| 1. |
- Bae, Juhee, et al.
(författare)
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Interactive clustering : A comprehensive review
- 2020
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Ingår i: ACM Computing Surveys. - New York, NY : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 53:1
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Tidskriftsartikel (refereegranskat)abstract
- In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.
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| 2. |
- Rajabi, Enayat, 1978-, et al.
(författare)
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A Knowledge-Based AI Framework for Mobility as a Service
- 2023
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Ingår i: Sustainability. - Basel : MDPI. - 2071-1050. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- Mobility as a Service (MaaS) combines various modes of transportation to present mobility services to travellers based on their transport needs. This paper proposes a knowledge-based framework based on Artificial Intelligence (AI) to integrate various mobility data types and provide travellers with customized services. The proposed framework includes a knowledge acquisition process to extract and structure data from multiple sources of information (such as mobility experts and weather data). It also adds new information to a knowledge base and improves the quality of previously acquired knowledge. We discuss how AI can help discover knowledge from various data sources and recommend sustainable and personalized mobility services with explanations. The proposed knowledge-based AI framework is implemented using a synthetic dataset as a proof of concept. Combining different information sources to generate valuable knowledge is identified as one of the challenges in this study. Finally, explanations of the proposed decisions provide a criterion for evaluating and understanding the proposed knowledge-based AI framework. © 2023 by the authors.
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| 3. |
- Abuella, Mohamed, 1980-, et al.
(författare)
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Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping
- 2023
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Ingår i: Machine Learning and Knowledge Discovery in Databases. - Cham : Springer. - 9783031434297 - 9783031434303 ; , s. 226-241
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Konferensbidrag (refereegranskat)abstract
- The maritime industry is under pressure to increase energy efficiency for climate change mitigation. Navigational data, combining vessel operational and environmental measurements from onboard instruments and external sources, are critical for achieving this goal. Short-sea shipping presents a unique challenge due to the significant influence of surrounding landscape characteristics. With high-resolution onboard data increasingly accessible through IoT devices, appropriate data representations and AI/ML analytical tools are needed for effective decision support. The aim of this study is to investigate the fuel consumption estimation model’s role in developing an energy efficiency decision support tool. ML models that lacking explainability may neglect important factors and essential constraints, such as the need to meet arrival time requirements. Onboard weather measurements are compared to external forecasts, and our findings demonstrate the necessity of eXplainable Artificial Intelligence (XAI) techniques for effective decision support. Real-world data from a short-sea passenger vessel in southern Sweden, consisting of 1754 voyages over 15 months (More of data description and code sources of this study can be found in the GitHub repository at https://github.com/MohamedAbuella/ST4EESSS), are used to support our conclusions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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| 4. |
- Abuella, Mohamed, Postdoktor, 1980-, et al.
(författare)
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Spatial Clustering Approach for Vessel Path Identification
- 2024
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Ingår i: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 12, s. 66248-66258
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five clusters achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation. © 2013 IEEE.
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| 5. |
- Altarabichi, Mohammed Ghaith, 1981-
(författare)
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Evolving intelligence : Overcoming challenges for Evolutionary Deep Learning
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Deep Learning (DL) has achieved remarkable results in both academic and industrial fields over the last few years. However, DL models are often hard to design and require proper selection of features and tuning of hyper-parameters to achieve high performance. These selections are tedious for human experts and require substantial time and resources. A difficulty that encouraged a growing number of researchers to use Evolutionary Computation (EC) algorithms to optimize Deep Neural Networks (DNN); a research branch called Evolutionary Deep Learning (EDL).This thesis is a two-fold exploration within the domains of EDL, and more broadly Evolutionary Machine Learning (EML). The first goal is to makeEDL/EML algorithms more practical by reducing the high computational costassociated with EC methods. In particular, we have proposed methods to alleviate the computation burden using approximate models. We show that surrogate-models can speed up EC methods by three times without compromising the quality of the final solutions. Our surrogate-assisted approach allows EC methods to scale better for both, expensive learning algorithms and large datasets with over 100K instances. Our second objective is to leverage EC methods for advancing our understanding of Deep Neural Network (DNN) design. We identify a knowledge gap in DL algorithms and introduce an EC algorithm precisely designed to optimize this uncharted aspect of DL design. Our analytical focus revolves around revealing avant-garde concepts and acquiring novel insights. In our study of randomness techniques in DNN, we offer insights into the design and training of more robust and generalizable neural networks. We also propose, in another study, a novel survival regression loss function discovered based on evolutionary search.
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| 6. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Extracting Invariant Features for Predicting State of Health of Batteries in Hybrid Energy Buses
- 2021
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Ingår i: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Porto, Portugal, 6-9 Oct., 2021. - : IEEE. ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- Batteries are a safety-critical and the most expensive component for electric vehicles (EVs). To ensure the reliability of the EVs in operation, it is crucial to monitor the state of health of those batteries. Monitoring their deterioration is also relevant to the sustainability of the transport solutions, through creating an efficient strategy for utilizing the remaining capacity of the battery and its second life. Electric buses, similar to other EVs, come in many different variants, including different configurations and operating conditions. Developing new degradation models for each existing combination of settings can become challenging from different perspectives such as unavailability of failure data for novel settings, heterogeneity in data, low amount of data available for less popular configurations, and lack of sufficient engineering knowledge. Therefore, being able to automatically transfer a machine learning model to new settings is crucial. More concretely, the aim of this work is to extract features that are invariant across different settings.In this study, we propose an evolutionary method, called genetic algorithm for domain invariant features (GADIF), that selects a set of features to be used for training machine learning models, in such a way as to maximize the invariance across different settings. A Genetic Algorithm, with each chromosome being a binary vector signaling selection of features, is equipped with a specific fitness function encompassing both the task performance and domain shift. We contrast the performance, in migrating to unseen domains, of our method against a number of classical feature selection methods without any transfer learning mechanism. Moreover, in the experimental result section, we analyze how different features are selected under different settings. The results show that using invariant features leads to a better generalization of the machine learning models to an unseen domain.
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| 7. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Fast Genetic Algorithm for feature selection — A qualitative approximation approach
- 2023
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Ingår i: Expert systems with applications. - Oxford : Elsevier. - 0957-4174 .- 1873-6793. ; 211
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Tidskriftsartikel (refereegranskat)abstract
- Evolutionary Algorithms (EAs) are often challenging to apply in real-world settings since evolutionary computations involve a large number of evaluations of a typically expensive fitness function. For example, an evaluation could involve training a new machine learning model. An approximation (also known as meta-model or a surrogate) of the true function can be used in such applications to alleviate the computation cost. In this paper, we propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. We define “Approximation Usefulness” to capture the necessary conditions to ensure correctness of the EA computations when an approximation is used. Based on this definition, we propose a procedure to construct a lightweight qualitative meta-model by the active selection of data instances. We then use a meta-model to carry out the feature selection task. We apply this procedure to the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation) to create a Qualitative approXimations variant, CHCQX. We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy (as compared to CHC), particularly for large datasets with over 100K instances. We also demonstrate the applicability of the thinking behind our approach more broadly to Swarm Intelligence (SI), another branch of the Evolutionary Computation (EC) paradigm with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available. © 2022 The Author(s)
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| 8. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Fast Genetic Algorithm For Feature Selection — A Qualitative Approximation Approach
- 2023
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Ingår i: Evolutionary Computation Conference Companion (GECCO ’23 Companion), July 15–19, 2023, Lisbon, Portugal. - New York, NY : Association for Computing Machinery (ACM). - 9798400701207 ; , s. 11-12
-
Konferensbidrag (refereegranskat)abstract
- We propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. The proposed approach involves constructing a lightweight qualitative meta-model by sub-sampling data instances and then using this meta-model to carry out the feature selection task. We define "Approximation Usefulness" to capture the necessary conditions that allow the meta-model to lead the evolutionary computations to the correct maximum of the fitness function. Based on our procedure we create CHCQX a Qualitative approXimations variant of the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation). We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy, particularly for large datasets with over 100K instances. We also demonstrate the applicability of our approach to Swarm Intelligence (SI), with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available2. This paper for the Hot-off-the-Press track at GECCO 2023 summarizes the original work published at [3].References[1] Mohammed Ghaith Altarabichi, Yuantao Fan, Sepideh Pashami, Peyman Sheikholharam Mashhadi, and Sławomir Nowaczyk. 2021. Extracting invariant features for predicting state of health of batteries in hybrid energy buses. In 2021 ieee 8th international conference on data science and advanced analytics (dsaa). IEEE, 1–6.[2] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2021. Surrogate-assisted genetic algorithm for wrapper feature selection. In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 776–785.[3] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2023. Fast Genetic Algorithm for feature selection—A qualitative approximation approach. Expert systems with applications 211 (2023), 118528.© 2023 Copyright held by the owner/author(s).
