Browsing by Author "Wickramasinghe, L"

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Open Access
Artificial neural network to estimate the paddy yield prediction using climatic data
(Hindawi, 2020-07) Amaratunga, V; Wickramasinghe, L; Perera, A; Jayasinghe, J; Rathnayake, U. S
Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.
Embargo
Real time deception detection for criminal investigation
(IEEE, 2019-10-08) Lakshan, I; Wickramasinghe, L; Disala, S; Chandrasegar, S; Haddela, P. S
Deception Detection System (PREDICTOR) is a solution to support the criminal investigation process by providing a technological analysis in justifying the guilt of an accused criminal in the investigation process. This study gives guidelines to substantiate decision making in the interrogation. In judicature, the importance of a platform that is capable of analyzing the genuineness and the (a) reliability of a lie and a truth, (b) emotion of the suspect and the (c) attentiveness has been recognized for a long period. The feasibility of using Machine Learning (ML) techniques to build such platforms has been explored before. However, no known platform could identify the suspect's authenticity, emotion, and attentiveness. The goal is to analyze the brain waves and build a real-time deception detection application to analyze lie/truth, emotion and the attentiveness, which will support the investigation process in a wide range of angles to decision making. Electroencephalogram (EEG) based real-time lie detection, emotion detection, and attention detection will be implemented using ML tools and techniques along with the help of special hardware equipment called MUSE 2 headband. Especially this equipment is required for the data acquisition as well as the creation of the final application. The outcome of this system is a solution to be used during the criminal investigation process as a deception detection system for lie, emotion and attentiveness of the suspect. This is more effective in the questioning process to get an idea of the suspect. This system will have a major impact on the Police Department, Criminal Investigation Department, and Judicial System to ensure the real criminal and reduce the workload of Criminal Investigation officers.
Open Access
Regression-Based Prediction of Power Generation at Samanalawewa Hydropower Plant in Sri Lanka Using Machine Learning
(Hindawi, 2021-07-31) Ekanayake, P; Wickramasinghe, L; Jayasinghe, J. M; Rathnayake, U. S
This paper presents the development of models for the prediction of power generation at the Samanalawewa hydropower plant, which is one of the major power stations in Sri Lanka. Four regression-based machine learning and statistical techniques were applied to develop the prediction models. Rainfall data at six locations in the catchment area of the Samanalawewa reservoir from 1993 to 2019 were used as the main input variables. The minimum and maximum temperature and evaporation at the reservoir site were also incorporated. The collinearities between the variables were investigated in terms of Pearson’s and Spearman’s correlation coefficients. It was found that rainfall at one location is less impactful on power generation, while that at other locations are highly correlated with each other. Prediction models based on monthly and quarterly data were developed, and their performance was evaluated in terms of the correlation coefficient (R), mean absolute percentage error (MAPE), ratio of the root mean square error (RMSE) to the standard deviation of measured data (RSR), BIAS, and the Nash number. Of the Gaussian process regression (GPR), support vector regression (SVR), multiple linear regression (MLR), and power regression (PR), the machine learning techniques (GPR and SVR) produced the comparably accurate prediction models. Being the most accurate prediction model, the GPR produced the best correlation coefficient closer to 1 with a very less error. This model could be used in predicting the hydropower generation at the Samanalawewa power station using the rainfall forecast.
Open Access
Relationships between climatic factors to the paddy yeild: A case study from North-Western province of Sri Lanka
(Smart Computing and Systems Engineering, 2020, 2020-09-23) Wickramasinghe, L; Jayasinghe, J. M. J. W; Rathnayake, U. S
Climate variation is one of the major impacting issues for paddy cultivation. It also highly impacts the harvest. Therefore, many researchers try to understand the relationships between climatic factors and harvest using numerous methods. Sri Lanka is still titled as a country with an agricultural-based economy and thus identifying the impact of climate variability on agriculture is very important. However, previous studies reveal a little information in the context of Sri Lanka on the impact of climate variabilities on agriculture. Therefore, this study showcases an artificial neural network (ANN) framework; that is an ordinary machine learning algorithm based on the model of the human neuron system, to evaluate the relationships among the climatic components and the paddy harvest in the North-Western province of Sri Lanka. This on-going study helps to analyze the relationships between the paddy harvest of the North-Western province and climate, including rainfall minimum atmospheric temperature and maximum atmospheric temperature. Correlation coefficient (R) and mean squared error (MSE) are used to test the performance of the ANN model. The results obtained from the analysis revealed that the predicted and real paddy yields have a significant correlation with rainfall, maximum temperature and minimum temperature.
Embargo
Student Teaching and Learning System for Academic Institutions
(IEEE, 2022-07-18) Kumarasiri, A. D. S. S; Delwita, C. E. M. S. M; Haddela, P. S; Samarasinghe, R. P; Udishan, R. P. I; Wickramasinghe, L
In today's global online environment, automation is essential for establishing a competitive advantage. Conversational Artificial Intelligence Systems are an example of an automation technology that has been embraced by some of world's most famous companies. In the field, higher education, these handy gadgets come in handy for administrators.Students' responses and performances are highly important and desired areas to improve the teaching-learning environment in the education organism. Evaluate the feedback to aid in the identification of flaws and actions. Institutes and universities in the field of education collect both quantitative and qualitative responses in order to improve the teaching-learning environment. However, most educational institutions and universities lack a sufficient student feedback evaluation system for determining students' feelings. The grading technique is currently being utilized to collect input. However, the grading process does not reveal the students' genuine feelings about the educational system, whereas written feedback allows students to describe specific areas and situations. Sentiment analysis is a type of qualitative feedback analysis that is based on records. The Nave Bayes (NB) classifier, Support Vector Machine (SVM), and Random Forest were employed in the majority of recent machine learning-based Sentiment Analysis prototypes. When it comes to measuring performance, most people utilize the kids' average grades, however this is not an accurate way. Because students can benefit from the experience indefinitely.This proposal offered a method for analyzing the sentiment of students' responses by combining Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence algorithms (AI). The goal is to combine machine learning algorithms and natural language processing approaches to achieve high accuracy. In this proposal, I suggested a collective model that connects machine learning algorithms to improve accuracy and performance. At the end of the presentation, AI planned to collect student responses in real time. The proposed method analyzes student comments from course reviews to determine mood, emotions, and satisfaction vs. displeasure. The approach categorizes sentimentalities into two categories: positive and negative, and senders' feelings into eight categories (8 emotion groups). The Fuzzy logic technique will be used to assess student performance. We intend to make our program available for broad use in the education industry so that organizations can improve the teaching-learning environment's quality.