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Sökning: WFRF:(Ahmed Mobyen Uddin Dr 1976 )

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1.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • A machine learning approach for biomass characterization
  • 2019
  • Ingår i: Energy Procedia. - : Elsevier Ltd. - 1876-6102. ; , s. 1279-1287
  • Konferensbidrag (refereegranskat)abstract
    • The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SG1) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications. © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 - The 10th International Conference on Applied Energy.
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2.
  • Islam, Mir Riyanul, Dr. 1991-, et al. (författare)
  • A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks
  • 2022
  • Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:3
  • 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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3.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • A Machine Learning Approach to Classify Pedestrians’ Event based on IMU and GPS
  • 2019
  • Ingår i: International Conference on Modern Intelligent Systems Concepts MISC'18. - : CESER Publications. ; 17:2, s. 154-167
  • Konferensbidrag (refereegranskat)abstract
    • This paper investigates and implements six Machine Learning (ML) algorithms, i.e. Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Gradient Boosted Trees (GBT) to classify different Pedestrians’ events based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS) signals. Pedestrians’ events are pedestrian movements as the first step of H2020 project called SimuSafe1 with a goal to reduce traffic fatalities by doing risk assessments of the pedestrians. The movements the MLs’ models are attempting to classify are standing, walking, and running. Data, i.e. IMU, GPS sensor signals and other contextual information are collected by a smartphone through a controlled procedure. The smartphone is placed in five different positions onto the body of participants, i.e. arm, chest, ear, hand and pocket. The recordings are filtered, trimmed, and labeled. Next, samples are generated from small overlapping sections from which time and frequency domain features are extracted. Three different experiments are conducted to evaluate the performances in term of accuracy of the MLs’ models in different circumstances. The best performing MLs’ models determined by the average accuracy across all experiments is Extra Tree (ET) with a classification accuracy of 91%. 
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4.
  • Ahmed, Mobyen Uddin, 1976- (författare)
  • A Multimodal Approach for Clinical Diagnosis and Treatment
  • 2011
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • A computer-aided Clinical Decision Support System (CDSS) for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSS could function as an expert for a less experienced clinician or as a second option/opinion of an experienced clinician to their decision making task. Nevertheless, it has been a real challenge to design and develop such a functional system where accuracy of the system performance is an important issue. This research work focuses on development of intelligent CDSS based on a multimodal approach for diagnosis, classification and treatment in medical domains i.e. stress and post-operative pain management domains. Several Artificial Intelligence (AI) techniques such as Case-Based Reasoning (CBR), textual Information Retrieval (IR), Rule-Based Reasoning (RBR), Fuzzy Logic and clustering approaches have been investigated in this thesis work. Patient’s data i.e. their stress and pain related information are collected from complex data sources for instance, finger temperature measurements through sensor signals, pain measurements using a Numerical Visual Analogue Scale (NVAS), patient’s information from text and multiple choice questionnaires. The proposed approach considers multimedia data management to be able to use them in CDSSs for both the domains. The functionalities and performance of the systems have been evaluated based on close collaboration with experts and clinicians of the domains. In stress management, 68 measurements from 46 subjects and 1572 patients’ cases out of ≈4000 in post-operative pain have been used to design, develop and validate the systems. In the stress management domain, besides the 68 measurement cases, three trainees and one senior clinician also have been involved in order to conduct the experimental work. The result from the evaluation shows that the system reaches a level of performance close to the expert and better than the senior and trainee clinicians. Thus, the proposed CDSS could be used as an expert for a less experienced clinician (i.e. trainee) or as a second option/opinion for an experienced clinician (i.e. senior) to their decision making process in stress management. In post-operative pain treatment, the CDSS retrieves and presents most similar cases (e.g. both rare and regular) with their outcomes to assist physicians. Moreover, an automatic approach is presented in order to identify rare cases and 18% of cases from the whole cases library i.e. 276 out of 1572 are identified as rare cases by the approach. Again, among the rare cases (i.e. 276), around 57.25% of the cases are classified as ‘unusually bad’ i.e. the average pain outcome value is greater or equal to 5 on the NVAS scale 0 to 10. Identification of rear cases is an important part of the PAIN OUT project and can be used to improve the quality of individual pain treatment.
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5.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • A vision-based indoor navigation system for individuals with visual impairment
  • 2019
  • Ingår i: International Journal of Artificial Intelligence. - : CESER Publications. - 0974-0635. ; 17:2, s. 188-201
  • Tidskriftsartikel (refereegranskat)abstract
    • Navigation and orientation in an indoor environment are a challenging task for visually impaired people. This paper proposes a portable vision-based system to provide support for visually impaired persons in their daily activities. Here, machine learning algorithms are used for obstacle avoidance and object recognition. The system is intended to be used independently, easily and comfortably without taking human help. The system assists in obstacle avoidance using cameras and gives voice message feedback by using a pre-trained YOLO Neural Network for object recognition. In other parts of the system, a floor plane estimation algorithm is proposed for obstacle avoidance and fuzzy logic is used to prioritize the detected objects in a frame and generate alert to the user about possible risks. The system is implemented using the Robot Operating System (ROS) for communication on a Nvidia Jetson TX2 with a ZED stereo camera for depth calculations and headphones for user feedback, with the capability to accommodate different setup of hardware components. The parts of the system give varying results when evaluated and thus in future a large-scale evaluation is needed to implement the system and get it as a commercialized product in this area.
