| 1. |
- Amirhossein, Berenji, et al.
(författare)
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curr2vib : Modality Embedding Translation for Broken-Rotor Bar Detection
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer Nature. - 9783031236334 ; , s. 423-437
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Konferensbidrag (refereegranskat)abstract
- Recently and due to the advances in sensor technology and Internet-of-Things, the operation of machinery can be monitored, using a higher number of sources and modalities. In this study, we demonstrate that Multi-Modal Translation is capable of transferring knowledge from a modality with higher level of applicability (more usefulness to solve an specific task) but lower level of accessibility (how easy and affordable it is to collect information from this modality) to another one with higher level of accessibility but lower level of applicability. Unlike the fusion of multiple modalities which requires all of the modalities to be available during the deployment stage, our proposed method depends only on the more accessible one; which results in the reduction of the costs regarding instrumentation equipment. The presented case study demonstrates that by the employment of the proposed method we are capable of replacing five acceleration sensors with three current sensors, while the classification accuracy is also increased by more than 1%.
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| 2. |
- Fan, Yuantao, 1989-, et al.
(författare)
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Incorporating Physics-based Models into Data-Driven Approaches for Air Leak Detection in City Buses
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer. - 9783031236327 - 9783031236334 ; , s. 438-450
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Konferensbidrag (refereegranskat)abstract
- In this work-in-progress paper two types of physics-based models, for accessing elastic and non-elastic air leakage processes, were evaluated and compared with conventional statistical methods to detect air leaks in city buses, via a data-driven approach. We have access to data streamed from a pressure sensor located in the air tanks of a few city buses, during their daily operations. The air tank in these buses supplies compressed air to drive various components, e.g. air brake, suspension, doors, gearbox, etc. We fitted three physics-based models only to the leakage segments extracted from the air pressure signal and used fitted model parameters as expert features for detecting air leaks. Furthermore, statistical moments of these fitted parameters, over predetermined time intervals, were compared to conventional statistical features on raw pressure values, under a classification setting in discriminating samples before and after the repair of air leak problems. The result of this exploratory study, on six air leak cases, shows that the fitted parameters of the physics-based models are useful for discriminating samples with air leak faults from the fault-free samples, which were observed right after the repair was performed to deal with the air leak problem. The comparison based on ANOVA F-score shows that the proposed features based on fitted parameters of physics-based models outrank the conventional features. It is observed that features of a non-elastic leakage model perform the best. © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
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| 3. |
- Rugolon, Franco, et al.
(författare)
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A Workflow for Generating Patient Counterfactuals in Lung Transplant Recipients
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer Nature. - 9783031236327 - 9783031236334 ; , s. 291-306
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Konferensbidrag (refereegranskat)abstract
- Lung transplantation is a critical procedure performed in end-stage pulmonary patients. The number of lung transplantations performed in the USA in the last decade has been rising, but the survival rate is still lower than that of other solid organ transplantations. First, this study aims to employ machine learning models to predict patient survival after lung transplantation. Additionally, the aim is to generate counterfactual explanations based on these predictions to help clinicians and patients understand the changes needed to increase the probability of survival after the transplantation and better comply with normative requirements. We use data derived from the UNOS database, particularly the lung transplantations performed in the USA between 2019 and 2021. We formulate the problem and define two data representations, with the first being a representation that describes only the lung recipients and the second the recipients and donors. We propose an explainable ML workflow for predicting patient survival after lung transplantation. We evaluate the workflow based on various performance metrics, using five classification models and two counterfactual generation methods. Finally, we demonstrate the potential of explainable ML for resource allocation, predicting patient mortality, and generating explainable predictions for lung transplantation.
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| 4. |
- Taghiyarrenani, Zahra, 1987-, et al.
