Blar i Fakultet for teknologi, kunst og design (TKD) på emneord "Machine learning"

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    • Advanced passive operating system fingerprinting using machine learning and deep learning 

      Hagos, Desta Haileselassie; Løland, Martin V.; Yazidi, Anis; Kure, Øivind; Engelstad, Paal E. (International Conference on Computer Communications and Networks (ICCCN); 2020 29th International Conference on Computer Communications and Networks (ICCCN), Journal article; Peer reviewed, 2020-09-30)
      Securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real-time. An accurate passive Operating System (OS) fingerprinting plays a critical role ...

    • Anomaly Detection in Cellular IoT with Machine Learning 

      Santos, Bernardo; Khan, Imran Qayyrm; Dzogovic, Bruno; Feng, Boning; Do, Thuan Van; Jacot, Niels; Do, van Thanh (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;Volume 401, Conference object, 2021-01-01)
      The number of Internet of Things (IoT) devices used in eldercare are increasing day by day and bringing big security challenges especially for health care organizations, IoT service providers and most seriously for the ...

    • Artificial Intelligence Based Approach for Predicting Fatigue Strength Using Composition and Process Parameters 

      Keprate, Arvind; Ratnayake Mudiyanselage, Chandima (International Conference on Offshore Mechanics and Arctic Engineering;Volume 3: Materials Technology, Conference object, 2020-12-18)
      Accurate prediction of the fatigue strength of steels is vital, due to the extremely high cost (and time) of fatigue testing and the often fatal consequences of fatigue failures. The work presented in this paper is an ...

    • Artificial intelligence in dry eye disease 

      Storås, Andrea Marheim; Strumke, Inga; Riegler, Michael Alexander; Grauslund, Jakob; Hammer, Hugo Lewi; Yazidi, Anis; Halvorsen, Pål; Gundersen, Kjell Gunnar; Utheim, Tor Paaske; Jackson, Catherine Joan (The ocular surface;Volume 23, January 2022, Peer reviewed; Journal article, 2021-12-01)
      Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. ...

    • Artificial intelligence in the fertility clinic: status, pitfalls and possibilities 

      Riegler, Michael Alexander; Stensen, Mette Haug; Witczak, Oliwia; Andersen, Jorunn Marie; Hicks, Steven; Hammer, Hugo Lewi; Delbarre, Erwan; Halvorsen, Pål; Yazidi, Anis; Holst, Nicolai; Haugen, Trine B. (Human Reproduction;Volume 36, Issue 9, Peer reviewed; Journal article, 2021-07-29)
      In recent years, the amount of data produced in the field of assisted reproduction technology [ART] has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, ...

    • Automated reporting system using deep convolutional neural network in the medical domain 

      Subedi, Matrika (MAUU;2021, Master thesis, 2021)
      Nowadays, in the healthcare sector, a massive volume of medical data sources is available. The data is growing at 153 Exabytes in 2013 and an estimated 2,314 exabytes in 2020 (Turner, Gantz et al. 2014). The medical data ...

    • Automatic Detection of Hateful Comments in Online Discussion 

      Hammer, Hugo Lewi (Peer reviewed; Journal article, 2016)
      Making violent threats towards minorities like immigrants or homosexuals is increasingly common on the Internet. We present a method to automatically detect threats of violence using machine learn- ing. A material of ...

    • Automatic detection of hateful comments in online discussion 

      Hammer, Hugo Lewi (Journal article; Peer reviewed, 2017)
      Making violent threats towards minorities like immigrants or homosexuals is increasingly common on the Internet. We present a method to automatically detect threats of violence using machine learning. A material of 24,840 ...

    • Automatic security classification by machine learning for cross-domain information exchange 

      Hammer, Hugo Lewi; Kongsgård, Kyrre Wahl; Yazidi, Anis; Bai, Aleksander; Nordbotten, Nils Agne; Engelstad, Paal E. (MILCOM IEEE Military Communications Conference;, Journal article; Peer reviewed, 2015)
      Cross-domain information exchange is necessary to obtain information superiority in the military domain, and should be based on assigning appropriate security labels to the information objects. Most of the data found ...

    • Autonomous configuration of network parameters in operating systems using evolutionary algorithms 

      Gembala, Bartosz Gembala; Yazidi, Anis; Haugerud, Hårek; Nichele, Stefano (Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems;, Chapter; Peer reviewed, 2018)
      By default, the Linux network stack is not configured for highspeed large file transfer. The reason behind this is to save memory resources. It is possible to tune the Linux network stack by increasing the network buffers ...

    • Can depth data improve the accuracy when classifying mops? 

