31 results on '"Nowaczyk, Sławomir"'
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2. Warranty Claim Rate Prediction Using Logged Vehicle Data
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Khoshkangini, Reza, Pashami, Sepideh, Nowaczyk, Slawomir, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Moura Oliveira, Paulo, editor, Novais, Paulo, editor, and Reis, Luís Paulo, editor
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- 2019
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3. Machine learning in healthcare - a system’s perspective
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Ashfaq, Awais and Nowaczyk, Sławomir
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System's thinking ,Machine learning ,Other Medical Engineering ,Electronic health records ,Healthcare complexity ,Annan medicinteknik - Abstract
A consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS. Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system's approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality - while duly respecting all ethical and legal concerns - to reap full benefits of ML.
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- 2019
4. Opportunities for Machine Learning in District Heating.
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Mbiydzenyuy, Gideon, Nowaczyk, Sławomir, Knutsson, Håkan, Vanhoudt, Dirk, Brage, Jens, and Calikus, Ece
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MACHINE learning ,HEATING from central stations ,WATER temperature ,LOW temperatures - Abstract
The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry. [ABSTRACT FROM AUTHOR]
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- 2021
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5. Learning-Based Dissimilarity for Clustering Categorical Data.
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Rivera Rios, Edgar Jacob, Medina-Pérez, Miguel Angel, Lazo-Cortés, Manuel S., Monroy, Raúl, Nowaczyk, Sławomir, Bouguelia, Mohamed-Rafik, and Fanaee, Hadi
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CATEGORIES (Mathematics) ,MACHINE learning ,KEY performance indicators (Management) ,METADATA - Abstract
Comparing data objects is at the heart of machine learning. For continuous data, object dissimilarity is usually taken to be object distance; however, for categorical data, there is no universal agreement, for categories can be ordered in several different ways. Most existing category dissimilarity measures characterize the distance among the values an attribute may take using precisely the number of different values the attribute takes (the attribute space) and the frequency at which they occur. These kinds of measures overlook attribute interdependence, which may provide valuable information when capturing per-attribute object dissimilarity. In this paper, we introduce a novel object dissimilarity measure that we call Learning-Based Dissimilarity, for comparing categorical data. Our measure characterizes the distance between two categorical values of a given attribute in terms of how likely it is that such values are confused or not when all the dataset objects with the remaining attributes are used to predict them. To that end, we provide an algorithm that, given a target attribute, first learns a classification model in order to compute a confusion matrix for the attribute. Then, our method transforms the confusion matrix into a per-attribute dissimilarity measure. We have successfully tested our measure against 55 datasets gathered from the University of California, Irvine (UCI) Machine Learning Repository. Our results show that it surpasses, in terms of various performance indicators for data clustering, the most prominent distance relations put forward in the literature. [ABSTRACT FROM AUTHOR]
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- 2021
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6. EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets.
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Florez-Revuelta, Francisco and Nowaczyk, Sławomir
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LABELS ,BIG data ,SUPERVISED learning ,MACHINE learning ,EVOLUTIONARY algorithms ,DATA distribution - Abstract
This paper presents a new evolutionary approach, EvoSplit, for the distribution of multi-label data sets into disjoint subsets for supervised machine learning. Currently, data set providers either divide a data set randomly or using iterative stratification, a method that aims to maintain the label (or label pair) distribution of the original data set into the different subsets. Following the same aim, this paper first introduces a single-objective evolutionary approach that tries to obtain a split that maximizes the similarity between those distributions independently. Second, a new multi-objective evolutionary algorithm is presented to maximize the similarity considering simultaneously both distributions (labels and label pairs). Both approaches are validated using well-known multi-label data sets as well as large image data sets currently used in computer vision and machine learning applications. EvoSplit improves the splitting of a data set in comparison to the iterative stratification following different measures: Label Distribution, Label Pair Distribution, Examples Distribution, folds and fold-label pairs with zero positive examples. [ABSTRACT FROM AUTHOR]
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- 2021
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7. Multi-Task Representation Learning
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Bouguelia, Mohamed-Rafik, Pashami, Sepideh, and Nowaczyk, Sławomir
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Machine Learning ,Feature Learning ,Signal Processing ,Representation Learning ,Signalbehandling ,Supervised Learning ,Multi-Task Learning - Abstract
The majority of existing machine learning algorithms assume that training examples are already represented with sufficiently good features, in practice ones that are designed manually. This traditional way of preprocessing the data is not only tedious and time consuming, but also not sufficient to capture all the different aspects of the available information. With big data phenomenon, this issue is only going to grow, as the data is rarely collected and analyzed with a specific purpose in mind, and more often re-used for solving different problems. Moreover, the expert knowledge about the problem which allows them to come up with good representations does not necessarily generalize to other tasks. Therefore, much focus has been put on designing methods that can automatically learn features or representations of the data instead of learning from handcrafted features. However, a lot of this work used ad hoc methods and the theoretical understanding in this area is lacking.
