Scopus Index Publications
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/2162
This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
Browse
3 results
Search Results
Publication Embargo Candidate Selection for the Interview using GitHub Profile and User Analysis for the Position of Software Engineer(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 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 perfo rmance 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 an d shown that the selected supervised machine learning algorithms and techniques can be used to evaluate the best possible candidates.Publication Embargo Symptomatic Analysis Prediction of Kidney Related Diseases using Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Lansakara, D.; Gunasekera, T.; Niroshana, C.; Weerasinghe, I.; Bandara, P.; Wijendra, D.Sri Lanka has been witnessing an increase in kidney disease issues for a while. Elderly kidney patients, kidney transplant patients who passed the risk level after the surgery are not treated in the emergency clinic. These patients are handed over to their families to take care of them. In any case, it is impossible to tackle a portion of the issues that emerge regarding the patient at home. It is hoped to enter patient’s data from home every day and to develop a system that can use that entered data to predict whether a patient is in an essential circumstance or not. Additionally, individuals in high-hazard regions cannot know whether they are in danger of creating kidney disappointments or not and individuals in danger of creating kidney sickness because of Diabetes Mellitus. Thus, we desire to emphasize the framework to improve answers for this issue. The research focuses on developing a system that includes early kidney disease prediction models involving machine learning classification algorithms by considering the relevant variables. In predictive analysis, six machine learning methods are used: Support Vector Machine (SVM with kernels), Random Forest (RF), Decision Tree, Logistic Regression, and Multilayer Perceptron. These classification algorithms' performance is evaluated using statistical measures such as sensitivity (recall), precision, accuracy, and F-score. In categorizing, accuracy determines which examples are accurate. The experimental results reveal that Support Vector Machine outperforms other classification algorithms in terms of accuracy.Publication Embargo E-Agrigo(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kartheepan, T.; SirigajanK, B.; Subangan, K.; Mohammed Azzam, M.A.; Bandara, P.; Mahaadikara, M.M.D.J.T.H.To feed this population, food production should be increased by at least 70%. Developing nations have a vast potential to increase the amount of food produced by doubling the current production. However, the traditional methods of farming are making agriculture unviable and inefficient. The increasing food production needs to be met by double the current level of farming. The conventional of farming is making industry uncompetitive and inefficient. This paper aims to analyze the various factors that affect the implementation of autonomous machinery in agriculture. The development of autonomous machinery for agriculture has emerged as vital step towards achieving this goal. Now a day’s farmers are planning their cultivation by finding proper weather and geographical condition on their own experience, but they are failing to cultivate profitable crop and unaware of the diseases that will affect their crops, sometimes these diseases may affect their whole crops and let the farmers to sink in zero profit. Despite these issues plays a major role, there are some other problems also have an impact like, lack of irrigation plans and question of how and where to sell their cultivated crops. By considering these major threats we have planned to propose a solution to some of the selected issues. This can be achieved by applying machine learning algorithm, Image processing and IOT systems. By using our platform farmers will get a chance to plan their yield in a profitable way by using our optimized weather and geographical data.