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.

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Subject: search.filters.subject.maternal health

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    Personalized AI System for Maternal Nutrition and Exercise
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Kumara P.S.D.N; Dahanayake Y.D.N.P; Jayalath U.W.T.P.; Ranawaka T.A; Samarakoon, U; Tissera, W
    This work presents a personalized AI system for maternal wellness that generates two coordinated, daily outputs for pregnant users: culturally adapted meal plans and risk-aware exercise prescriptions. In its nutrition module, the combination of a library of curated local dishes and trimester-specific energy corridors with condition-aware rules (diabetes/gestational diabetes, anemia, hypertension, allergies) highlights iron, folate, protein, and calcium instead of full nutrient panels. The exercise module comprises a rules-first risk screen (Low/High) corroborated by a calibrated gradient-boosted classifier, followed by a template-based planner that renders day-level sessions with safety guardrails, trimester adaptations, and video guidance. Evaluation on de-identified datasets (2,000 meal profiles; 970 risk records; 1,934 exercise prescriptions) showed high nutritional adequacy and reliable safety enforcement: daily energy corridors were met on 94.7% of days and ≥3/4 essential nutrients on 91.3%, with 0 allergen violations. The risk classifier achieved AUROC 0.86, accuracy 0.82, sensitivity 0.79, and specificity 0.84. The exercise recommender successfully enforced trimester- and risk-based guardrails, with all plans passing safety checks. These findings, from a study focused on Sri Lanka, suggest that an integrated, objective and culturally based method can provide personalized, safe guidance daily in resource-constrained environments, and serve as a basis for future clinical validation and predictive modeling.