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| 9. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Improving Concordance Index in Regression-based Survival Analysis : Discovery of Loss Function for Neural Networks
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this work, we use an Evolutionary Algorithm (EA) to discover a novel Neural Network (NN) regression-based survival loss function with the aim of improving the C-index performance. Our contribution is threefold; firstly, we propose an evolutionary meta-learning algorithm SAGA$_{loss}$ for optimizing a neural-network regression-based loss function that maximizes the C-index; our algorithm consistently discovers specialized loss functions that outperform MSCE. Secondly, based on our analysis of the evolutionary search results, we highlight a non-intuitive insight that signifies the importance of the non-zero gradient for the censored cases part of the loss function, a property that is shown to be useful in improving concordance. Finally, based on this insight, we propose MSCE$_{Sp}$, a novel survival regression loss function that can be used off-the-shelf and generally performs better than the Mean Squared Error for censored cases. We performed extensive experiments on 19 benchmark datasets to validate our findings.
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| 10. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Predicting state of health and end of life for batteries in hybrid energy buses
- 2020
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Ingår i: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. - Singapore : Research Publishing Services. - 9789811485930 ; , s. 1231-1231
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Konferensbidrag (refereegranskat)abstract
- There is a major ongoing transition from utilizing fossil fuel to electricity in buses for enabling a more sustainable, environmentally friendly, and connected transportation ecosystem. Batteries are expensive, up to 30% of the total cost for the vehicle (A. Fotouhi 2016), and considered safety-critical components for electric vehicles (EV). As they deteriorate over time, monitoring the health status and performing the maintenance accordingly in a proactive manner is crucial to achieving not only a safe and sustainable transportation system but also a cost-effective operation and thus a greater market satisfaction. As a widely used indicator, the State of Health (SOH) is a measurement that reflects the current capability of the battery in comparison to an ideal condition. Accurate estimation of SOH is important to evaluate the validity of the batteries for the intended application and can be utilized as a proxy to estimate the remaining useful life (RUL) and predict the end-of-life (EOL) of batteries for maintenance planning. The SOH is computed via an on-board computing device, i.e. battery management unit (BMU), which is commonly developed based on controlled experiments and many of them are physical-model based approaches that only depend on the internal parameters of the battery (B. Pattipati 2008; M. H. Lipu 2018). However, the deterioration processes of batteries in hybrid and full-electric buses depend not only on the designing parameters but also on the operating environment and usage patterns of the vehicle. Therefore, utilizing multiple data sources to estimate the health status and EOL of the batteries is of potential internet. In this study, a data-driven prognostic method is developed to estimate SOH and predict EOL for batteries in heterogeneous fleets of hybrid buses, using various types of data sources, e.g. physical configuration of the vehicle, deployment information, on-board sensor readings, and diagnostic fault codes. A set of new features was generated from the existing sensor readings by inducing artificial resets on each battery replacement. A neural network-based regression model achieved accurate estimates of battery SOH status. Another network was used to indicate the EOL of batteries and the result was evaluated using battery replacement based on the current maintenance strategy. © ESREL2020-PSAM15 Organizers. Published by Research Publishing, Singapore.
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| 11. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Rolling The Dice For Better Deep Learning Performance : A Study Of Randomness Techniques In Deep Neural Networks
- 2024
-
Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 667, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a comprehensive empirical investigation into the interactions between various randomness techniques in Deep Neural Networks (DNNs) and how they contribute to network performance. It is well-established that injecting randomness into the training process of DNNs, through various approaches at different stages, is often beneficial for reducing overfitting and improving generalization. However, the interactions between randomness techniques such as weight noise, dropout, and many others remain poorly understood. Consequently, it is challenging to determine which methods can be effectively combined to optimize DNN performance. To address this issue, we categorize the existing randomness techniques into four key types: data, model, optimization, and learning. We use this classification to identify gaps in the current coverage of potential mechanisms for the introduction of noise, leading to proposing two new techniques: adding noise to the loss function and random masking of the gradient updates.In our empirical study, we employ a Particle Swarm Optimizer (PSO) to explore the space of possible configurations to answer where and how much randomness should be injected to maximize DNN performance. We assess the impact of various types and levels of randomness for DNN architectures applied to standard computer vision benchmarks: MNIST, FASHION-MNIST, CIFAR10, and CIFAR100. Across more than 30\,000 evaluated configurations, we perform a detailed examination of the interactions between randomness techniques and their combined impact on DNN performance. Our findings reveal that randomness in data augmentation and in weight initialization are the main contributors to performance improvement. Additionally, correlation analysis demonstrates that different optimizers, such as Adam and Gradient Descent with Momentum, prefer distinct types of randomization during the training process. A GitHub repository with the complete implementation and generated dataset is available. © 2024 The Author(s)
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| 12. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Stacking Ensembles of Heterogenous Classifiers for Fault Detection in Evolving Environments
- 2020
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Ingår i: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. - Singapore : Research Publishing Services. - 9789811485930 ; , s. 1068-1068
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Konferensbidrag (refereegranskat)abstract
- Monitoring the condition, detecting faults, and modeling the degradation of industrial equipment are important challenges in Prognostics and Health Management (PHM) field. Our solution to the challenge demonstrated a multi-stage approach for detecting faults in a group of identical industrial equipment, composed of four identical interconnected components, that have been deployed to the evolving environment with changes in operational and environmental conditions. In the first stage, a stacked ensemble of heterogeneous classifiers was applied to predict the state of each component of the equipment individually. In the second stage, a low pass filter was applied to smoothen the predictions cast by stacked ensembles, utilizing temporal information of the prediction sequence. © ESREL2020-PSAM15 Organizers. Published by Research Publishing, Singapore.
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| 13. |
- Altarabichi, Mohammed Ghaith, 1981-, et al.
(författare)
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Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection
- 2021
-
Ingår i: 2021 IEEE Congress on Evolutionary Computation (CEC). - : IEEE. - 9781728183930 ; , s. 776-785
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Konferensbidrag (refereegranskat)abstract
- Feature selection is an intractable problem, therefore practical algorithms often trade off the solution accuracy against the computation time. In this paper, we propose a novel multi-stage feature selection framework utilizing multiple levels of approximations, or surrogates. Such a framework allows for using wrapper approaches in a much more computationally efficient way, significantly increasing the quality of feature selection solutions achievable, especially on large datasets. We design and evaluate a Surrogate-Assisted Genetic Algorithm (SAGA) which utilizes this concept to guide the evolutionary search during the early phase of exploration. SAGA only switches to evaluating the original function at the final exploitation phase.We prove that the run-time upper bound of SAGA surrogate-assisted stage is at worse equal to the wrapper GA, and it scales better for induction algorithms of high order of complexity in number of instances. We demonstrate, using 14 datasets from the UCI ML repository, that in practice SAGA significantly reduces the computation time compared to a baseline wrapper Genetic Algorithm (GA), while converging to solutions of significantly higher accuracy. Our experiments show that SAGA can arrive at near-optimal solutions three times faster than a wrapper GA, on average. We also showcase the importance of evolution control approach designed to prevent surrogates from misleading the evolutionary search towards false optima.
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| 14. |
- 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
-
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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| 15. |
- Ashfaq, Awais, 1990-, et al.
