| 11. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Dilemmas in designing e-learning experiences for professionals
- 2021
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Ingår i: Proceedings of the European Conference on e-Learning, ECEL. ; , s. 10-17
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Konferensbidrag (refereegranskat)abstract
- The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.
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| 12. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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FUZZY RULE-BASED CLASSIFICATION TO BUILD INITIAL CASE LIBRARY FOR CASE-BASED STRESS DIAGNOSIS
- 2009
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Ingår i: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009. - 9780889867802 ; , s. 225-230
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Konferensbidrag (refereegranskat)abstract
- Case-Based Reasoning (CBR) is receiving increasedinterest for applications in medical decision support.Clinicians appreciate the fact that the system reasons withfull medical cases, symptoms, diagnosis, actions takenand outcomes. Also for experts it is often appreciated toget a second opinion. In the initial phase of a CBR systemthere are often a limited number of cases available whichreduces the performance of the system. If past cases aremissing or very sparse in some areas the accuracy isreduced. This paper presents a fuzzy rule-basedclassification scheme which is introduced into the CBRsystem to initiate the case library, providing improvedperformance in the stress diagnosis task. Theexperimental results showed that the CBR system usingthe enhanced case library can correctly classify 83% ofthe cases, whereas previously the correctness of theclassification was 61%. Consequently the proposedsystem has an improved performance with 22% in termsof accuracy. In terms of the discrepancy in classificationcompared to the expert, the goodness-of-fit value of thetest results is on average 87%. Thus by employing thefuzzy rule-based classification, the new hybrid system cangenerate artificial cases to enhance the case library.Furthermore, it can classify new problem cases previouslynot classified by the system.
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| 13. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Machine learning for cognitive load classification : A case study on contact-free approach
- 2020
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer. - 9783030491604 ; , s. 31-42
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Konferensbidrag (refereegranskat)abstract
- The most common ways of measuring Cognitive Load (CL) is using physiological sensor signals e.g., Electroencephalography (EEG), or Electrocardiogram (ECG). However, these signals are problematic in situations e.g., in dynamic moving environments where the user cannot relax with all the sensors attached to the body and it provides significant noises in the signals. This paper presents a case study using a contact-free approach for CL classification based on Heart Rate Variability (HRV) collected from ECG signal. Here, a contact-free approach i.e., a camera-based system is compared with a contact-based approach i.e., Shimmer GSR+ system in detecting CL. To classify CL, two different Machine Learning (ML) algorithms, mainly, Support Vector Machine (SVM) and k-Nearest-Neighbor (k-NN) have been applied. Based on the gathered Inter-Beat-Interval (IBI) values from both the systems, 13 different HRV features were extracted in a controlled study to determine three levels of CL i.e., S0: low CL, S1: normal CL and S2: high CL. To get the best classification accuracy with the ML algorithms, different optimizations such as kernel functions were chosen with different feature matrices both for binary and combined class classifications. According to the results, the highest average classification accuracy was achieved as 84% on the binary classification i.e. S0 vs S2 using k-NN. The highest F1 score was achieved 88% using SVM for the combined class considering S0 vs (S1 and S2) for contact-free approach i.e. the camera system. Thus, all the ML algorithms achieved a higher classification accuracy while considering the contact-free approach than contact-based approach. © IFIP International Federation for Information Processing 2020.
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| 14. |
- Ahmed, Mobyen Uddin, 1976-, et al.
(författare)
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Quality index analysis on camera- A sed R-eak identification considering movements and light illumination
- 2018
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Ingår i: Studies in Health Technology and Informatics, vol 249. - : IOS Press. - 9781614998679 ; , s. 84-92
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Konferensbidrag (refereegranskat)abstract
- This paper presents a quality index (QI) analysis on R-peak extracted by a camera system considering movements and light illumination. Here, the proposed camera system is compared with a reference system named Shimmer PPG sensor. The study considers five test subjects with a 15 minutes measurement protocol, where the protocol consists of several conditions. The conditions are: Normal sittings, head movements i.e., up/down/left/right/forward/backword, with light on/off and with moving flash on/off. A percentage of corrected R-peaks are calculated based on time difference in milliseconds (MS) between the R-peaks extracted both from camera-based and sensor-based systems. A comparison results between normal, movements, and lighting condition is presented as individual and group wise. Furthermore, the comparison is extended considering gender and origin of the subjects. According to the results, more than 90% R-peaks are correctly identified by the camera system with ±200 MS time differences, however, it decreases with while there is no light than when it is on. At the same time, the camera system shows more 95% accuracy for European than Asian men.
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| 15. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Study on Human Subjects – Influence of Stress and Alcohol in Simulated Traffic Situations
- 2021
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Ingår i: Open Research Europe. - : F1000 Research Ltd. - 2732-5121. ; 1:83
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Tidskriftsartikel (refereegranskat)abstract
- This report presents a research study plan on human subjects – the influence of stress and alcohol in simulated traffic situations under an H2020 project named SIMUSAFE. This research study focuses on road-users’, i.e., car drivers, motorcyclists, bicyclists and pedestrians, behaviour in relation to retrospective studies, where interaction between the users are considered. Here, the study includes sample size, inclusion/exclusion criteria, detailed study plan, protocols, potential test scenarios and all related ethical issues. The study plan has been included in a national ethics application and received approval for implementation.
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| 16. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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When a CBR in Hand is Better than Twins in the Bush
- 2022
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Ingår i: CEUR Workshop Proceedings, vol. 3389. - : CEUR-WS. ; , s. 141-152
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Konferensbidrag (refereegranskat)abstract
- AI methods referred to as interpretable are often discredited as inaccurate by supporters of the existence of a trade-off between interpretability and accuracy. In many problem contexts however this trade-off does not hold. This paper discusses a regression problem context to predict flight take-off delays where the most accurate data regression model was trained via the XGBoost implementation of gradient boosted decision trees. While building an XGB-CBR Twin and converting the XGBoost feature importance into global weights in the CBR model, the resultant CBR model alone provides the most accurate local prediction, maintains the global importance to provide a global explanation of the model, and offers the most interpretable representation for local explanations. This resultant CBR model becomes a benchmark of accuracy and interpretability for this problem context, and hence it is used to evaluate the two additive feature attribute methods SHAP and LIME to explain the XGBoost regression model. The results with respect to local accuracy and feature attribution lead to potentially valuable future work. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)
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| 17. |
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| 18. |
- Barua, Arnab, et al.
(författare)
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A Systematic Literature Review on Multimodal Machine Learning : Applications, Challenges, Gaps and Future Directions
- 2023
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers Inc.. - 2169-3536. ; 11, s. 14804-14831
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Forskningsöversikt (refereegranskat)abstract
- 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.
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| 19. |
- Barua, Arnab, et al.
(författare)
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Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification
- 2023
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Ingår i: ICSET 2023 - 2023 IEEE 13th International Conference on System Engineering and Technology, Proceeding. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350340891 ; , s. 296-301
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 20. |
- Barua, Arnab, et al.
(författare)
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Second-Order Learning with Grounding Alignment : A Multimodal Reasoning Approach to Handle Unlabelled Data
- 2024
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Ingår i: International Conference on Agents and Artificial Intelligence. - : Science and Technology Publications, Lda. ; , s. 561-572
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Konferensbidrag (refereegranskat)abstract
- Multimodal machine learning is a critical aspect in the development and advancement of AI systems. However, it encounters significant challenges while working with multimodal data, where one of the major issues is dealing with unlabelled multimodal data, which can hinder effective analysis. To address the challenge, this paper proposes a multimodal reasoning approach adopting second-order learning, incorporating grounding alignment and semi-supervised learning methods. The proposed approach illustrates using unlabelled vehicular telemetry data. During the process, features were extracted from unlabelled telemetry data using an autoencoder and then clustered and aligned with true labels of neurophysiological data to create labelled and unlabelled datasets. In the semi-supervised approach, the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms are applied to the labelled dataset, achieving a test accuracy of over 97%. These algorithms are then used to predict labels for the unlabelled dataset, which is later added to the labelled dataset to retrain the model. With the additional prior labelled data, both algorithms achieved a 99% test accuracy. Confidence in predictions for unlabelled data was validated using counting samples based on the prediction score and Bayesian probability. RF and XGBoost scored 91.26% and 97.87% in counting samples and 98.67% and 99.77% in Bayesian probability, respectively.
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