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Author: search.filters.author.Kasthurirathna, D

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    Hybrid Model-Based Automated Exterior Vehicle Damage Assessment and Severity Estimation for Insurance Operations
    (Institute of Electrical and Electronics Engineers Inc., 2025) Jayagoda, N.M; Kasthurirathna, D
    After a vehicle accident, insurance companies face the critical task of assessing the damage sustained by the involved vehicles, a process essential for maintaining the insurer's credibility, building consumer trust, and meeting legal and ethical obligations. This assessment is crucial for ensuring clients' financial protection and proper compensation, upholding the integrity of the insurance process. Traditionally, evaluations have been conducted through manual inspections by experienced professionals who meticulously document vehicle damage. Despite its thoroughness, this approach suffers from significant inefficiencies, high costs, and extended time requirements. Moreover, the method is vulnerable to human errors and subjective biases, which can result in inflated valuations. To overcome these challenges, this research introduces an innovative system designed to leverage technology for analyzing images of damaged vehicles uploaded by the user. This system aims to accurately identify the damaged external components, assess the severity of the damage, and determine the repair needs based on the compromised sections of the vehicle. The findings reveal that the hybrid model used in this research is capable of determining vehicle damage severity with an overall accuracy of 73.3%. This level of accuracy demonstrates the model's robust capability to effectively navigate and analyze complex damage patterns, underscoring its practical applications. By accurately determining damage levels on the first assessment, the model reduces the need for further assessments and disagreements, which frequently cause claim delays. This enhancement increases productivity, reduces administrative costs, and improves the customer experience, resulting in a more efficient, transparent, and satisfactory resolution of vehicle insurance claims.
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    Intelligent Systems for Comprehensive Dog Management
    (Association for Computing Machinery, 2025-06-28) Katipearachchi, M.E; Sachethana, O; Gunawardena, G. N.A; Ruwanara, D.C; Krishara, J; Kasthurirathna, D
    In recent years, the integration of advanced technologies with canine welfare has gained significant attention, leading to the development of comprehensive platforms for dog management. The "Research Pooch-Paw"initiative addresses the multifaceted needs of dog owners and stray dog populations through an innovative platform that incorporates machine learning, wearable sensors, and real-time data processing. The platform facilitates early disease detection, behaviour analysis, and health monitoring using IoT-enabled devices, and provides personalized care guidance. Additionally, it includes features for stray dog identification and emergency response using deep learning algorithms and image processing techniques. The research underscores the potential of leveraging modern technology to enhance the quality of life for dogs and improve the effectiveness of canine welfare strategies.