(författare)
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DEED : DEep Evidential Doctor
- 2023
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Ingår i: Artificial Intelligence. - Amsterdam : Elsevier. - 0004-3702 .- 1872-7921. ; 325
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Tidskriftsartikel (refereegranskat)abstract
- As Deep Neural Networks (DNN) make their way into safety-critical decision processes, it becomes imperative to have robust and reliable uncertainty estimates for their predictions for both in-distribution and out-of-distribution (OOD) examples. This is particularly important in real-life high-risk settings such as healthcare, where OOD examples (e.g., patients with previously unseen or rare labels, i.e., diagnoses) are frequent, and an incorrect clinical decision might put human life in danger, in addition to having severe ethical and financial costs. While evidential uncertainty estimates for deep learning have been studied for multi-class problems, research in multi-label settings remains untapped. In this paper, we propose a DEep Evidential Doctor (DEED), which is a novel deterministic approach to estimate multi-label targets along with uncertainty. We achieve this by placing evidential priors over the original likelihood functions and directly estimating the parameters of the evidential distribution using a novel loss function. Additionally, we build a redundancy layer (particularly for high uncertainty and OOD examples) to minimize the risk associated with erroneous decisions based on dubious predictions. We achieve this by learning the mapping between the evidential space and a continuous semantic label embedding space via a recurrent decoder. Thereby inferring, even in the case of OOD examples, reasonably close predictions to avoid catastrophic consequences. We demonstrate the effectiveness of DEED on a digit classification task based on a modified multi-label MNIST dataset, and further evaluate it on a diagnosis prediction task from a real-life electronic health record dataset. We highlight that in terms of prediction scores, our approach is on par with the existing state-of-the-art having a clear advantage of generating reliable, memory and time-efficient uncertainty estimates with minimal changes to any multi-label DNN classifier. © 2023 The Author(s)
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| 16. |
- Ashfaq, Awais, 1990-
(författare)
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Deep Evidential Doctor
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recent years have witnessed an unparalleled surge in deep neural networks (DNNs) research, surpassing traditional machine learning (ML) and statistical methods on benchmark datasets in computer vision, audio processing and natural language processing (NLP). Much of this success can be attributed to the availability of numerous open-source datasets, advanced computational resources and algorithms. These algorithms learn multiple levels of simple to complex abstractions (or representations) of data resulting in superior performances on downstream applications. This has led to an increasing interest in reaping the potential of DNNs in real-life safety-critical domains such as autonomous driving, security systems and healthcare. Each of them comes with their own set of complexities and requirements, thereby necessitating the development of new approaches to address domain-specific problems, even if building on common foundations.In this thesis, we address data science related challenges involved in learning effective prediction models from structured electronic health records (EHRs). In particular, questions related to numerical representation of complex and heterogeneous clinical concepts, modelling the sequential structure of EHRs and quantifying prediction uncertainties are studied. From a clinical perspective, the question of predicting onset of adverse outcomes for individual patients is considered to enable early interventions, improve patient outcomes, curb unnecessary expenditures and expand clinical knowledge.This is a compilation thesis including five articles. It begins by describing a healthcare information platform that encapsulates clinical, operational and financial data of patients across all public care delivery units in Halland, Sweden. Thus, the platform overcomes the technical and legislative data-related challenges inherent to the modern era's complex and fragmented healthcare sector. The thesis presents evidence that expert clinical features are powerful predictors of adverse patient outcomes. However, they are well complemented by clinical concept embeddings; gleaned via NLP inspired language models. In particular, a novel representation learning framework (KAFE: Knowledge And Frequency adapted Embeddings) has been proposed that leverages medical knowledge schema and adversarial principles to learn high quality embeddings of both frequent and rare clinical concepts. In the context of sequential EHR modelling, benchmark experiments on cost-sensitive recurrent nets have shown significant improvements compared to non-sequential networks. In particular, an attention based hierarchical recurrent net is proposed that represents individual patients as weighted sums of ordered visits, where visits are, in turn, represented as weighted sums of unordered clinical concepts. In the context of uncertainty quantification and building trust in models, the field of deep evidential learning has been extended. In particular for multi-label tasks, simple extensions to current neural network architecture are proposed, coupled with a novel loss criterion to infer prediction uncertainties without compromising on accuracy. Moreover, a qualitative assessment of the model behaviour has also been an important part of the research articles, to analyse the correlations learned by the model in relation to established clinical science.Put together, we develop DEep Evidential Doctor (DEED). DEED is a generic predictive model that learns efficient representations of patients and clinical concepts from EHRs and quantifies its confidence in individual predictions. It is also equipped to infer unseen labels.Overall, this thesis presents a few small steps towards solving the bigger goal of artificial intelligence (AI) in healthcare. The research has consistently shown impressive prediction performance for multiple adverse outcomes. However, we believe that there are numerous emerging challenges to be addressed in order to reap the full benefits of data and AI in healthcare. For future works, we aim to extend the DEED framework to incorporate wider data modalities such as clinical notes, signals and daily lifestyle information. We will also work to equip DEED with explainability features.
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| 17. |
- Ashfaq, Awais, 1990-, et al.
(författare)
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KAFE : Knowledge and Frequency Adapted Embeddings
- 2022
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Ingår i: Machine Learning, Optimization, and Data Science. - Cham : Springer. - 9783030954697 - 9783030954703 ; , s. 132-146
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Konferensbidrag (refereegranskat)abstract
- Word embeddings are widely used in several Natural Language Processing (NLP) applications. The training process typically involves iterative gradient updates of each word vector. This makes word frequency a major factor in the quality of embedding, and in general the embedding of words with few training occurrences end up being of poor quality. This is problematic since rare and frequent words, albeit semantically similar, might end up far from each other in the embedding space.In this study, we develop KAFE (Knowledge And Frequency adapted Embeddings) which combines adversarial principles and knowledge graph to efficiently represent both frequent and rare words. The goal of adversarial training in KAFE is to minimize the spatial distinguishability (separability) of frequent and rare words in the embedding space. The knowledge graph encourages the embedding to follow the structure of the domain-specific hierarchy, providing an informative prior that is particularly important for words with low amount of training data. We demonstrate the performance of KAFE in representing clinical diagnoses using real-world Electronic Health Records (EHR) data coupled with a knowledge graph. EHRs are notorious for including ever-increasing numbers of rare concepts that are important to consider when defining the state of the patient for various downstream applications. Our experiments demonstrate better intelligibility through visualisation, as well as higher prediction and stability scores of KAFE over state-of-the-art. © Springer Nature Switzerland AG 2022
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| 18. |
- Berenji, Amirhossein, 1995-, et al.
(författare)
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Data-Centric Perspective on Explainability Versus Performance Trade-Off
- 2023
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Ingår i: Advances in Intelligent Data Analysis XXI. - Cham : Springer. - 9783031300462 - 9783031300479 ; , s. 42-54
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Konferensbidrag (refereegranskat)abstract
- The performance versus interpretability trade-off has been well-established in the literature for many years in the context of machine learning models. This paper demonstrates its twin, namely the data-centric performance versus interpretability trade-off. In a case study of bearing fault diagnosis, we found that substituting the original acceleration signal with a demodulated version offers a higher level of interpretability, but it comes at the cost of significantly lower classification performance. We demonstrate these results on two different datasets and across four different machine learning algorithms. Our results suggest that “there is no free lunch,” i.e., the contradictory relationship between interpretability and performance should be considered earlier in the analysis process than it is typically done in the literature today; in other words, already in the preprocessing and feature extraction step. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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| 19. |
- Bergquist, Magnus, 1960-, et al.
(författare)
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OSMaaS Toolkit : Designing Open and Self Organising Mechanisms for Sustainable Mobility as a Service
- 2024
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Rapport (populärvet., debatt m.m.)abstract
- The project Open and Self Organizing Mechanisms for Sustainable Mobility as a Service (OSMaaS) ran between 2020 and 2024, hosted by Halmstad University and funded by The Knowledge Foundation. The project was a collaboration between researchers from service design, design ethnography, business model innovation, and intelligent systems, and the companies Volvo Cars, WirelessCar, Polestar, and Devoteam. One of the project’s outputs is the OSMaaS Service Design Framework that integrates research from the different activities in the project into a toolkit for service designers. This booklet provides a guide for how to apply the framework. Each canvas can be used standalone or in any order, but our experience is that the framework is most powerful when following the design process presented here. The canvases can be downloaded from the OSMaaS webpage and are free to use.
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| 20. |
- Bobek, Szymon, et al.
(författare)
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Towards Explainable Deep Domain Adaptation
- 2024
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Ingår i: Artificial Intelligence. ECAI 2023 International Workshops. - Cham : Springer. - 9783031503955 - 9783031503962 ; , s. 101-113
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Konferensbidrag (refereegranskat)abstract
- In many practical applications data used for training a machine learning model and the deployment data does not always preserve the same distribution. Transfer learning and, in particular, domain adaptation allows to overcome this issue, by adapting the source model to a new target data distribution and therefore generalizing the knowledge from source to target domain. In this work, we present a method that makes the adaptation process more transparent by providing two complementary explanation mechanisms. The first mechanism explains how the source and target distributions are aligned in the latent space of the domain adaptation model. The second mechanism provides descriptive explanations on how the decision boundary changes in the adapted model with respect to the source model. Along with a description of a method, we also provide initial results obtained on publicly available, real-life dataset. © The Author(s) 2024.
