Research Papers - Dept of Information Technology
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/593
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Publication Open Access Exploring nontoxic perovskite materials for perovskite solar cells using machine learning(Discover, 2025-07-06) Pabasara W.G.A; Wijerathne H.A.H.M; Karunarathne M.G.M.M.; Sandaru D.M.C; Abeygunawardhana, Pradeep K. W; Sewvandi, GPerovskite solar cells are promising renewable energy technology that faces significant challenges due to the Pb induced toxicity. The current study addresses this issue by leveraging machine learning techniques to explore Pb-free perovskite materials that ensure environmental sustainability and human safety. A highly accurate machine learning model was developed to predict Goldschmidt factor and the band gap, aiming to discover lead-free perovskites. Extreme Gradient Boost (XGBoost), Random Forest (RF), Gradient Boost Regression (GBR), and Ada Boost Regression (ABR) models were employed for this purpose. The findings exhibit that XGBoost delivers the most precise and reliable results for Goldsmith tolerance factor prediction with an accuracy of 98.5%. Furthermore, GBR model, combined with K-nearest neighbors (KNN) model delivers an impressive accuracy of 98.7% for the band gap predictions. 49 Pb-free perovskite materials were screened out considering the toxicity and the abundance. Utilizing Principal Component Analysis (PCA) and K-means clustering, six optimal materials (KBiBr3, KZnBr3, RbBiBr 3, RbZnBr3, MAGeI3, and FAGeI3null) were identified as the potential environment-friendly materials for photovoltaic applications. These results show the crucial role of machine learning and statistical analysis in discovering nontoxic and environmental-friendly perovskite materials, advancing the development of sustainable energy solutionsPublication Embargo EasyChat: A Chat Application for Deaf/Dumb People to Communicate with the General Community(Springer, Cham, 2022-07-07) Sriyaratna, D; Samararathne, W. A. H. K.; Gurusinghe, P. M.; Gunathilake, M. D. S. S.; Wijenayake, W. W. G. P. A.Sign Language is closely associated with the deaf and dumb community to communicate with each other. However, not everyone understands sign language or verbal languages, so these communities need proper ways to communicate online. Therefore, this paper presents EasyChat, a sign language chat application that can translate three main sign languages into Simple English text as well as Simple English text into sign language, which would benefit for deaf/dumb community to express their ideas with the general community by simply capturing their British Sign Language (BSL) or Makaton gestures/symbols or lip movements. These steps are handled by four components. The first component, Convert BSL into Simple English, and the second component, handles Lip Reading conversion. The Makaton gesture and symbol conversion component produces a simple English text-formatted output for identified Makaton hand signs. Finally, the Text/voice to Sign Converter works on converting entered English text back into the sign language-based images. By using these components, EasyChat can detect relevant gestures and lip movement inputs with superior accuracy and translate. This can lead to more effective and efficient online communication between the community of deaf/dumb individuals and the general public.Publication Open Access Machine learning approach for predicting career suitability, career progression and attrition of IT graduates(IEEE, 2021-12-02) Bannaka, B. M. D. E; Dhanasekara, D. M. H. S. G; Sheena, M. K; Karunasena, A; Pemadasa, NThe IT industry in Sri Lanka is associated with a massive work force consisting of skillful professionals and it also provides many job opportunities for fresh graduates at the present. For a fresh graduate entering the IT industry there is a wide variety of job opportunities available and in order to have a satisfactory and rewarding career they should identify the most suitable career for them. On the other hand, employees change their careers and regularly seeking for career advancements and more benefits while the employers struggle to retain employees. Under such circumstances, this research focuses on developing a career mentoring system which comprises of the prediction of career suitability, career and salary progression, and employee attrition to assist IT employees to achieve career goals by overcoming barriers in their career path. For this purpose, data are collected from IT employees, and several models were implemented using classification algorithms such as XGBoost, Random Forest, Support Vector Machine, K-Nearest Neighbors, Decision tree, Naive Bayes, and their performance are compared using accuracy, precision, recall, and F1-Score to select accurate models. XGBoost resulted with higher accuracies for prediction of career suitability, initial salary, career and salary progression with values of 92.31, 90.35, 86.45 and 88.76 respectively. Furthermore, for the prediction of professional courses and employee attrition, Random Forest resulted higher accuracies of 93.52 and 89.70. The ultimate goal of this research is to guide IT graduates and employees to have better performances and to assist them in embracing responsibilities throughout their career life.Publication Embargo Individualized Edutainment and Parent Supportive Tool for ADHD Children(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Thennakoon, A.; Perera, D.; Sugathapala, S.; Weerasingha, S.; Samarasinghe, P.; Dahanayake, D.; Piyawardan, V.S.Attention-Deficit/Hyperactivity Disorder (ADHD) is a comorbid disorder that can impact a child and his/her family. ADHD children have considerable obstacles in managing time, understanding instructions, and paying attention to the activities. To address these perplexities, this research has designed a mobile application to help parents to have better interaction with the children and for the children to enjoy their learning activities. The specialty of this application is the models are trained on individual child skills and needs. Issues with time management are handled by the Scheduler component while the Instruction Predictor module supports the parent in recognizing the child's understandability level. Furthermore, the children are provided with edutainment activities based on their attention and ability levels. Different models have been used in predicting the results through these modules and the prediction result accuracy exceeds 90% in most of the cases. Out of the many models, The Random Forest model resulted in the best overall performance. The application was tried by many parents and health professionals and received satisfactory and commendable reviews.Publication Embargo Agro-Mate: A Virtual Assister to Maximize Crop Yield in Agriculture Sector(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Dayalini, S.; Sathana, M.; Navodya, P. R. N.; Weerakkodi, R.W. A. I. M. N.; Jayakody, A.; Gamage, N.Information Technology plays a vital role in the agriculture industry. The main goal of the project is to develop a mobile application to support farmers to take accurate decisions and help them with activities such as soil quality determination, best crop selection, rice disease prediction, and disaster prediction for the wet zone of Sri Lanka. To achieve the main goal the project has incorporated advanced technologies such as Deep Learning, Image Processing (IP), Internet of Things (IoT), and Machine Learning that can support farmers or investors in a way to maximize yield. ‘Agro-Mate’ application is developed in a way to facilitate the agriculture industry. ‘Agro-Mate’ consists of four components such as soil quality determination and fertilizer recommendation, best crop selection, rice disease prediction and recommendation, and natural disaster prediction and providing the recommendation. Also, the application suggests fertilizer when soil is lacking quality and provides recommendations whenever rice diseases or natural disasters are identified. The usage of android mobile devices in agriculture is one of the key components of the sector's growth, which facilitates the farmer's inaccurate decision-making to gain more quality and quantity of crops. Agro-mate’ is more likely to increase the productivity of crops and indirectly increase the GDP of Sri Lanka.Publication Embargo Smart Mirror with Virtual Twin(IEEE, 2019-12-05) Abeydeera, S. S; Bandaranayake, M; Karunarathna, H. U; Pallewatta, S; Dharmasiri, P; Gunathilake, B; Saparamadu, S; Senanayake, B; Jayawardena, CSmart Mirror with a virtual twin who helps the user as a close companion. The virtual twin monitors the user's physical appearance and tracks the data gathered from given inputs. Since this is an intelligent virtual twin it uses machine learning techniques. It helps to improve the user's mental and physical health by detecting medical conditions and providing suitable suggestions in a more personalized way. This virtual twin not only focuses on physical or mental health conditions but also gives friendly suggestions about suitable styles which helps to improve the person's life quality.