Malmö University Publications
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  • 1.
    Jamali, Mahtab
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Khoshkangini, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Ljungqvist, Martin Georg
    Axis Communications AB, Lund, Sweden.
    Mihailescu, Radu-Casian
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Specialized Indoor and Outdoor Scene-specific Object Detection Models2023In: Sixteenth International Conference on Machine Vision (ICMV 2023) / [ed] Osten, Wolfgang, 2023Conference paper (Refereed)
    Abstract [en]

    Object detection is a critical task in computer vision with applications across various domains, ranging from autonomous driving to surveillance systems. Despite extensive research on improving the performance of object detection systems, identifying all objects in different places remains a challenge. The traditional object detection approaches focus primarily on extracting and analyzing visual features without considering the contextual information about the places of objects. However, entities in many real-world scenarios closely relate to their surrounding environment, providing crucial contextual cues for accurate detection. This study investigates the importance and impact of places of images (indoor and outdoor) on object detection accuracy. To this purpose, we propose an approach that first categorizes images into two distinct categories: indoor and outdoor. We then train and evaluate three object detection models (indoor, outdoor, and general models) based on YOLOv5 and 19 classes of the PASCAL VOC dataset and 79 classes of COCO dataset that consider places. The experimental evaluations show that the specialized indoor and outdoor models have higher mAP (mean Average Precision) to detect objects in specific environments compared to the general model that detects objects found both indoors and outdoors. Indeed, the network can detect objects more accurately in similar places with common characteristics due to semantic relationships between objects and their surroundings, and the network’s misdetection is diminished. All the results were analyzed statistically with t-tests.

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  • 2.
    Khoshkangini, Reza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Mashhadi, Peyman
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden.
    Tegnered, Daniel
    Volvo Group Connected Solutions, Gothenburg, Sweden.
    Lundström, Jens
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden.
    Rögnvaldsson, Thorsteinn
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden.
    Predicting Vehicle Behavior Using Multi-task Ensemble Learning2023In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 212, p. 118716-118716, article id 118716Article in journal (Refereed)
    Abstract [en]

    Vehicle utilization analysis is an essential tool for manufacturers to understand customer needs, improve equipment uptime, and to collect information for future vehicle and service development. Typically today, this behavioral modeling is done on high-resolution time-resolved data with features such as GPS position and fuel consumption. However, high-resolution data is costly to transfer and sensitive from a privacy perspective. Therefore, such data is typically only collected when the customer pays for extra services relying on that data. This motivated us to develop a multi-task ensemble approach to transfer knowledge from the high-resolution data and enable vehicle behavior prediction from low-resolution but high dimensional data that is aggregated over time in the vehicles. This study proposes a multi-task snapshot-stacked ensemble (MTSSE) deep neural network for vehicle behavior prediction by considering vehicles’ low-resolution operational life records. The multi-task ensemble approach utilizes the measurements to map the low-frequency vehicle usage to the vehicle behaviors defined from the high-resolution time-resolved data. Two data sources are integrated and used: high-resolution data called Dynafleet, and low-resolution so-called Logged Vehicle Data (LVD). The experimental results demonstrate the proposed approach’s effectiveness in predicting the vehicle behavior from low frequency data. With the suggested multi-task snapshot-stacked ensemble deep network, it is shown how low-resolution sensor data can highly contribute to predicting multiple vehicle behaviors simultaneously while using only one single training process.

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  • 3.
    Khoshkangini, Reza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Rani Kalia, Nidhi
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden.
    Ashwathanarayana, Sachin
    Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden.
    Orand, Abbas
    Arriver Software AB, a Qualcomm Company, Linköping, Sweden.
    Maktobian, Jamal
    Information and Communication Technology, University of Tasmania, Hobart, Tasmania, Australia.
    Tajgardan, Mohsen
    Faculty of Electrical and Computer Engineering Qom University of Technology, Qom University.
    Vehicle Usage Extraction Using Unsupervised Ensemble Approach2022In: Proceedings of SAI Intelligent Systems Conference, Springer, 2022, p. 588-604Conference paper (Refereed)
    Abstract [en]

