Research Papers - Dept of Information Technology

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    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. M
    The 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.
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    A Mobile App for Location Based Customer Notifications about Sales Offers
    (2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Saluwadana, R.B.; Hemachandra, K.A.N.W.; Jayasinghe, L.M.R.; Ahnaf Hassanar; Gamage, M.P.
    Nowadays merchants’ focus on sending specifics about their sales offers to prospective customers through electronic means. But customers are neutral about those messages if they are away from those shops. Therefore, the authors decided to implement a mobile application to send location-based sales offer notifications to customers in order to overcome this problem, with some additional features. The main features in the proposed system are to filter out sales offer details from social media, send location-based notifications containing details of offers to customers, provide personalized search predictions during search, and provide recommendations to merchants to improve their business. Modern technologies like Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) are used to build the solution for this problem. The main advantage of the proposed system is that customers are attracted more towards the sales offers since they receive them when they are close by to the relevant shop. Also, merchants can reach targeted customers resulting in a more effective marketing campaign. The survey conducted proved that both customers and merchants are highly satisfied with the effectiveness of the product.