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- 2017
8. Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data
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Nowaczyk, Sławomir, Prytz, Rune, Rögnvaldsson, Thorsteinn, and Byttner, Stefan
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Machine Learning ,Datavetenskap (datalogi) ,Computer Sciences ,Relational Learning ,Automotive Diagnostics ,Logged Vehicle Data ,Fault Prediction ,AQ - Abstract
Predictive maintenance is becoming more and more important for the commercial vehicle manufactures, as focus shifts from product- to service-based operation. The idea is to provide a dynamic maintenance schedule, fulfilling specific needs of individual vehicles. Luckily, the same shift of focus, as well as technological advancements in the telecommunication area, make long-term data collection more widespread, delivering the necessary data. We have found, however, that the standard attribute-value knowledge representation is not rich enough to capture important dependencies in this domain. Therefore, we are proposing a new rule induction algorithm, inspired by Michalski's classical AQ approach. Our method is aware that data concerning each vehicle consists of time-ordered sequences of readouts. When evaluating candidate rules, it takes into account the composite performance for each truck, instead of considering individual readouts in separation. This allows us more exibility, in particular in defining desired prediction horizon in a fuzzy, instead of crisp, manner. © 2013 The authors and IOS Press. All rights reserved. ReDi2Service
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- 2013
9. Analysis of Truck Compressor Failures Based on Logged Vehicle Data
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Prytz, Rune, Nowaczyk, Sławomir, Rögnvaldsson, Thorsteinn, and Byttner, Stefan
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Machine Learning ,Datavetenskap (datalogi) ,Computer Sciences ,Automotive Diagnostics ,Logged Vehicle Data ,Data Mining ,Fault Prediction - Abstract
In multiple industries, including automotive one, predictive maintenance is becoming more and more important, especially since the focus shifts from product to service-based operation. It requires, among other, being able to provide customers with uptime guarantees. It is natural to investigate the use of data mining techniques, especially since the same shift of focus, as well as technological advancements in the telecommunication solutions, makes long-term data collection more widespread. In this paper we describe our experiences in predicting compressor faults using data that is logged on-board Volvo trucks. We discuss unique challenges that are posed by the specifics of the automotive domain. We show that predictive maintenance is possible and can result in significant cost savings, despite the relatively low amount of data available. We also discuss some of the problems we have encountered by employing out-of-the-box machine learning solutions, and identify areas where our task diverges from common assumptions underlying the majority of data mining research.
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- 2013
10. Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data.
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Prytz, Rune, Nowaczyk, Sławomir, Rögnvaldsson, Thorsteinn, and Byttner, Stefan
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COMPRESSOR maintenance & repair , *PREDICTION models , *INFORMATION storage & retrieval systems , *ACQUISITION of data , *MACHINE learning , *FAULT-tolerant computing - Abstract
Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on a large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit. Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain. [ABSTRACT FROM AUTHOR]
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- 2015
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11. Early Prediction of Quality Issues in Automotive Modern Industry.
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Khoshkangini, Reza, Sheikholharam Mashhadi, Peyman, Berck, Peter, Gholami Shahbandi, Saeed, Pashami, Sepideh, Nowaczyk, Sławomir, and Niklasson, Tobias
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QUALITY function deployment ,FORECASTING ,ORIGINAL equipment manufacturers ,AUTOMOBILE industry ,INDUSTRIAL costs ,PRODUCT design - Abstract
Many industries today are struggling with early the identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with the customer requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the original equipment manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using machine learning (ML) to forecast the failures of a given component across the large population of units. In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage. We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently. [ABSTRACT FROM AUTHOR]
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- 2020
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12. Missing Data Imputation: Do Advanced ML/DL Techniques Outperform Traditional Approaches?
