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  • 1.
    Barua, Shaibal
    et al.
    Malardalen Univ, Sweden.
    Uddin Ahmed, Mobyen Uddin
    Malardalen Univ, Sweden.
    Ahlström, Christer
    Linköping University, Department of Biomedical Engineering, Physiological Measurements. Linköping University, Faculty of Science & Engineering. Swedish Natl Rd and Transport Res Inst VTI, SE-58195 Linkoping, Sweden.
    Begum, Shahina
    Malardalen Univ, Sweden.
    Automatic driver sleepiness detection using EEG, EOG and contextual information2019In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 115, p. 121-135Article in journal (Refereed)
    Abstract [en]

    The many vehicle crashes that are caused by driver sleepiness each year advocates the development of automated driver sleepiness detection (ADSD) systems. This study proposes an automatic sleepiness classification scheme designed using data from 30 drivers who repeatedly drove in a high-fidelity driving simulator, both in alert and in sleep deprived conditions. Driver sleepiness classification was performed using four separate classifiers: k-nearest neighbours, support vector machines, case-based reasoning, and random forest, where physiological signals and contextual information were used as sleepiness indicators. The subjective Karolinska sleepiness scale (KSS) was used as target value. An extensive evaluation on multiclass and binary classifications was carried out using 10-fold cross-validation and leave-one-out validation. With 10-fold cross-validation, the support vector machine showed better performance than the other classifiers (79% accuracy for multiclass and 93% accuracy for binary classification). The effect of individual differences was also investigated, showing a 10% increase in accuracy when data from the individual being evaluated was included in the training dataset. Overall, the support vector machine was found to be the most stable classifier. The effect of adding contextual information to the physiological features improved the classification accuracy by 4% in multiclass classification and by and 5% in binary classification. (C) 2018 Elsevier Ltd. All rights reserved.

  • 2.
    Gerdes, Mike
    Philotech GmbH, Buxtehude, Germany.
    Decision trees and genetic algorithms for condition monitoring forecasting of aircraft air conditioning2013In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 40, no 12, p. 5021-5026Article in journal (Refereed)
    Abstract [en]

    Unscheduled maintenance of aircraft can cause significant costs. The machine needs to be repaired before it can operate again. Thus it is desirable to have concepts and methods to prevent unscheduled maintenance. This paper proposes a method for forecasting the condition of aircraft air conditioning system based on observed past data. Forecasting is done in a point by point way, by iterating the algorithm. The proposed method uses decision trees to find and learn patterns in past data and use these patterns to select the best forecasting method to forecast future data points. Forecasting a data point is based on selecting the best applicable approximation method. The selection is done by calculating different features/attributes of the time series and then evaluating the decision tree. A genetic algorithm is used to find the best feature set for the given problem to increase the forecasting performance. The experiments show a good forecasting ability even when the function is disturbed by noise.

  • 3.
    Saar de Moraes, Rodrigo
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fed Univ Rio Grande do Sul UFRGS, Brazil.
    de Freitas, Edison P.
    Fed Univ Rio Grande do Sul UFRGS, Brazil.
    Experimental analysis of heuristic solutions for the moving target traveling salesman problem applied to a moving targets monitoring system2019In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 136, p. 392-409Article in journal (Refereed)
    Abstract [en]

    The Traveling Salesman Problem (TSP) is an important problem in computer science which consists in finding a path linking a set of cities so that each of then can be visited once, before the traveler comes back to the starting point. This is highly relevant because several real world problems can be mapped to it. A special case of TSP is the one in which the cities (the points to be visited) are not static as the cities, but mobile, changing their positions as the time passes. This variation is known as Moving Target TSP (MT-TSP). Emerging systems for crowd monitoring and control based on unmanned aerial vehicles (UAVs) can be mapped to this variation of the TSP problem, as a number of persons (targets) in the crowd can be assigned to be monitored by a given number of UAVs, which by their turn divide the targets among them. These target persons have to be visited from time to time, in a similar way to the cities in the traditional TSP. Aiming at finding a suitable solution for this type of crowd monitoring application, and considering the fact that exact solutions are too complex to perform in a reasonable time, this work explores and compares different heuristic methods for the intended solution. The performed experiments showed that the Genetic Algorithms present the best performance in finding acceptable solutions for the problem in restricted time and processing power situations, performing better compared to Ant Colony Optimization and Simulated Annealing Algorithms. (C) 2019 Published by Elsevier Ltd.

  • 4.
    Åström, Freddie
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Koker, Rasit
    Engineering Faculty Esentepe Kampus, Computer Engineering Department, Sakarya University, Turkey.
    A parallel neural network approach to prediction of Parkinson´s Disease2011In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 38, no 10, p. 12470-12474Article in journal (Refereed)
    Abstract [en]

    Recently the neural network based diagnosis of medical diseases has taken a great deal of attention. In this paper a parallel feed-forward neural network structure is used in the prediction of Parkinson’s Disease. The main idea of this paper is using more than a unique neural network to reduce the possibility of decision with error. The output of each neural network is evaluated by using a rule-based system for the final decision. Another important point in this paper is that during the training process, unlearned data of each neural network is collected and used in the training set of the next neural network. The designed parallel network system significantly increased the robustness of the prediction. A set of nine parallel neural networks yielded an improvement of 8.4% on the prediction of Parkinson’s Disease compared to a single unique network. Furthermore, it is demonstrated that the designed system, to some extent, deals with the problems of imbalanced data sets.

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