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Department of Physical Science: Recent submissions

  • Extracting knowledge from Noise Information 
    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 ...
  • Infoshare: A Digital Governance Community platform for Knowledge Sharing 
    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 ...
  • Telematics and Road Safety 
    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 ...
  • Scene-Specific Dark Channel Prior for Single Image Fog Removal 
    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 ...
  • Underwater Image Enhancement using Scene-Specific Red Channel Prior and Fusion 
    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 ...
  • Electricity Usage Monitoring and Alerting System 
    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 ...
  • Single Image Defogging using Depth Estimation and Scene-Specific Dark Channel Prior 
    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 ...
  • Mean square error comparison between ridge and lasso estimators in the Misspecified linear regression model 
    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 ...
  • Sparse Matrix Vector multiplication using parallel algorithm with block striped partitioning 
    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. ...
  • Restricted least angle regression algorithm for lasso 
    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 ...
  • Comparison between r-d and r-k class estimators in Misspecified linear regression model 
    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 ...
  • Combining Lasso and Liu type estimator in the linear regression model 
    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 ...
  • The knowledge gathering and study pattern of undergraduate students of the Vavuniya campus of the university of Jaffna 
    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 ...
  • Multiclass classification approach to identify the stage of autism spectrum disorder 
    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. ...
  • Modified Stochastic Restricted Ridge Estimator in Misspecified Linear Regression Model 
    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 ...
  • Variable Selection via Biased Estimators in the Linear Regression Model 
    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 ...
  • Stochastic Restricted LASSO-Type Estimator in the Linear Regression Model 
    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 ...
  • Performance of LASSO and Elastic net estimators in Misspecified Linear Regression Model 
    Kayanan, M.; Wijekoon, P. (Faculty of Science, University of Peradeniya, Sri Lanka, 16-09-19)
    Ridge Estimator (RE) 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 heavy bias when the ...
  • Optimal estimators in misspecified linear regression model with an application to real-world data 
    Kayanan, M.; Wijekoon, P. (Taylor & Francis, 15-04-19)
    In this article, we propose the sample information optimal estimator and the stochastic restricted optimal estimator for misspecified linear regression model when multicollinearity exists among explanatory variables. ...
  • Stochastic Restricted Biased Estimators in Misspecified Regression Model with Incomplete Prior Information 
    Kayanan, M.; Wijekoon, P. (Hindawi, )
    The analysis of misspecification was extended to the recently introduced stochastic restricted biased estimators when multi collinearity exists among the explanatory variables. The Stochastic Restricted Ridge Estimator ...

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