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Publications (10 of 122) Show all publications
Jamshidi, P., Nowaczyk, S. & Rahat, M. (2024). EcoShap: Save Computations by only Calculating Shapley Values for Relevant Features. In: Nowaczyk, Sławomir et al. (Ed.), Artificial Intelligence. ECAI 2023 International Workshops: XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 – October 4, 2023, Proceedings, Part I. Paper presented at International Workshops of the 26th European Conference on Artificial Intelligence (ECAI 2023), Kraków, Poland, 30 September-4 October, 2023 (pp. 24-42). Cham: Springer, 1947
Open this publication in new window or tab >>EcoShap: Save Computations by only Calculating Shapley Values for Relevant Features
2024 (English)In: Artificial Intelligence. ECAI 2023 International Workshops: XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 – October 4, 2023, Proceedings, Part I / [ed] Nowaczyk, Sławomir et al., Cham: Springer, 2024, Vol. 1947, p. 24-42Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cham: Springer, 2024
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1947
Keywords
Explainable Artificial Intelligence (XAI), Feature Importance, Shapley Value
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-52744 (URN)10.1007/978-3-031-50396-2_2 (DOI)2-s2.0-85184111581 (Scopus ID)978-3-031-50395-5 (ISBN)978-3-031-50396-2 (ISBN)
Conference
International Workshops of the 26th European Conference on Artificial Intelligence (ECAI 2023), Kraków, Poland, 30 September-4 October, 2023
Funder
Swedish Research Council, CHIST-ERA-19-XAI-012
Available from: 2024-02-23 Created: 2024-02-23 Last updated: 2024-02-23Bibliographically approved
Altarabichi, M. G., Alabdallah, A., Pashami, S., Ohlsson, M., Rögnvaldsson, T. & Nowaczyk, S. (2024). Improving Concordance Index in Regression-based Survival Analysis: Discovery of Loss Function for Neural Networks.
Open this publication in new window or tab >>Improving Concordance Index in Regression-based Survival Analysis: Discovery of Loss Function for Neural Networks
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2024 (English)Manuscript (preprint) (Other academic)
Abstract [en]

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.

Keywords
evolutionary meta-learning, loss function, neural networks, survival analysis, regression
National Category
Computer Systems
Identifiers
urn:nbn:se:hh:diva-52468 (URN)
Note

As manuscript in thesis.

Available from: 2024-01-24 Created: 2024-01-24 Last updated: 2024-02-05Bibliographically approved
Altarabichi, M. G., Nowaczyk, S., Pashami, S., Sheikholharam Mashhadi, P. & Handl, J. (2024). Rolling the Dice for Better Deep Learning Performance: A Study of Randomness Techniques in Deep Neural Networks.
Open this publication in new window or tab >>Rolling the Dice for Better Deep Learning Performance: A Study of Randomness Techniques in Deep Neural Networks
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2024 (English)Manuscript (preprint) (Other (popular science, discussion, etc.))
Abstract [en]

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\footnote[1]{https://github.com/Ghaith81/Radnomness\_in\_Neural\_Network}.

Keywords
Neural Networks, Randomized Neural Networks, Convolutional Neural Network, hyperparameter optimization, Particle swarm optimization
National Category
Computer Systems
Identifiers
urn:nbn:se:hh:diva-52467 (URN)
Note

As manuscript in thesis.

