Faculty of Computing

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AI and Machine Learning Based E - Learning System For Secondary Education
(IEEE, 2022-07-18) Wijayawardena, G. C. S; Subasinghe, S. G. T. S; Bismi, K. H. P; Gamage, A
One of the key functions directly shifted to online platforms under COVID-19 is education. The paper is about an E-learning system for secondary education in Sri Lanka. Learners and teachers can access information, resources, and tools through an E-Learning system, which is a Learning Management System that integrates a number of online activities. The main functions provided through the proposed system are chatbot, final grade prediction and weak area prediction of the students. Chatbots are becoming increasingly popular in a wide range of applications, especially in those that provide intelligence support to the user, according to recent research. So, in order to speed up the aid process, these systems are often integrated with Chatbots, which can quickly and accurately read the user's questions. This paper describes the implementation of a Chatbot prototype in the educational domain: a system for providing support to students. In the beginning, the goal was to design a special architecture and communication model that would help students get the proper answers. The final grade prediction component plays major role in the system. Because when the students are graded by their marks, they can review which areas that they have to improve and work on them. This is helpful for students as well as teachers. Weak area prediction also plays a significant role, because it can help to find out the weak areas of each subject and generate Individual Student Progress Plans to predict the students’ weak subjects and the subject areas of the students. This motivates students to get higher marks easily because this part is mainly focused on weak areas of students and improve those weak areas by providing several learning activities. These are the major parts of this system to have a good E-learning system for both students and Teachers.
Embargo
Machine Learning to Aid in the Process of Disease Detection and Management in Soilless Farming
(IEEE, 2022-07-18) Fernando, S. D; Gamage, A; De Silva, D. H
This research aims at enhancing the methods and techniques that are being used in disease detection when it comes to soilless farming. Soilless farming is quite famous among the Sri Lankan farmers farming in urban areas. A mobile application is launched by us and this application is capable of identifying diseases in plants, therefore, farmers do not have to rely on their years of experience to identify the diseases. A novice farmer may struggle to say what is wrong with their plants, while another farmer with many years of experience may say what the disease is with no hesitation. Both those types of farmers benefit from our mobile application equally. The said mobile application consists of four components and each of them focuses on a different service. One of those components is to detect and manage diseases in plant leaves and that component is what this research paper showcases. This particular component allows the user to capture live-images of plant leaves. Then the application processes the captured image to identify if the plant is suffering from a disease. After that, it generates a report with a set of treatments. It further analyses and alerts the user if this disease detected is going to affect the harvest.
Open Access
Agro-Genius: Crop Prediction Using Machine Learning
(2019-10) Gamage, A; Kasthurirathna, D
This paper present a way to aid farmers focusing on profitable vegetable cultivation in Sri Lanka. As agriculture creates an economic future for developing countries, the demand of modern technologies in this sector is higher. Key technologies used for this problem are Deep Learning, Machine Learning and Visualization. As the product, an android mobile application is developed. In this application the users should input their location to start the prediction process. Data preprocessing is started when the location is received to the system. The collected dataset divided into 3 parts. 80 percent for training, 10 percent for testing and 10 percent for validation. After that the model is created using LSTM RNN for vegetable prediction and ARIMA for price prediction. Finally, for given location profitable crop and predicted future price of vegetables are shown in the application. Other than the prediction, optimizing for multiple crop sowing according to the user requirements and visualizing cultivation and production data on map and graphs are also given in the application. This paper elaborates the procedure of model development, model training and model testing.

Faculty of Computing

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