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

2022 - 20254

Advanced Search

Filter by

Settings

Author: search.filters.author.Krishara, J

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemEmbargo
    FITGEN - AI Smart Fitness Companion: An Integrated Multi-Modal Approach to Personalized Health and Fitness Management
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Gunawardena S.N.A; Jayamali P.L.W; Methsandi K.A.N.; Gamage R.P.D.D; Wijendra, D; Krishara, J
    This research presents FITGEN, a comprehensive AI-powered fitness companion system that integrates four core modules: AI Gym Trainer with Gamification, Medical Guidance System, AI Nutritionist, and Social Bridge for inclusive fitness engagement. Unlike existing solutions that address singular aspects of fitness technology, FITGEN provides a holistic approach combining real-time pose detection, chronic disease management, personalized nutrition planning, and accessibility features for special abled youth. The system utilizes Flutter framework with Firebase backend, implementing Google ML Kit for pose estimation, TensorFlow models for nutrition recommendations, and ESP32-based IoT sensors for vital sign monitoring. Comprehensive evaluation demonstrates 93.5% pose detection accuracy, 96% nutrition recommendation accuracy, 73% user retention after 4 weeks, and significant improvements in accessibility compliance. The integrated approach addresses critical gaps in existing fitness technologies while maintaining high user engagement and safety standards for diverse user populations including those with chronic conditions and developmental disabilities.
  • Thumbnail Image
    ItemEmbargo
    Context-Aware Incremental Learning Pipeline Triggers
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Perara, S; Senadheera, D; Gunathilake, P; Gunawardana, C; Thelijjagoda, S; Krishara, J
    Modern Continuous Integration/Continuous Delivery pipelines require intelligent and adaptive learning mechanisms to enhance software deployment stability and efficiency. While previous studies have introduced Pull Request prioritization techniques and predictive models for build failures, they do not offer real-time recommendations based on Pull Request criticality and build stability. This research presents a GitHub app based on reinforcement learning for intelligent Pull Request prioritization and deployment advisory in Continuous Integration/Continuous Delivery workflows. T he system combines natural language processing with contextual features to dynamically determine whether a Pull Request should be prioritized for early deployment or flagged for urgent review based on its priority level and associated build failure risk. Due to limited labeled feedback data, the study employs a hybrid approach using supervised learning for initial classification, enhanced by reinforcement learning for continuous adaptation. Using a dataset of 682148 Pull Requests from 28835 repositories, experimental results demonstrate significant improvements in deployment efficiency, achieving 92.01% accuracy in Pull Request classification with strong developer alignment. This research presents a comprehensive solution for optimizing Continuous Integration/Continuous Delivery pipelines through intelligent Pull Request prioritization with deployment advisory.
  • Thumbnail Image
    ItemEmbargo
    CINNOVA: Advancing Sustainable Cinnamon Farming through AI and Collaborative Solutions
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Bandara D.; Fernando M.K.K.L; Senadeera N.A.D.N; De Silva R.C.T; Wijendra, D; Krishara, J
    Cinnamon is one of the most economically significant export crops in Sri Lanka. However, its cultivation is challenged by plant diseases, nutrient deficiencies, and inefficient harvesting practices, which reduce yield quality and productivity. Traditional methods for identifying plant health issues are time-consuming and require expert evaluation, often inaccessible to rural farmers. To address these limitations, this study introduces an AI-driven intelligent monitoring system for sustainable cinnamon cultivation, a mobile-based solution specifically designed to enhance cinnamon farming practices. The system leverages Artificial Intelligence (AI), Deep Learning (DL), and Image Processing techniques to support real-time plant health diagnostics. It integrates multiple AI-powered components for early detection of bark diseases such as Rough Bark Disease (RBD) and Canker Disease (CD) using a contrastive learning-based model, severity prediction of leaf diseases, including Leaf Gall and Leaf Blight, using the YOLO model, identification of nutrient deficiencies, particularly Magnesium and Potassium, through transfer learning and prediction of cinnamon bark maturity and quality grades utilizing spatial attention mechanisms based on diameter and color. Each model is optimized for mobile deployment to provide real-time feedback and enable efficient decision-making. This AI-driven approach enhances disease management and improves yield quality while promoting sustainable, data-driven cinnamon cultivation in Sri Lanka.
  • Thumbnail Image
    PublicationEmbargo
    Face Skin Disease Detection and Community based Doctor Recommendation System
    (IEEE, 2022-12-09) Udara, M.A.A.; Wimalki Dilshani, D.G.; Mahalekam, M.S.W.; Wickramaarachchi, V.Y.; Krishara, J; Wijendra, D
    In our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.