Faculty of Computing

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Author: search.filters.author.Nawinna, D. PSubject: search.filters.subject.Identification

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    Digital Tool for Prevention, Identification and Emergency Handling of Heart Attacks
    (IEEE, 2021-09-30) Mihiranga, A; Shane, D; Indeewari, B; Udana, A; Nawinna, D. P; Attanayaka, B
    Heart attack is one of the most frequent causes of death in adults. The majority of heart attacks lead to death before any treatment is given to patients. The conventional mode of healthcare is passive, whereby patients themselves call the healthcare services requesting assistance. Consequently, if they are unconscious when heart failure occurs, they normally fail to call the service. To prevent patients from further harm and save their lives, the early and on-time diagnosis important. This paper presents an innovative web and mobile solution designed using it as Internet of Things (IoT) technology and Machine learning concepts to effectively manage heart patients, the ‘CARDIIAC’ system. This system can predict potential heart attack based on a set of identified risk factors. The system also can identify an actual heart attack using the readings from a wearable IoT device and notify the patient. The system is also equipped with emergency event coordination functionalities. Therefore, ‘CARDIIAC’ provides a holistic care for heart patients by effectively monitoring and managing emergencies related to heart diseases. This would be a socially important system to reduce the number of heart patients who die due to the inability to get immediate treatment.
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    Identification of Medicinal Plants by Visual Characteristics of Leaves and Flowers
    (IEEE, 2019-12-18) Jayalath, A. D. A. D. S; Amarawanshaline, T. G. A. G. D; Nawinna, D. P; Nadeeshan, P. V. D; Jayasuriya, H. P
    In Ayurveda medicine, correct identification of medicinal plants is of great importance. Plants are identified by human experts using their visual features and aroma. Incorrect identification of medicinal plants may lead to adverse results. Plant identification can be automated using visual morphological characteristics such as the shape, color, and texture of the leaves and flowers. This paper presents how rare medicinal plants were identified with high accuracy by applying image processing and machine learning capabilities. For this study, a database was created from scanned images of leaves and flowers of rare medicinal plants used in Sri Lankan Ayurveda medicine. Both the front and back sides of leaves and flowers were captured. The leaves are classified based on the unique feature combination. Identification rates up to 98% have been obtained when tested over 10 plants.