    Current heavy vehicles are equipped with hundreds of sensors that are used to continuously collect data in motion. The logged data enables researchers and industries to address three main transportation issues related to performance (e.g. fuel consumption, breakdown), environment (e.g., emission reduction), and safety (e.g. reducing vehicle accidents and incidents during maintenance activities). While according to the American Transportation Research Institute (ATRI), the operational cost of heavy vehicles is around 59%59% of overall costs, there are limited studies demonstrating the specific impacts of external factors (e.g. weather and road conditions, driver behavior) on vehicle performance. In this work, vehicle usage modeling was studied based on time to determine the different usage styles of vehicles and how they can affect vehicle performance. An ensemble clustering approach was developed to extract vehicle usage patterns and vehicle performance taking into consideration logged vehicle data (LVD) over time. Analysis results showed a strong correlation between driver behavior and vehicle performance that would require further investigation.

  • 4.
    Khoshkangini, Reza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP). Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, S-30118 Halmstad, Sweden..
    Tajgardan, Mohsen
    Qom Univ Technol, Fac Elect & Comp Engn, Qom 151937195, Iran..
    Lundström, Jens
    Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, S-30118 Halmstad, Sweden..
    Rabbani, Mahdi
    Univ New Brunswick UNB, Canadian Inst Cybersecur CIC, Fredericton, NB E3B 9W4, Canada..
    Tegnered, Daniel
    Volvo Grp Connected Solut, S-41756 Gothenburg, Sweden..
    A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 12, article id 5621Article in journal (Refereed)
    Abstract [en]

    Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system's effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

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  • 5.
    Khoshkangini, Reza
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP). Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, Halmstad, Sweden..
    Tajgardan, Mohsen
    Qom Univ Technol, Fac Elect & Comp Engn, Qom, Iran..
    Mashhadi, Peyman
    Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, Halmstad, Sweden..
    Rognvaldsson, Thorsteinn
    Halmstad Univ, Ctr Appl Intelligent Syst Res CAISR, Halmstad, Sweden..
    Tegnered, Daniel
    Volvo Grp Connected Solut, Gothenburg, Sweden..
    Optimal Task Grouping Approach in Multitask Learning2024In: Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VI / [ed] Luo, B Wu, ZG Cheng, C Li, H Li, C, Springer, 2024, Vol. 14452, p. 206-225Conference paper (Refereed)
    Abstract [en]

    Multi-task learning has become a powerful solution in which multiple tasks are trained together to leverage the knowledge learned from one task to improve the performance of the other tasks. However, the tasks are not always constructive on each other in the multi-task formulation and might play negatively during the training process leading to poor results. Thus, this study focuses on finding the optimal group of tasks that should be trained together for multi-task learning in an automotive context. We proposed a multi-task learning approach to model multiple vehicle long-term behaviors using low-resolution data and utilized gradient descent to efficiently discover the optimal group of tasks/vehicle behaviors that can increase the performance of the predictive models in a single training process. In this study, we also quantified the contribution of individual tasks in their groups and to the other groups' performance. The experimental evaluation of the data collected from thousands of heavy-duty trucks shows that the proposed approach is promising.

  • 6.
    Zolfaghari, Mahshid
    et al.
    University of Bojnord,Computer Engineering Department,Bojnord,Iran.
    Fadishei, Hamid
    University of Bojnord,Computer Engineering Department,Bojnord,Iran.
    Tajgardan, Mohsen
    Qom University of Technology,Faculty of Electrical and Computer Engineering,Qom,Iran.
    Khoshkangini, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Stock Market Prediction Using Multi-Objective Optimization2022In: 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper (Refereed)
    Abstract [en]

    Forecasting in financial markets is challenging due to the inherent randomness of financial data sources and the vast number of factors that affect the market trends. Thus, it is essential to find informative elements within the vast number of available factors to enhance the performance of the predictive models in such a vital context. This makes the feature selection process an integral part of the financial prediction. In this paper, we propose a multi-objective evolutionary algorithm to reduce the number of features employed to predict the yearly performance of the US stock market. The primary idea is to select a smaller set of features with the slightest similarity and the best prediction accuracy. In this practice, we have utilized genetic algorithm, XGBoost and correlation in order to obtain a more diverse set of features which increases the performance. Experiential results show that our proposed approach is able to reduce the number of features significantly while maintaining comparable prediction accuracy.

1 - 6 of 6
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