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Begum, S. & Ahmed, M. U. (2024). Artificial Intelligence in Predictive Maintenance: A Systematic Literature Review on Review Papers. In: Lecture Notes in Mechanical Engineering: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023 (pp. 251-261). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Artificial Intelligence in Predictive Maintenance: A Systematic Literature Review on Review Papers
2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 251-261Conference paper, Published paper (Refereed)
Abstract [en]

The fourth industrial revolution, colloquially referred to as “industry 4.0”, has garnered substantial global attention in recent years. There, Artificial intelligence (AI) driven industrial intelligence has been increasingly deployed in predictive maintenance (PdM), emerging as a vital enabler of smart manufacturing and industry 4.0. Since in recent years the number of articles focusing on Artificial Intelligence (AI) in PdM is high a review on the available literature reviews in this domain would be useful for the future researchers who would like to advance the research in this area and also for the persons who would like to apply PdM in their application domains. Therefore, this study identifies the AI revolution in PdM and focuses on the next stages available in the literature reviews in this area by quality assessment of secondary study. A well-known structured review approach (Systematic Literature Review, or SLR) was employed to perform this tertiary study. In addition, the Scale for the Assessment of Narrative Review Articles (SANRA) approach for evaluating the quality of review papers has been employed to support a few of the research questions. Here, This tertiary study scrutinizes four crucial aspects of secondary articles: (1) their specific research domains, (2) the annual trends in the quantity, variety, and quality (3) a footsteps of top researchers, and (4) the research constraints that review articles face during the time frame of 2015 to 2022. The results show that the majority of the application areas are applied to the manufacturing industry. It also leads to the identification of the revolution of AI in PdM as well. Our final findings indicate that Dr. Cheng et al.’s (2022) review has emerged as the predominant source of information in this field. As newcomers or industrial practitioners, we can benefit greatly from following his insights. The final outcome is that there is a lack of progress in SLR formulation and in adding explainable or interpretive AI methodologies in secondary studies.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Keywords
Artificial Intelligence, Predictive maintenance, Systematic literature review, Industry 4.0, Maintenance, Applications domains, Industrial revolutions, Literature reviews, Quality assessment, Review papers, Smart manufacturing, Structured review, Tertiary study
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-65371 (URN)10.1007/978-3-031-39619-9_18 (DOI)2-s2.0-85181976041 (Scopus ID)9783031396182 (ISBN)
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-02-26Bibliographically approved
Islam, M. R., Ahmed, M. U. & Begum, S. (2024). iXGB: improving the interpretability of XGBoost using decision rules and counterfactuals. In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART: . Paper presented at 16th International Conference on Agents and Artificial Intelligence (ICAART 2024) (pp. 1345-1353). , 3
Open this publication in new window or tab >>iXGB: improving the interpretability of XGBoost using decision rules and counterfactuals
2024 (English)In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 2024, Vol. 3, p. 1345-1353Conference paper, Published paper (Other academic)
Abstract [en]

Tree-ensemble models, such as Extreme Gradient Boosting (XGBoost), are renowned Machine Learning models which have higher prediction accuracy compared to traditional tree-based models. This higher accuracy, however, comes at the cost of reduced interpretability. Also, the decision path or prediction rule of XGBoost is not explicit like the tree-based models. This paper proposes the iXGB--interpretable XGBoost, an approach to improve the interpretability of XGBoost. iXGB approximates a set of rules from the internal structure of XGBoost and the characteristics of the data. In addition, iXGB generates a set of counterfactuals from the neighbourhood of the test instances to support the understanding of the end-users on their operational relevance. The performance of iXGB in generating rule sets is evaluated with experiments on real and benchmark datasets which demonstrated reasonable interpretability. The evaluation result also supports that the interpretability of XGBoost can be improved without using surrogate methods.