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Zhou, Youran, Bouadjenek, Mohamed Reda, Aryal, Sunil, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bifet, Albert, editor, Krilavičius, Tomas, editor, Miliou, Ioanna, editor, and Nowaczyk, Slawomir, editor
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- 2024
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13. Integrating Machine Learning into an SMT-Based Planning Approach for Production Planning in Cyber-Physical Production Systems
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Heesch, René, Ehrhardt, Jonas, Niggemann, Oliver, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nowaczyk, Sławomir, editor, Biecek, Przemysław, editor, Chung, Neo Christopher, editor, Vallati, Mauro, editor, Skruch, Paweł, editor, Jaworek-Korjakowska, Joanna, editor, Parkinson, Simon, editor, Nikitas, Alexandros, editor, Atzmüller, Martin, editor, Kliegr, Tomáš, editor, Schmid, Ute, editor, Bobek, Szymon, editor, Lavrac, Nada, editor, Peeters, Marieke, editor, van Dierendonck, Roland, editor, Robben, Saskia, editor, Mercier-Laurent, Eunika, editor, Kayakutlu, Gülgün, editor, Owoc, Mieczyslaw Lech, editor, Mason, Karl, editor, Wahid, Abdul, editor, Bruno, Pierangela, editor, Calimeri, Francesco, editor, Cauteruccio, Francesco, editor, Terracina, Giorgio, editor, Wolter, Diedrich, editor, Leidner, Jochen L., editor, Kohlhase, Michael, editor, and Dimitrova, Vania, editor
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- 2024
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14. Do Datapoints Argue?: Argumentation for Hierarchical Agreement in Datasets
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Bahuguna, Ayush, Haydar, Sajjad, Brännström, Andreas, Nieves, Juan Carlos, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nowaczyk, Sławomir, editor, Biecek, Przemysław, editor, Chung, Neo Christopher, editor, Vallati, Mauro, editor, Skruch, Paweł, editor, Jaworek-Korjakowska, Joanna, editor, Parkinson, Simon, editor, Nikitas, Alexandros, editor, Atzmüller, Martin, editor, Kliegr, Tomáš, editor, Schmid, Ute, editor, Bobek, Szymon, editor, Lavrac, Nada, editor, Peeters, Marieke, editor, van Dierendonck, Roland, editor, Robben, Saskia, editor, Mercier-Laurent, Eunika, editor, Kayakutlu, Gülgün, editor, Owoc, Mieczyslaw Lech, editor, Mason, Karl, editor, Wahid, Abdul, editor, Bruno, Pierangela, editor, Calimeri, Francesco, editor, Cauteruccio, Francesco, editor, Terracina, Giorgio, editor, Wolter, Diedrich, editor, Leidner, Jochen L., editor, Kohlhase, Michael, editor, and Dimitrova, Vania, editor
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- 2024
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15. Learning Process Steps as Dynamical Systems for a Sub-Symbolic Approach of Process Planning in Cyber-Physical Production Systems
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Ehrhardt, Jonas, Heesch, René, Niggemann, Oliver, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nowaczyk, Sławomir, editor, Biecek, Przemysław, editor, Chung, Neo Christopher, editor, Vallati, Mauro, editor, Skruch, Paweł, editor, Jaworek-Korjakowska, Joanna, editor, Parkinson, Simon, editor, Nikitas, Alexandros, editor, Atzmüller, Martin, editor, Kliegr, Tomáš, editor, Schmid, Ute, editor, Bobek, Szymon, editor, Lavrac, Nada, editor, Peeters, Marieke, editor, van Dierendonck, Roland, editor, Robben, Saskia, editor, Mercier-Laurent, Eunika, editor, Kayakutlu, Gülgün, editor, Owoc, Mieczyslaw Lech, editor, Mason, Karl, editor, Wahid, Abdul, editor, Bruno, Pierangela, editor, Calimeri, Francesco, editor, Cauteruccio, Francesco, editor, Terracina, Giorgio, editor, Wolter, Diedrich, editor, Leidner, Jochen L., editor, Kohlhase, Michael, editor, and Dimitrova, Vania, editor
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- 2024
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16. Automatic Coral Detection with YOLO: A Deep Learning Approach for Efficient and Accurate Coral Reef Monitoring
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Younes, Ouassine, Jihad, Zahir, Noël, Conruyt, Mohsen, Kayal, Philippe, A. Martin, Eric, Chenin, Lionel, Bigot, Regine, Vignes Lebbe, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nowaczyk, Sławomir, editor, Biecek, Przemysław, editor, Chung, Neo Christopher, editor, Vallati, Mauro, editor, Skruch, Paweł, editor, Jaworek-Korjakowska, Joanna, editor, Parkinson, Simon, editor, Nikitas, Alexandros, editor, Atzmüller, Martin, editor, Kliegr, Tomáš, editor, Schmid, Ute, editor, Bobek, Szymon, editor, Lavrac, Nada, editor, Peeters, Marieke, editor, van Dierendonck, Roland, editor, Robben, Saskia, editor, Mercier-Laurent, Eunika, editor, Kayakutlu, Gülgün, editor, Owoc, Mieczyslaw Lech, editor, Mason, Karl, editor, Wahid, Abdul, editor, Bruno, Pierangela, editor, Calimeri, Francesco, editor, Cauteruccio, Francesco, editor, Terracina, Giorgio, editor, Wolter, Diedrich, editor, Leidner, Jochen L., editor, Kohlhase, Michael, editor, and Dimitrova, Vania, editor