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6.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • Analysis of Breakdown Reports Using Natural Language Processing and Machine Learning
  • 2022
  • Ingår i: Lecture Notes in Mechanical Engineering. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030936389 ; , s. 40-52
  • Konferensbidrag (refereegranskat)abstract
    • Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.
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7.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • Artificial intelligence, machine learning and reasoning in health informatics—an overview
  • 2021
  • Ingår i: Intelligent Systems Reference Library, Vol. 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 171-192
  • 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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8.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • Artificial intelligence, machine learning and reasoning in health informatics—case studies
  • 2021
  • Ingår i: Intelligent Systems Reference Library, Vol 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 261-291
  • 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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9.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • Convolutional Neural Network for Driving Maneuver Identification Based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS)
  • 2020
  • Ingår i: Frontiers in Sustainable Cities. - : Frontiers Media SA. - 2624-9634. ; 2
  • Tidskriftsartikel (refereegranskat)abstract
    • Identification and translation of different driving manoeuvre are some of the key elements to analysis driving risky behavior. However, the major obstacles to manoeuvre identification are the wide variety of styles of driving manoeuvre which are performed during driving. The objective in this contribution through the paper is to automatic identification of driver manoeuvre e.g. driving in roundabouts, left and right turns, breaks, etc. based on Inertia Measurement Unit (IMU) and Global Positioning System (GPS). Here, several Machine Learning (ML) algorithms i.e. Artificial Neural Network (ANN), Convolutional Neural Network (CNN), K-nearest neighbor (k-NN), Hidden Markov Model (HMM), Random Forest (RF), and Support Vector Machine (SVM) have been applied for automatic feature extraction and classification on the IMU and GPS data sets collected through a Naturalistic Driving Studies (NDS) under an H2020 project called SimuSafe . The CNN is further compared with HMM, RF, ANN, k-NN and SVM to observe the ability to identify a car manoeuvre through roundabouts. According to the results, CNN outperforms (i.e. average F1-score of 0.88 both roundabout and not roundabout) among the other ML classifiers and RF presents better correlation than CNN, i.e. MCC = -.022.
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10.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • Data Analysis on Powered Two Wheelers Riders’ Behaviour using Machine Learning
  • 2019
  • Ingår i: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019. - Barcelona, Spain.
  • Konferensbidrag (refereegranskat)abstract
    • Analyzing powered two-wheeler rider behavior, i.e. classification of riding patterns based on 3-D accelerometer/gyroscope sensors mounted on motorcycles is challenging. This paper presents machine learning approach to classify four different riding events performed by powered two wheeler riders’ as a step towards increasing traffic safety. Three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been used to classify riding patterns. The classification is conducted based on features extracted in time and frequency domains from accelerometer/gyroscope sensors signals. A comparison result between different filter frequencies, window sizes, features sets, as well as machine learning algorithms is presented. According to the results, the Random Forest method performs most consistently through the different data sets and scores best.
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11.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • Dilemmas in designing e-learning experiences for professionals
  • 2021
  • Ingår i: Proceedings of the European Conference on e-Learning, ECEL. ; , s. 10-17
  • 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, Dr, 1976-, et al. (författare)
  • Machine learning for cognitive load classification : A case study on contact-free approach
  • 2020
  • Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer. - 9783030491604 ; , s. 31-42
  • 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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13.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • Study on Human Subjects – Influence of Stress and Alcohol in Simulated Traffic Situations
  • 2021
  • Ingår i: Open Research Europe. - : F1000 Research Ltd. - 2732-5121. ; 1:83
  • 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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14.
  • Ahmed, Mobyen Uddin, Dr, 1976-, et al. (författare)
  • When a CBR in Hand is Better than Twins in the Bush
  • 2022
  • Ingår i: CEUR Workshop Proceedings, vol. 3389. - : CEUR-WS. ; , s. 141-152
  • 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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15.
  • Altarabichi, Mohammed Ghaith, et al. (författare)
  • Reaction Time Variability Association with Unsafe Driving
  • 2020
  • Ingår i: Transport Research Arena TRA2020. - Helsinki, Finland.
  • Konferensbidrag (refereegranskat)abstract
    • This paper investigates several human factors including visual field, reaction speed, driving behavior and personality traits based on results of a cognitive assessment test targeting drivers in a Naturalistic Driving Study (NDS). Frequency of being involved in Near Miss event (fnm) and Frequency of committing Traffic Violation (ftv) are defined as indexes of safe driving in this work. Inference of association shows statistically significant correlation between Standard Deviation of Reaction Time (σRT) and both safe driving indexes fnm and ftv. Causal relationship analysis excludes age as confounding factor as variations in behavioral responses is observed in both younger and older drivers of this study.
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16.
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17.
  • Barua, Arnab, et al. (författare)
  • A Systematic Literature Review on Multimodal Machine Learning : Applications, Challenges, Gaps and Future Directions
  • 2023
  • Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers Inc.. - 2169-3536. ; 11, s. 14804-14831
  • 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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18.
  • Barua, Arnab, et al. (författare)
  • Multi-scale Data Fusion and Machine Learning for Vehicle Manoeuvre Classification
  • 2023
  • 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
  • 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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19.
  • Barua, Arnab, et al. (författare)
  • Second-Order Learning with Grounding Alignment : A Multimodal Reasoning Approach to Handle Unlabelled Data
  • 2024
  • Ingår i: International Conference on Agents and Artificial Intelligence. - : Science and Technology Publications, Lda. ; , s. 561-572
  • 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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20.
