Browsing by Author "Chandrasekara, N.V."

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An Application of Artificial Neural Networks to Predict the Milk Yield of a Typical Dairy Farm in Sri Lanka
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Hewage, S.S.; Chandrasekara, N.V.
It is quite interesting that milk and dairy products play an important role in a healthy, balanced diet thus contributing to certain indispensable nutritional benefits. Hence, the need for dairy is crucial, which means dairy farms provide a vital necessity to the people in both rural and other areas across the country. Therefore, accurate forecast of milk yield is important for dairy farmers to utilize and optimize their production process. The present study is aimed at using Artificial Neural Networks (ANN) for predicting the milk yield of a dairy farm by considering the potential factors that affect the milk production. Further, it is important to note that this dairy farm has kept records of the daily milk yield, the amount of food given to cows, and weather condition. Data from January 2016 to June 2018 were used for the study. In this regard, a feedforward neural network (FFNN), non-linear auto regressive neural network (NAR), and a non-linear auto regressive exogenous neural network (NARX) were fitted. Analysis was done using Matlab software and all three implemented models took around 30 seconds for execution. While all the three models exhibited quite strong model performances, the NARX model exhibited prominently outstanding results. The best forecasting performance was shown by the NARX neural network which contained one hidden layer with five neurons having saturating linear transfer function. Normalized Mean Squared Error (NMSE) was 0.0247 for the overall model while the Mean Absolute Error (MAE) value was 6.6245.
Application of fuzzy goal programming model to assess optimal multi crop cultivation planning
(Agriculture and Natural Resources, 2022) Hakmanage, N.M.; Chandrasekara, N.V.; Jayasundara, M.
Importance of the work: Planning for the optimal use of resources in agricultural systems considering uncertainty, with the objective of maximizing profit and production, will improve the social and economic conditions of farmers. Objectives: A rural farming area in Sri Lanka was used as a study site to apply the fuzzy goal programming (FGP) approach to identify the optimal cultivation plan and land resource allocation under uncertainty to optimize profit, production, labor, water use, fertilizer costs and land allocation. Materials & Methods: A tolerance-based FGP technique was used to quantify the fuzziness of different goals for the model. This study was carried out using 24 crops on a total land area under cultivation of 47.4 ha. These crops were categorized into three varieties: vegetable, fruit and other. Furthermore, the crops were classified into seven groups according to the required period of cultivation. Results: The proposed model suggested statistically significant increments of 11% and 10.6% for the net return and harvest amount, respectively, for the 24 crops compared to existing cultivation techniques.Main finding: The FGP multi-crop cultivation planning approach is a new application for the Sri Lankan rural farming community and it should be useful for agricultural planners, by allowing them to make more informed recommendations to the farming community. Crops that provide higher levels of production and profit than those currently being cultivated should be developed to extend cultivation under the supervision of agricultural experts or officers to obtain sustainable development of cultivation.
An Approach for Prediction of Weekly Prices of Green Chili in Sri Lanka: Application of Artificial Neural Network Techniques
(Journal of Agricultural Sciences – Sri Lanka, 2022) Basnayake, B.R.P.M.; Kaushalya, K.D.; Wickaramarathne, R.H.M.; Kushan, M.A.K.; Chandrasekara, N.V.
Purpose: Predicting the prices of crops is a principal task for producers, suppliers, governments and international businesses. The purpose of the study is to forecast the prices of green chili, which is a cash crop in Sri Lanka. Artificial neural networks were applied as they help to extract important insights from the bulk of data with a scientific approach. Research Method: The Time Delay Neural Network (TDNN), Feedforward Neural Network (FFNN) with Levenberg-Marquardt (LM) algorithm and FFNN with Scaled Conjugate Gradient (SCG) algorithm were employed on weekly average retail prices of green chili in Sri Lanka from the 1st week of January 2011 to the 4th week of December 2018. The performance of models was evaluated through the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Normalized Mean Squared Error (NMSE). Findings: Among the three methods implemented, the FFNN model using the LM algorithm exhibited the highest accuracy with a minimum MSE of 0.0033, MAE of 0.0437 and NMSE of 0.2542. The model built using the SCG algorithm fitted data with a minimum MSE of 0.0033, MAE of 0.0458 and NMSE of 0.2549. Among the fitted TDNN models, the model with 8 input delays were a better model with an MSE of 0.0036, MAE of 0.0470 and NMSE of 0.3221. FFNNs outperformed TDNN in forecasting green chili prices of Sri Lanka. Originality/ Value: The neural network approach in forecasting the prices of green chili provides more accurate results to make decisions based on the trends and to identify future opportunities.
