Browsing by Author "Mahendran, R"

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item: Article-Full-text
Assessment of environmental variability on malaria transmission in a malaria-endemic rural dry zone locality of Sri Lanka: The wavelet approach
(Public Library of Science, 2020) Mahendran, R; Pathirana, S; Piyatilake, ITS; Perera, SSN; Weerasinghe, MC
Malaria is a global public health concern and its dynamic transmission is still a complex process. Malaria transmission largely depends on various factors, including demography, geography, vector dynamics, parasite reservoir, and climate. The dynamic behaviour of malaria transmission has been explained using various statistical and mathematical methods. Of them, wavelet analysis is a powerful mathematical technique used in analysing rapidly changing time-series to understand disease processes in a more holistic way. The current study is aimed at identifying the pattern of malaria transmission and its variability with environmental factors in Kataragama, a malaria-endemic dry zone locality of Sri Lanka, using a wavelet approach. Monthly environmental data including total rainfall and mean water flow of the “Menik Ganga” river; mean temperature, mean minimum and maximum temperatures and mean relative humidity; and malaria cases in the Kataragama Medical Officer of Health (MOH) area were obtained from the Department of Irrigation, Department of Meteorology and Malaria Research Unit (MRU) of University of Colombo, respectively, for the period 1990 to 2005. Wavelet theory was applied to analyze these monthly time series data. There were two significant periodicities in malaria cases during the period of 1992–1995 and 1999–2000. The cross-wavelet power spectrums revealed an anti-phase correlation of malaria cases with mean temperature, minimum temperature, and water flow of “Menik Ganga” river during the period 1991–1995, while the in-phase correlation with rainfall is noticeable only during 1991–1992. Relative humidity was similarly associated with malaria cases between 1991–1992. It appears that environmental variables have contributed to a higher incidence of malaria cases in Kataragama in different time periods between 1990 and 2005.
item: Conference-Abstract
Using CNN to identify the condition of edible fish
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2022-12) Mahendran, R; Seneviratne, GP; Sumathipala, KASN; Ganegoda, GU; Piyathilake, ITS; Manawadu, IN
Evaluating the edibility of fish by it’s freshness is an essential process for the fisheries industry as it contributes to customers’ health and the taste of food. In general, identifying freshness of fish is a difficult task for the customers due to lack of experience or knowledge. Using a real-time application which employs real-time images of fish is the best solution to identify their freshness. In this study, a model was developed using VGG16 architecture in a deep convolutional neural network (CNN) to extract the features of the fish and to classify them based on their freshness. Here, Bluefin Trevally fish was selected as a sample and its freshness was detected using real-time images. Those images were collected in various backgrounds with different lightning by different devices. In real-time images, features of fish such as the colour of the eye and frozen blood colour of the operculum were used to identify the freshness of fish. An accuracy of 99% on identification of freshness was achieved by this model.