      Blomdal, Stian (ACIT;2022, Master thesis, 2022)
      Since the emergence of low cost RGB-D cameras, a new world of possibilities have opened up in the field of Computer Vision. This projects focuses on both the practical and theoretical part of how depth data can improve ...

    • Cross-Subject emotion recognition from EEG using Convolutional Neural Networks 

      Zhong, Xiaolong; Yin, Zhong; Zhang, Jianhua (Chinese Control Conference (CCC);2020 39th Chinese Control Conference (CCC), Peer reviewed; Journal article, 2020-09-09)
      Using electroencephalogram (EEG) signals for emotion detection has aroused widespread research concern. However, across subjects emotional recognition has become an insurmountable gap which researchers cannot step across ...

    • Deep Learning for Classifying Physical Activities from Accelerometer Data 

      Nunavath, Vimala; Johansen, Sahand; Johannessen, Tommy Sandtorv; Jiao, Lei; Hansen, Bjørge Hermann; Stølevik, Sveinung Berntsen; Goodwin, Morten (Sensors;Volume 21 / Issue 16, Peer reviewed; Journal article, 2021-08-18)
      Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening ...

    • DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine 

      Thambawita, Vajira; Isaksen, Jonas L.; Hicks, Steven A.; Ghouse, Jonas; Ahlberg, Gustav; Linneberg, Allan; Grarup, Niels; Ellervik, Christina; Olesen, Morten Salling; Hansen, Torben; Graff, Claus; Holstein-Rathlou, Niels-Henrik; Strümke, Inga; Hammer, Hugo L.; Maleckar, Mary M.; Halvorsen, Pål; Riegler, Michael A.; Kanters, Jørgen K. (Scientific Reports;11, Article number: 21896 (2021), Peer reviewed; Journal article, 2021-11-09)
      Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic ...

    • DeepSynthBody: the beginning of the end for data deficiency in medicine 

      Thambawita, Vajira Lasantha Bandara (OsloMet Avhandling 2021;Nr. 45, Doctoral thesis; Peer reviewed, 2021)
      Recent advancements in technology have made arti ficial intelligence (AI) a popular tool in the medical domain, especially machine learning (ML) methods, which is a subset of AI. In this context, a goal is to research and ...

    • Design of Reinforced Concrete Isolated Footings Under Axial Loading with Artificial Neural Networks 

      Ramirez, German Solorzano; Plevris, Vagelis (EUROGEN;Proceedings of the 14th International Conference on Evolutionary and Deterministic Methods For Design, Optimization and Control, Conference object, 2021)
      In engineering practice, the design of structural elements is a repetitive task that has proven to be difficult to fully automate. This is mainly because of the complex relations of the design variables and the multiple ...

    • Detection of DNS tunneling in mobile networks using machine learning 

      Do, Van Thuan; Engelstad, Paal E.; Feng, Boning; Do, van Thanh (Lecture Notes in Electrical Engineering;Information Science and Applications 2017, Volume 424, Journal article; Peer reviewed, 2017)
      Lately, costly and threatening DNS tunnels on the mobile networks bypassing the mobile operator’s Policy and Charging Enforcement Function (PCEF), has shown the vulnerability of the mobile networks caused by the Domain ...

    • Dyadic Aggregated Autoregressive Model (DASAR) for Automatic Modulation Classification 

      Pinto-Orellana, Marco Antonio; Hammer, Hugo Lewi (IEEE Access;Volume 8, Journal article; Peer reviewed, 2020-08-27)
      In this article, we presented a novel spectral estimation method, the dyadic aggregated autoregressive model (DASAR), that characterizes the spectrum dynamics of a modulated signal. DASAR enhances automatic modulation ...

    • Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review 

      Zhang, Jianhua; Yin, Zhong; Chen, Peng; Nichele, Stefano (Information Fusion;Volume 59, July 2020, Peer reviewed; Journal article, 2020-01-31)
      In recent years, the rapid advances in machine learning (ML) and information fusion has made it possible to endow machines/computers with the ability of emotion understanding, recognition, and analysis. Emotion recognition ...

    • Evading a Machine Learning-based Intrusion Detection System through Adversarial Perturbations 

      Fladby, Torgeir; Haugerud, Hårek; Nichele, Stefano; Begnum, Kyrre; Yazidi, Anis (RACS: Research in Applied Computation Symposium; RACS '20: International Conference on Research in Adaptive and Convergent Systems, Chapter; Peer reviewed; Conference proceeding, 2020-10)
      Machine-learning based Intrusion Detection and Prevention Systems provide significant value to organizations because they can efficiently detect previously unseen variations of known threats, new threats related to known ...