Automated response recognition system for questionnaires
(Sri Lanka Association for the Advancement of Science, 2012) Fernando, M.A.I.D.; Chandrasekara, N.V.
Comparison of support vector regression and artificial neural network models to forecast daily Colombo Stock Exchange
(Proceedings of the International Statistics Conference, Institute of Applied Statistics, Sri Lanka, 2011) Rangana, D.L.M.; Chandrasekara, N.V.; Tilakaratne, C.D.
Determining and Comparing Multivariate Distributions: An Application to AORD and GSPC with their related financial markets
(2016) Chandrasekara, N.V.; Mammadov, M.; Tilakaratne, C.D.
Many real world applications are associated with more than one variable and hence, identifying multivariate distributions associated with real world problems portrays great importance today. Many studies can be found in the literature in this aspect and most of them are associated with two variables/dimensions and the maximum dimension of multivariate distribution found in the literature is four. Different optimization techniques have been used by researchers to find multivariate distributions in their studies. Numerical methods can be identified as more preferable than analytical methods when the dimension of the problem is high. The main objective of this study is to identify the multivariate distribution associated with the return series of Australian all ordinary index (AORD) and those of the related financial markets and compare it with the multivariate distribution of return series of the US GSPC index and its related financial markets. No research were found in the literature which were aimed at finding aforesaid multivariate distribution and comparisons. Moreover no evidence found for identifying a multivariate distribution with six dimensions. Five financial markets: Amex oil index, Amex gold index, world cocoa index, exchange rate of Australian dollar to United States dollar and US GSPC index were found to be associated with AORD. Hence the attempt was to derive the multivariate distribution of return series of AORD and these five return series and therefore the optimization problem of the study is a six dimension problem which associated with forty three parameters need to be estimated. A local optimization technique and a global optimization technique were used to estimate the parameters of the multivariate distribution. Results exhibit that the parameter estimates obtained from the global optimization technique are better than the parameter estimates obtained from the local optimization technique. The multivariate distribution of return series of AORD and related financial markets is central, less peaked and have fat tails. A comparison was done with another multivariate distribution of a return series of a leading stock market index: GSPC and return series of its associated financial markets and found that both distributions are alike in shape. Two periods were identified in the AORD series and found that the shape of the multivariate distribution of one period is similar to the shape of the multivariate distribution of full data set while the shape of the multivariate distribution of the other period is dissimilar to that of full data set.
Effect of Common Culinary Methods Practiced in Sri Lanka on the Nutrient Composition of Commonly Consumed Vegetables and Other Foods
(International Journal of Food Science, 2021) Dewangani, H.G.N.; Jayawardena, B.M.; Chandrasekara, N.V.; Wijayagunaratne, H.D.S.P.
In Sri Lankan traditional cooking, coconut and spices are incorporated to enhance the taste, flavor, and aroma. However, little attention has been given to assess the effect of these ingredients on the nutritional and chemical composition of the consumed food. The objective of this study was to ascertain the effect of traditional cooking methods on the chemical composition of vegetables, cereals and cereal-based foods, legumes, and selected nonvegetarian food varieties consumed in the daily diet. The results indicate that the addition of coconut milk (CM), coconut scraps, and coconut oil (CO) had a significant impact on the fat content of the prepared foods (p < 0.05). Cooking facilitated the incorporation of fat into food. According to the results, more percentage increases of fat content were observed in tempered string beans (97.51%) and cauliflower milk curry (96.6%). Data revealed that boiling helped to reduce the fat content in cereals and legumes. The cooked foods prepared using traditional recipes with CM, CO, or scraps have higher nutritional content than raw foods and have a significant nourishing potential that meets the daily energy requirements (p < 0.05). It can be concluded that the chemical composition of cooked food serves as a more realistic guideline in recommending dietary interventions in disease and weight management.
An Ensemble Technique For Multi Class Imbalanced Problem Using Probabilistic Neural Networks
(Advances and Applications in Statistics, 2018) Chandrasekara, N.V.; Tilakaratne, C.D.; Mammadov, M.A.