Keywords
Counterfactuals, Explainability, Explainable Artificial Intelligence, Interpretability, Regression, Rule-based Explanation, XGBoost.
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-64916 (URN)10.5220/0012474000003636 (DOI)978-989-758-680-4 (ISBN)
Conference
16th International Conference on Agents and Artificial Intelligence (ICAART 2024)
Available from: 2023-11-30 Created: 2023-11-30 Last updated: 2024-02-29Bibliographically approved
Barua, A., Ahmed, M. U. & Begum, S. (2023). A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions. IEEE Access, 11, 14804-14831
Open this publication in new window or tab >>A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 14804-14831Article, review/survey (Refereed) Published
Abstract [en]

Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role in tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) was applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
alignment, co-learning, fusion, Multimodal machine learning, representation, systematic literature review, translation, Machine learning applications, Machine-learning, Multi-disciplinary research, Multi-modal, Multiple modalities, Machine learning
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:mdh:diva-62038 (URN)10.1109/ACCESS.2023.3243854 (DOI)000936312800001 ()2-s2.0-85149020788 (Scopus ID)
Available from: 2023-03-08 Created: 2023-03-08 Last updated: 2023-03-15Bibliographically approved
Rehman, A. U., Ahmed, M. U. & Begum, S. (2023). Cognitive Digital Twin in Manufacturing: A Heuristic Optimization Approach. In: IFIP Advances in Information and Communication Technology: . Paper presented at IFIP Advances in Information and Communication Technology (pp. 441-453). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Cognitive Digital Twin in Manufacturing: A Heuristic Optimization Approach
2023 (English)In: IFIP Advances in Information and Communication Technology, Springer Science and Business Media Deutschland GmbH , 2023, p. 441-453Conference paper, Published paper (Refereed)
Abstract [en]

Complex systems that link virtualization and simulation platforms with actual data from industrial processes are vital for the next generation of production. Digital twins are such systems that have several advantages, notably in manufacturing where they can boost productivity throughout the whole manufacturing life-cycle. Enterprises will be able to creatively, efficiently, and effectively leverage implicit information derived from the experience of current production processes, thanks to cognitive digital twins. The development of numerous technologies has made the digital twin notion more competent and sophisticated throughout time. This article proposes a heuristic approach for cognitive digital twin technology as the next development in a digital twin that will aid in the realization of the goal of Industry 4.0. In creating cognitive digital twins, this article suggests the use of a heuristic approach as a possible route to allowing cognitive functionalities. Here, heuristic optimization is proposed as a feature selection tool to enhance the cognitive capabilities of a digital twin throughout the product design phase of production. The proposed approach is validated using the use-case of Power Transfer Unit (PTU) production, which resulted in an improvement of 8.83% in classification accuracy to predict the faulty PTU in the assembly line. This leads to an improved throughput of the PTU assembly line and also saves the resources utilized by faulty PTUs.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Keywords
Cognitive Digital Twins, Cyber-Physical Systems, Heuristic Optimization, Industrial Manufacturing, Assembly, Cognitive systems, Embedded systems, Energy transfer, Heuristic methods, Life cycle, Optimization, Product design, Simulation platform, Assembly line, Cognitive digital twin, Cybe-physical systems, Heuristics approaches, Optimization approach, Power transfer units, Virtualizations, Cyber Physical System
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64546 (URN)10.1007/978-3-031-34107-6_35 (DOI)2-s2.0-85173568013 (Scopus ID)9783031341069 (ISBN)
Conference
IFIP Advances in Information and Communication Technology
Available from: 2023-10-24 Created: 2023-10-24 Last updated: 2023-10-24Bibliographically approved
Jmoona, W., Ahmed, M. U., Islam, M. R., Barua, S., Begum, S., Ferreira, A. & Cavagnetto, N. (2023). Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction. In: AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676.: . Paper presented at 19th IFIP WG 12.5 International Conference, AIAI 2023 León, Spain, June 14–17, 2023 (pp. 81-93). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction
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2023 (English)In: AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676., Springer Science and Business Media Deutschland GmbH , 2023, p. 81-93Conference paper, Published paper (Refereed)
Abstract [en]

