Research papers published by Syigen.https://research.syigen.com/https://research.syigen.com/papers/intent-oriented-programming-language/https://research.syigen.com/papers/intent-oriented-programming-language/Modern software systems increasingly demand that developers specify what a system should accomplish rather than how it should be implemented, motivating declarative languages, intent-based domain-specific systems, and AI-assisted programming tools; however, many existing approaches remain confined to narrow domains, are tightly coupled to particular execution engines, or implicitly outsource program meaning to probabilistic models, weakening determinism, reproducibility, and formal reasoning. This paper introduces the Intent-Oriented Programming Language (IOPL), a general-purpose language that elevates intent to a first-class semantic construct while preserving deterministic, verifiable execution: IOPL separates semantic intent from execution strategy via a staged compiler pipeline and a canonical Execution Intermediate Representation (EIR) that freezes observable semantics prior to code generation, and it constrains AI components to non-authoritative roles whose outputs must conform to EIR and be subject to verification. We outline the language model, compiler architecture, runtime semantics, determinism contract, and verification approach, and situate IOPL within the broader research landscape.Sat, 07 Feb 2026 00:00:00 GMTIntent-Oriented ProgrammingDeclarative LanguagesCompiler ArchitectureDeterministic SemanticsAI-Assisted CompilationDewmal Handapangoda, Sadika Sumanapalahttps://research.syigen.com/papers/elephant-movement-prediction/https://research.syigen.com/papers/elephant-movement-prediction/This research aims to develop a machine learning-based mobile application, Safe App, to predict elephant movements to reduce the impacts of human-elephant conflicts in rural Sri Lanka. Traditional prevention methods have been unable to manage hundreds of annual deaths. In this respect, the Safe App applies AI to predict movement, map the locations of elephants, and visualize high-risk areas using heat maps. It is an early warning system that will integrate machine learning and spatial analysis. The primary data is collected from the users' reported sights, and secondary data is collected from GPS-tracked elephant movements, enhancing predictive accuracy. Safe App provides AI-driven wildlife insights to help conservationists, authorities, and communities in improving human and elephant safety.Wed, 01 Jan 2025 00:00:00 GMTElephant Movement PredictionMachine LearningHuman-Elephant ConflictWildlife ConservationDewmal Handapangoda, Sadika Sumanapala, S. D. S. Medhangani, P. H. S. N. Herath, N. G. M. S. Karunathilake, R. M. I. U. Rathnayake, A. K. A. U. Kularathne, D. M. Weerakoon, N. N. Jayasinghe, U. P. H. Pramodya, O. M. Jayamanne, E. D. T. N. Gunarathne