SLIIT Conference and Symposium Proceedings
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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.
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Publication Embargo A Mobile Application to Predict and Manage High Blood Pressure and Personalized Recommendations(IEEE, 2019-12-05) Rajapaksha, S. K; Abhayarathne, W. J. A; Kumari, S. G. K; De Silva, M. V. L. U; Wijesuriya, W. M. S. MThe purpose of this investigation is to present a mobile application using AI expert and how to predict and manage high blood pressure and provide personalized recommendations to lower it. Basically, the system interprets the inadequate and inappropriate intake of food is known to cause various health issues and diseases. Due to the diversity of food components and a large number of dietary sources, it is challenging to perform a real-time selection of diet patterns that must fulfill one's nutrition needs and with considering your health issues and diseases. In this research, to address this issue to present an android based system, called Smart Blood Pressure Recommendation app. The purpose of this system is to allow patients to have an easy way to monitor their health and to see how their blood pressure has changed over time. This offer advice or suggestions, without having to schedule an appointment. As the system continues to gather data from a patient, it begins to offer advice its own if it finds that the patient's current conditions fit a certain condition or pattern. To generate a recommendation, it refers to an Ontology based data model. The data model gains information about its knowledge by doctors and nutritionists that can be used by AI expert. This research helps users to identify their previous record charts of blood pressure, reliable alarms for user blood pressure medication, popup notifications, build health diary and also share log data processing through the AI expert.Publication Embargo Responsive drone autopilot system for uncertain natural language commands(IEEE, 2019-12-05) Rajapaksha, S. K; Illankoon, V; Halloluwa, N. D; Satharana, M; Umayanganie, DThe purpose of this investigation is to realize how possible is it to control a drone with human's English natural language commands, even the command contains some quantitative uncertain words. Basically, the system interprets the commands with uncertain words, into a machine understandable format. According to the literature review, human-robot association does not go with uncertain words and it is a considerable gap. This proposed system has a client side mobile application to input voice. To build a machine understandable command, it refers to an ontology based knowledge store. The knowledge store gains information and expands its knowledge by crawling websites. Once the user commands, the drone to make a movement, the system will analyze the instruction to check if there is uncertainty and if it points to a surrounding object that is visible to the drone's camera. For extracting the uncertainty it uses NLTK along with a specific grammar rule and that methodology is succeeded according to the results. After collecting that information the system can navigate through the user's instruction. This research helps non-skilled drone pilots to have a smooth flying experience, and also other researchers to discover about natural language processing with robotics.