Flight Take-Off Time (TOT) delay prediction is essential to optimizing capacity-related tasks in Air Traffic Management (ATM) systems. Recently, the ATM domain has put afforded to predict TOT delays using machine learning (ML) algorithms, often seen as “black boxes”, therefore it is difficult for air traffic controllers (ATCOs) to understand how the algorithms have made this decision. Hence, the ATCOs are reluctant to trust the decisions or predictions provided by the algorithms. This research paper explores the use of explainable artificial intelligence (XAI) in explaining flight TOT delay to ATCOs predicted by ML-based predictive models. Here, three post hoc explanation methods are employed to explain the models’ predictions. Quantitative and user evaluations are conducted to assess the acceptability and usability of the XAI methods in explaining the predictions to ATCOs. The results show that the post hoc methods can successfully mimic the inference mechanism and explain the models’ individual predictions. The user evaluation reveals that user-centric explanation is more usable and preferred by ATCOs. These findings demonstrate the potential of XAI to improve the transparency and interpretability of ML models in the ATM domain.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Keywords
Air Traffic Management, DALEX, Explainable Artificial Intelligence, Flight Take-off Time Delay Prediction, LIME, SHAP, Advanced traffic management systems, Air traffic control, Forecasting, Machine learning, Timing circuits, Air traffic controller, Flight take off, Management domains, Take off time, Time-delay predictions, Time delay
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64550 (URN)10.1007/978-3-031-34107-6_7 (DOI)2-s2.0-85173565890 (Scopus ID)9783031341069 (ISBN)
Conference
19th IFIP WG 12.5 International Conference, AIAI 2023 León, Spain, June 14–17, 2023
Available from: 2023-10-24 Created: 2023-10-24 Last updated: 2024-02-29Bibliographically approved
Islam, M. R., Ahmed, M. U. & Begum, S. (2023). Interpretable Machine Learning for Modelling and Explaining Car Drivers' Behaviour: An Exploratory Analysis on Heterogeneous Data. In: : . Paper presented at 15th International Conference on Agents and Artificial Intelligence (ICAART 2023).
Open this publication in new window or tab >>Interpretable Machine Learning for Modelling and Explaining Car Drivers' Behaviour: An Exploratory Analysis on Heterogeneous Data
2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Understanding individual car drivers’ behavioural variations and heterogeneity is a significant aspect of developingcar simulator technologies, which are widely used in transport safety. This also characterizes the heterogeneity in drivers’ behaviour in terms of risk and hurry, using both real-time on-track and in-simulator driving performance features. Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain drivers’ behaviour while being classified and the explanations for them are evaluated. However, the high predictive power of ML algorithms ignore the characteristics of non-stationary domain relationships in spatiotemporal data (e.g., dependence, heterogeneity), which can lead to incorrect interpretations and poor management decisions. This study addresses this critical issue of ‘interpretability’ in ML-based modelling of structural relationships between the events and corresponding features of the car drivers’ behavioural variations. In this work, an exploratory experiment is described that contains simulator and real driving concurrently with a goal to enhance the simulator technologies. Here, initially, with heterogeneous data, several analytic techniques for simulator bias in drivers’ behaviour have been explored. Afterwards, five different ML classifier models were developed to classify risk and hurry in drivers’ behaviour in real and simulator driving. Furthermore, two different feature attribution-based explanation models were developed to explain the decision from the classifiers. According to the results and observation, among the classifiers, Gradient Boosted Decision Trees performed best with a classification accuracy of 98.62%. After quantitative evaluation, among the feature attribution methods, the explanation from Shapley Additive Explanations (SHAP) was found to be more accurate. The use of different metrics for evaluating explanation methods and their outcome lay the path toward further research in enhancing the feature attribution methods.

