| 11. |
- Jmoona, Waleed, et al.
(author)
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Explaining the Unexplainable : Role of XAI for Flight Take-Off Time Delay Prediction
- 2023
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In: AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676.. - : Springer Science and Business Media Deutschland GmbH. - 9783031341069 ; , s. 81-93
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Conference paper (peer-reviewed)abstract
- 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.
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| 12. |
- Rahman, Hamidur, et al.
(author)
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Non-contact-based driver's cognitive load classification using physiological and vehicular parameters
- 2020
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In: Biomedical Signal Processing and Control. - : ELSEVIER SCI LTD. - 1746-8094 .- 1746-8108. ; 55
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Journal article (peer-reviewed)abstract
- Classification of cognitive load for vehicular drivers is a complex task due to underlying challenges of the dynamic driving environment. Many previous works have shown that physiological sensor signals or vehicular data could be a reliable source to quantify cognitive load. However, in driving situations, one of the biggest challenges is to use a sensor source that can provide accurate information without interrupting diverging tasks. In this paper, instead of traditional wire-based sensors, non-contact camera and vehicle data are used that have no physical contact with the driver and do not interrupt driving. Here, four machine learning algorithms, logistic regression (LR), support vector machine (SVM), linear discriminant analysis (LDA) and neural networks (NN), are investigated to classify the cognitive load using the collected data from a driving simulator study. In this paper, physiological parameters are extracted from facial video images, and vehicular parameters are collected from controller area networks (CAN). The data collection was performed in close collaboration with industrial partners in two separate studies, in which study-1 was designed with a 1-back task and study-2 was designed with both 1-back and 2-back task. The goal of the experiment is to investigate how accurately the machine learning algorithms can classify drivers' cognitive load based on the extracted features in complex dynamic driving environments. According to the results, for the physiological parameters extracted from the facial videos, the LR model with logistic function outperforms the other three classification methods. Here, in study-1, the achieved average accuracy for the LR classifier is 94% and in study-2 the average accuracy is 82%. In addition, the classification accuracy for the collected physiological parameters was compared with reference wire-sensor signals. It is observed that the classification accuracies between the sensor and the camera are very similar; however, better accuracy is achieved with the camera data due to having lower artefacts than the sensor data.
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| 13. |
- Rahman, Hamidur, Doctoral Student, 1984-, et al.
(author)
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Vision-based driver’s cognitive load classification considering eye movement using machine learning and deep learning
- 2021
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In: Sensors. - : MDPI. - 1424-8220. ; 21:23
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Journal article (peer-reviewed)abstract
- Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drivers’ unsafe behaviors. Therefore, to make the traffic environment safe it is important to keep the driver alert and awake both in human and autonomous driving cars. A driver’s cognitive load is considered a good indication of alertness, but determining cognitive load is challenging and the acceptance of wire sensor solutions are not preferred in real-world driving scenarios. The recent development of a non-contact approach through image processing and decreasing hardware prices enables new solutions and there are several interesting features related to the driver’s eyes that are currently explored in research. This paper presents a vision-based method to extract useful parameters from a driver’s eye movement signals and manual feature extraction based on domain knowledge, as well as automatic feature extraction using deep learning architectures. Five machine learning models and three deep learning architectures are developed to classify a driver’s cognitive load. The results show that the highest classification accuracy achieved is 92% by the support vector machine model with linear kernel function and 91% by the convolutional neural networks model. This non-contact technology can be a potential contributor in advanced driver assistive systems.
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| 14. |
- Ramentol, Enislay, et al.
(author)
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Machine Learning Models for Industrial Applications
- 2021
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In: AI and Learning Systems. - : IntechOpen. - 9781789858785 - 9781789858778
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Book chapter (other academic/artistic)abstract
- More and more industries are aspiring to achieve a successful production using the known artificial intelligence. Machine learning (ML) stands as a powerful tool for making very accurate predictions, concept classification, intelligent control, maintenance predictions, and even fault and anomaly detection in real time. The use of machine learning models in industry means an increase in efficiency: energy savings, human resources efficiency, increase in product quality, decrease in environmental pollution, and many other advantages. In this chapter, we will present two industrial applications of machine learning. In all cases we achieve interesting results that in practice can be translated as an increase in production efficiency. The solutions described cover areas such as prediction of production quality in an oil and gas refinery and predictive maintenance for micro gas turbines. The results of the experiments carried out show the viability of the solutions.
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| 15. |
- Sheuly, Sharmin Sultana, et al.
(author)
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Data Analytics using Statistical Methods and Machine Learning : A Case Study of Power Transfer Units
- 2021
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In: The International Journal of Advanced Manufacturing Technology. - Sweden : Springer Science and Business Media LLC. - 0268-3768 .- 1433-3015. ; 114:5-6, s. 1859-1870
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Journal article (peer-reviewed)abstract
- Sensors can produce large amounts of data related to products, design and materials; however, it is important to use the right data for the right purposes. Therefore, detailed analysis of data accumulated from different sensors in production and assembly manufacturing lines is necessary to minimize faulty products and understand the production process. Additionally, when selecting analytical methods, manufacturing companies must select the most suitable techniques. This paper presents a data analytics approach to extract useful information, such as important measurements for the dimensions of a shim, a small part for aligning shafts, from the manufacturing data of a Power Transfer Unit (PTU). This paper also identifies the best techniques and analytical approaches within the following six individual areas: 1) identifying measurements associated with faults; 2) identifying measurements associated with shim dimensions; 3) identifying associations between station codes; 4) predicting shim dimensions; 5) identifying duplicate samples in faulty data; and 6) identifying error distributions associated with measurement. These areas are analysed in accordance with two analytical approaches: a) statistical analysis and b) machine learning (ML)-based analysis. The results show a) the relative importance of measurements with regard to the faulty unit and shim dimensions, b) the error distribution of measurements, and c) the reproduction rate of faulty units. Additionally, both statistical analysis and ML-based analysis have shown that the measurement ‘PTU housing measurement’ is the most important measurement among available shim dimensions. Additionally, certain faulty stations correlated with one another. ML is shown to be the most suitable technique in three areas (e.g., identifying measurements associated with faults), while statistical analysis is sufficient for the other three areas (e.g., identifying measurements associated with shim dimensions) because they do not require a complex analytical model. This study provides a clearer understanding of assembly line production and identifies highly correlated and significant measurements of a faulty unit.
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