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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 101 条记录，以下是21-30 订阅

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Explainable Artificial Intelligence for Improved Modeling of Processes 23rd

Explainable Artificial Intelligence for Improved Modeling of...

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

作者： Velioglu, Riza Goepfert, Jan Philip Artelt, Andre Hammer, Barbara Bielefeld Univ CITEC Bielefeld Germany Recommendy UG Bielefeld Germany

ISBN: (纸本)9783031217524;9783031217531

In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowledge extraction based on process mining has been proposed, among other techniques, to provide users with intuitive access to the information contained therein. At present, the majority of technologies aim to reconstruct explicit business process models. These are directly interpretable but limited concerning the integration of diverse and real-valued information sources. On the other hand, Machine learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes. In this contribution, we evaluate the capability of modern Transformer architectures as well as more classical ML technologies of modeling process regularities, as can be quantitatively evaluated by their prediction capability. In addition, we demonstrate the capability of attentional properties and feature relevance determination by highlighting features that are crucial to the processes' predictive abilities. We demonstrate the efficacy of our approach using five benchmark datasets and show that the ML models are capable of predicting critical outcomes and that the attention mechanisms or XAI components offer new insights into the underlying processes.

关键词： Machine learning Process mining Transformer XAI

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Endowing intelligent Vehicles with the Ability to Learn User's Habits and Preferences with Machine learning Methods 23rd

Endowing Intelligent Vehicles with the Ability to Learn User...

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

作者： Barbosa, Paulo Ferreira, Flora Fernandes, Carlos Erlhagen, Wolfram Guimaraes, Pedro Wojtak, Weronika Monteiro, Sergio Bicho, Estela Univ Minho Res Ctr Algoritmi P-4800058 Guimaraes Portugal Univ Minho Res Ctr Math P-4800058 Guimaraes Portugal

ISBN: (纸本)9783031217524;9783031217531

A private vehicle frequently carries the same passengers who routinely take specific objects with them, have their vehicle comfort preferences, and visit the same places at relatively the same time of a given day or day of the week. Thus, developing intelligent vehicles that are able to reduce the cognitive workload of the drivers by learning and adapting to their occupants' routines is of the highest interest. In this paper, we present two independent models based on machine learning methods, including artificial neural networks and linear and ridge regressions, to learn the habits and preferences of the vehicle's users. The first model is responsible for predicting the next vehicle trip state, i.e., the departure location and time, and the driver, passenger, and object states. The second model anticipates the comfort setting inside the cockpit - temperature, cockpit mirror, and driver seat poses. The developed models were trained, evaluated, and validated with different datasets in the Portuguese city of Braga. The results prove that the vehicle efficiently learns the routines of several users with varying complexities. Prediction errors happen in cases of an exceptional, one-time deviation from routine behavior.

关键词： learning driver habits Human mobility patterns Time and space prediction Deep learning intelligent vehicles

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A Comparative Study of LAD, CNN and DNN for Detecting Intrusions 23rd

A Comparative Study of LAD, CNN and DNN for Detecting Intrus...

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

作者： Chauhan, Sneha Mahmoud, Loreen Gangopadhyay, Sugata Gangopadhyay, Aditi Kar IIT Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India NIT Uttarakhand Dept Comp Sci & Engn Srinagar India IIT Roorkee Dept Math Roorkee Uttar Pradesh India

ISBN: (纸本)9783031217524;9783031217531

In recent years, with the growth of the Internet and network devices, a significant amount of information has been exposed to the attackers and intruders. Due to vulnerabilities in the system, the adversaries plan new ways of network intrusions. Many Intrusion Detection Systems (IDSs) are developed to protect the networks from malicious attacks and ensure reliability and availability within the organizations. IDSs built using various machine learning and data mining techniques are effective in detecting attacks. However, their performance decreases with an increase in the size of data. In this paper, we focus on developing an IDS model using Logical Analysis of data (LAD). It is a supervised learning technique where patterns are generated using partially defined Boolean functions (pdBf), which can detect attacks based on certain features of the data. We compare the performance of LAD model with Deep Neural Network (DNN) and Convolutional Neural Network (CNN) IDS models. UNSW-NB15 and CSE-CIC-IDS2018 datasets are used for training and testing our proposed model. The results show that the performance of LAD model is competitive to CNN, DNN and other existing IDS models based on accuracy, precision, recall and F1 score.

关键词： Logical analysis of data Intrusion detection system Deep neural network Convolutional neural network

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Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes 23rd

Using Design of Experiments to Support the Commissioning of ...