Keywords
Artificial Intelligence, Driving Behaviour, Feature Attribution, Evaluation, Explainable Artificial Intelligence, Interpretability, Road Safety.
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-64912 (URN)10.5220/0011801000003393 (DOI)2-s2.0-85182555765 (Scopus ID)
Conference
15th International Conference on Agents and Artificial Intelligence (ICAART 2023)
Available from: 2023-11-30 Created: 2023-11-30 Last updated: 2024-01-31Bibliographically approved
Islam, M. R., Weber, R. O., Ahmed, M. U. & Begum, S. (2023). Investigating Additive Feature Attribution for Regression.
Open this publication in new window or tab >>Investigating Additive Feature Attribution for Regression
2023 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Feature attribution is a class of explainable artificial intelligence (XAI) methods that produce the contributions of data features to a model's decision. There are multiple accounts stating that feature attribution methods produce inconsistent results and should always be evaluated. However, the existing body of literature on evaluation techniques is still immature with multiple proposed techniques and a lack of widely adopted methods, making it difficult to recognize the best approach for each circumstance. This article investigates an approach to creating synthetic data for regression that can be used to evaluate the results of feature attribution methods. From a real-world dataset, the proposed approach describes how to create synthetic data that preserves the patterns of the original data and enables comprehensive evaluation of XAI methods. This research also demonstrates how global and local feature attributions can be represented in the additive form of case-based reasoning as a benchmark method for evaluation. Finally, this work demonstrates the case where a method that includes a standardization step does not produce feature attributions of the same quality as one that does not use standardization in the context of a regression task.

Keywords
Explainability, Additive Feature Attribution, Regression, Additive CBR, CBR, Evaluation, Interpretability, LIME, SHAP, Synthetic Data, XAI.
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-64913 (URN)
Note

Submitted to the journal of Artificial Intelligence (AIJ)

Available from: 2023-11-30 Created: 2023-11-30 Last updated: 2024-02-28
Barua, A., Ahmed, M. U. & Begum, S. (2023). Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification. In: ICSET 2023 - 2023 IEEE 13th International Conference on System Engineering and Technology, Proceeding: . Paper presented at 13th IEEE International Conference on System Engineering and Technology, ICSET 2023, Shah Alam, 2 October 2023 (pp. 296-301). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification
2023 (English)In: ICSET 2023 - 2023 IEEE 13th International Conference on System Engineering and Technology, Proceeding, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 296-301Conference paper, Published paper (Refereed)
Abstract [en]

Vehicle manoeuvre analysis is vital for road safety as it helps understand driver behaviour, traffic flow, and road conditions. However, classifying data from in-vehicle acquisition systems or simulators for manoeuvre recognition is complex, requiring data fusion and machine learning (ML) algorithms. This paper proposes a hybrid approach that combines multivariate multiscale entropy (MMSE) and one-dimensional convolutional neural networks (1D-CNNs). MMSE is utilised for early feature extraction and data fusion, and the extracted features are classified using 1D-CNNs, achieving an impressive 87% test accuracy in multiclass classification. This paper provides insights into improving vehicle manoeuvre classification using advanced ML techniques and data fusion methods to handle complex data sets effectively. Ultimately, this approach can enhance the understanding of driver behaviour, inform policy decisions, and develop more effective strategies to enhance road safety. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Data Extraction, Data Fusion, Multivariate Multiscale Entropy (MMSE), Vehicle Manoeuvre, Accident prevention, Classification (of information), Complex networks, Data mining, Entropy, Extraction, Learning algorithms, Machine learning, Motor transportation, Roads and streets, Driver's behavior, Flow condition, Machine-learning, Multi-scale datum, Multivariate multiscale entropies, Multivariate multiscale entropy, Road safety, Traffic flow, Vehicle maneuver, Vehicles
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:mdh:diva-65013 (URN)10.1109/ICSET59111.2023.10295109 (DOI)2-s2.0-85178031651 (Scopus ID)9798350340891 (ISBN)
Conference
13th IEEE International Conference on System Engineering and Technology, ICSET 2023, Shah Alam, 2 October 2023
Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-13Bibliographically approved
Sheuly, S. S., Ahmed, M. U. & Begum, S. (2023). Quantitative Performance Analysis from Discrete Perspective: A Case Study of Chip Detection in Turning Process. In: International Conference on Agents and Artificial Intelligence: . Paper presented at 15th International Conference on Agents and Artificial Intelligence, Lisbon, Portugal, 22-24 February, 2023 (pp. 368-379). Science and Technology Publications, Lda
Open this publication in new window or tab >>Quantitative Performance Analysis from Discrete Perspective: A Case Study of Chip Detection in Turning Process
2023 (English)In: International Conference on Agents and Artificial Intelligence, Science and Technology Publications, Lda , 2023, p. 368-379Conference paper, Published paper (Refereed)
Abstract [en]

