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Author: search.filters.author.Kumara, B.T.G.S.

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    A Machine Learning Influenced Recommendation System for Predicting the Rainfall and Price for Crops in Badulla District
    (Faculty of Computing and Technology, University of Kelaniya Sri Lanka, 2022) Nandasiri, K.P. Sasindu Madushan; Banujan, Kuhaneswaran; Kumara, B.T.G.S.; Jayasinghe, Sadeeka; Ekanayake, E.M.U.W.J.B.; Senthan, Prasanth
    Every day, agriculture becomes more vital to the global economy. Daily population expansion necessitates substantial crop output for human existence. But as the population has increased, human activity has also altered the environment. Therefore, it has resulted in challenges with weather forecasting, which is crucial for crop planting in the agricultural sector. Thus, the globe needs a method to forecast agrarian weather. In addition, it is highly advantageous for farmers to understand the production rate they can achieve and the price range they may expect for their efforts. As a result, Machine learning technologies have become unique and fashionable in the agricultural industry due to their ability to provide accurate farming predictions. Selecting suitable plants for planting has evolved into a necessity. This study focuses on the application of machine learning to estimate the optimal crop for a given period. In this work, the author addresses the beginning part of the study: precipitation prediction under the weather forecast and pricing forecast. The authors have employed six distinct machine-learning models to forecast rainfall and crop prices.
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    Stack Ensemble Model to Detect the Stress in Humans by Considering the Sleeping Habits
    (Faculty of Computing and Technology, University of Kelaniya Sri Lanka, 2022) Kanagarathnam, Mauran; Premisha, P.; Prasanth, Senthan; Banujan, Kuhaneswaran; Kumara, B.T.G.S.
    Recently, one of the big challenges encountered by humans is experiencing and managing stress. Beyond the age restriction, people of all ages, from teenagers to seniors, experience issues as a result of stress. Acute and chronic stress are the two main categories of stress. Acute stress is a typical human response that aids in your body’s adaptation to a new situation. In actuality, this form of stress has positive effects. However, the second type of stress, chronic stress, is a crucial type of stress, and this study focused on determining the stress level of this type in advance. This research examined eight attributes related to chronic stress to investigate the chosen person’s sleeping patterns. The Kaggle website provided the dataset that was used in this study. The user’s snoring range, body temperature, limb movement rate, blood oxygen levels, eye movement, number of hours of sleep, heart rate, and stress levels (0-low/normal, 1-medium low, 2-medium, 3-medium high, 4 - high) were all taken into account. The stack ensemble approach was utilized with two levels during this approach. At level 0, the classifiers such as Random Forest, Decision tree, K-nearest neighbour, and XGBoost were considered. At level 1, as a Metamodel, Logistic regression was adopted. Moreover, the predictions obtained from the level 0 models added an additional attribute to the original dataset and fed it to the level 1 model as a new training dataset. Additionally, five folds of fold cross-validation were performed along with the basic assessment to validate further the model for various ratios of training and testing data. Following the cross-validation, the model’s mean accuracy obtained for RF, DT, KNN, XGB and stack ensemble models. From the results discovered, it was represented that the combined model (stack ensemble model) produced more precise results rather than the models considered in isolation.
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    Database Management System Deployment on Docker Containerization for Distributed Systems
    (Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021) Kithulwatta, W.M.C.J.T.; Jayasena, K.P.N.; Kumara, B.T.G.S.; Rathnayaka, R.M.K.T.
    Containerization is a novel technology that brings an alternative for virtualization. Due to the most infrastructure-based features, most computer system administration engineers use Docker as the infrastructure level platform. On the Docker containers, any such kind of software service can be deployed. This study aims to evaluate Docker container based relational database management system container behavior. Currently, most scholarly research articles are existing for the database engine performance evaluation under different metrics and measurements of the database management systems. Therefore, without repeating them: this study evaluated the data storage mechanisms, security approaches, container resource usages and container features on the launching mechanism. According to the observed features and factors on the containerized database management systems, containerized database management systems are presenting more value-added features. Hence containerized database management system Docker containers can be recommended for the distributed computer systems for getting the benefit of effectiveness and efficiency.
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    Comparative Analysis between K-mean and EM Clustering for Investigate Appropriate Algorithm for Landslide Risk Evaluation
    (4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Madawala, C.N.; Kumara, B.T.G.S.
    Irregular development activities on mountains and inadequate attention to construction aspects have led to increasing of landslide and sustaining damages to lives and properties. Within the study area, nearly 3275 sq.km of the area expanded over the Ratnapura District; and is to be highly prone to land sliding of 2178 sq.km. Landslides transpired in many regions of this area, and nearly 90 deaths have reported by National Research Building Organization (NBRO), Sri Lanka. If the suitable investigations were performed at the right time, most of the landslides could be predicted relatively and accurately. The main objective behind this of study is to evaluate the landslide risk levels to discover the real extent, timing and the intensity of landslide processes in Ratnapura district, such knowledge will present vital benefit to government officials, and the general public to avoid landslide hazards and mitigate the losses. Clustering Approaches can be used to developed the Risk Analysing model using actual data. This method was based on K-mean and Expectation Maximization (EM) Algorithms by concerning triggering factor; rainfall and causative factors; slope angle, elevation, and intensity. Such data were collected and applied to the Clustering algorithms. In this study, comparing the multiple Clustering algorithms and investigate the most appropriate risk evaluation approach where it can be used to advance hazard monitoring, early warning, and disaster mitigation. The results indicate that EM clustering algorithm showed accuracy over 84% with the highest speed. The highest accuracy over 92% was acquired by the K-means algorithm, but it was more time-consuming than EM algorithm. Therefore, this research proposed that an EM clustering has a strong capability to fit for the Landslide risk evaluation and producing a more relevant and accurate prediction of the landslide vulnerability within the study area.