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

作者： Voigt, Tim Schoene, Marvin Kohlhase, Martin Nelles, Oliver Kuhn, Martin Bielefeld Univ Appl Sci Fac Engn & Math Ctr Appl Data Sci Gutersloh Bielefeld Germany Univ Siegen Automat Control Mechatron Siegen Germany Miele & Cie KG Prod Qual Gutersloh Germany

ISBN: (纸本)9783031217524;9783031217531

Ensuring high product quality is an important success factor in modern industry. data-driven models are increasingly used for this purpose and need to be integrated into industrial processes as early as possible. As these models require high-quality training data, strategies for obtaining such data in the commissioning phase of a process are needed. Design of experiments (DoE) methods can be used for this task, but are not directly applicable for all types of processes. A common class of processes, to which most DoE methods cannot be applied, are assembly processes. The approach described in this paper aims to gather as much information as possible from an assembly process with minimal effort. It makes use of given sets of components from which optimal combinations are created, in order to achieve a space-filling design. Further design improvements are achieved by additionally specifying different mounting position of parts in the design and by using a subset selection procedure to remove less informative design points. The approach was successfully applied to an assembly process of washing machine drums to predict the radial deviation of assembled drums.

关键词： Design of experiments Optimal design Quality prediction Assembly processes

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Design of Federated learning Engagement Method for Autonomous Vehicle Privacy Protection 12

Design of Federated Learning Engagement Method for Autonomou...

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Joint 12th international conference on Soft Computing and intelligent Systems / 23rd international Symposium on Advanced intelligent Systems (SCIS and ISIS)

作者： Kim, Jung-Sook Kimpo Univ Sch Contents & Design Gimpo Si Geyonggi Do South Korea

ISBN: (纸本)9781665499248

An autonomous car is defined as a vehicle that can be operated only by an autonomous driving system and does not require a driver or intervention of a driver or passenger. Various types of data collected for fully autonomous car operation include personal information such as location, image, physical, behavior information, and license number. It is hard for a driver or pedestrian to recognize or control automated collection and processing of personal information by autonomous vehicles in real time. It is, therefore, necessary to prepare and apply appropriate countermeasures such as minimizing the collection of personal information, deleting and encrypting unnecessary information, etc. Federated learning is a distributed machine learning technique that can learn AI models in cooperation with each other without directly sharing data distributed and stored in multiple locations, such as devices or institutions. Instead of transmitting individual client data to the central server (cloud), the AI model of the central server is sent to the client to train the model with each data. By repeating this process, the global AI model of the central server becomes more general and the accuracy of the local AI model of the client improves. In order to protect personal information collected while driving an autonomous car, this paper proposed a federated learning engagement method, where cross-device was used to engage in the federated learning consisting of a very large number of mobile devices, and based on this, personal information can he protected for the efficient operation of autonomous vehicles.

关键词： autonomous car federated learning cross-device privacy protection security

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Zero-Shot Knowledge Graph Completion for Recommendation System 23rd

Zero-Shot Knowledge Graph Completion for Recommendation Syst...

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

作者： Wang, Zhiyuan Chen, Cheng Tang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Key Lab Brain Inspired Intelligent Comp Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China

ISBN: (纸本)9783031217524;9783031217531

Knowledge graphs are structured representations of actual entities and relations. They are widely used to improve the performance of downstream tasks such as recommendation systems and semantic searching. Knowledge graph completion (KGC) is a technology for discovering the missing relations between the entities in a knowledge graph (KG). Existing methods leverage known relations on a KG to build a model to predict missing relations. Such methods implicitly require a substantial number of relations to be known in advance, which might not be available in practice. To cope with the cold-start scenario for KGC, i.e., no relation is known in advance, we propose a zero-shot approach in this paper. Our approach converts the KGC process to an optimization problem. It uses the Evolutionary Strategy (ES) algorithm to optimize a model used to complete the KG according to the performance of the recommendation system constructed based on the completed KG. Experiments on a movie dataset demonstrate that our approach can complete the KG in the cold-start scenario and improve the performance of the recommendation system built based on the completed KG.

关键词： Knowledge graph completion Zero-shot Recommendation system Evolutionary strategy

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Ensemble Stack Architecture for Lungs Segmentation from X-ray Images 23rd

Ensemble Stack Architecture for Lungs Segmentation from X-ra...

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

作者： Lasker, Asifuzzaman Ghosh, Mridul Obaidullah, Sk Md Chakraborty, Chandan Goncalves, Teresa Roy, Kaushik Aliah Univ Dept Comp Sci & Engn Kolkata 700160 India Shyampur Siddheswari Mahavidyalaya Dept Comp Sci Howrah 711312 India Natl Inst Tech Teachers Training & Res Dept Comp Sci & Engn Howrah 700106 India Univ Evora Dept Comp Sci Evora Portugal Univ Evora ALGORITMI Ctr Evora Portugal West Bengal State Univ Dept Comp Sci Barasat 700126 India

ISBN: (纸本)9783031217524;9783031217531

In healthcare, chest X-rays are an inexpensive medical imaging diagnostic tools. The lung images segmentation from chest X-rays (CXRs) is important for screening and diagnosing diseases. The lungs are opacified in many patients' CXRs, making it difficult to segment them. A segmentation algorithm based on U-Net is proposed in this paper to address this problem. The proposed architecture was developed using three pre-trained models: MobileNetV2, InceptionResNetV2, and EfficientNetB0. In this architecture, we designed a ensemble stacked framework which is based on the pre-trained models to improve segmentation performance. Compared with the conventional U-Net model, our method improves by 3.02% dice coefficient and 3.43% IoU experimenting on the three public lung segmentation datasets.

