SLIIT Conference and Symposium Proceedings
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/295
All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.
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Publication Embargo Intelligent Timetable Scheduler: A Comparison of Genetic, Graph Coloring, Heuristic and Iterated Local Search Algorithms(IEEE, 2019-12-05) Ekanayake, T. W; Subasinghe, P; Ragel, S; Gamage, A; Attanayaka, SA Timetable scheduling is a monotonous task and a problem in an educational institute. This is because many rules and constraints are involved, which can be categorized as hard and soft constraints. Mainly, a university must produce two types of timetables, which are examination, and semester timetables. This paper has reviewed the Exam Timetabling problem with Genetic and Graph Coloring algorithms and the Semester Timetabling problem with Heuristic and Iterated Local Search algorithms. Our aim here is to develop a possible and correct solution for each timetabling problem using the above-mentioned four different approaches.Publication Embargo WoKnack – A Professional Social Media Platform for Women Using Machine Learning Approach(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Shanmugarajah, S.; Praisoody, A.; Rakib Uddin, M.D.Today’s generation is heavily influenced by social media. However, most users decline to post their abilities on these platforms for a variety of reasons, including security, a lack of basic skills, and a lack of knowledge about the various skill sets. It's understandable that women face many security risks on these platforms. WoKnack is a professional social networking platform dedicated to women. This opens opportunities for women to demonstrate their abilities and teach other women. This paper targets onfunctionalities like registration limited to female users, skill categorization, post verification and privacy preservation. Facial image, identification document and Voice related gender verification done using machine learning approaches to identify thegender before registration. Accuracy of 91% gained during the process. Skills have been categorized using Natural language processing and post verification done based on these categories. Usage of the best accurate algorithm gives an accuracy of 94% during this process. In order to preserve the privacy of users Data anonymization, skill and location clustering have been added to the system.