The class imbalanced problem is one of the major difficulties encountered by many researchers when using classification tools. Multi class problems are especially severe in this regard. The main objective of this study is to propose a suitable technique to handle multi class imbalanced problem. Probabilistic neural network (PNN) is used as the classification tool and the directional prediction of Australian, United States and Srilankan stock market indices is considered as the application. We propose an ensemble technique to handle multi class imbalanced problem that is called multi class undersampling based bagging (MCUB) technique. This is a new initiative that has not been considered in the literature to handle multi class imbalanced problem by employing PNN. The results obtained demonstrate that the proposed MCUB technique is capable of handling multi class imbalanced problem. Therefore, the PNN with the proposed ensemble technique can be used effectively in data classification. As a further study, other classification tools can be used to investigate the performance of the proposed MCUB technique in solving class imbalanced problems.
Estimating parameters of multivariate scaled t distribution of GSPC and its associated financial indices
(2015) Chandrasekara, N.V.; Mammadov, M.A.; Thilakaratne, C.D.
forecasting exchange rates using artificial neural networks
(Journal of Applied Statistics, 2009) Chandrasekara, N.V.; tilakaratne, C.D.
Forecasting Exchange Rates using Time Series and Neural Network Approaches
(Proceedings of the 69th Annual Session of the Sri Lanka Association for Advancement of Science -SLASS, 2013) Nanayakkara, K.A.D.S.A.; Chandrasekara, N.V.; Jayasundara, D.D.M.
Forecasting exchange rates using time series and neural network approaches
(Sri Lanka Association for the Advancement of Science, 2013) Nanayakkara, K.A.D.S.A.; Chandrasekara, N.V.; Jayasundara, D.D.M.
HOURLY SOLAR RADIATION FORECASTING USING ARTIFICIAL NEURAL NETWORK MODEL FOR COLOMBO, SRI LANKA
(Advances and Applications in Statistics, 2019) Saumyamala, M.G.A.; Chandrasekara, N.V.
Sri Lanka is a tropical country located close to the equator with abundant sunlight throughout the year. For efficient utilization of this solar resource for power generation in photovoltaic (PV) systems and agricultural modelling, prior knowledge of global solar radiation (GSR) in the future is important. Limited availability of onsite GSR data and the high cost are the main barriers in forecasting GSR for Sri Lanka. As a solution this study suggests an artificial neural network (ANN) model to forecast hourly solar radiation using weather data and solar angles to forecast GSR in Colombo, specifically using feedforward neural network (FFNN) trained with Levenberg- Marquardt (LM) back propagation algorithm. Hourly weather data for 6 weather variables and two solar angles from 1st of March 2017 to 14th of February 2018 were used for training, validation and testing the network. Input parameters and training parameters were adjusted to identify the most accurate network configuration and the performance of the network was measured using normalized mean squared error (NMSE). Coefficient of determination (R2) measured to identify the appropriateness of using weather variables and solar angles to forecast solar radiation. The final hourly FFNN model consists of 2 hidden layers and there are 5 neurons and 3 neurons in each layer respectively. This model was able to forecast hourly solar radiation with 0.0961 NMSE and the R2 was 90.39%. This implies the capability of this model for prediction of global solar radiation when unseen weather data input supply to the model and ensure the accuracy of the result.
Identification of factors and classifying the accident severity in Colombo - Katunayake expressway, Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Kushan, M.A.K.; Chandrasekara, N.V.
Sri Lanka’s expressway system was launched in 2011 and now owns three major expressways. Many peoples choose expressways rather than normal ways due to the reasons of time, traffic, easy of driving, etc. According to police reports of highway main traffic police branch, in recent years the number of accidents occurring in expressways is increasing drastically. Nowadays, the rate of accident occurrence in Colombo-Katunayake Expressway is high compared to the other two expressways and there was no previous research has been done in Sri Lanka regarding accidents on ColomboKatunayake expressway. Therefore, the objective of the study was to identify the factors contributing to accidents on the Colombo-Katunayake Expressway and to develop appropriate machine learning models to classify the severity of the accidents. In this study, 704 total accident cases were considered during the period 2013-2019. Chi-square test, logistic regression, and Kruskal–Wallis tests were used to identify the association between the accident severity and other influential variables found from the literature. Finally, seven variables: time category, driver’s age category, vehicle type, the reason for the accident, number of vehicles involved, cause for accident and rainfall were identified as influencing variables to accident severity under 5% level of significance. Naïve Bayes classification algorithm and probabilistic neural network (PNN) were used in the study to forecast accident severity. A random under-sampling technique was used to overcome the class imbalanced problem persists in the data set considered in the study. The final models developed using the Naïve Bayes algorithm and PNN exhibit 72.14% and 74.29% overall classification accuracy respectively. Both aforementioned models can be considered as suitable models to forecast accident severity in the Colombo-Katunayake expressway where the PNN model exhibits slightly higher accuracy. The final models developed by this study can be used to implement safety improvements against traffic accidents in expressways of Sri Lanka.
