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  • 1.
    Islam, Mir Riyanul
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Flumeri, Gianluca Di
    Cognitive States in Operative Environment, BrainSigns, Via Sesto Celere, 7/C Rome, Italy.
    Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification2019In: Communications in Computer and Information Science, Volume 1107, 2019, p. 121-135Conference paper (Refereed)
    Abstract [en]

    In the pursuit of reducing traffic accidents, drivers' mental workload (MWL) has been considered as one of the vital aspects. To measure MWL in different driving situations Electroencephalography (EEG) of the drivers has been studied intensely. However, in the literature, mostly, manual analytic methods are applied to extract and select features from the EEG signals to quantify drivers' MWL. Nevertheless, the amount of time and effort required to perform prevailing feature extraction techniques leverage the need for automated feature extraction techniques. This work investigates deep learning (DL) algorithm to extract and select features from the EEG signals during naturalistic driving situations. Here, to compare the DL based and traditional feature extraction techniques, a number of classifiers have been deployed. Results have shown that the highest value of area under the curve of the receiver operating characteristic (AUC-ROC) is 0.94, achieved using the features extracted by CNN-AE and support vector machine. Whereas, using the features extracted by the traditional method, the highest value of AUC-ROC is 0.78 with the multi-layer perceptron. Thus, the outcome of this study shows that the automatic feature extraction techniques based on CNN-AE can outperform the manual techniques in terms of classification accuracy.

  • 2.
    Islam, Mir Riyanul
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hypothyroid Disease Diagnosis with Causal Explanation using Case-based Reasoning and Domain-specific Ontology2019In: Workshop on CBR in the Health Science WS-HealthCBR, 2019Conference paper (Refereed)
    Abstract [en]

    Explainability of intelligent systems in health-care domain is still in its initial state. Recently, more efforts are made to leverage machine learning in solving causal inference problems of disease diagnosis, prediction and treatments. This research work presents an ontology based causal inference model for hypothyroid disease diagnosis using case-based reasoning. The effectiveness of the proposed method is demonstrated with an example from hypothyroid disease domain. Here, the domain knowledge is mapped into an ontology and causal inference is performed based on this domain-specific ontology. The goal is to incorporate this causal inference model in traditional case-based reasoning cycle enabling explanation for each solved problem. Finally, a mechanism is defined to deduce explanation for a solution to a problem case from the combined causal statements of similar cases. The initial result shows that case-based reasoning can retrieve relevant cases with 95% accuracy.

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