Available from: 2024-01-24 Created: 2024-01-24 Last updated: 2024-02-05Bibliographically approved
Rajabi, E., Nowaczyk, S., Pashami, S., Bergquist, M., Ebby, G. S. & Wajid, S. (2023). A Knowledge-Based AI Framework for Mobility as a Service. Sustainability, 15(3), Article ID 2717.
Open this publication in new window or tab >>A Knowledge-Based AI Framework for Mobility as a Service
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2023 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 15, no 3, article id 2717Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Basel: MDPI, 2023
Keywords
mobility as a service, knowledge-based, explainability
National Category
Computer Sciences
Research subject
Smart Cities and Communities
Identifiers
urn:nbn:se:hh:diva-49970 (URN)10.3390/su15032717 (DOI)000929663500001 ()2-s2.0-85148043364 (Scopus ID)
Funder
Knowledge Foundation, 20180181
Available from: 2023-02-14 Created: 2023-02-14 Last updated: 2023-08-21Bibliographically approved
Jamshidi, S., Nowaczyk, S., Fanaee Tork, H. & Rahat, M. (2023). A systematic approach for tracking the evolution of XAI as a field of research. In: Irena Koprinska; Paolo Mignone; Riccardo Guidotti; Szymon Jaroszewicz; Holger Fröning; Francesco Gullo; Pedro M. Ferreira; Damian Roqueiro (Ed.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II. Paper presented at Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Workshop on IoT Streams for Predictive Maintenance, Grenoble, France, September 19-23, 2022 (pp. 461-476). Cham: Springer, 1753
Open this publication in new window or tab >>A systematic approach for tracking the evolution of XAI as a field of research
2023 (English)In: Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II / [ed] Irena Koprinska; Paolo Mignone; Riccardo Guidotti; Szymon Jaroszewicz; Holger Fröning; Francesco Gullo; Pedro M. Ferreira; Damian Roqueiro, Cham: Springer, 2023, Vol. 1753, p. 461-476Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
Communications in Computer and Information Science, ISSN 978-3-031-23632-7, E-ISSN 978-3-031-23633-4 ; 2
Keywords
Field's Evolution, XAI, Explainable AI
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-49831 (URN)10.1007/978-3-031-23633-4_31 (DOI)000967761200031 ()2-s2.0-85149954978 (Scopus ID)
Conference
Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Workshop on IoT Streams for Predictive Maintenance, Grenoble, France, September 19-23, 2022
Funder
Swedish Research Council, CHIST-ERA-19-XAI-012
Available from: 2023-01-16 Created: 2023-01-16 Last updated: 2023-08-11Bibliographically approved
Kharazian, Z., Rahat, M., Gama, F., Sheikholharam Mashhadi, P., Nowaczyk, S., Lindgren, T. & Magnússon, S. (2023). AID4HAI: Automatic Idea Detection for Healthcare-Associated Infections from Twitter, A Framework based on Active Learning and Transfer Learning. In: Crémilleux, B.; Hess, S.; Nijssen, S. (Ed.), Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Paper presented at Symposium on Intelligent Data Analysis (IDA 2023), Louvain-la-Neuve, Belgium, 12-14 April, 2023 (pp. 195-207). Cham: Springer, 13876
Open this publication in new window or tab >>AID4HAI: Automatic Idea Detection for Healthcare-Associated Infections from Twitter, A Framework based on Active Learning and Transfer Learning
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2023 (English)In: Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings / [ed] Crémilleux, B.; Hess, S.; Nijssen, S., Cham: Springer, 2023, Vol. 13876, p. 195-207Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13876
Keywords
automatic idea detection, healthcare-associated infections, human-in-the-loop, active learning, feedback loops, supervised machine learning, natural language processing
National Category
Computer Systems Health Care Service and Management, Health Policy and Services and Health Economy
Research subject
Health Innovation, Information driven care
Identifiers
urn:nbn:se:hh:diva-50007 (URN)10.1007/978-3-031-30047-9_16 (DOI)000999877600016 ()2-s2.0-85152539906 (Scopus ID)978-3-031-30046-2 (ISBN)978-3-031-30047-9 (ISBN)
Conference
Symposium on Intelligent Data Analysis (IDA 2023), Louvain-la-Neuve, Belgium, 12-14 April, 2023
Projects
AID project
Funder
Knowledge Foundation, 220023Vinnova
Note

Funding: KK-Foundation, Scania CV AB and the Vinnova program for Strategic Vehicle Research and Innovation (FFI).

Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-08-11Bibliographically approved
Taghiyarrenani, Z., Nowaczyk, S. & Pashami, S. (2023). Analysis of Statistical Data Heterogeneity in Federated Fault Identification. In: Takehisa Yairi; Samir Khan; Seiji Tsutsumi (Ed.), Proceedings of the Asia Pacific Conference of the PHM Society 2023: . Paper presented at 4th Asia Pacific Conference of the Prognostics and Health Management, Tokyo, Japan, September 11-14, 2023. New York: The Prognostics and Health Management Society, 4
Open this publication in new window or tab >>Analysis of Statistical Data Heterogeneity in Federated Fault Identification
2023 (English)In: Proceedings of the Asia Pacific Conference of the PHM Society 2023 / [ed] Takehisa Yairi; Samir Khan; Seiji Tsutsumi, New York: The Prognostics and Health Management Society , 2023, Vol. 4Conference paper, Published paper (Refereed)
Abstract [en]

Federated Learning (FL) is a setting where different clients collaboratively train a Machine Learning model in a privacy-preserving manner, i.e., without the requirement to share data. Given the importance of security and privacy in real-world applications, FL is gaining popularity in many areas, including predictive maintenance. For example, it allows independent companies to construct a model collaboratively. However, since different companies operate in different environments, their working conditions may differ, resulting in heterogeneity among their data distributions. This paper considers the fault identification problem and simulates different scenarios of data heterogeneity. Such a setting remains challenging for popular FL algorithms, and thus we demonstrate the considerations to be taken into account when designing federated predictive maintenance solutions.  

Place, publisher, year, edition, pages
New York: The Prognostics and Health Management Society, 2023
Series
Proceedings of the Asia Pacific Conference of the PHM Society, E-ISSN 2994-7219
Keywords
Predictive Maintenance, Federated Learning, Predictive Maintenance Federated Learning Statistical Heterogeneity
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-52478 (URN)10.36001/phmap.2023.v4i1.3708 (DOI)
Conference
4th Asia Pacific Conference of the Prognostics and Health Management, Tokyo, Japan, September 11-14, 2023
Funder
Vinnova
Available from: 2024-01-25 Created: 2024-01-25 Last updated: 2024-01-31Bibliographically approved
Amirhossein, B., Taghiyarrenani, Z. & Nowaczyk, S. (2023). curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection. In: Irena Koprinska et al. (Ed.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II. Paper presented at ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Grenoble, France, September 19–23, 2022 (pp. 423-437). Cham: Springer Nature, 1753
Open this publication in new window or tab >>curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection
2023 (English)In: Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II / [ed] Irena Koprinska et al., Cham: Springer Nature, 2023, Vol. 1753, p. 423-437Conference paper, Published paper (Refereed)
Abstract [en]

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%.

