Research Papers - Dept of Information Technology

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Now showing 1 - 6 of 6
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    Analyzing Fisheries Market, Shrimp Farming & Identifying Fish Species using Image Processing
    (IEEE, 2022-12-09) Sumeera, S; Pesala, N; Thilani, M; Gamage, A; Bandara, P
    The fisheries industry is vital to the Sri Lankan economy because it provides a living for more than 2.5 million coastal communities and meets more than half of the country’s animal protein needs. Today, the fishery community in Sri Lanka is facing several grant problems. Among them, not getting a decent fish price for their harvesting, the inability to identify diseases in shrimp cages in the early stages, and the inability to identify fish species by observing their external appearance. This research developed a prototype mobile application “Malu Malu” to avoid the above-mentioned problems. It facilitates to the prediction of market fish prices, identifying shrimp diseases in their early stages, and identifying fish species by observing their external appearance. The proposed predictive models of the “Malu Malu” contains three main models developed using inseption V3 Convolutional Neural Network (CNN) model for image classification and Linear Regression is used for creating a model for predictions. The experimental results of these models showed above 85% of accuracy.
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    Machine Learning-Based Skin And Heart Disease Diagnose Mobile App
    (IEEE, 2021-07-01) Tharushika, G. K. A. A; Rasanga, D. M.T; Weerathunge, I; Bandara, P
    This research aims to develop a Mobile app for predicting major diseases we have to face nowadays. These days the heart disease is the main source of death around the world. It is a complex task to predict a heart attack with a doctor because more experience and knowledge are needed. Sometimes it may be gastritis or asthma symptoms. Also, the following most common disease is a skin disease. Most people have some skin disease, and they don’t even have time to check it from a medical centre. These diseases led to deadly cancers kind of things. Implementing the Smart health care application, the skin disease classification and treatment, and the heart disease predictions can be made domestically. The application is taken images of skin disease through the device camera. It classifies the disease with the Keras ResNet trained to classify the accuracy as eighty-seven point eighty-three as a percentage. The heart disease prediction module takes 14 different attributes that can access by the personal and predict the heart disease probability with the model of sklearn KNeighborsClassifier is trained as a percentage with an accuracy of eighty-three point nine. The application was developed on top of the android platform with the SQL Lite database integration.
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    PublicationEmbargo
    Machine Learning-Based Skin And Heart Disease Diagnose Mobile App
    (IEEE, 2021-07-01) Tharushika, G. K. A . A; Rasanga, D. M. T; Weerathunge, I; Bandara, P
    This research aims to develop a Mobile app for predicting major diseases we have to face nowadays. These days the heart disease is the main source of death around the world. It is a complex task to predict a heart attack with a doctor because more experience and knowledge are needed. Sometimes it may be gastritis or asthma symptoms. Also, the following most common disease is a skin disease. Most people have some skin disease, and they don’t even have time to check it from a medical centre. These diseases led to deadly cancers kind of things. Implementing the Smart health care application, the skin disease classification and treatment, and the heart disease predictions can be made domestically. The application is taken images of skin disease through the device camera. It classifies the disease with the Keras ResNet trained to classify the accuracy as eighty-seven point eighty-three as a percentage. The heart disease prediction module takes 14 different attributes that can access by the personal and predict the heart disease probability with the model of sklearn KNeighborsClassifier is trained as a percentage with an accuracy of eighty-three point nine. The application was developed on top of the android platform with the SQL Lite database integration.
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    PublicationOpen Access
    Inclination of teachers towards incorporating mobile game based learning into primary education: A Sri Lankan case study
    (MECS (http://www.mecs-press.org/), 2018-04-08) Bandara, P
    Incorporating information and communication technology (ICT), especially computer/mobile games into teaching and learning has been identified as a proven method of increasing primary grade students’ intrinsic motivation towards learning. However, in countries like Sri Lanka with teacher centric education cultures, the teacher still plays a significant role in the child’s education process. Therefore, it is imperative to look at the teachers’ willingness and inclination to integrate technology enhanced games in their classrooms. The purpose of this study is to investigate the teachers’ preparedness, attitude towards integrating mobile games in teaching and the issues faced by the teachers when trying to use technology in the Sri Lankan primary classroom. A questionnaire for assessing mobile game based learning readiness was designed and used as the research instrument to assess the inclination of teachers to incorporate mobile-based games for learning in their classroom. The survey was conducted involving primary school teachers in four Type 3 schools of Gampaha district in Sri Lanka. Type 3 schools have classes only up to grade 8. It was identified that the teachers in Type 3 schools of Gampaha district are moderately inclined towards incorporating mobile games into their day-to-day teaching activities.
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    EduEasy-Smart Learning Assistant System
    (IEEE, 2021-03-27) Karunasena, A; Bandara, P; Jayasuriya, J. A. T. P; Gallage, P. D; Jayasundara, J. M. S. D; Nuwanjaya, L. A. P. Y. P
    Usage of smart learning concepts have increased rapidly recently as better teaching and learning methods. Smart learning concepts are especially useful for students to learn better in large classes. In large classes, the lecture method is the most popular method of teaching. In the lecture method, the lecturer presents the content mostly using lecture slides and the students make their own notes based on the content presented. However, some students may find difficulties with the above method due to various issues such as speed in delivery. The purpose of this research is to assist students in large classes in the following content. The research proposes a solution with four components namely note taker, slide matcher, reference finder, and question presenter which are helpful for the students to obtain a summarized version of the lecture note, easily navigate to the content and find resources, and revise content using questions.
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    Candidate Selection for the Interview using GitHub Profile and User Analysis for the Position of Software Engineer
    (IEEE, 2020-12-10) Gajanayake, R. G. U. S; Hiras, M. H. M; Gunathunga, P. I. N; Supun, E. G. J; Karunasenna, A; Bandara, P
    Selecting the most suitable candidates for interviews is an important process for organizations that can affect their overall work performance. Typically, recruiters check Curriculum Vitae (CV), shortlist them and call candidates for interviews which have been the way of recruiting new employees for a long time. To minimize the time spent on the above process, pre-screening mechanisms are nowadays implemented by organizations. However, those mechanisms need sufficient information to evaluate the candidate. For example, in case of a software engineer, the recruiters are interested on the programming ability, academic performance as well as personality traits of potential candidates. In this research, a pre-screening solution is proposed to screen the applicants for the post of Software Engineer where candidates are screen based on an initial call transcript, GitHub profile, LinkedIn profile, CV, Academic transcript and, Recommendation letters. This approach extracts textual features of different dimensions based on Natural Language Processing to identify the Big Five personality traits, CV and GitHub insights, candidate's skills, background, and capabilities from Recommendation letters as well as programming skills and knowledge from Academic transcript and Linked Profile. The results obtained from the different areas are presented and shown that the selected supervised machine learning algorithms and techniques can be used to evaluate the best possible candidates.