{
  "id": 11859,
  "url": "https://arxiv.org/abs/2607.15775v1",
  "title": "AquaAugmentor: A Novel Feature Augmentation Algorithm for Water Potability Prediction",
  "summary": "Access to potable water is crucial for health, economic development, and sustainability. However, accurately classifying water quality remains a significant challenge due to the complexity and variability of water source data. This paper addresses the challenge of predicting water potability through machine learning and deep learning algorithms. It introduces a novel feature augmentation algorithm, AquaAugmentor, to enhance the predictive performance of these models for low-dimensional datasets.",
  "authors": "Muntasir Tabasum, Al Zadid Sultan Bin Habib, Tanpia Tasnim, Md. Ekramul Islam, Md Younus Ahamed, Md Asif Bin Syed",
  "category": "research",
  "topics": "healthcare,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-17T09:11:24.000Z",
  "fetched_at": "2026-07-20T05:10:09.534Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/11859",
  "original_url": "https://arxiv.org/abs/2607.15775v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}