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The study applies machine learning to address Ghana’s solid waste disposal challenges, using a Random Forest classifier to predict illness categories from waste practices and demographics, achieving a macro F1 score of 0.63. A MobileNetV2 image classifier was also developed for automated waste sorting, reaching 88.2% accuracy and a macro F1 of 0.87 on 415 images. These quantitative results confirm a previously qualitative link between waste disposal and health, while demonstrating the feasibility of low‑cost, camera‑based sorting in resource‑constrained settings.
arXiv:2607. 02230v1 Announce Type: cross Abstract: The complexity of waste disposal regulations across European countries poses significant challenges for the residents and hinders the transition to a Circular Economy.
arXiv:2606. 15547v1 Announce Type: cross Abstract: Waste classification models have become highly accurate at sorting waste, often exceeding 95% on benchmark datasets.
arXiv:2607. 10610v1 Announce Type: cross Abstract: Efficient waste segregation is critical for sustainable urban management and environmental governance.
Housing-level urban physical examination is essential for identifying residential building problems and supporting targeted urban renewal. Existing automated inspection studies primarily rely on individual images and rarely examine whether surrounding urban functional context can provide supplementary information for building-level assessment.
arXiv:2602. 10381v2 Announce Type: replace Abstract: Childhood malnutrition remains a major public health concern in Nepal and other low-resource settings, while conventional case-finding approaches are labor-intensive and frequently unavailable in remote areas.