  • Barua, Shaibal, et al. (författare)
  • Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual data
  • 2019
  • Ingår i: First International Conference on Advances in Signal Processing and Artificial Intelligence ASPAI' 2019.
  • Konferensbidrag (refereegranskat)abstract
    • Analysing physiological parameters together with contextual information of car drivers to identify drivers’ sleepiness is a challenging issue. Machine learning algorithms show high potential in data analysis and classification tasks in many domains. This paper presents a use case of machine learning approach for drivers’ sleepiness classification. The classifications are conducted based on drivers’ physiological parameters and contextual information. The sleepiness classification shows receiver operating characteristic (ROC) curves for KNN, SVM and RF were 0.98 on 10-fold cross-validation and 0.93 for leave-one-out (LOO) for all classifiers.
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21.
  • Barua, Shaibal, et al. (författare)
  • Towards Intelligent Data Analytics : A Case Study in Driver Cognitive Load Classification
  • 2020
  • Ingår i: Brain Sciences. - Switzerland : MDPI AG. - 2076-3425. ; 10:8
  • 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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22.
  • Begum, Shahina, 1977-, et al. (författare)
  • Artificial Intelligence in Predictive Maintenance : A Systematic Literature Review on Review Papers
  • 2024
  • Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 251-261
  • Konferensbidrag (refereegranskat)abstract
    • The fourth industrial revolution, colloquially referred to as “industry 4.0”, has garnered substantial global attention in recent years. There, Artificial intelligence (AI) driven industrial intelligence has been increasingly deployed in predictive maintenance (PdM), emerging as a vital enabler of smart manufacturing and industry 4.0. Since in recent years the number of articles focusing on Artificial Intelligence (AI) in PdM is high a review on the available literature reviews in this domain would be useful for the future researchers who would like to advance the research in this area and also for the persons who would like to apply PdM in their application domains. Therefore, this study identifies the AI revolution in PdM and focuses on the next stages available in the literature reviews in this area by quality assessment of secondary study. A well-known structured review approach (Systematic Literature Review, or SLR) was employed to perform this tertiary study. In addition, the Scale for the Assessment of Narrative Review Articles (SANRA) approach for evaluating the quality of review papers has been employed to support a few of the research questions. Here, This tertiary study scrutinizes four crucial aspects of secondary articles: (1) their specific research domains, (2) the annual trends in the quantity, variety, and quality (3) a footsteps of top researchers, and (4) the research constraints that review articles face during the time frame of 2015 to 2022. The results show that the majority of the application areas are applied to the manufacturing industry. It also leads to the identification of the revolution of AI in PdM as well. Our final findings indicate that Dr. Cheng et al.’s (2022) review has emerged as the predominant source of information in this field. As newcomers or industrial practitioners, we can benefit greatly from following his insights. The final outcome is that there is a lack of progress in SLR formulation and in adding explainable or interpretive AI methodologies in secondary studies.
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23.
  • Bengtsson, Marcus, 1977-, et al. (författare)
  • Combining Ontology and Large Language Models to Identify Recurring Machine Failures in Free-Text Fields
  • 2024
  • Ingår i: Sustainable Production Through Advanced Manufacturing, Intelligent Automation And Work Integrated Learning, Sps 2024. - : IOS Press BV. - 9781643685106 - 9781643685113 ; , s. 27-38
  • Konferensbidrag (refereegranskat)abstract
    • Companies must enhance total maintenance effectiveness to stay competitive, focusing on both digitalization and basic maintenance procedures. Digitalization offers technologies for data-driven decision-making, but many maintenance decisions still lack a factual basis. Prioritizing efficiency and effectiveness require analyzing equipment history, facilitated by using Computerized Maintenance Management Systems (CMMS). However, CMMS data often contains unstructured free-text, leading to manual analysis, which is resource-intensive and reactive, focusing on short time periods and specific equipment. Two approaches are available to solve the issue: minimizing free-text entries or using advanced methods for processing them. Free-text allows detailed descriptions but may lack completeness, while structured reporting aids automated analysis but may limit fault description richness. As knowledge and experience are vital assets for companies this research uses a hybrid approach by combining Natural Language Processing with domain specific ontology and Large Language Models to extract information from free-text entries, enabling the possibility of real-time analysis e.g., identifying recurring failure and knowledge sharing across global sites.
  •  
24.
  • D'Cruze, Ricky Stanley, et al. (författare)
  • A Case Study on Ontology Development for AI Based Decision Systems in Industry
  • 2024
  • Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 693-706
  • Konferensbidrag (refereegranskat)abstract
    • Ontology development plays a vital role as it provides a structured way to represent and organize knowledge. It has the potential to connect and integrate data from different sources, enabling a new class of AI-based services and systems such as decision support systems and recommender systems. However, in large manufacturing industries, the development of such ontology can be challenging. This paper presents a use case of an application ontology development based on machine breakdown work orders coming from a Computerized Maintenance Management System (CMMS). Here, the ontology is developed using a Knowledge Meta Process: Methodology for Ontology-based Knowledge Management. This ontology development methodology involves steps such as feasibility study, requirement specification, identifying relevant concepts and relationships, selecting appropriate ontology languages and tools, and evaluating the resulting ontology. Additionally, this ontology is developed using an iterative process and in close collaboration with domain experts, which can help to ensure that the resulting ontology is accurate, complete, and useful for the intended application. The developed ontology can be shared and reused across different AI systems within the organization, facilitating interoperability and collaboration between them. Overall, having a well-defined ontology is critical for enabling AI systems to effectively process and understand information.