Good performance of the Machine Learning (ML) model is an important requirement associated with ML-integrated manufacturing. An increase in performance improvement methods such as hyperparameter tuning, data size increment, feature extraction, and architecture change leads to random attempts while improving performance. This can result in unnecessary consumption of time and performance improvement solely depending on luck. In the proposed study, a quantitative performance analysis on the case study of chip detection is performed from six perspectives: hyperparameter change, feature extraction method, data size increment, and concatenated Artificial Neural Network (ANN) architecture. The focus of the analysis is to create a consolidated knowledge of factors affecting ML model performance in turning process quality prediction. Metal peels such as chips are designed at the time of metal cutting (turning process) and the shape of these chips indicates the quality of the turning process. The result of the proposed study shows that following a fixed recipe does not always improve performance. In the case of performance improvement, data quality plays the main role. Additionally, the choice of an ML algorithm and hyperparameter tuning plays an essential role in performance.

Place, publisher, year, edition, pages
Science and Technology Publications, Lda, 2023
Keywords
Machine Learning, Manufacturing System, Performance Analysis, Quantification
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-65799 (URN)10.5220/0011800100003393 (DOI)2-s2.0-85182554050 (Scopus ID)
Conference
15th International Conference on Agents and Artificial Intelligence, Lisbon, Portugal, 22-24 February, 2023
Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-01-31Bibliographically approved
Degas, A., Islam, M. R., Hurter, C., Barua, S., Rahman, H., Poudel, M., . . . Aricó, P. (2022). A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory. Applied Sciences, 12(3), Article ID 1295.
Open this publication in new window or tab >>A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory
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2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 3, article id 1295Article, review/survey (Refereed) Published
Abstract [en]

Air Traffic Management (ATM) will be more complex in the coming decades due to the growth and increased complexity of aviation and has to be improved in order to maintain aviation safety. It is agreed that without significant improvement in this domain, the safety objectives defined by international organisations cannot be achieved and a risk of more incidents/accidents is envisaged. Nowadays, computer science plays a major role in data management and decisions made in ATM. Nonetheless, despite this, Artificial Intelligence (AI), which is one of the most researched topics in computer science, has not quite reached end users in ATM domain. In this paper, we analyse the state of the art with regards to usefulness of AI within aviation/ATM domain. It includes research work of the last decade of AI in ATM, the extraction of relevant trends and features, and the extraction of representative dimensions. We analysed how the general and ATM eXplainable Artificial Intelligence (XAI) works, analysing where and why XAI is needed, how it is currently provided, and the limitations, then synthesise the findings into a conceptual framework, named the DPP (Descriptive, Predictive, Prescriptive) model, and provide an example of its application in a scenario in 2030. It concludes that AI systems within ATM need further research for their acceptance by end-users. The development of appropriate XAI methods including the validation by appropriate authorities and end-users are key issues that needs to be addressed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
Air traffic management (ATM), Artificial intelligence (AI), Explainable artificial intelligence (XAI), User-centric XAI (UCXAI)
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-57255 (URN)10.3390/app12031295 (DOI)000756561800001 ()2-s2.0-85123696145 (Scopus ID)
Available from: 2022-02-09 Created: 2022-02-09 Last updated: 2024-02-28
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1212-7637

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