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- 2024
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17. Remote Learning Technologies in Achieving the Fourth Sustainable Development Goal
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Chomiak-Orsa, Iwona, Smolag, Klaudia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Nowaczyk, Sławomir, editor, Biecek, Przemysław, editor, Chung, Neo Christopher, editor, Vallati, Mauro, editor, Skruch, Paweł, editor, Jaworek-Korjakowska, Joanna, editor, Parkinson, Simon, editor, Nikitas, Alexandros, editor, Atzmüller, Martin, editor, Kliegr, Tomáš, editor, Schmid, Ute, editor, Bobek, Szymon, editor, Lavrac, Nada, editor, Peeters, Marieke, editor, van Dierendonck, Roland, editor, Robben, Saskia, editor, Mercier-Laurent, Eunika, editor, Kayakutlu, Gülgün, editor, Owoc, Mieczyslaw Lech, editor, Mason, Karl, editor, Wahid, Abdul, editor, Bruno, Pierangela, editor, Calimeri, Francesco, editor, Cauteruccio, Francesco, editor, Terracina, Giorgio, editor, Wolter, Diedrich, editor, Leidner, Jochen L., editor, Kohlhase, Michael, editor, and Dimitrova, Vania, editor
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- 2024
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18. Italian Debate on Measles Vaccination: How Twitter Data Highlight Communities and Polarity
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Ugwu, Cynthia Ifeyinwa, Casarin, Sofia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
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- 2023
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19. A Workflow for Generating Patient Counterfactuals in Lung Transplant Recipients
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Rugolon, Franco, Bampa, Maria, Papapetrou, Panagiotis, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
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- 2023
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20. Towards a General Model for Intrusion Detection: An Exploratory Study
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Zoppi, Tommaso, Ceccarelli, Andrea, Bondavalli, Andrea, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
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- 2023
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21. Predicting Drug Treatment for Hospitalized Patients with Heart Failure
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Zhou, Linyi, Miliou, Ioanna, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
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- 2023
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22. Auto-clustering of Financial Reports Based on Formatting Style and Author’s Fingerprint
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Blanco Lambruschini, Braulio C., Brorsson, Mats, Zurad, Maciej, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
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- 2023
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23. Evaluation of the Limit of Detection in Network Dataset Quality Assessment with PerQoDA
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Wasielewska, Katarzyna, Soukup, Dominik, Čejka, Tomáš, Camacho, José, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
- Published
- 2023
- Full Text
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24. Financial Distress Model Prediction Using Machine Learning: A Case Study on Indonesia’s Consumers Cyclical Companies
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Martono, Niken Prasasti, Ohwada, Hayato, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
- Published
- 2023
- Full Text
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25. Privacy-Preserving Machine Learning in Life Insurance Risk Prediction
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Pereira, Klismam, Vinagre, João, Alonso, Ana Nunes, Coelho, Fábio, Carvalho, Melânia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
- Published
- 2023
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26. Evaluation of Group Fairness Measures in Student Performance Prediction Problems
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Le Quy, Tai, Nguyen, Thi Huyen, Friege, Gunnar, Ntoutsi, Eirini, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
- Published
- 2023
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27. A Temporal Fusion Transformer for Long-Term Explainable Prediction of Emergency Department Overcrowding
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Caldas, Francisco M., Soares, Cláudia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