  •  
25.
  • Degas, A., et al. (författare)
  • A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management : Current Trends and Development with Future Research Trajectory
  • 2022
  • Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:3
  • 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.
  •  
26.
  • Giorgi, Andrea, et al. (författare)
  • Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving
  • 2023
  • Ingår i: Frontiers in Neurorobotics. - : FRONTIERS MEDIA SA. - 1662-5218. ; 17
  • Tidskriftsartikel (refereegranskat)abstract
    • The human factor plays a key role in the automotive field since most accidents are due to drivers' unsafe and risky behaviors. The industry is now pursuing two main solutions to deal with this concern: in the short term, there is the development of systems monitoring drivers' psychophysical states, such as inattention and fatigue, and in the medium-long term, there is the development of fully autonomous driving. This second solution is promoted by recent technological progress in terms of Artificial Intelligence and sensing systems aimed at making vehicles more and more accurately aware of their "surroundings." However, even with an autonomous vehicle, the driver should be able to take control of the vehicle when needed, especially during the current transition from the lower (SAE < 3) to the highest level (SAE = 5) of autonomous driving. In this scenario, the vehicle has to be aware not only of its "surroundings" but also of the driver's psychophysical state, i.e., a user-centered Artificial Intelligence. The neurophysiological approach is one the most effective in detecting improper mental states. This is particularly true if considering that the more automatic the driving will be, the less available the vehicular data related to the driver's driving style. The present study aimed at employing a holistic approach, considering simultaneously several neurophysiological parameters, in particular, electroencephalographic, electrooculographic, photopletismographic, and electrodermal activity data to assess the driver's mental fatigue in real time and to detect the onset of fatigue increasing. This would ideally work as an information/trigger channel for the vehicle AI. In all, 26 professional drivers were engaged in a 45-min-lasting realistic driving task in simulated conditions, during which the previously listed biosignals were recorded. Behavioral (reaction times) and subjective measures were also collected to validate the experimental design and to support the neurophysiological results discussion. Results showed that the most sensitive and timely parameters were those related to brain activity. To a lesser extent, those related to ocular parameters were also sensitive to the onset of mental fatigue, but with a delayed effect. The other investigated parameters did not significantly change during the experimental session.
  •  
27.
  • Hurter, C., et al. (författare)
  • Usage of more transparent and explainable conflict resolution algorithm : Air traffic controller feedback
  • 2022
  • Ingår i: Transportation Research Procedia. - : Elsevier B.V.. - 2352-1457 .- 2352-1465. ; 66:C, s. 270-278
  • 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.
  •  
28.
  • Islam, Mir Riyanul, Doctoral Student, 1991-, et al. (författare)
  • A Novel Mutual Information based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning
  • 2020
  • Ingår i: Brain Sciences. - Switzerland : MDPI AG. - 2076-3425. ; 10:8, s. 1-23
  • 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.
  •  
29.
  • Islam, Mir Riyanul, Doctoral Student, 1991-, et al. (författare)
  • Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification
  • 2019
  • Ingår i: Communications in Computer and Information Science, Volume 1107. - Cham : Springer International Publishing. - 9783030324223 ; , s. 121-135
  • Konferensbidrag (refereegranskat)abstract
    • 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.
  •  
30.
  • Islam, Mir Riyanul, et al. (författare)
  • Hypothyroid Disease Diagnosis with Causal Explanation using Case-based Reasoning and Domain-specific Ontology
  • 2019
  • Ingår i: Workshop on CBR in the Health Science WS-HealthCBR.
  • Konferensbidrag (refereegranskat)abstract
    • 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.
  •  
31.
  • Islam, Mir Riyanul, Doctoral Student, 1991-, et al. (författare)
  • Interpretable Machine Learning for Modelling and Explaining Car Drivers' Behaviour : An Exploratory Analysis on Heterogeneous Data
  • 2023
  • Konferensbidrag (refereegranskat)abstract
    • Understanding individual car drivers’ behavioural variations and heterogeneity is a significant aspect of developingcar simulator technologies, which are widely used in transport safety. This also characterizes the heterogeneity in drivers’ behaviour in terms of risk and hurry, using both real-time on-track and in-simulator driving performance features. Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain drivers’ behaviour while being classified and the explanations for them are evaluated. However, the high predictive power of ML algorithms ignore the characteristics of non-stationary domain relationships in spatiotemporal data (e.g., dependence, heterogeneity), which can lead to incorrect interpretations and poor management decisions. This study addresses this critical issue of ‘interpretability’ in ML-based modelling of structural relationships between the events and corresponding features of the car drivers’ behavioural variations. In this work, an exploratory experiment is described that contains simulator and real driving concurrently with a goal to enhance the simulator technologies. Here, initially, with heterogeneous data, several analytic techniques for simulator bias in drivers’ behaviour have been explored. Afterwards, five different ML classifier models were developed to classify risk and hurry in drivers’ behaviour in real and simulator driving. Furthermore, two different feature attribution-based explanation models were developed to explain the decision from the classifiers. According to the results and observation, among the classifiers, Gradient Boosted Decision Trees performed best with a classification accuracy of 98.62%. After quantitative evaluation, among the feature attribution methods, the explanation from Shapley Additive Explanations (SHAP) was found to be more accurate. The use of different metrics for evaluating explanation methods and their outcome lay the path toward further research in enhancing the feature attribution methods.
  •  
32.