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| 21. |
- Cabunagan-Cinco, Gerjane Joy, et al.
(författare)
-
Cluster Analysis on Sustainable Transportation : The Case of New York City Open Data
- 2022
-
Ingår i: 2022 International Conference on Applied Artificial Intelligence (ICAPAI). - : IEEE. - 9781665467810 - 9781665467827
-
Konferensbidrag (refereegranskat)abstract
- Artificial Intelligence (AI) provides the opportunity to analyze complex transportation domains from various perspectives. Sustainability is one of the important transportation factors vital for a robust, fair, and efficient living environment and the livability of a city. This article leverages different feature engineering techniques on the New York City mobility dataset to identify the significant sustainability factors and employ the k-means clustering technique to cluster the commuters based on their transportation modes and demographics. Cluster analysis is performed based on the specified features and sustainable mode of transportation. Our cluster analysis of commuters on the New York City dataset shows that demographic information such as gender or race does not influence the sustainable mode of transportation, while the "start location"of travellers and their car access are influencing factors on sustainability. © 2022 IEEE.
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| 22. |
- Calikus, Ece, 1990-, et al.
(författare)
-
No free lunch but a cheaper supper : A general framework for streaming anomaly detection
- 2020
-
Ingår i: Expert systems with applications. - Oxford : Elsevier. - 0957-4174 .- 1873-6793. ; 155
-
Tidskriftsartikel (refereegranskat)abstract
- In recent years, there has been increased research interest in detecting anomalies in temporal streaming data. A variety of algorithms have been developed in the data mining community, which can be divided into two categories (i.e., general and ad hoc). In most cases, general approaches assume the one-size-fits-all solution model where a single anomaly detector can detect all anomalies in any domain. To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets. On the other hand, ad hoc approaches that are designed for a specific application lack flexibility. Adapting an existing algorithm is not straightforward if the specific constraints or requirements for the existing task change. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing us to isolate the effects of shared and unique characteristics of different algorithms on detection performance. Using SAFARI, we have implemented various anomaly detectors and identified a research gap that motivates us to propose a novel learning strategy in this work. We conducted an extensive evaluation study of 20 detectors that are composed using SAFARI and compared their performances using real-world benchmark datasets with different properties. The results indicate that there is no single superior detector that works well for every case, proving our hypothesis that "there is no free lunch" in the streaming anomaly detection world. Finally, we discuss the benefits and drawbacks of each method in-depth and draw a set of conclusions to guide future users of SAFARI.
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| 23. |
- Calikus, Ece, 1990-
(författare)
-
Self-Monitoring using Joint Human-Machine Learning : Algorithms and Applications
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The ability to diagnose deviations and predict faults effectively is an important task in various industrial domains for minimizing costs and productivity loss and also conserving environmental resources. However, the majority of the efforts for diagnostics are still carried out by human experts in a time-consuming and expensive manner. Automated data-driven solutions are needed for continuous monitoring of complex systems over time. On the other hand, domain expertise plays a significant role in developing, evaluating, and improving diagnostics and monitoring functions. Therefore, automatically derived solutions must be able to interact with domain experts by taking advantage of available a priori knowledge and by incorporating their feedback into the learning process.This thesis and appended papers tackle the problem of generating a real-world self-monitoring system for continuous monitoring of machines and operations by developing algorithms that can learn data streams and their relations over time and detect anomalies using joint-human machine learning. Throughout this thesis, we have described a number of different approaches, each designed for the needs of a self-monitoring system, and have composed these methods into a coherent framework. More specifically, we presented a two-layer meta-framework, in which the first layer was concerned with learning appropriate data representations and detectinganomalies in an unsupervised fashion, and the second layer aimed at interactively exploiting available expert knowledge in a joint human-machine learning fashion.Furthermore, district heating has been the focus of this thesis as the application domain with the goal of automatically detecting faults and anomalies by comparing heat demands among different groups of customers. We applied and enriched different methods on this domain, which then contributed to the development and improvement of the meta-framework. The contributions that result from the studies included in this work can be summarized into four categories: (1) exploring different data representations that are suitable for the self-monitoring task based on data characteristics and domain knowledge, (2) discovering patterns and groups in data that describe normal behavior of the monitored system/systems, (3) implementing methods to successfully discriminate anomalies from the normal behavior, and (4) incorporating domain knowledge and expert feedback into self-monitoring.
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| 24. |
- Calikus, Ece, 1990-
(författare)
-
Together We Learn More : Algorithms and Applications for User-Centric Anomaly Detection
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Anomaly detection is the problem of identifying data points or patterns that do not conform to normal behavior. Anomalies in data often correspond to important and actionable information such as frauds in financial applications, faults in production units, intrusions in computer systems, and serious diseases in patient records. One of the fundamental challenges of anomaly detection is that the exact notion of anomaly is subjective and varies greatly in different applications and domains. This makes distinguishing anomalies that match with the end-user's expectations from other observations difficult. As a result, anomaly detectors produce many false alarms that do not correspond to semantically meaningful anomalies for the analyst. Humans can help, in different ways, to bridge this gap between detected anomalies and ''anomalies-of-interest'': by giving clues on features more likely to reveal interesting anomalies or providing feedback to separate them from irrelevant ones. However, it is not realistic to assume a human to easily provide feedback without explaining why the algorithm classifies a certain sample as an anomaly. Interpretability of results is crucial for an analyst to be able to investigate the candidate anomaly and decide whether it is actually interesting or not. In this thesis, we take a step forward to improve the practical use of anomaly detection in real-life by leveraging human-algorithm collaboration. This thesis and appended papers study the problem of formulating and implementing algorithms for user-centric anomaly detection-- a setting in which people analyze, interpret, and learn from the detector's results, as well as provide domain knowledge or feedback. Throughout this thesis, we have described a number of diverse approaches, each addressing different challenges and needs of user-centric anomaly detection in the real world, and combined these methods into a coherent framework. By conducting different studies, this thesis finds that a comprehensive approach incorporating human knowledge and providing interpretable results can lead to more effective and practical anomaly detection and more successful real-world applications. The major contributions that result from the studies included in this work and led the above conclusion can be summarized into five categories: (1) exploring different data representations that are suitable for anomaly detection based on data characteristics and domain knowledge, (2) discovering patterns and groups in data that describe normal behavior in the current application, (3) implementing a generic and extensible framework enabling use-case-specific detectors suitable for different scenarios, (4) incorporating domain knowledge and expert feedback into anomaly detection, and (5) producing interpretable detection results that support end-users in understanding and validating the anomalies.
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| 25. |
- Calikus, Ece, 1990-, et al.
(författare)
-
Wisdom of the contexts : active ensemble learning for contextual anomaly detection
- 2022
-
Ingår i: Data mining and knowledge discovery. - New York : Springer-Verlag New York. - 1384-5810 .- 1573-756X. ; 36, s. 2410-2458
-
Tidskriftsartikel (refereegranskat)abstract
- In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several "useful" contexts to unveil them. In this work, we propose a novel approach, called WisCon (Wisdom of the Contexts), to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. We estimate the importance of each context using an active learning approach with a novel query strategy. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active classifiers, unsupervised contextual, and non-contextual anomaly detectors) on 18 datasets. Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the "wisdom" of multiple contexts is necessary. © 2022, The Author(s).
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| 26. |
- Chen, Kunru, 1993-, et al.
(författare)
-
Forklift Truck Activity Recognition from CAN Data
- 2021
-
Ingår i: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. - Heidelberg : Springer. - 9783030667696 - 9783030667702 ; , s. 119-126
-
Konferensbidrag (refereegranskat)abstract
- Machine activity recognition is important for accurately esti- mating machine productivity and machine maintenance needs. In this paper, we present ongoing work on how to recognize activities of forklift trucks from on-board data streaming on the controller area network. We show that such recognition works across different sites. We first demon- strate the baseline classification performance of a Random Forest that uses 14 signals over 20 time steps, for a 280-dimensional input. Next, we show how a deep neural network can learn low-dimensional representa- tions that, with fine-tuning, achieve comparable accuracy. The proposed representation achieves machine activity recognition. Also, it visualizes the forklift operation over time and illustrates the relationships across different activities. © Springer Nature Switzerland AG 2020
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| 27. |
- Chen, Kunru, 1993-, et al.
