Smart Computing and Systems Engineering - 2025 (SCSE 2025)

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    FITSTYLE: An Application Revolutionizing Online Shopping by Enhancing the Virtual Try-On Experience
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Divyanjalee, G.; Ilmini, K.; Uwanthika, I.
    In the last decade, the fashion industry has been significantly influenced by the rise of e-commerce and mobile commerce. As online consumers, we often face the challenge of selecting the right clothing size without the ability to physically try on garments, leading to frustration, uncertainty, and high return rates. These issues negatively impact customer satisfaction and online sales productivity, highlighting the need for advanced virtual shopping solutions. To address this problem, FITSTYLE proposes a sophisticated virtual try-on tool based on Generative Adversarial Networks (GANs) to simulate how clothes fit a consumer’s body. The FITSTYLE system tackles challenges in online clothing shopping by utilizing advanced technologies such as ResNet101 for image preprocessing, OpenPose for pose estimation, image segmentation to isolate users from backgrounds, and garment deformation algorithms for accurate fitting. Designed for ease of use and realistic visualization, it enhances customer satisfaction and reduces return rates. The research employed a mixed quantitative and qualitative methodology, collecting data from online shoppers to understand user needs and preferences. Based on these insights, the system was implemented using the most suitable technologies, with initial results showing improved customer decision-making and engagement. The system enhances satisfaction, reduces return rates, and boosts productivity in online sales, while also paving the way for future advancements in virtual try-on technologies and the broader fashion e-commerce landscape.
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    Accelerating Meta-Learning with the Enhanced Reptile Algorithm for Rapid Adaptation in Neural Networks
    (Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Shree Smeka J.; SheejaKumari V.; Vijaya Raj M.; Santhosh Kumar S. P.; Angalaeswari S.
    Reptile is an innovative meta-learning approach that improves the process of neural networks training across diverse tasks. Reptile differs from the preference gradient strategies. It searches for a model weight vector which can enable a model to learn a new set of tasks with a small number of weight updates. This is accomplished via a first-order optimization process which makes it less intricate than other strategies like model-agnostic meta-learning MAML. Reptile is said to sample a number of tasks and then trains the model on each of the tasks via performing a series of a few gradient steps, systematically updating the model towards the average gradient direction across all the tasks. This enables the model to generalize well with new tasks trained with few iterations, thus proving beneficial in few-shot learning. Reptile enables faster adaptation of the model by focusing on the learning of better initial parameters thereby lowering computational overhead and the training duration. The algorithm is noted for its ability not only to learn but also to adapt itself to new tasks in a short period of time, which greatly extends the scope of its application, especially in areas where data is scarce. Examples of these areas are robotics, personalized advertisements, and decision-making approaches that need to operate in real time. Reptile Algorithm builds good on gradient-based approaches and can spearhead volumetric applications of meta reasoning by being exceptionally efficient, scalable and less computationally intensive.