  • Islam, Mir Riyanul, Doctoral Student, 1991-, et al. (författare)
  • Investigating Additive Feature Attribution for Regression
  • 2023
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • Feature attribution is a class of explainable artificial intelligence (XAI) methods that produce the contributions of data features to a model's decision. There are multiple accounts stating that feature attribution methods produce inconsistent results and should always be evaluated. However, the existing body of literature on evaluation techniques is still immature with multiple proposed techniques and a lack of widely adopted methods, making it difficult to recognize the best approach for each circumstance. This article investigates an approach to creating synthetic data for regression that can be used to evaluate the results of feature attribution methods. From a real-world dataset, the proposed approach describes how to create synthetic data that preserves the patterns of the original data and enables comprehensive evaluation of XAI methods. This research also demonstrates how global and local feature attributions can be represented in the additive form of case-based reasoning as a benchmark method for evaluation. Finally, this work demonstrates the case where a method that includes a standardization step does not produce feature attributions of the same quality as one that does not use standardization in the context of a regression task.
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33.
  • Islam, Mir Riyanul, Doctoral Student, 1991-, et al. (författare)
  • iXGB : improving the interpretability of XGBoost using decision rules and counterfactuals
  • 2024
  • Ingår i: Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART. - 9789897586804 ; , s. 1345-1353
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Tree-ensemble models, such as Extreme Gradient Boosting (XGBoost), are renowned Machine Learning models which have higher prediction accuracy compared to traditional tree-based models. This higher accuracy, however, comes at the cost of reduced interpretability. Also, the decision path or prediction rule of XGBoost is not explicit like the tree-based models. This paper proposes the iXGB--interpretable XGBoost, an approach to improve the interpretability of XGBoost. iXGB approximates a set of rules from the internal structure of XGBoost and the characteristics of the data. In addition, iXGB generates a set of counterfactuals from the neighbourhood of the test instances to support the understanding of the end-users on their operational relevance. The performance of iXGB in generating rule sets is evaluated with experiments on real and benchmark datasets which demonstrated reasonable interpretability. The evaluation result also supports that the interpretability of XGBoost can be improved without using surrogate methods.
  •  
34.
  • Islam, Mir Riyanul, Doctoral Student, 1991-, et al. (författare)
  • Local and Global Interpretability Using Mutual Information in Explainable Artificial Intelligence
  • 2021
  • Ingår i: 2021 8TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING &amp; MACHINE INTELLIGENCE (ISCMI 2021). - : IEEE. - 9781728186832 ; , s. 191-195
  • Konferensbidrag (refereegranskat)abstract
    • Numerous studies have exploited the potential of Artificial Intelligence (AI) and Machine Learning (ML) models to develop intelligent systems in diverse domains for complex tasks, such as analysing data, extracting features, prediction, recommendation etc. However, presently these systems embrace acceptability issues from the end-users. The models deployed at the back of the systems mostly analyse the correlations or dependencies between the input and output to uncover the important characteristics of the input features, but they lack explainability and interpretability that causing the acceptability issues of intelligent systems and raising the research domain of eXplainable Artificial Intelligence (XAI). In this study, to overcome these shortcomings, a hybrid XAI approach is developed to explain an AI/ML model's inference mechanism as well as the final outcome. The overall approach comprises of 1) a convolutional encoder that extracts deep features from the data and computes their relevancy with features extracted using domain knowledge, 2) a model for classifying data points using the features from autoencoder, and 3) a process of explaining the model's working procedure and decisions using mutual information to provide global and local interpretability. To demonstrate and validate the proposed approach, experimentation was performed using an electroencephalography dataset from road safety to classify drivers' in-vehicle mental workload. The outcome of the experiment was found to be promising that produced a Support Vector Machine classifier for mental workload with approximately 89% performance accuracy. Moreover, the proposed approach can also provide an explanation for the classifier model's behaviour and decisions with the combined illustration of Shapely values and mutual information.
  •  
35.
  • Jmoona, Waleed, et al. (författare)
  • Explaining the Unexplainable : Role of XAI for Flight Take-Off Time Delay Prediction
  • 2023
  • Ingår i: AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676.. - : Springer Science and Business Media Deutschland GmbH. - 9783031341069 ; , s. 81-93
  • Konferensbidrag (refereegranskat)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.
  •  
36.
  • Kabir, Md Alamgir, et al. (författare)
  • CODE : A Moving-Window-Based Framework for Detecting Concept Drift in Software Defect Prediction
  • 2022
  • Ingår i: Symmetry. - : MDPI AG. - 2073-8994. ; 14:12
  • Tidskriftsartikel (refereegranskat)abstract
    • Concept drift (CD) refers to data distributions that may vary after a minimum stable period. CD negatively influences models’ performance of software defect prediction (SDP) trained on past datasets when applied to the new datasets. Based on previous studies of SDP, it is confirmed that the accuracy of prediction models is negatively affected due to changes in data distributions. Moreover, cross-version (CV) defect data are naturally asymmetric due to the nature of their class imbalance. In this paper, a moving window-based concept-drift detection (CODE) framework is proposed to detect CD in chronologically asymmetric defective datasets and to investigate the feasibility of alleviating CD from the data. The proposed CODE framework consists of four steps, in which the first pre-processes the defect datasets and forms CV chronological data, the second constructs the CV defect models, the third calculates the test statistics, and the fourth provides a hypothesis-test-based CD detection method. In prior studies of SDP, it is observed that in an effort to make the data more symmetric, class-rebalancing techniques are utilized, and this improves the prediction performance of the models. The ability of the CODE framework is demonstrated by conducting experiments on 36 versions of 10 software projects. Some of the key findings are: (1) Up to 50% of the chronological-defect datasets are drift-prone while applying the most popular classifiers used from the SDP literature. (2) The class-rebalancing techniques had a positive impact on the prediction performance for CVDP by correctly classifying the CV defective modules and detected CD by up to 31% on the resampled datasets.