(författare)
-
Material handling machine activity recognition by context ensemble with gated recurrent units
- 2023
-
Ingår i: Engineering applications of artificial intelligence. - Oxford : Elsevier. - 0952-1976 .- 1873-6769. ; 126:Part C
-
Tidskriftsartikel (refereegranskat)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. © 2023 The Author(s)
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| 28. |
- Chen, Kunru, 1993-, et al.
(författare)
-
Semi-Supervised Learning for Forklift Activity Recognition from Controller Area Network (CAN) Signals
- 2022
-
Ingår i: Sensors. - Basel : MDPI. - 1424-8220. ; 22:11
-
Tidskriftsartikel (refereegranskat)abstract
- Machine Activity Recognition (MAR) can be used to monitor manufacturing processes and find bottlenecks and potential for improvement in production. Several interesting results on MAR techniques have been produced in the last decade, but mostly on construction equipment. Forklift trucks, which are ubiquitous and highly important industrial machines, have been missing from the MAR research. This paper presents a data-driven method for forklift activity recognition that uses Controller Area Network (CAN) signals and semi-supervised learning (SSL). The SSL enables the utilization of large quantities of unlabeled operation data to build better classifiers; after a two-step post-processing, the recognition results achieve balanced accuracy of 88% for driving activities and 95% for load-handling activities on a hold-out data set. In terms of the Matthews correlation coefficient for five activity classes, the final score is 0.82, which is equal to the recognition results of two non-domain experts who use videos of the activities. A particular success is that context can be used to capture the transport of small weight loads that are not detected by the forklift’s built-in weight sensor. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 29. |
- Chen, Kunru, 1993-, et al.
(författare)
-
Toward Solving Domain Adaptation with Limited Source Labeled Data
- 2023
-
Ingår i: 2023 IEEE International Conference on Data Mining Workshops (ICDMW). - Piscataway, NJ : IEEE Computer Society. - 9798350381641 ; , s. 1240-1246
-
Konferensbidrag (refereegranskat)abstract
- The success of domain adaptation relies on high-quality labeled data from the source domain, which is a luxury setup for applied machine learning problems. This article investigates a particular challenge: the source labeled data are neither plentiful nor sufficiently representative. We studied the challenge of limited data with an industrial application, i.e., forklift truck activity recognition. The task is to develop data-driven methods to recognize forklift usage performed in different warehouses with a large scale of signals collected from the onboard sensors. The preliminary results show that using pseudo-labeled data from the source domain can significantly improve classification performance on the target domain in some tasks. As the real-world problems are much more complex than typical research settings, it is not clearly understood in what circumstance the improvement may occur. Therefore, we provided discussions regarding this phenomenon and shared several inspirations on the difficulty of understanding and debugging domain adaptation problems in practice. © 2023 IEEE.
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| 30. |
- Dahl, Oskar, et al.
(författare)
-
Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters : Using Clustering and Rule-Based Machine Learning
- 2020
-
Ingår i: Proceedings of the 2020 3rd International Conference on Information Management and Management Science, IMMS 2020. - New York : Association for Computing Machinery (ACM). - 9781450375467 ; , s. 13-22
-
Konferensbidrag (refereegranskat)abstract
- Trucks are designed, configured and marketed for various working environments. There lies a concern whether trucks are used as intended by the manufacturer, as usage may impact the longevity, efficiency and productivity of the trucks. In this paper we propose a framework that aims to extract costumers' vehicle behaviours from Logged Vehicle Data (LVD) in order to evaluate whether they align with vehicle configurations, so-called Global Transport Application (GTA) parameters. Gaussian mixture model (GMM)s are employed to cluster and classify various vehicle behaviors from the LVD. Rule-based machine learning (RBML) was applied on the clusters to examine whether vehicle behaviors follow the GTA configuration. Particularly, we propose an approach based on studying associations that is able to extract insights on whether the trucks are used as intended. Experimental results shown that while for the vast majority of the trucks' behaviors seemingly follows their GTA configuration, there are also interesting outliers that warrant further analysis. © 2020 ACM.
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| 31. |
- Davari, Narjes, et al.
(författare)
-
A Fault Detection Framework Based on LSTM Autoencoder : A Case Study for Volvo Bus Data Set
- 2022
-
Ingår i: <em>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</em>Volume 13205 LNCS, Pages 39 - 522022. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031013324 - 9783031013331 ; , s. 39-52
-
Konferensbidrag (refereegranskat)abstract
- This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network. © 2022, The Author(s)
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| 32. |
- Del Moral, Pablo, 1989-, et al.
(författare)
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Filtering Misleading Repair Log Labels to Improve Predictive Maintenance Models
- 2022
-
Ingår i: Proceedings of the 7th European Conference of the Prognostics and Health Management Society 2022. - State College, PA : PHM Society. - 9781936263363 ; , s. 110-117
-
Konferensbidrag (refereegranskat)abstract
- One of the main challenges for predictive maintenance in real applications is the quality of the data, especially the labels. In this paper, we propose a methodology to filter out the misleading labels that harm the performance of Machine Learning models. Ideally, predictive maintenance would be based on the information of when a fault has occurred in a machine and what specific type of fault it was. Then, we could train machine learning models to identify the symptoms of such fault before it leads to a breakdown. However, in many industrial applications, this information is not available. Instead, we approximate it using a log of component replacements, usually coming from the sales or maintenance departments. The repair history provides reliable labels for fault prediction models only if the replaced component was indeed faulty, with symptoms captured by collected data, and it was going to lead to a breakdown.However, very often, at least for complex equipment, this assumption does not hold. Models trained using unreliable labels will then, necessarily, fail. We demonstrate that filtering misleading labels leads to improved results. Our central claim is that the same fault, happening several times, should have similar symptoms in the data; thus, we can train a model to predict them. On the contrary, replacements of the same component that do not exhibit similar symptoms will be confusing and harm the ML models. Therefore, we aim to filter the maintenance operations, keeping only those that can be used to predict each other. Suppose we can train a successful model using the data before a component replacement to predict another component replacement. In that case, those maintenance operations must be motivated by the same, or a very similar, type of fault.We test this approach on a real scenario using data from a fleet of sterilizers deployed in hospitals. The data includes sensor readings from the machines describing their operations and the service logs indicating the replacement of components when the manufacturing company performs the service. Since sterilizers are complex machines consisting of many components and systems interacting with each other, there is the possibility of faults happening simultaneously.
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| 33. |
- Del Moral, Pablo, 1989-, et al.
(författare)
-
Hierarchical Multi-class Classification for Fault Diagnosis
- 2021
-
Ingår i: Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021). - Singapore : Research Publishing Services. - 9789811820168 ; , s. 2457-2464
-
Konferensbidrag (refereegranskat)abstract
- This paper formulates the problem of predictive maintenance for complex systems as a hierarchical multi-class classification task. This formulation is useful for equipment with multiple sub-systems and components performing heterogeneous tasks. Often, the data available describes the whole system's operation and is not ideal for accurate condition monitoring. In this setup, specialized predictive models analyzing one component at a time rarely perform much better than random. However, using machine learning and hierarchical approaches, we can still exploit the data to build a fault isolation system that provides measurable benefits for technicians in the field. We propose a method for creating a taxonomy of components to train hierarchical classifiers that aim to identify the faulty component. The output of this model is a structured set of predictions with different probabilities for each component. In this setup, traditional machine learning metrics fail to capture the relationship between the performance of the models and its usefulness in the field.We introduce a new metric to evaluate our approach's benefits; it measures the number of tests a technician needs to perform before pinpointing the faulty component. Using a dataset from a real-case problem coming fro the automotive industry, we demonstrate how traditional machine learning performance metrics, like accuracy, fail to capture practical benefits. Our proposed hierarchical approach succeeds in exploiting the information in the data and outperforms non-hierarchical machine learning solutions. In addition, we can identify the weakest link of our fault isolation model, allowing us to improve it efficiently.
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| 34. |
- Del Moral, Pablo, 1989-, et al.
(författare)
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Why Is Multiclass Classification Hard?
