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

20251

Advanced Search

Filter by

Settings

Author: search.filters.author.Amarasena, NSubject: search.filters.subject.Internet of Things (IoT)

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    IoT based System for Early Detection and Monitoring of Mosquito Breeding Sites
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Samarakoon, U; Amarasena, N; Marage P.M.B.S; Rajapaksha R.M.K.P.; Thathsara B.M.L; Thennakoon K.T.A.T.
    This research develops an IoT-based dengue prediction and prevention system integrating real-time environmental monitoring, automated species identification, predictive analytics, and community engagement to combat dengue in vulnerable regions. The study addresses critical gaps in existing literature by fusing IoT sensor networks (temperature, humidity, water-level, and MEMS microphones), epidemiological data from the National Dengue Control Unit (NDCU), meteorological records, and community-reported data via a multilingual mobile application. Methodologically, we employed stratified sensor deployment across urban, semi-urban, and rural zones, coupled with machine learning models (SVM/Random Forest for mosquito classification, CNNs for image-based breeding site detection, and time-series forecasting for outbreak prediction). Results demonstrated 89.25% prediction accuracy, dynamic heatmaps for risk visualization, and high community engagement (120 reports from urban areas alone). The system enables data-driven public health interventions, reducing dengue spread through proactive breeding site elimination and real-time risk communication, representing a scalable paradigm shift in vector-borne disease control.