  •  
37.
  • Köckemann, Uwe, 1983-, et al. (författare)
  • Open-source data collection and data sets for activity recognition in smart homes
  • 2020
  • Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:3
  • Tidskriftsartikel (refereegranskat)abstract
    • As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.
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38.
  • Rahman, Hamidur, Doctoral Student, 1984-, et al. (författare)
  • Artificial Intelligence-Based Life Cycle Engineering in Industrial Production : A Systematic Literature Review
  • 2022
  • Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 10, s. 133001-133015
  • Forskningsöversikt (refereegranskat)abstract
    • For the last few years, cases of applying artificial intelligence (AI) to engineering activities towards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematically reach higher and productivity levels, owing to its holistic perspective and consideration of economic and environmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE) we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AI techniques, (2) the current AI-improved LCE subfields and (3) the subfields with highly enhanced by AI. A specific set of inclusion and exclusion criteria were used to identify and select academic papers from several fields, i.e. production, logistics, marketing and supply chain and after the selection process described in the paper we ended up with 42 scientific papers. The study and analysis show that there are many AI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, and Infrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall, the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance and Repair are the top explored LCE subfields whereas logistics and Procurement are the least explored subareas. Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strong research funding and focus on Industry 4.0; Germany is standing out with numbers of publications. The in-depth analysis of selected and relevant scientific papers are helpful in getting a more correct picture of the area which enables a more systematic approach to AI-LCE in the future.
  •  
39.
  • Rahman, Hamidur, Doctoral Student, 1984- (författare)
  • Artificial Intelligence for Non-Contact-Based Driver Health Monitoring
  • 2021
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • In clinical situations, a patient’s physical state is often monitored by sensors attached to the patient, and medical staff are alerted if the patient’s status changes in an undesirable or life-threatening direction. However, in unsupervised situations, such as when driving a vehicle, connecting sensors to the driver is often troublesome and wired sensors may not produce sufficient quality due to factors such as movement and electrical disturbance. Using a camera as a non-contact sensor to extract physiological parameters based on video images offers a new paradigm for monitoring a driver’s health and mental state. Due to the advanced technical features in modern vehicles, driving is now faster, safer and more comfortable than before. To enhance transport security (i.e. to avoid unexpected traffic accidents), it is necessary to consider a vehicle driver as a part of the traffic environment and thus monitor the driver’s health and mental state. Such a monitoring system is commonly developed based on two approaches: driving behaviour-based and physiological parameters-based.This research work demonstrates a non-contact approach that classifies a driver’s cognitive load based on physiological parameters through a camera system and vehicular data collected from control area networks considering image processing, computer vision, machine learning (ML) and deep learning (DL). In this research, a camera is used as a non-contact sensor and pervasive approach for measuring and monitoring the physiological parameters. The contribution of this research study is four-fold: 1) Feature extraction approach to extract physiological parameters (i.e. heart rate [HR], respiration rate [RR], inter-beat interval [IBI], heart rate variability [HRV] and oxygen saturation [SpO2]) using a camera system in several challenging conditions (i.e. illumination, motion, vibration and movement); 2) Feature extraction based on eye-movement parameters (i.e. saccade and fixation); 3) Identification of key vehicular parameters and extraction of useful features from lateral speed (SP), steering wheel angle (SWA), steering wheel reversal rate (SWRR), steering wheel torque (SWT), yaw rate (YR), lanex (LAN) and lateral position (LP); 4) Investigation of ML and DL algorithms for a driver’s cognitive load classification. Here, ML algorithms (i.e. logistic regression [LR], linear discriminant analysis [LDA], support vector machine [SVM], neural networks [NN], k-nearest neighbours [k-NN], decision tree [DT]) and DL algorithms (i.e. convolutional neural networks [CNN], long short-term memory [LSTM] networks and autoencoders [AE]) are used. One of the major contributions of this research work is that physiological parameters were extracted using a camera. According to the results, feature extraction based on physiological parameters using a camera achieved the highest correlation coefficient of .96 for both HR and SpO2 compared to a reference system. The Bland Altman plots showed 95% agreement considering the correlation between the camera and the reference wired sensors. For IBI, the achieved quality index was 97.5% considering a 100 ms R-peak error. The correlation coefficients for 13 eye-movement features between non-contact approach and reference eye-tracking system ranged from .82 to .95.For cognitive load classification using both the physiological and vehicular parameters, two separate studies were conducted: Study 1 with the 1-back task and Study 2 with the 2-back task. Finally, the highest average accuracy achieved in terms of cognitive load classification was 94% for Study 1 and 82% for Study 2 using LR algorithms considering the HRV parameter. The highest average classification accuracy of cognitive load was 92% using SVM considering saccade and fixation parameters. In both cases, k-fold cross-validation was used for the validation, where the value of k was 10. The classification accuracies using CNN, LSTM and autoencoder were 91%, 90%, and 90.3%, respectively. This research study shows such a non-contact-based approach using ML, DL, image processing and computer vision is suitable for monitoring a driver’s cognitive state.
  •  
40.