Identification of factors and classifying the accident severity in Colombo Katunayake expressway, Sri Lanka using multinomial logistic regression
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Kushan, M.A.K.; Chandrasekara, N.V.
Accidents are one of the main social problems in the World, which cause damages or injuries unintentionally and unexpectedly. This is a major issue affecting not only in developing countries like Sri Lanka but also in developed countries. Sri Lanka's expressway system was launched in 2011 and currently has three major expressways: Southern Expressway, Colombo-Katunayake Expressway and Outer-Circle Expressway. After the construction of expressways, many people opted for expressways based on time, traffic, ease of driving, etc., rather than ordinary roads. The number of accidents on expressways has been on the rise in recent years compared to the past. At present, the accident rate on the Colombo-Katunayake Expressway, which connects the Sri Lankan capital, Colombo with Bandaranaike International Airport, Katunayake and Negombo, is high compared to the other two expressways, but no research has been done to date regarding this. Therefore, the objective of the study was to identify the factors contributing to accidents on the Colombo-Katunayake Expressway and to develop an appropriate regression model to classify the severity of the accidents. In this study, 704 total accident cases of Colombo-Katunayake expressway were considered during the period from 2013 to 2019. Initially, Pearson Chi-square, Logistic regression and Kruskal–Wallis H tests were used to identify the association between the multinomial response variable (accident severity) and eleven predictor variables identified based on the literature. Finally, from selected predictor variables, seven variables: time category, driver’s age category, vehicle type, reason for the accident, number of vehicles involved, cause for accident and rainfall were identified as influencing variables to accident severity under 5% level of significance. Since this is not a time series data, 80% of the data were selected in various ways for model building and the remaining 20% were used to test the performance of the built models. Considering significant variables identified above, Multinomial Logistic Regression (MLR) was trained using the stepwise enter method with different data selections criteria. The Random under-sampling technique was used to overcome the class imbalance problem that persists in the data set considered in the study and after selecting the best model, the adequacy of the model was examined and classified the severity of accidents in Colombo-Katunayake Expressway. The final MLR model predicts accident severity with an overall accuracy of 64.3% and rainfall, cause for accident and time category (it is a categorical variable that divides 24 hours into four equal parts) have been identified as the most influential factors affecting accidents on the Colombo-Katunayake Expressway. Furthermore, the final model depicts, with rainy weather, high speed, sleepiness, technical faults and reckless driving increased the likelihood of an accident on the Colombo-Katunayake Expressway, and [0-6] and [12-18] hours were identified as dangerous time categories. The final model developed by this study can be used to implement safety improvements against traffic accidents in expressways of Sri Lanka. As a future study, machine learning techniques can be employed to identify better models with higher classification accuracy.
Identifying distributions of selected stock returns
(2015) Chandrasekara, N.V.; Thilakaratne, C.D.; Mammadov, M.A.
Identifying Factors affecting the severity of Asthma
(European International Journal of Science and Technology, 2013) Peramuna, P.R.N.L.; Chandrasekara, N.V.; Jayasundara, D.D.M.
Identifying factors associated with severity of asthma in Kegalle District
(Sri Lanka Association for the Advancement of Science, 2013) Peramuna, P.R.N.L.; Chandrasekara, N.V.; Jayasundara, D.D.M.
Impact of Exchange Rate on Imports and Exports in Sri Lanka from 1960 to 2019
(Faculty of Commerce and Management Studies, University of Kelaniya, 2021) Gunarathne, K.M.U.; Perera, D.H.N.; Liyanage, L.T.H.; Chandrasekara, N.V.
Exchange rate volatility has had a significant effect on industrial export and import performance in Sri Lanka as a third world country. This study examines the effect of exchange rates on imports and exports in Sri Lanka using annual data from 1960 to 2019. Vector Auto Regression (VAR) consisting of import, export and exchange rate is considered to check the impact of exchange rate on exports and imports in Sri Lanka. Our findings revealed that the exchange rate negatively affects Sri Lanka's exports and imports.
The impact of mothers’ migration for work abroad on children’s education
(Sri Lanka Association for the Advancement of Science, 2013) Dissanayake, P.L.; Chandrasekara, N.V.; Jayasundara, D.D.M.