关键词： Deep learning Lung segmentation Unet Medical imaging Chest X-rays

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Go-Around Prediction in Non-Stabilized Approach Scenarios Through a Regression Machine-learning Model Trained from Pilots' Expertise 23rd

Go-Around Prediction in Non-Stabilized Approach Scenarios Th...

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

作者： Cantero, Jesus Colomer, Adrian Launet, Laetitia Duchevet, Alexandre De La Hogue, Theo Imbert, Jean-Paul Naranjo, Valery Univ Politecn Valencia Inst Res & Innovat Bioengn Valencia Spain ENAC Ecole Natl Aviat Civile 7 Ave Edouard Belin CS 54005 F-31055 Touluse France

ISBN: (纸本)9783031217524;9783031217531

To face the pilots' shortage that could hit the aeronautical world in the future, single-pilot operations are envisaged as a solution. Even if the single pilot will have to assume the tasks that are today done by two pilots in the cockpits, the safety level of the flight should remain the same compared to standard operations. The challenge will be significant during high workload phases when complex decisions are to be made. We focused our work on the final approach phase during which the decision to perform a go-around or to land must be taken. This decision reveals to be particularly difficult as studies show that 97% of the time, the choice to land is made whereas a go-around was needed. In this paper, we propose a machine learning regression algorithm based on expert users classification to predict the need for a go-around during the final approach. An average precision of 0.96, a sensitivity of 0.84 and an F1-score of 0.88 shows a promising behaviour for the GA automatic identification task.

关键词： Go-around prediction Non-stabilized approach Pilot expertise Regression neural network Aircraft parameters

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Topological Analysis of Credit data: Preliminary Findings 23rd

Topological Analysis of Credit Data: Preliminary Findings

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

作者： Cooper, James Mitic, Peter Reinert, Gesine Temcinas, Tadas Santander US Boston MA 02109 USA UCL Dept Comp Sci London England Santander UK London England Univ Oxford Dept Stat Oxford England

ISBN: (纸本)9783031217524;9783031217531

Intuitively, similar customers should have similar credit risk. Capturing this similarity is often attempted using Euclidean distances between customer features and predicting credit default via logistic regression. Here we explore the use of topological data analysis for describing this similarity. In particular, persistent homology algorithms provide summaries of point clouds which relate to their topology. This approach has been shown to be useful in many applications but to the best of our knowledge, applying topological data analysis to prediction of credit risk is novel. We develop a pipeline which is based on the topological analysis of neighbourhoods of customers, with the neighbourhoods determined by a geometric network construction. We find a modest signal using three data sets from the Lending Club, and the Japan Credit Screening data set. The Cleveland oncological data set is used to validate the pipeline. The results have high variance, but they indicate that including such topological features could improve credit risk prediction when used as additional explanatory variable in a logistic regression.

关键词： Credit risk Topological data analysis Barcode Landscape Logistic regression

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Federating Unlabeled Samples: A Semi-supervised Collaborative Framework for Whole Slide Image Analysis 23rd

Federating Unlabeled Samples: A Semi-supervised Collaborativ...

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

作者： Launet, Laetitia del Amor, Rocio Colomer, Adrian Mosquera-Zamudio, Andres Moscardo, Anais Monteagudo, Carlos Zhao, Zhiming Naranjo, Valery Univ Politecn Valencia I3B Valencia Spain Univ Valencia Hosp Clin Univ Valencia Dept Pathol Valencia Spain Univ Amsterdam Multiscale Networked Syst Amsterdam Netherlands

ISBN: (纸本)9783031217524;9783031217531

Over the last decades, deep learning-based algorithms have witnessed tremendous progress in the medical field to assist pathologists in clinical decisions and reduce their workload. For these models to reach their full potential, access to large and diverse datasets is essential, but collaborations between hospitals are highly limited by privacy-related regulations. At the same time, medical institutions do not always have specialized pathologists to diagnose biopsies and label local data. To address these limitations, federated learning gained traction to enable multi-institution model training without sharing sensitive patient data. However, this technique is still in its infancy when it comes to digital pathology applications, and does not consider institutions with unlabeled data in federations. In this paper, we introduce a novel semi-supervised federated learning approach that promotes multi-institutional training of deep learning models while integrating unlabeled collaborating data sources into the federated setup. The experimental results show a better performance for models trained under a federated setting with both labeled and unlabeled data. Optimally, this framework will also bring the promise of assisting clinical decisions in hospitals that do not have specialized pathologists.

关键词： Federated learning Self-training Whole slide images Multiple instance learning

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