ICITR - 2023

Permanent URI for this collectionhttp://192.248.9.226/handle/123/22075

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Browsing ICITR - 2023 by Subject "Artificial intelligence"

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    Ai-driven user experience design: exploring innovations and challenges in delivering tailored user experiences
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Padmasiri, P; Kalutharage, P; Jayawardhane, N; Wickramarathne, J; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
    In today’s digital landscape, providing user experiences is considered paramount in respective of user satisfaction and engagement. Artificial Intelligence (AI) has emerged as a transformative force in the User Experience (UX) design field, offering innovative solutions. Our research delves into key innovations and challenges enabled by AI in UX design particularly guided by Design Thinking (DT) process. The methodology involved administering a questionnaire to UX professionals in Sri Lanka using a snowball sampling method. The questionnaire, distributed through online platforms, explored participants’ familiarity with AI-driven UX design, contributions of AI in the DT process, and challenges faced, and the responses were analyzed using MS Excel and R Studio. The results demonstrate that AI technologies certainly empower UX professionals to design usercentric solutions adhering to DT process. A “Recommendation Guide” is provided, featuring a set of recommended tools for UX professionals to integrate AI technologies into the DT process.
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    Enhanced timetable scheduling: a high-performance computational approach
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Sovis, A; Patikirige, C; Pandigama, Y; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
    Timetable scheduling is a complicated, expensive and resource-intensive Optimization Problem. This project aims to suggest a solution to this problem using multiple strategies. The core strategy is to use Artificial Intelligence and Machine Learning to optimize a timetable. The result is optimized further by reapplying this optimization mechanism iteratively without aiming to build a perfect result in a single iteration. The project uses the concepts of High-Performance Computing and Cluster Computing to provide flexibility and efficiency on a hardware level. These form the basis of Project Almanac: a robust and flexible timetable optimization architecture. Project Almanac aims to generate a ‘good enough’ timetable by adjusting the expenses according to the end-user requirements. Alternatively, the solution also intends to offer a faster, cheaper and more flexible hardware-software architecture to generate optimized timetables for diverse applications.