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Author: search.filters.author.Weerasinghe, NHas files: Yes

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    PublicationEmbargo
    Conquerors of poverty – a case study of Colombo slum dwellers
    (Emerald Publishing, 2025-02-07) Nanayakkara Wasam Mudage, K; Weerasinghe, N; Madusanka, M; Saliya, C.A; Lokeshwara, A. A; Jayatissa, C. D
    Purpose: While extensive research has explored poverty in various dimensions, there remains a notable dearth of studies focusing on success stories of slum dwellers overcoming adversity. This research seeks to address this gap by investigating the strategies employed by individuals to transcend poverty. Moreover, it evaluates the practicality and effectiveness of existing theories in real-world contexts through careful interpretation. Design/methodology/approach: Data were gathered from two distinct cases through comprehensive, in-depth interviews. These narratives were subjected to scrutiny employing the Description Analysis Interpretation method. Subsequent interpretation and theoretical exploration were guided by Bourdieu’s class theory. The overarching goal was to shed light on the remarkable journeys of specific slum residents who surpassed the constraints of poverty. This endeavor not only highlighted the practical efficacy of these theories but also underscored their relevance in illuminating real-world scenarios through interpretation. Findings: The findings underscore the capacity of slum dwellers to rise above poverty, thereby emphasizing the practical utility of specific theories aimed at poverty alleviation in elucidating their experiences. Notably, social capital, a cornerstone of Bourdieu’s class theory, emerges as equally pivotal as economic capital in shaping individuals' trajectories. Research limitations/implications: The study’s scope is confined to narratives within the Colombo slums, offering a platform for future researchers to extend their investigations beyond this context. By employing alternative methodologies and exploring diverse geographical regions, scholars can broaden their understanding of poverty alleviation strategies and their applicability across varied socioeconomic landscapes. This calls for wider research for comprehensive exploration and comprehension of poverty dynamics beyond singular locales. Practical implications: The study provides invaluable insights for policymakers, governmental bodies and nongovernmental organizations, urging them to reconsider and reformulate policies, educational strategies and community development programs tailored to the needs of slum dwellers and their children. These insights offer a pathway toward more effective interventions aimed at fostering sustainable upliftment within these marginalized communities. Originality/value: This research fills a critical gap in poverty literature by exploring success stories of slum dwellers overcoming adversity, an area often overlooked. It uniquely investigates the strategies these individuals employ to transcend poverty, offering fresh insights into the practical application of poverty alleviation theories. Utilizing Bourdieu’s class theory, the study highlights the importance of both social and economic capital in these success narratives. By focusing on real-life experiences, it underscores the relevance and utility of these theories in real-world contexts, enriching the theoretical discourse and providing valuable perspectives for policymakers and practitioners.Poverty
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    PublicationOpen Access
    Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-07-15) Fernando, N; Seneviratne, L; Weerasinghe, N; Rathnayake, N; Hoshino, Y
    Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies.