| 1. |
- 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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| 2. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Artificial intelligence, machine learning and reasoning in health informatics—an overview
- 2021
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Ingår i: Intelligent Systems Reference Library, Vol. 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 171-192
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Bokkapitel (refereegranskat)abstract
- As humans are intelligent, to mimic or models of human certain intelligent behavior to a computer or a machine is called Artificial Intelligence (AI). Learning is one of the activities by a human that helps to gain knowledge or skills by studying, practising, being taught, or experiencing something. Machine Learning (ML) is a field of AI that mimics human learning behavior by constructing a set of algorithms that can learn from data, i.e. it is a field of study that gives computers the ability to learn without being explicitly programmed. The reasoning is a set of processes that enable humans to provide a basis for judgment, making decisions, and prediction. Machine Reasoning (MR), is a part of AI evolution towards human-level intelligence or the ability to apply prior knowledge to new situations with adaptation and changes. This book chapter presents some AI, ML and MR techniques and approached those are widely used in health informatics domains. Here, the overview of each technique is discussed to show how they can be applied in the development of a decision support system.
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| 3. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Artificial intelligence, machine learning and reasoning in health informatics—case studies
- 2021
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Ingår i: Intelligent Systems Reference Library, Vol 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 261-291
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Bokkapitel (refereegranskat)abstract
- To apply Artificial Intelligence (AI), Machine Learning (ML) and Machine Reasoning (MR) in health informatics are often challenging as they comprise with multivariate information coming from heterogeneous sources e.g. sensor signals, text, etc. This book chapter presents the research development of AI, ML and MR as applications in health informatics. Five case studies on health informatics have been discussed and presented as (1) advanced Parkinson’s disease, (2) stress management, (3) postoperative pain treatment, (4) driver monitoring, and (5) remote health monitoring. Here, the challenges, solutions, models, results, limitations are discussed with future wishes.
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| 4. |
- 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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| 5. |
- 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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| 6. |
- Barua, Shaibal, et al.
(författare)
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Towards Intelligent Data Analytics : A Case Study in Driver Cognitive Load Classification
- 2020
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Ingår i: Brain Sciences. - Switzerland : MDPI AG. - 2076-3425 .- 2076-3425. ; 10:8
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Tidskriftsartikel (refereegranskat)abstract
- One debatable issue in traffic safety research is that cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform ‘intelligent multivariate data analytics’ based on machine learning (ML). Here, k-nearest neighbour (k-NN), support vector machine (SVM) and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., Sequential Forward Floating Selection (SFFS) and Random Forest have been applied where out of 323 features, a sub-set of 42 features has been selected as the best feature subset. For the classification, the RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Also, the result shows that using multicomponent features classifiers could classify better than using features from a single source.
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| 7. |
- Degas, A., et al.
(författare)
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A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management : Current Trends and Development with Future Research Trajectory
- 2022
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:3
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Forskningsöversikt (refereegranskat)abstract
- 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.
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| 8. |
- Hurter, C., et al.
(författare)
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Usage of more transparent and explainable conflict resolution algorithm : Air traffic controller feedback
- 2022
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Ingår i: Transportation Research Procedia. - : Elsevier B.V.. - 2352-1457 .- 2352-1465. ; 66:C, s. 270-278
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Tidskriftsartikel (refereegranskat)abstract
- Recently, Artificial intelligence (AI) algorithms have received increasable interest in various application domains including in Air Transportation Management (ATM). Different AI in particular Machine Learning (ML) algorithms are used to provide decision support in autonomous decision-making tasks in the ATM domain e.g., predicting air transportation traffic and optimizing traffic flows. However, most of the time these automated systems are not accepted or trusted by the intended users as the decisions provided by AI are often opaque, non-intuitive and not understandable by human operators. Safety is the major pillar to air traffic management, and no black box process can be inserted in a decision-making process when human life is involved. To address this challenge related to transparency of the automated system in the ATM domain, we investigated AI methods in predicting air transportation traffic conflict and optimizing traffic flows based on the domain of Explainable Artificial Intelligence (XAI). Here, AI models’ explainability in terms of understanding a decision i.e., post hoc interpretability and understanding how the model works i.e., transparency can be provided for air traffic controllers. In this paper, we report our research directions and our findings to support better decision making with AI algorithms with extended transparency.
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| 9. |
- Islam, Mir Riyanul, Doctoral Student, 1991-, et al.
(författare)
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A Novel Mutual Information based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning
- 2020
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Ingår i: Brain Sciences. - Switzerland : MDPI AG. - 2076-3425 .- 2076-3425. ; 10:8, s. 1-23
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Tidskriftsartikel (refereegranskat)abstract
- Analysis of physiological signals, electroencephalography in more specific notion, is considered as a very promising technique to obtain objective measures for mental workload evaluation, however, it requires complex apparatus to record and thus with poor usability in monitoring in-vehicle drivers’mental workload. This study proposes amethodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through real driving experiment and deployed in evaluating drivers’ mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. In predicting mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers’ mental workload.
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| 10. |
- Islam, Mir Riyanul, Dr. 1991-, et al.
(författare)
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A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks
- 2022
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:3
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Forskningsöversikt (refereegranskat)abstract
- Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. The literature shows evidence from numerous studies on the philosophy and methodologies of XAI. Nonetheless, there is an evident scarcity of secondary studies in connection with the application domains and tasks, let alone review studies following prescribed guidelines, that can enable researchers’ understanding of the current trends in XAI, which could lead to future research for domain- and application-specific method development. Therefore, this paper presents a systematic literature review (SLR) on the recent developments of XAI methods and evaluation metrics concerning different application domains and tasks. This study considers 137 articles published in recent years and identified through the prominent bibliographic databases. This systematic synthesis of research articles resulted in several analytical findings: XAI methods are mostly developed for safety-critical domains worldwide, deep learning and ensemble models are being exploited more than other types of AI/ML models, visual explanations are more acceptable to end-users and robust evaluation metrics are being developed to assess the quality of explanations. Research studies have been performed on the addition of explanations to widely used AI/ML models for expert users. However, more attention is required to generate explanations for general users from sensitive domains such as finance and the judicial system.
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