- Published
- 2023
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28. Intelligently Detecting Information Online-Weaponisation Trends (IDIOT)
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Kara-Isitt, Fawzia Zehra, Swift, Stephen, Tucker, Allan, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Koprinska, Irena, editor, Mignone, Paolo, editor, Guidotti, Riccardo, editor, Jaroszewicz, Szymon, editor, Fröning, Holger, editor, Gullo, Francesco, editor, Ferreira, Pedro M., editor, Roqueiro, Damian, editor, Ceddia, Gaia, editor, Nowaczyk, Slawomir, editor, Gama, João, editor, Ribeiro, Rita, editor, Gavaldà, Ricard, editor, Masciari, Elio, editor, Ras, Zbigniew, editor, Ritacco, Ettore, editor, Naretto, Francesca, editor, Theissler, Andreas, editor, Biecek, Przemyslaw, editor, Verbeke, Wouter, editor, Schiele, Gregor, editor, Pernkopf, Franz, editor, Blott, Michaela, editor, Bordino, Ilaria, editor, Danesi, Ivan Luciano, editor, Ponti, Giovanni, editor, Severini, Lorenzo, editor, Appice, Annalisa, editor, Andresini, Giuseppina, editor, Medeiros, Ibéria, editor, Graça, Guilherme, editor, Cooper, Lee, editor, Ghazaleh, Naghmeh, editor, Richiardi, Jonas, editor, Saldana, Diego, editor, Sechidis, Konstantinos, editor, Canakoglu, Arif, editor, Pido, Sara, editor, Pinoli, Pietro, editor, Bifet, Albert, editor, and Pashami, Sepideh, editor
- Published
- 2023
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29. Material handling machine activity recognition by context ensemble with gated recurrent units.
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Chen, Kunru, Rögnvaldsson, Thorsteinn, Nowaczyk, Sławomir, Pashami, Sepideh, Klang, Jonas, and Sternelöv, Gustav
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MATERIALS handling , *MACHINING , *MACHINE learning , *FORKLIFT trucks , *PRODUCT improvement , *PRODUCT design - Abstract
Research on machine activity recognition (MAR) is drawing more attention because MAR can provide productivity monitoring for efficiency optimization, better maintenance scheduling, product design improvement, and potential material savings. A particular challenge of MAR for human-operated machines is the overlap when transiting from one activity to another: during transitions, operators often perform two activities simultaneously, e.g., lifting the fork already while approaching a rack, so the exact time when one activity ends and another begins is uncertain. Machine learning models are often uncertain during such activity transitions, and we propose a novel ensemble-based method adapted to fuzzy transitions in a forklift MAR problem. Unlike traditional ensembles, where models in the ensemble are trained on different subsets of data, or with costs that force them to be diverse in their responses, our approach is to train a single model that predicts several activity labels, each under a different context. These individual predictions are not made by independent networks but are made using a structure that allows for sharing important features, i.e., a context ensemble. The results show that the gated recurrent unit network can provide medium or strong confident context ensembles for 95% of the cases in the test set, and the final forklift MAR result achieves accuracies of 97% for driving and 90% for load-handling activities. This study is the first to highlight the overlapping activity issue in MAR problems and to demonstrate that the recognition results can be significantly improved by designing a machine learning framework that addresses this issue. [ABSTRACT FROM AUTHOR]
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- 2023
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30. Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening
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Kopitar, Leon, Cilar, Leona, Kocbek, Primoz, Stiglic, Gregor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Marcos, Mar, editor, Juarez, Jose M., editor, Lenz, Richard, editor, Nalepa, Grzegorz J., editor, Nowaczyk, Slawomir, editor, Peleg, Mor, editor, Stefanowski, Jerzy, editor, and Stiglic, Gregor, editor
- Published
- 2019
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31. Modelling ICU Patients to Improve Care Requirements and Outcome Prediction of Acute Respiratory Distress Syndrome: A Supervised Learning Approach
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Sayed, Mohammed, Riaño, David, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Marcos, Mar, editor, Juarez, Jose M., editor, Lenz, Richard, editor, Nalepa, Grzegorz J., editor, Nowaczyk, Slawomir, editor, Peleg, Mor, editor, Stefanowski, Jerzy, editor, and Stiglic, Gregor, editor
- Published
- 2019
- Full Text
- View/download PDF
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