- 2022
-
Ingår i: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 10, s. 80448-80462
-
Tidskriftsartikel (refereegranskat)abstract
- In classification problems, as the number of classes increases, correctly classifying a new instance into one of them is assumed to be more challenging than making the same decision in the presence of fewer classes. The essence of the problem is that using the learning algorithm on each decision boundary individually is better than using the same learning algorithm on several of them simultaneously. However, why and when it happens is still not well-understood today. This work’s main contribution is to introduce the concept of heterogeneity of decision boundaries as an explanation of this phenomenon. Based on the definition of heterogeneity of decision boundaries, we analyze and explain the differences in the performance of state of the art approaches to solve multi-class classification. We demonstrate that as the heterogeneity increases, the performances of all approaches, except one-vs-one, decrease. We show that by correctly encoding the knowledge of the heterogeneity of decision boundaries in a decomposition of the multi-class problem, we can obtain better results than state of the art decompositions. The benefits can be an increase in classification performance or a decrease in the time it takes to train and evaluate the models. We first provide intuitions and illustrate the effects of the heterogeneity of decision boundaries using synthetic datasets and a simplistic classifier. Then, we demonstrate how a real dataset exhibits these same principles, also under realistic learning algorithms. In this setting, we devise a method to quantify the heterogeneity of different decision boundaries, and use it to decompose the multi-class problem. The results show significant improvements over state-of-the-art decompositions that do not take the heterogeneity of decision boundaries into account. © 2013 IEEE.
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| 35. |
- Del Moral Pastor, Pablo José, 1989-
(författare)
-
Hierarchical Methods for Self-Monitoring Systems : Theory and Application
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Self-monitoring solutions first appeared to avoid catastrophic breakdowns in safety-critical mechanisms. The design behind these solutions relied heavily on the physical knowledge of the mechanism and its fault. They usually involved installing specialized sensors to monitor the state of the mechanism and statistical modeling of the recorded data. Mainly, these solutions focused on specific components of a machine and rarely considered more than one type of fault.In our work, on the other hand, we focus on self-monitoring of complex machines, systems composed of multiple components performing heterogeneous tasks and interacting with each other: systems with many possible faults. Today, the data available to monitor these machines is vast but usually lacks the design and specificity to monitor each possible fault in the system accurately. Some faults will show distinctive symptoms in the data; some faults will not; more interestingly, there will be groups of faults with common symptoms in the recorded data.The thesis in this manuscript is that we can exploit the similarities between faults to train machine learning models that can significantly improve the performance of self-monitoring solutions for complex systems that overlook these similarities. We choose to encode these similarity relationships into hierarchies of faults, which we use to train hierarchical supervised models. We use both real-life problems and standard benchmarks to prove the adequacy of our approach on tasks like fault diagnosis and fault prediction.We also demonstrate that models trained on different hierarchies result in significantly different performances. We analyze what makes a good hierarchy and what are the best practices to develop methods to extract hierarchies of classes from the data. We advance the state-of-the-art by defining the concept of heterogeneity of decision boundaries and studying how it affects the performance of different class decompositions.
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| 36. |
- Etminani, Kobra, 1984-, et al.
(författare)
-
How Behavior Change Strategies are Used to Design Digital Interventions to Improve Medication Adherence and Blood Pressure Among Patients With Hypertension : Systematic Review
- 2020
-
Ingår i: Journal of Medical Internet Research. - Toronto : J M I R Publications, Inc.. - 1438-8871. ; 22:4
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Information on how behavior change strategies have been used to design digital interventions (DIs) to improve blood pressure (BP) control or medication adherence (MA) for patients with hypertension is currently limited.Objective: Hypertension is a major modifiable risk factor for cardiovascular diseases and can be controlled with appropriate medication. Many interventions that target MA to improve BP are increasingly using modern digital technologies. This systematic review was conducted to discover how DIs have been designed to improve MA and BP control among patients with hypertension in the recent 10 years. Results were mapped into a matrix of change objectives using the Intervention Mapping framework to guide future development of technologies to improve MA and BP control.Methods: We included all the studies regarding DI development to improve MA or BP control for patients with hypertension published in PubMed from 2008 to 2018. All the DI components were mapped into a matrix of change objectives using the Intervention Mapping technique by eliciting the key determinant factors (from patient and health care team and system levels) and targeted patient behaviors.Results: The analysis included 54 eligible studies. The determinants were considered at two levels: patient and health care team and system. The most commonly described determinants at the patient level were lack of education, lack of self-awareness, lack of self-efficacy, and forgetfulness. Clinical inertia and an inadequate health workforce were the most commonly targeted determinants at the health care team and system level. Taking medication, interactive patient-provider communication, self-measurement, and lifestyle management were the most cited patient behaviors at both levels. Most of the DIs did not include support from peers or family members, despite its reported effectiveness and the rate of social media penetration.Conclusions: This review highlights the need to design a multifaceted DI that can be personalized according to patient behavior(s) that need to be changed to overcome the key determinant(s) of low adherence to medication or uncontrolled BP among patients with hypertension, considering different levels including patient and healthcare team and system involvement. © Kobra Etminani, Arianna Tao Engström, Carina Göransson, Anita Sant’Anna, Sławomir Nowaczyk.
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| 37. |
- Etminani, Kobra, 1984-, et al.
(författare)
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Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension : Protocol for an Interrupted Time Series Study
- 2021
-
Ingår i: JMIR Research Protocols. - Toronto : JMIR. - 1929-0748. ; 10:5
-
Tidskriftsartikel (refereegranskat)abstract
- Background: There is a strong need to improve medication adherence (MA) for individuals with hypertension in order to reduce long-term hospitalization costs. We believe this can be achieved through an artificial intelligence agent that helps the patient in understanding key individual adherence risk factors and designing an appropriate intervention plan. The incidence of hypertension in Sweden is estimated at approximately 27%. Although blood pressure control has increased in Sweden, barely half of the treated patients achieved adequate blood pressure levels. It is a major risk factor for coronary heart disease and stroke as well as heart failure. MA is a key factor for good clinical outcomes in persons with hypertension.Objective: The overall aim of this study is to design, develop, test, and evaluate an adaptive digital intervention called iMedA, delivered via a mobile app to improve MA, self-care management, and blood pressure control for persons with hypertension.Methods: The study design is an interrupted time series. We will collect data on a daily basis, 14 days before, during 6 months of delivering digital interventions through the mobile app, and 14 days after. The effect will be analyzed using segmented regression analysis. The participants will be recruited in Region Halland, Sweden. The design of the digital interventions follows the just-in-time adaptive intervention framework. The primary (distal) outcome is MA, and the secondary outcome is blood pressure. The design of the digital intervention is developed based on a needs assessment process including a systematic review, focus group interviews, and a pilot study, before conducting the longitudinal interrupted time series study.Results: The focus groups of persons with hypertension have been conducted to perform the needs assessment in a Swedish context. The design and development of digital interventions are in progress, and the interventions are planned to be ready in November 2020. Then, the 2-week pilot study for usability evaluation will start, and the interrupted time series study, which we plan to start in February 2021, will follow it.Conclusions: We hypothesize that iMedA will improve medication adherence and self-care management. This study could illustrate how self-care management tools can be an additional (digital) treatment support to a clinical one without increasing burden on health care staff. © Kobra Etminani, Carina Göransson, Alexander Galozy, Margaretha Norell Pejner, Sławomir Nowaczyk.
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| 38. |
- 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
-
Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer. - 9783031236327 - 9783031236334 ; , s. 438-450
-
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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| 39. |
- Fan, Yuantao, 1989-, et al.
