the introduction of the selected data set about the emotion regulation questionnaires, we detail the association rules mining method and discuss the obtained results about the significant association rules between the items. This can lead to uphold the items' reduction without loss of reliability.

关键词： Emotion regulation questionnaire (ERQ) Reducing items Association rules mining

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ICU Mortality Prediction Using Long Short-Term Memory Networks 23rd

ICU Mortality Prediction Using Long Short-Term Memory Networ...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Mili, Manel Kerkeni, Asma Ben Abdallah, Asma Bedoui, Mohamed Hedi Univ Monastir Fac Med Monastir Tunisia Univ Monastir Higher Inst Comp Sci & Math Monastir Tunisia Univ Monastir Lab Technol & Med Imaging Fac Med Monastir Tunisia

ISBN: (纸本)9783031217524;9783031217531

Extensive bedside monitoring in Intensive Care Units (ICUs) has resulted in complex temporal data regarding patient physiology, which presents an upscale context for clinical data analysis. In the other hand, identifying the time-series patterns within these data may provide a high aptitude to predict clinical events. Hence, we investigate, during this work, the implementation of an automatic data-driven system, which analyzes large amounts of multivariate temporal data derived from Electronic Health Records (EHRs), and extracts high-level information so as to predict in-hospital mortality and Length of Stay (LOS) early. Practically, we investigate the applicability of LSTM network by reducing the time-frame to 6-hour so as to enhance clinical tasks. The experimental results highlight the efficiency of LSTM model with rigorous multivariate time-series measurements for building real-world prediction engines.

关键词： Electronic health record Multivariate time-series data MIMIC-III

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The Covid-19 Influence on the Desire to Stay at Home: A Big data Architecture 23rd

The Covid-19 Influence on the Desire to Stay at Home: A Big ...

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23rd international conference on intelligent data engineering and automated learning (ideal)

作者： Sousa, Regina Oliveira, Daniela Carneiro, Ana Pinto, Luis Pereira, Ana Peixoto, Ana Peixoto, Hugo Machado, Jose Univ Minho LASI ALGORITMI Res Ctr Braga Portugal

ISBN: (纸本)9783031217524;9783031217531

The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic's insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people's desire to stay at home.

关键词： Big data Business intelligence COVID-19 Stay at home Behavior analysis

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