ICATC 2023

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/27835

Browse

Filters

20232

Settings

Author: search.filters.author.Welhenge, Chiranthi

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Fog Computing based Heart Disease Prediction System using Deep Learning for Medical IoT
    (Faculty of Computing and Technology, University of Kelaniya Sri Lanka, 2023) Welhenge, Anuradhi; Welhenge, Chiranthi; Subodhani, Shanika
    Internet of Things (IoT) is used in all areas because of the benefits it is offering. All most anything can be connected to the internet and data created by these devices can be analyzed to predict results. IoT is helpful in the medical field because it can connect the patients with the healthcare professionals, and the healthcare professionals can monitor their patients remotely and analyze their data and take necessary actions. Because of the huge amount of data in IoT systems, cloud services are utilized to store the data. But this is not a feasible option in medical IoT, because the predictions should be available as quickly as possible, since patients’ lives are at risk. Therefore, edge-fog- cloud architecture is used. Fog nodes can be used to analyze data closer to the edge devices, resulting in much faster predictions and the cloud can be used for storage. This paper proposes a novel fog based architecture for medical IoT based on deep learning. Deep learning is used on the fog nodes to make accurate predictions. This study used data collected from heart patients to predict the heart disease to evaluate the system and yielded a good accuracy.
  • Thumbnail Image
    Item
    Effect of Finite Impulse Response Filters on Activities of Daily Living Classification Algorithms
    (Faculty of Computing and Technology, University of Kelaniya Sri Lanka., 2023) Welhenge, Anuradhi; Welhenge, Chiranthi
    With the increasing aging population, improving the healthcare system is an important task in every country. The largest number of hospitalizations of the elderly people is due to falls. Therefore, many researchers have come up with different fall detection mechanisms. Improving the accuracy of these algorithms is an important task. This paper focuses on the use of Finite Impulse Response filters to improve the accuracy.