(författare)
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Domain Adaptation with Maximum Margin Criterion with application to network traffic classification
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer. - 9783031236327 - 9783031236334 ; , s. 159-169
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Konferensbidrag (refereegranskat)abstract
- A fundamental assumption in machine learning is that training and test samples follow the same distribution. Therefore, for training a machine learning-based network traffic classifier, it is necessary to use samples obtained from the desired network. Collecting enough training data, however, can be challenging in many cases. Domain adaptation allows samples from other networks to be utilized. In order to satisfy the aforementioned assumption, domain adaptation reduces the distance between the distribution of the samples in the desired network and that of the available samples in other networks. However, it is important to note that the applications in two different networks can differ considerably. Taking this into account, in this paper, we present a new domain adaptation method for classifying network traffic. Thus, we use the labeled samples from a network and adapt them to the few labeled samples from the desired network; In other words, we adapt shared applications while preserving the information about non-shared applications. In order to demonstrate the efficacy of our method, we construct five different cross-network datasets using the Brazil dataset. These results indicate the effectiveness of adapting samples between different domains using the proposed method.
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| 5. |
- Taghiyarrenani, Zahra, 1987-, et al.
(författare)
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Towards Geometry-Preserving Domain Adaptation for Fault Identification
- 2022
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer Nature. - 9783031236327 - 9783031236334
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Konferensbidrag (refereegranskat)abstract
- In most industries, the working conditions of equipment vary significantly from one site to another, from one time of a year to another, and so on. This variation poses a severe challenge for data-driven fault identification methods: it introduces a change in the data distribution. This contradicts the underlying assumption of most machine learning methods, namely that training and test samples follow the same distribution. Domain Adaptation (DA) methods aim to address this problem by minimizing the distribution distance between training (source) and test (target) samples.However, in the area of predictive maintenance, this idea is complicated by the fact that different classes – fault categories – also vary across domains. Most of the state-of-the-art DA methods assume that the data in the target domain is complete, i.e., that we have access to examples from all the possible classes or faulty categories during adaptation. In reality, this is often very difficult to guarantee.Therefore, there is a need for a domain adaptation method that is able to align the source and target domains even in cases of having access to an incomplete set of test data. This paper presents our work in progress as we propose an approach for such a setting based on maintaining the geometry information of source samples during the adaptation. This way, the model can capture the relationships between different fault categories and preserve them in the constructed domain-invariant feature space, even in situations where some classes are entirely missing. This paper examines this idea using artificial data sets to demonstrate the effectiveness of geometry-preserving transformation. We have also started investigations on real-world predictive maintenance datasets, such as CWRU.
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| 6. |
- Zhou, Linyi, et al.
(författare)
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Predicting Drug Treatment for Hospitalized Patients with Heart Failure
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer Nature Switzerland. - 9783031236327 - 9783031236334 ; , s. 275-290
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Konferensbidrag (refereegranskat)abstract
- Heart failure and acute heart failure, the sudden onset or worsening of symptoms related to heart failure, are leading causes of hospital admission in the elderly. Treatment of heart failure is a com- plex problem that needs to consider a combination of factors such as clinical manifestation and comorbidities of the patient. Machine learning approaches exploiting patient data may potentially improve heart failure patients disease management. However, there is a lack of treatment prediction models for heart failure patients. Hence, in this study, we propose a workflow to stratify patients based on clinical features and predict the drug treatment for hospitalized patients with heart failure. Initially, we train the k-medoids and DBSCAN clustering methods on an extract from the MIMIC III dataset. Subsequently, we carry out a multi-label treatment prediction by assigning new patients to the pre-defined clusters. The empirical evaluation shows that k-medoids and DBSCAN successfully identify patient subgroups, with different treatments in each subgroup. DSBCAN outperforms k-medoids in patient stratification, yet the performance for treatment prediction is similar for both algorithms. Therefore, our work supports that clustering algorithms, specifically DBSCAN, have the potential to successfully perform patient profiling and predict individualized drug treatment for patients with heart failure.
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