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Department of Physical Science: Recent submissions

  • Vigneswaran, R.; Thilaganathan, S. (Advances in mathematical sciences, knowvel, 2019-03-31)
    We consider a phase space stability error control for numerical simulation of dynamical systems. Standard adaptive algorithm used to solve the linear systems perform well during the finite time of integration with fixed ...
  • Vigneswaran, R.; Thilaganathan, S. (World Academy of Science, Engineering and Technology, 2016-05-12)
    Variable time-stepping algorithms for solving dynamical systems performed poorly for long time computations which pass close to a fixed point. To overcome this difficulty, several authors considered phase space error ...
  • Kirushnath, S.; Kabaso, B. (IEEE, 2018-07-24)
    Driving overloaded vehicle causes road infrastructural damages, accidents, air pollution by excessive fuel consumption, and unusual expenses. Measuring the gross weight of a vehicle on a particular road segment without ...
  • Chowuraya, T.; Kirushanth, S.; Kabaso, B. (Academic Conference International (ACI), 2018-11-12)
    Information is an important part of creating knowledge. We live at a time when so much information is created. Unfortunately, much of the information is redundant. There is huge amount of online information in the form of ...
  • Xolani, V.; Kirushanth, S.; Boniface, K. (Academic Conference International (ACI), 2018-11-12)
    With the easy availability of mobile devices and affordable bandwidth, there has been a proliferation of sharing of perceived expert information, with unreliable source, which have often not been tested in a specialized ...
  • Kirushanth, S.; Kabaso, B. (IEEE, 2018-07-24)
    Road Safety is a major concern around the world. Telematics solutions have been available for more than a decade, and several studies have been done in the use of telematics data in road safety. However, these studies are ...
  • Anparasy, Sivaanpu; Kokul, Thanikasalam (University of Colombo School of Computing, 31-07-21)
    Extensive presence of fog in outdoor images severely alters the scene appearance and hence reduces the visibility. Image processing based defogging algorithms are used to restore the details and colour in a single foggy ...
  • Anparasy, S.; Kokul, T. (IEEE, 10-12-21)
    Quality of underwater images are reduced with the depth since light is absorbed and scattered by the suspended particles in water. Although several optical model-based and model-free underwater image enhancement methods ...
  • Amrithaa, P.; Shorubiga, P.; Thanoojan, T.; Kartheeswaran, T. (IEEE, 03-12-20)
    Electricity is one of the inevitable sources of energy in our day to day life. Almost every single device we use from the moment we wake up until going to bed is electrically powered. Meanwhile, there is always a problem ...
  • Kokul, T.; Anparasy, S. (IEEE, 19-01-21)
    Quality of outdoor images are often degraded under bad weather conditions by the extensive presence of suspended particles in the atmosphere such as fog and haze. Although, many single image defogging approaches have been ...
  • Kayanan, M.; Wijekoon, P. (Postgraduate Institute of Science , University of Peradeniya, Sri Lanka, 09-11-18)
    It is well-known that the Ridge Estimator has been used as an alternative estimator for Ordinary Least Squared Estimator (OLSE) to handle multicollinearity problem in the linear regression model. However, it introduces ...
  • Kayanan, M.; Kanaganathan, S. (South Eastern University of Sri Lanka, 06-07-13)
    Parallel algorithm may be executed as a piece at a time on many processing devices, and then return results at the end. A sparse matrix is huge and consists many zero elements. Its execution process is extremely time-consuming. ...
  • Kayanan, M.; Wijekoon, P. (Postgraduate Institute of Science , University of Peradeniya, Sri Lanka, 11-10-19)
    Least Absolute Shrinkage and Selection Operator (LASSO) method has been used for variable selection in the linear regression model when multicollinearity exists among the predictor variables. A popular algorithm to find ...
  • Kayanan, M.; Wijekoon, P. (Postgraduate Institute of Science , University of Peradeniya, Sri Lanka, 08-09-17)
    A significance attention has been paid by researchers about misspecification due to excluding some relevant explanatory variables and multicollinearity among the explanatory variables in linear regression model. Several ...
  • Kayanan, M.; Wijekoon, P. (International Statistical Institute, 21-08-19)
    The Ordinary Least Square Estimator (OLSE) has been widely used to estimate unknown parameters in the linear regression model. Since OLSE produces high variance on the estimates when multicollinearity exists among the ...
  • Kayanan, M.; Kuhanesan, S. (South Eastern University of Sri Lanka, 20-12-16)
    The study examined the knowledge gathering and study pattern as contemplate example of undergraduates of Vavuniya Campus of the University of Jaffna. Precisely, the analysis made to determine the impact of gender and level ...
  • Kayanan, M.; Wijekoon, P. (Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Sri Lanka, 09-01-20)
    Autism Spectrum Disorder (ASD) is known as a neurodevelopmental disorder that affects communication, social interaction and behavioural skills. ASD affects the children at the age of two years old and continues lifelong. ...
  • Kayanan, M.; Wijekoon, P. (Institute of Applied Statistics, Sri Lanka, 28-12-18)
    Misspecification of the linear model due to omitting relevant explanatory variable from the model and multicollinearity among the explanatory variables are considered as two of the problems which are considered seriously ...
  • Kayanan, M.; Wijekoon, P. (Scientific Research Publishing, 28-02-20)
    Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates having ...
  • Kayanan, M.; Wijekoon, P. (Hindawi, 30-03-20)
    Among several variable selection methods, LASSO is the most desirable estimation procedure for handling regularization and variable selection simultaneously in the high-dimensional linear regression models when multicollinearity ...

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