Malmö University Publications
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  • 1.
    Munir, Hussan
    et al.
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Vogel, Bahtijar
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Jacobsson, Andreas
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision2022In: Information, E-ISSN 2078-2489, Vol. 13, no 4, article id 203Article, review/survey (Refereed)
    Abstract [en]

    The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature's themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

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  • 2.
    Vogel, Bahtijar
    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).
    Kajtazi, Miranda
    Department of Informatics, Lund University.
    Bugeja, Joseph
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Varshney, Rimpu
    Department of Security, Booking.com.
    Openness and Security Thinking Characteristics for IoT Ecosystems2020In: Information, E-ISSN 2078-2489, Vol. 11, no 12Article in journal (Refereed)
    Abstract [en]

    While security is often recognized as a top priority for organizations and a push for competitive advantage, repeatedly, Internet of Things (IoT) products have become a target of diverse security attacks. Thus, orchestrating smart services and devices in a more open, standardized and secure way in IoT environments is yet a desire as much as it is a challenge. In this paper, we propose a model for IoT practitioners and researchers, who can adopt a sound security thinking in parallel with open IoT technological developments. We present the state-of-the-art and an empirical study with IoT practitioners. These efforts have resulted in identifying a set of openness and security thinking criteria that are important to consider from an IoT ecosystem point of view. Openness in terms of open standards, data, APIs, processes, open source and open architectures (flexibility, customizability and extensibility aspects), by presenting security thinking tackled from a three-dimensional point of view (awareness, assessment and challenges) that highlight the need to develop an IoT security mindset. A novel model is conceptualized with those characteristics followed by several key aspects important to design and secure future IoT systems.

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    fulltext
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