(författare)
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Transfer learning for remaining useful life prediction based on consensus self-organizing models
- 2020
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Ingår i: Reliability Engineering & System Safety. - London : Elsevier. - 0951-8320 .- 1879-0836. ; 203
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Tidskriftsartikel (refereegranskat)abstract
- The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very similar distribution to the experiment data. However, many complex machines operate under dynamic environmental conditions and are used in many different ways. This makes collecting comprehensive data very challenging, and the assumption that pre-deployment data and post-deployment data follow very similar distributions is unlikely to hold.In this work, we present a feature-representation based transfer learning (TL) method for predicting Remaining Useful Life (RUL) of equipment, under scenarios that samples with previously unseen conditions are presented in the target domain and the labels are available only for the source domain, but not the target domain. This setting corresponds to generalizing from a limited number of run-to-failure experiments performed prior to deployment into making prognostics with data coming from deployed equipment that is being used under multiple new operating conditions and experiencing previously unseen faults. We employ a deviation detection method, Consensus Self-Organizing Models (COSMO), to create transferable features for building the RUL regression model. These features capture how different a particular equipment is in comparison to its peers.The efficiency of the proposed TL method is demonstrated using the NASA Turbofan Engine Degradation Simulation Data Set. Models using the COSMO transferable features show better performance than other methods on predicting RUL when the target domain is more complex than the source domain. © 2020 Elsevier Ltd
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| 40. |
- Fan, Yuantao, 1989-
(författare)
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Wisdom of the Crowd for Fault Detection and Prognosis
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Monitoring and maintaining the equipment to ensure its reliability and availability is vital to industrial operations. With the rapid development and growth of interconnected devices, the Internet of Things promotes digitization of industrial assets, to be sensed and controlled across existing networks, enabling access to a vast amount of sensor data that can be used for condition monitoring. However, the traditional way of gaining knowledge and wisdom, by the expert, for designing condition monitoring methods is unfeasible for fully utilizing and digesting this enormous amount of information. It does not scale well to complex systems with a huge amount of components and subsystems. Therefore, a more automated approach that relies on human experts to a lesser degree, being capable of discovering interesting patterns, generating models for estimating the health status of the equipment, supporting maintenance scheduling, and can scale up to many equipment and its subsystems, will provide great benefits for the industry. This thesis demonstrates how to utilize the concept of "Wisdom of the Crowd", i.e. a group of similar individuals, for fault detection and prognosis. The approach is built based on an unsupervised deviation detection method, Consensus Self-Organizing Models (COSMO). The method assumes that the majority of a crowd is healthy; individual deviates from the majority are considered as potentially faulty. The COSMO method encodes sensor data into models, and the distances between individual samples and the crowd are measured in the model space. This information, regarding how different an individual performs compared to its peers, is utilized as an indicator for estimating the health status of the equipment. The generality of the COSMO method is demonstrated with three condition monitoring case studies: i) fault detection and failure prediction for a commercial fleet of city buses, ii) prognosis for a fleet of turbofan engines and iii) finding cracks in metallic material. In addition, the flexibility of the COSMO method is demonstrated with: i) being capable of incorporating domain knowledge on specializing relevant expert features; ii) able to detect multiple types of faults with a generic data- representation, i.e. Echo State Network; iii) incorporating expert feedback on adapting reference group candidate under an active learning setting. Last but not least, this thesis demonstrated that the remaining useful life of the equipment can be estimated from the distance to a crowd of peers.
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| 41. |
- Galozy, Alexander, 1991-
(författare)
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Data-driven personalized healthcare : Towards personalized interventions via reinforcement learning for Mobile Health
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Medical and technological advancement in the last century has led to the unprecedented increase of the populace's quality of life and lifespan. As a result, an ever-increasing number of people live with chronic health conditions that require long-term treatment, resulting in increased healthcare costs and managerial burden to the healthcare provider. This increase in complexity can lead to ineffective decision-making and reduce care quality for the individual while increasing costs. One promising direction to tackle these issues is the active involvement of the patient in managing their care. Particularly for chronic diseases, where ongoing support is often required, patients must understand their illness and be empowered to manage their care. With the advent of smart devices such as smartphones, it is easier than ever to provide personalised digital interventions to patients, help them manage their treatment in their daily lives, and raise awareness about their illness. If such new approaches are to succeed, scalability is necessary, and solutions are needed that can act autonomously without costly human intervention. Furthermore, solutions should exhibit adaptability to the changing circumstances of an individual patient's health, needs and goals. Through the ongoing digitisation of healthcare, we are presented with the unique opportunity to develop cost-effective and scalable solutions through Artificial Intelligence (AI).This thesis presents work that we conducted as part of the project improving Medication Adherence through Person-Centered Care and Adaptive Interventions (iMedA) that aims to provide personalised adaptive interventions to hypertensive patients, supporting them in managing their medication regiment. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA and discussions on limitations of such approaches for analysing MA. Furthermore, providing personalised adaptive interventions is framed in the contextual bandit setting and addresses the challenge of delivering relevant interventions in environments where contextual information is significantly corrupted. The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs and (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments.
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| 42. |
- Galozy, Alexander, 1991-, et al.
(författare)
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Information-gathering in latent bandits
- 2023
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Ingår i: Knowledge-Based Systems. - Amsterdam : Elsevier. - 0950-7051 .- 1872-7409. ; 260
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Tidskriftsartikel (refereegranskat)abstract
- In the latent bandit problem, the learner has access to reward distributions and – for the non-stationary variant – transition models of the environment. The reward distributions are conditioned on the arm and unknown latent states. The goal is to use the reward history to identify the latent state, allowing for the optimal choice of arms in the future. The latent bandit setting lends itself to many practical applications, such as recommender and decision support systems, where rich data allows the offline estimation of environment models with online learning remaining a critical component. Previous solutions in this setting always choose the highest reward arm according to the agent’s beliefs about the state, not explicitly considering the value of information-gathering arms. Such information-gathering arms do not necessarily provide the highest reward, thus may never be chosen by an agent that chooses the highest reward arms at all times.In this paper, we present a method for information-gathering in latent bandits. Given particular reward structures and transition matrices, we show that choosing the best arm given the agent’s beliefs about the states incurs higher regret. Furthermore, we show that by choosing arms carefully, we obtain an improved estimation of the state distribution, and thus lower the cumulative regret through better arm choices in the future. Through theoretical analysis we show that the proposed method retains the sub-linear regret rate of previous methods while having much better problem dependent constants. We evaluate our method on both synthetic and real-world data sets, showing significant improvement in regret over state-of-the-art methods. © 2022 The Author(s).
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| 43. |
- Galozy, Alexander, 1991-
(författare)
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Mobile Health Interventions through Reinforcement Learning
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis presents work conducted in the domain of sequential decision-making in general and Bandit problems in particular, tackling challenges from a practical and theoretical perspective, framed in the contexts of mobile Health. The early stages of this work have been conducted in the context of the project ``improving Medication Adherence through Person-Centred Care and Adaptive Interventions'' (iMedA) which aims to provide personalized adaptive interventions to hypertensive patients, supporting them in managing their medication regimen. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA, and discussions on limitations of such approaches for analyzing MA. Providing personalized adaptive interventions is framed in several bandit settings and addresses the challenge of delivering relevant interventions in environments where contextual information is unreliable and full of noise. Furthermore, the need for good initial policies is explored and improved in the latent-bandits setting, utilizing prior collected data to optimal selection the best intervention at every decision point. As the final concluding work, this thesis elaborates on the need for privacy and explores different privatization techniques in the form of noise-additive strategies using a realistic recommendation scenario. The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs, (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments. (4) Algorithmic improvements, equipping the agent with information-gathering capabilities for active action selection in the latent bandit setting, and (5) exploring important privacy aspects using a realistic recommender scenario.
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| 44. |
- Galozy, Alexander, 1991-, et al.
(författare)
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Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data
- 2020
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Ingår i: Journal of Biomedical Informatics: X. - New York, NY : Elsevier. - 2590-177X .- 1532-0464. ; 6-7
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Tidskriftsartikel (refereegranskat)abstract
- Background and purposeLow adherence to medication in chronic disease patients leads to increased morbidity, mortality, and healthcare costs. The widespread adoption of electronic prescription and dispensation records allows a more comprehensive overview of medication utilization. In combination with electronic health records (EHR), such data provides new opportunities for identifying patients at risk of nonadherence and provide more targeted and effective interventions. The purpose of this article is to study the predictability of medication adherence for a cohort of hypertensive patients, focusing on healthcare utilization factors under various predictive scenarios. Furthermore, we discover common proportion of days covered patterns (PDC-patterns) for patients with index prescriptions and simulate medication-taking behaviours that might explain observed patterns.ProceduresWe predict refill adherence focusing on factors of healthcare utilization, such as visits, prescription information and demographics of patient and prescriber. We train models with machine learning algorithms, using four different data splits: stratified random, patient, temporal forward prediction with and without index patients. We extract frequent, two-year long PDC-patterns using K-means clustering and investigate five simple models of medication-taking that can generate such PDC-patterns.FindingsModel performance varies between data splits (AUC test set: 0.77–0.89). Including historical information increases the performance slightly in most cases (approx. 1–2% absolute AUC uplift). Models show low predictive performance (AUC test set: 0.56–0.66) on index-prescriptions and patients with sudden drops in PDC (Recall: 0.58–0.63). We find 21 distinct two-year PDC-patterns, ranging from good adherence to intermittent gaps and early discontinuation in the first or second year. Simulations show that observed PDC-patterns can only be explained by specific medication consumption behaviours.ConclusionsPrediction models developed using EHR exhibit bias towards patients with high healthcare utilization. Even though actual medication-taking is not observable, consumption patterns may not be as arbitrary, provided that medication refilling and consumption is linked. © 2020 The Authors. Published by Elsevier Inc.
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| 45. |
- Gama, Joao, et al.
