International Conference on Advancements in Computing [ICAC]

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/312

The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.

The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.

https://icac.lk

Browse

Filters

20252

Advanced Search

Filter by

Settings

Author: search.filters.author.De Silva L.K.NSubject: search.filters.subject.machine learning

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemEmbargo
    Data-Driven Insights for Improved Diabetes Management
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Gunawardhana D.H.M.G; Kajeevan J; De Silva L.K.N; Wimansa P.P.H.S.D.; Anjana, J; Dassanayake, G.T
    Effective diabetes management requires understanding the complex interplay between multiple physiological and behavioral factors affecting glycemic control. This study presents a comprehensive analysis of diabetes management data from six distinct datasets containing continuous glucose monitoring (CGM) data, insulin administration records, carbohydrate intake, physical activity, sleep quality, and stress measurements. We employed time series analysis techniques such as STL decomposition, Dynamic Time Warping, LSTM neural networks, and XGBoost modeling to identify patterns in glucose variability and their relationships with modifiable factors. Our findings revealed significant time-of-day variations in glucose levels, with highest variability in late afternoon and evening (CV = 0.41 vs. 0.34, p < 0.01). Integrated prediction models achieved 83% sensitivity for hypoglycemia and 81% for hyperglycemia detection. The insulin-to-carbohydrate ratio varied by time of day, with morning ratios (1:8) differing from evening (1:12). Physical activity demonstrated intensity-dependent effects, with moderate-intensity exercise reducing glucose levels by 42 mg/dL for approximately 12 hours post-activity. Sleep quality below rating 3 (on a 5-point scale) was associated with a 37% increase in next-day glucose variability. These findings provide evidence-based insights for developing personalized diabetes management strategies that account for chronological variations in insulin sensitivity, meal timing, physical activity scheduling, and lifestyle interventions targeting sleep and stress management.
  • Thumbnail Image
    ItemEmbargo
    Data-Driven Insights for Improved Diabetes Management
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Gunawardhana D.H.M.G; Kajeevan J.; De Silva L.K.N; Wimansa P.P.H.S.D; Anjana, J; Dassanayake, G.T
    Effective diabetes management requires understanding the complex interplay between multiple physiological and behavioral factors affecting glycemic control. This study presents a comprehensive analysis of diabetes management data from six distinct datasets containing continuous glucose monitoring (CGM) data, insulin administration records, carbohydrate intake, physical activity, sleep quality, and stress measurements. We employed time series analysis techniques such as STL decomposition, Dynamic Time Warping, LSTM neural networks, and XGBoost modeling to identify patterns in glucose variability and their relationships with modifiable factors. Our findings revealed significant time-of-day variations in glucose levels, with highest variability in late afternoon and evening (CV = 0.41 vs. 0.34, p < 0.01). Integrated prediction models achieved 83% sensitivity for hypoglycemia and 81% for hyperglycemia detection. The insulin-to-carbohydrate ratio varied by time of day, with morning ratios (1:8) differing from evening (1:12). Physical activity demonstrated intensity-dependent effects, with moderate-intensity exercise reducing glucose levels by 42 mg/dL for approximately 12 hours post-activity. Sleep quality below rating 3 (on a 5-point scale) was associated with a 37% increase in next-day glucose variability. These findings provide evidence-based insights for developing personalized diabetes management strategies that account for chronological variations in insulin sensitivity, meal timing, physical activity scheduling, and lifestyle interventions targeting sleep and stress management.