  • Rahman, Hamidur, Doctoral Student, 1984-, et al. (författare)
  • Deep Learning in Remote Sensing : An Application to Detect Snow and Water in Construction Sites
  • 2021
  • Ingår i: Proceedings - 2021 4th International Conference on Artificial Intelligence for Industries, AI4I 2021. - 9781665434102 ; , s. 52-56
  • Konferensbidrag (refereegranskat)abstract
    • It is important for a construction and property development company to know weather conditions in their daily operation. In this paper, a deep learning-based approach is investigated to detect snow and rain conditions in construction sites using drone imagery. A Convolutional Neural Network (CNN) is developed for the feature extraction and performing classification on those features using machine learning (ML) algorithms. Well-known existing deep learning algorithms AlexNet and VGG16 models are also deployed and tested on the dataset. Results show that smaller CNN architecture with three convolutional layers was sufficient at extracting relevant features to the classification task at hand compared to the larger state-of-the-art architectures. The proposed model reached a top accuracy of 97.3% in binary classification and 96.5% while also taking rain conditions into consideration. It was also found that ML algorithms,i.e., support vector machine (SVM), logistic regression and k-nearest neighbors could be used as classifiers using feature maps extracted from CNNs and a top accuracy of 90% was obtained using SVM algorithms.
  •  
41.
  • Rahman, Hamidur, Doctoral Student, 1984-, et al. (författare)
  • Driver’s Cognitive Load Classification based on Eye Movement through Facial Image using Machine Learning
  • Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
    • The driver's cognitive load is considered a good indication if the driver is alert or distracted but determing cognitive load is challenging and the acceptance of wire sensor solutions like EEG and ECG are not not preferred in real-world driving scenario. The recent development of image processing, machine learning, 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. Two different wireless sensor systems, one commercial giving eye position (SmartEye) and one Microsoft LifeCam Studio with resolution 1920 x 1080 were used for data collection. In this paper, two eye movement parameters, saccade, and fixation are investigated through facial images and 13 features are manually extracted. Five machine learning algorithms, support vector machine (SVM), logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), and decision tree (DT), are investigated to classify the cognitive load. According to the results, the SVM model with linear kernel function outperforms the other four classification methods. Here, the achieved average accuracy is 92% using SVM. Again, three deep learning architectures, convolutional neural networks (CNN),  long short-term memory (LSTM), and autoencoder (AE) are designed both for automatic feature extraction and cognitive load classification. The results show that CNN architecture achieves the highest classification accuracy which is 91%.  Besides, the classification accuracy for the extracted eye movement parameters is compared with reference eye tracker signals. It is observed that the classification accuracies between the eye tracker and the camera are very similar. 
  •  
42.
  • Rahman, Hamidur, et al. (författare)
  • Non-contact-based driver's cognitive load classification using physiological and vehicular parameters
  • 2020
  • Ingår i: Biomedical Signal Processing and Control. - : ELSEVIER SCI LTD. - 1746-8094 .- 1746-8108. ; 55
  • Tidskriftsartikel (refereegranskat)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. 
  •  
43.
  • Rahman, Hamidur, et al. (författare)
  • Non-Contact Physiological Parameters Extraction Using Facial Video Considering Illumination, Motion, Movement and Vibration
  • 2020
  • Ingår i: IEEE Transactions on Biomedical Engineering. - : IEEE Computer Society. - 0018-9294 .- 1558-2531. ; 67:1, s. 88-98
  • Tidskriftsartikel (refereegranskat)abstract
    • Objective: In this paper, four physiological parameters, i.e., heart rate (HR), inter-beat-interval (IBI), heart rate variability (HRV), and oxygen saturation (SpO2), are extracted from facial video recordings. Methods: Facial videos were recorded for 10 min each in 30 test subjects while driving a simulator. Four regions of interest (ROIs) are automatically selected in each facial image frame based on 66 facial landmarks. Red-green-blue color signals are extracted from the ROIs and four physiological parameters are extracted from the color signals. For the evaluation, physiological parameters are also recorded simultaneously using a traditional sensor 'cStress,' which is attached to hands and fingers of test subjects. Results: The Bland Altman plots show 95% agreement between the camera system and 'cStress' with the highest correlation coefficient R = 0.96 for both HR and SpO2. The quality index is estimated for IBI considering 100 ms R-peak error; the accumulated percentage achieved is 97.5%. HRV features in both time and frequency domains are compared and the highest correlation coefficient achieved is 0.93. One-way analysis of variance test shows that there are no statistically significant differences between the measurements by camera and reference sensors. Conclusion: These results present high degrees of accuracy of HR, IBI, HRV, and SpO2 extraction from facial image sequences. Significance: The proposed non-contact approach could broaden the dimensionality of physiological parameters extraction using cameras. This proposed method could be applied for driver monitoring application under realistic conditions, i.e., illumination, motion, movement, and vibration.
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44.
  • Rahman, Hamidur, Doctoral Student, 1984-, et al. (författare)
  • Vision-based driver’s cognitive load classification considering eye movement using machine learning and deep learning
  • 2021
  • Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:23
  • 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. 
  •  
45.