Place, publisher, year, edition, pages
Cham: Springer Nature, 2023
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1753
Keywords
Induction Motor, Broken Rotor Bar, Fault Diagnosis, Predictive Maintenance, Contrastive pre-training, Multi-Modal Latent Translation
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-49990 (URN)10.1007/978-3-031-23633-4_28 (DOI)000967761200028 ()2-s2.0-85149919657 (Scopus ID)978-3-031-23633-4 (ISBN)
Conference
ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Grenoble, France, September 19–23, 2022
Available from: 2023-02-18 Created: 2023-02-18 Last updated: 2023-08-11Bibliographically approved
Berenji, A., Nowaczyk, S. & Taghiyarrenani, Z. (2023). Data-Centric Perspective on Explainability Versus Performance Trade-Off. In: Bruno Crémilleux, Sibylle Hess, Siegfried Nijssen (Ed.), Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Paper presented at 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023 (pp. 42-54). Cham: Springer, 13876
Open this publication in new window or tab >>Data-Centric Perspective on Explainability Versus Performance Trade-Off
2023 (English)In: Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings / [ed] Bruno Crémilleux, Sibylle Hess, Siegfried Nijssen, Cham: Springer, 2023, Vol. 13876, p. 42-54Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13876
Keywords
Explainable AI, SHAP, Intelligent Fault Diagnosis, Bearings, Hilbert Transform, Envelope Spectrum
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-52004 (URN)10.1007/978-3-031-30047-9_4 (DOI)000999877600004 000999877600004 ()2-s2.0-85152557513 (Scopus ID)978-3-031-30046-2 (ISBN)978-3-031-30047-9 (ISBN)
Conference
21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023
Funder
VinnovaSwedish Research Council, CHIST-ERA-19-XAI-012
Available from: 2023-11-14 Created: 2023-11-14 Last updated: 2023-11-21Bibliographically approved
Abuella, M., Atoui, M. A., Nowaczyk, S., Johansson, S. & Faghani, E. (2023). Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping. In: Gianmarco De Francisci Morales; Claudia Perlich; Natali Ruchansky; Nicolas Kourtellis; Elena Baralis; Francesco Bonchi (Ed.), Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII. Paper presented at European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023 (pp. 226-241). Cham: Springer, 14175
Open this publication in new window or tab >>Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping
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2023 (English)In: Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII / [ed] Gianmarco De Francisci Morales; Claudia Perlich; Natali Ruchansky; Nicolas Kourtellis; Elena Baralis; Francesco Bonchi, Cham: Springer, 2023, Vol. 14175, p. 226-241Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14175
Keywords
Short-sea shipping, Energy efficiency, Explainability, Spatio-temporal aggregation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-51888 (URN)10.1007/978-3-031-43430-3_14 (DOI)2-s2.0-85174447269 (Scopus ID)978-3-031-43429-7 (ISBN)978-3-031-43430-3 (ISBN)
Conference
European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023
Projects
Research projects within Aware Intelligent Systems
Funder
Vinnova
Available from: 2023-11-02 Created: 2023-11-02 Last updated: 2023-12-07Bibliographically approved
Projects
iMedA: Improving MEDication Adherence through Person Centered Care and Adaptive Interventions [2017-04617_Vinnova]; Halmstad University; Publications
Galozy, A. (2021). Data-driven personalized healthcare: Towards personalized interventions via reinforcement learning for Mobile Health. (Licentiate dissertation). Halmstad: Halmstad University PressGalozy, A., Nowaczyk, S., Pinheiro Sant'Anna, A., Ohlsson, M. & Lingman, M. (2020). Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation. International Journal of Medical Informatics, 136, Article ID 104092. Galozy, A. & Nowaczyk, S. (2020). Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data. Journal of Biomedical Informatics: X, 6-7, Article ID 100075. Galozy, A., Nowaczyk, S. & Ohlsson, M.Corrupted Contextual Bandits with Action Order Constraints.
eXplainable Predictive Maintenance [2020-00767_VR]; Halmstad University; Publications
Amirhossein, B., Taghiyarrenani, Z. & Nowaczyk, S. (2023). curr2vib: Modality Embedding Translation for Broken-Rotor Bar Detection. In: Irena Koprinska et al. (Ed.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II. Paper presented at ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Grenoble, France, September 19–23, 2022 (pp. 423-437). Cham: Springer Nature, 1753Berenji, A., Nowaczyk, S. & Taghiyarrenani, Z. (2023). Data-Centric Perspective on Explainability Versus Performance Trade-Off. In: Bruno Crémilleux, Sibylle Hess, Siegfried Nijssen (Ed.), Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Paper presented at 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023 (pp. 42-54). Cham: Springer, 13876Alabdallah, A., Pashami, S., Rögnvaldsson, T. & Ohlsson, M. (2022). SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP. In: Joshua Zhexue Huang; Yi Pan; Barbara Hammer; Muhammad Khurram Khan; Xing Xie; Laizhong Cui; Yulin He (Ed.), 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA): . Paper presented at The 9th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2022), Shenzhen, China, October 13-16, 2022. Piscataway, NJ: IEEE
Automatic Idea Detection: Implementing artificial intelligence in medical technology innovation (AID); Halmstad UniversityFrom Connected to Sustainable Mobility (FREEDOM) [2021-02548_Vinnova]; Halmstad UniversityAI-driven Automotive Service Market: Towards more Resource-Efficient and Sustainable Vehicle Maintenance [2023-02594_Vinnova]; Halmstad University
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