(författare)
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XAI for Predictive Maintenance
- 2023
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Ingår i: KDD '23. - New York, NY : Association for Computing Machinery (ACM). - 9798400701030 ; , s. 5798-5799
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Konferensbidrag (refereegranskat)abstract
- The field of Explainable Predictive Maintenance (PM) is concerned with developing methods that can clarify how AI systems operate in the PM domain. One of the challenges of creating maintenance plans is integrating AI output with human decision-making processes and expertise. For AI to be helpful and trustworthy, fault predictions must be contextualized and easily comprehensible to humans. This involves providing tailored explanations to different actors depending on their roles and needs. For example, engineers can be connected to technical installation blueprints, while managers can evaluate system downtime costs, and lawyers can assess safety-threatening failures' potential liability. In many industries, black-box AI systems analyze sensor data to predict failures by detecting anomalies and deviations from typical behavior with impressive accuracy. However, PM is just one part of a broader context that aims to identify the most probable causes, develop a recovery plan, and estimate remaining useful life while providing alternative solutions. Achieving this requires complex interactions among various actors in industrial and decision-making processes. Our tutorial explores current trends, and promising research directions in Explainable AI (XAI) relevant to Explainable Predictive Maintenance (XPM), and future challenges and open issues on this topic. We will also present three case studies that highlight XPM's challenges in bus and train operations and steel factories. © 2023 Owner/Author.
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| 46. |
- Jamshidi, Parisa, 1991-, et al.
(författare)
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EcoShap : Save Computations by only Calculating Shapley Values for Relevant Features
- 2024
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Ingår i: Artificial Intelligence. ECAI 2023 International Workshops. - Cham : Springer. - 9783031503955 - 9783031503962 ; , s. 24-42
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Konferensbidrag (refereegranskat)abstract
- One of the most widely adopted approaches for eXplainable Artificial Intelligence (XAI) involves employing of Shapley values (SVs) to determine the relative importance of input features. While based on a solid mathematical foundation derived from cooperative game theory, SVs have a significant drawback: high computational cost. Calculating the exact SV is an NP-hard problem, necessitating the use of approximations, particularly when dealing with more than twenty features. On the other hand, determining SVs for all features is seldom necessary in practice; users are primarily interested in the most important ones only. This paper introduces the Economic Hierarchical Shapley values (ecoShap) method for calculating SVs for the most crucial features only, with reduced computational cost. EcoShap iteratively expands disjoint groups of features in a tree-like manner, avoiding the expensive computations for the majority of less important features. Our experimental results across eight datasets demonstrate that the proposed technique efficiently identifies top features; at a 50% reduction in computational costs, it can determine between three and seven of the most important features. © The Author(s) 2024.
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| 47. |
- Jamshidi, Samaneh, 1991-, et al.
(författare)
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A systematic approach for tracking the evolution of XAI as a field of research
- 2023
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Ingår i: Machine Learning and Principles and Practice of Knowledge Discovery in Databases. - Cham : Springer. ; , s. 461-476
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Konferensbidrag (refereegranskat)abstract
- The increasing use of AI methods in various applications has raised concerns about their explainability and transparency. Many solutions have been developed within the last few years to either explain the model itself or the decisions provided by the model. However, the number of contributions in the field of eXplainable AI (XAI) is increasing at such a high pace that it is almost impossible for a newcomer to identify key ideas, track the field’s evolution, or find promising new research directions. Typically, survey papers serve as a starting point, providing a feasible entry point into a research area. However, this is not trivial for some fields with exponential growth in the literature, such as XAI. For instance, we analyzed 23 surveys in the XAI domain published within the last three years and surprisingly found no common conceptualization among them. This makes XAI one of the most challenging research areas to enter. To address this problem, we propose a systematic approach that enables newcomers to identify the principal ideas and track their evolution. The proposed method includes automating the retrieval of relevant papers, extracting their semantic relationship, and creating a temporal graph of ideas by post-analysis of citation graphs. The main outcome of our method is Field’s Evolution Graph (FEG), which can be used to find the core idea of each approach in this field, see how a given concept has developed and evolved over time, observe how different notions interact with each other, and perceive how a new paradigm emerges through combining multiple ideas. As for demonstration, we show that FEG successfully identifies the field’s key articles, such as LIME or Grad-CAM, and maps out their evolution and relationships.© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.
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| 48. |
- Kharazian, Zahra, et al.
(författare)
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AID4HAI : Automatic Idea Detection for Healthcare-Associated Infections from Twitter, A Framework based on Active Learning and Transfer Learning
- 2023
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Ingår i: Advances in Intelligent Data Analysis XXI. - Cham : Springer. - 9783031300462 - 9783031300479 ; , s. 195-207
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Konferensbidrag (refereegranskat)abstract
- This research is an interdisciplinary work between data scientists, innovation management researchers, and experts from a Swedish hygiene and health company. Based on this collaboration, we have developed a novel package for automatic idea detection to control and prevent healthcare-associated infections (HAI). The principal idea of this study is to use machine learning methods to extract informative ideas from social media to assist healthcare professionals in reducing the rate of HAI. Therefore, the proposed package offers a corpus of data collected from Twitter, associated expert-created labels, and software implementation of an annotation framework based on the Active Learning paradigm. We employed Transfer Learning and built a two-step deep neural network model that incrementally extracts the semantic representation of the collected text data using the BERTweet language model in the first step and classifies these representations as informative or non-informative using a multi-layer perception (MLP) in the second step. The package is AID4HAI (Automatic Idea Detection for controlling and preventing Healthcare-Associated Infections) and is made fully available (software code and the collected data) through a public GitHub repository (https://github.com/XaraKar/AID4HAI). We believe that sharing our ideas and releasing these ready-to-use tools contributes to the development of the field and inspires future research.
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| 49. |
- Khoshkangini, Reza, 1984-, et al.
(författare)
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Bayesian network for failure prediction in different seasons
- 2020
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Ingår i: 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020. - : Research Publishing Services. - 9789811485930 ; , s. 1710-1710
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Konferensbidrag (refereegranskat)
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| 50. |
- Khoshkangini, Reza, 1984-, et al.
(författare)
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Baysian Network for Failure Prediction in Different Seasons
- 2020
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Ingår i: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. - 9789811485930 ; , s. 1710-1710
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Konferensbidrag (populärvet., debatt m.m.)abstract
- In recent years, there have been many attentions in developing technologies with the aim of monitoring and predicting emerging issues such as break downs, component failures, and quality degradations e.g., R, Prytz et al. (2015), as a means to provide predictive maintenance solution in modern vehicle industries. These existing technologies exploit several fault predictions and diagnostic pipelines ranging from statistics methods to machine learning algorithms e.g., M, You et al. (2010), Y, Lei et al. (2016). However, these solutions have not particularly concentrated on the ability to predict the component failures and the cause of the failures taking into consideration vehicle usage patterns and history of failures over time in different seasons.This is not a trivial task since modern vehicles with their huge functionalities and dependency among their components bring out a challenge to the manufacturer to plan their maintenance strategy in this complex domain. This is truly a complex challenge since failures can be sourced and affected by multiple features, which are highly related to each other and change over time in different contexts (e.g., location, time, season). Under such conditions, an advanced early prediction capability is desired, because manufacturers can exceedingly serve from early prediction of potential vehicle component failures, and more specifically the chain of the features and their dependencies which may lead to a failure over time in different seasons. This is considered important due to the fact that different seasons may have a potential effect on certain component failures, so predicting these dependencies and the actual failure enables a higher level of maintenance for optimally planning and managing total cost and more importantly safety. In this study, we build a probabilistic prediction model in a time series, on top of vehicle usage pattern, which is represented by the Live Vehicle Data (LVD). LVD logged and captured using multiple sensors located in Volvo vehicles that includes usage and specification of the vehicles aggregated in a cumulative fashion. We exploit and apply a type of supervised machine learning algorithm called Bayesian Network N, Friedman. (1997), on the engineered LVD (we applied a type of data engineering process to extract hidden patterns from LVD), which is logged through different seasons. These result a very complex network of dependency in each time stamp that indicates how a failure sourced by different features and their quantitative influences. In addition, integrating all these networks reveal how the usage can influence failure over time. Moreover, the quantitative influences allow us to extract the main chain of effect on a failure. This is strongly beneficial for the manufacturers and maintenance strategy to find out the main reason of failures, which can be extracted by vehicle usage pattern during their operation. © ESREL2020-PSAM15 Organizers
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