  • Rehman, Atiq Ur, et al. (författare)
  • Cognitive Digital Twin in Manufacturing : A Heuristic Optimization Approach
  • 2023
  • Ingår i: IFIP Advances in Information and Communication Technology. - : Springer Science and Business Media Deutschland GmbH. - 9783031341069 ; , s. 441-453
  • Konferensbidrag (refereegranskat)abstract
    • Complex systems that link virtualization and simulation platforms with actual data from industrial processes are vital for the next generation of production. Digital twins are such systems that have several advantages, notably in manufacturing where they can boost productivity throughout the whole manufacturing life-cycle. Enterprises will be able to creatively, efficiently, and effectively leverage implicit information derived from the experience of current production processes, thanks to cognitive digital twins. The development of numerous technologies has made the digital twin notion more competent and sophisticated throughout time. This article proposes a heuristic approach for cognitive digital twin technology as the next development in a digital twin that will aid in the realization of the goal of Industry 4.0. In creating cognitive digital twins, this article suggests the use of a heuristic approach as a possible route to allowing cognitive functionalities. Here, heuristic optimization is proposed as a feature selection tool to enhance the cognitive capabilities of a digital twin throughout the product design phase of production. The proposed approach is validated using the use-case of Power Transfer Unit (PTU) production, which resulted in an improvement of 8.83% in classification accuracy to predict the faulty PTU in the assembly line. This leads to an improved throughput of the PTU assembly line and also saves the resources utilized by faulty PTUs.
  •  
46.
  • Sakao, T., et al. (författare)
  • AI-LCE : Adaptive and Intelligent Life Cycle Engineering by applying digitalization and AI methods-An emerging paradigm shift in Life Cycle Engineering
  • 2021
  • Ingår i: Procedia CIRP. - : Elsevier B.V.. - 2212-8271. ; , s. 571-576
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a vision for a much-needed paradigm shift in Life Cycle Engineering (LCE), which is termed Adaptive and Intelligent Life Cycle Engineering (AI-LCE). To do so, interdisciplinary analysis of literature in domains of AI and LCE is performed. Needed concepts and methods are described: key enabling technologies are Artificial Intelligence (AI), the Internet of Things, and data lakes, which are becoming cost-effective and increasingly implemented in industry. Both artificial and human intelligence are used in combination. Its advantages compared with the conventional LCE include shorter time for changing activities in the lifecycle and the accuracy of changes made.
  •  
47.
  • Sheuly, Sharmin Sultana, et al. (författare)
  • Data Analytics using Statistical Methods and Machine Learning : A Case Study of Power Transfer Units
  • 2021
  • 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
  • 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.
  •  
48.
  • Sheuly, Sharmin Sultana, et al. (författare)
  • Explainable Machine Learning to Improve Assembly Line Automation
  • 2021
  • Ingår i: Proceedings - 2021 4th International Conference on Artificial Intelligence for Industries, AI4I 2021. - 9781665434119 ; , s. 81-85
  • Konferensbidrag (refereegranskat)abstract
    • Faulty manufactured product causes huge economic loss in the manufacturing industry. A local company produces a power transfer unit (PTU) for the vehicle industry and in this production 3% of PTU are rejected due to a mismatch of shim (a small mechanical part supporting PTU). Today the dimension of a shim is predicted manually by human experts. However, there are several problems due to the manual prediction of shim dimension, automatic central control from the cloud cannot be done. Additionally, it increases rejection rates and as a consequence decreases the reliability of the systems. To solve these problems, in this study shim prediction is implemented in the manufacturing of PTU with explainable Machine Learning (ML) which automates the manual shim selection process in the assembly line and explains the ML prediction. A hybrid approach that combines support vector regression (SVR) and k nearest neighbours (kNN) for the first part of the assembly line and Partial Least Squares (PLS) and kNN for the second part of the assembly line is used for shim prediction. The hybrid approach is selected due to better performance compared to the single ML model approach. Then, the most important features of the hybrid approach were identified with SHAP (SHapley Additive exPlanations). The result indicates due to this improved automation faulty PTU rate decreased from 3% to only 1%. Additionally, it enabled control from the cloud and increased reliability. From the explanation of the hybrid approach, it is evident that one of the features values has more impact on the prediction output and controlling this feature will reduce the rejection rate.
  •  
49.
  • Sheuly, Sharmin Sultana, et al. (författare)
  • Machine-Learning-Based Digital Twin in Manufacturing : A Bibliometric Analysis and Evolutionary Overview
  • 2022
  • Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 12:13
  • Tidskriftsartikel (refereegranskat)abstract
    • The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.
  •  
50.
  • Sheuly, Sharmin Sultana, et al. (författare)
  • Quantitative Performance Analysis from Discrete Perspective : A Case Study of Chip Detection in Turning Process
  • 2023
  • Ingår i: International Conference on Agents and Artificial Intelligence. - : Science and Technology Publications, Lda. ; , s. 368-379
  • Konferensbidrag (refereegranskat)abstract
    • Good performance of the Machine Learning (ML) model is an important requirement associated with ML-integrated manufacturing. An increase in performance improvement methods such as hyperparameter tuning, data size increment, feature extraction, and architecture change leads to random attempts while improving performance. This can result in unnecessary consumption of time and performance improvement solely depending on luck. In the proposed study, a quantitative performance analysis on the case study of chip detection is performed from six perspectives: hyperparameter change, feature extraction method, data size increment, and concatenated Artificial Neural Network (ANN) architecture. The focus of the analysis is to create a consolidated knowledge of factors affecting ML model performance in turning process quality prediction. Metal peels such as chips are designed at the time of metal cutting (turning process) and the shape of these chips indicates the quality of the turning process. The result of the proposed study shows that following a fixed recipe does not always improve performance. In the case of performance improvement, data quality plays the main role. Additionally, the choice of an ML algorithm and hyperparameter tuning plays an essential role in performance.
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