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  • 1.
    Aghanavesi, Somayeh
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Memedi, Mevludin
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Feasibility of using smartphones for quantification of Parkinson’s disease motor states during hand rotation tests2019Conference paper (Refereed)
  • 2.
    Aghanavesi, Somayeh
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Memedi, Mevludin
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Nyholm, Dag
    Westin, Jerker
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Verification of a method for measuring Parkinson’s disease related temporal irregularity in spiral drawings2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 10, article id 2341Article in journal (Refereed)
    Abstract [en]

    Parkinson's disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.

  • 3.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Something old, something new, something borrowed, something blue: Part 3 - An elephant never forgets2017In: Journal of Intelligent Systems, ISSN 0334-1860, E-ISSN 2191-026X, Vol. 26, no 3, p. 433-437Article in journal (Refereed)
    Abstract [en]

    Forgetting is an oft-forgotten art. Many artificial intelligence (AI) systems deliver good performance when first implemented; however, as the contextual environment changes, they become out of date and their performance degrades. Learning new knowledge is part of the solution, but forgetting outdated facts and information is a vital part of the process of renewal. However, forgetting proves to be a surprisingly difficult concept to either understand or implement. Much of AI is based on analogies with natural systems, and although all of us have plenty of experiences with having forgotten something, as yet we have only an incomplete picture of how this process occurs in the brain. A recent judgment by the European Court concerns the "right to be forgotten" by web index services such as Google. This has made debate and research into the concept of forgetting very urgent. Given the rapid growth in requests for pages to be forgotten, it is clear that the process will have to be automated and that intelligent systems of forgetting are required in order to meet this challenge.

  • 4.
    Elf, Marie
    et al.
    Chalmers Tekniska Högskola.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    The role for simulation in the design of new health care environments2003In: The 8th International Congress in Nursing Informatics - NI2003, Rio de Janeiro, Brazil, 2003Conference paper (Refereed)
  • 5.
    Grek, Åsa
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Hartwig, Fredrik
    Dalarna University, School of Technology and Business Studies, Business Administration and Management.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Auxiliary variables for nonresponse adjustment in business surveysManuscript (preprint) (Other academic)
  • 6.
    Grek, Åsa
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Hartwig, Fredrik
    Dalarna University, School of Technology and Business Studies, Business Administration and Management.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Determinants of debt leverage ratios in Swedish listed companiesManuscript (preprint) (Other academic)
  • 7.
    Hansson, Karl
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Fleyeh, Hasan
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Machine Learning Algorithms in Heavy Process Manufacturing2016In: American Journal of Intelligent Systems, ISSN 2165-8978, E-ISSN 2165-8994, Vol. 6, no 1, p. 1-13Article in journal (Refereed)
    Abstract [en]

    In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.

  • 8.
    Jomaa, Diala
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Yella, Siril
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A comparative study between vehicle activated signs and speed indicator devices2017In: Transportation Research Procedia, ISSN 2324-9935, E-ISSN 2352-1465, Vol. 22, p. 115-123Article in journal (Refereed)
    Abstract [en]

    Vehicle activated signs and Speed indicator devices are safety signs used to warn and remind drivers that they are exceeding the speed limit on a particular road segment. This article has analysed and compared such signs with the aim of reporting the most suitable sign for relevant situations. Vehicle speeds were recorded at different test sites and the effects of the signs were studied by analyzing the mean and standard deviation. Preliminary results from the work indicate that both types of signs have variable effects on the mean and standard deviation of speed on a given road segment. Speed indicator devices were relatively more effective than vehicle activated signs on local roads; in contrast their effectivity was only comparable when tested on highways.

  • 9.
    Yella, Siril
    et al.
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Nyberg, Roger G
    Dalarna University, School of Technology and Business Studies, Information Systems.
    Gupta, Narendra K.
    Edinburgh Napier University.
    Dougherty, Mark
    Dalarna University, School of Technology and Business Studies, Computer Engineering.
    Reliability of manual assessments in determining the types of vegetation on railway tracks2015In: Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part II / [ed] Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y., Springer, 2015, Vol. 9149, p. 391-399Conference paper (Refereed)
    Abstract [en]

    Current day vegetation assessments within railway maintenance are (to a large extent) carried out manually. This study has investigated the reliability of such manual assessments by taking three non-domain experts into account. Thirty-five track images under different conditions were acquired for the purpose. For each image, the raters’ were asked to estimate the cover of woody plants, herbs and grass separately (in %) using methods such as aerial canopy cover, aerial foliar cover and sub-plot frequency. Visual estimates of raters’ were recorded and analysis-of-variance tests on the mean cover estimates were investigated to see whether if there were disagreements between the raters’.  In tra-correl ation coefficient was used to study the differences between the estimates. Results achieved in this work revealed that seven out of the nine analysis-of-variance tests conducted in this study have demonstrated significant difference in the mean estimates of cover (p < 0.05).

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