| 1. |
- 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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| 2. |
- 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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| 3. |
- 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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| 4. |
- Rahman, Hamidur, Doctoral Student, 1984-, et al.
(författare)
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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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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:23
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Tidskriftsartikel (refereegranskat)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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| 5. |
- Ramentol, Enislay, et al.
(författare)
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Machine Learning Models for Industrial Applications
- 2021
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Ingår i: AI and Learning Systems. - : IntechOpen. - 9781789858785 - 9781789858778
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Bokkapitel (övrigt vetenskapligt/konstnärligt)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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| 6. |
- Sheuly, Sharmin Sultana, et al.
(författare)
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Data Analytics using Statistical Methods and Machine Learning : A Case Study of Power Transfer Units
- 2021
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Ingår i: 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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Tidskriftsartikel (refereegranskat)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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