A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters

A Comparative Study of Advanced Transformer Learning Frameworks for Water Potability Analysis Using Physicochemical Parameters

Keeping drinking water safe is a critical aspect of protecting public health. Traditional laboratory-based methods for evaluating water potability are often time-consuming, costly, and labour-intensive. This paper presents a comparative analysis of four transformer-based deep learning models in the...

Full description

Saved in:
Bibliographic Details
Main Authors: Enes Algül, Saadin Oyucu, Onur Polat, Hüseyin Çelik, Süleyman Ekşi, Faruk Kurker, Ahmet Aksoz
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
water potability
transformer models
deep learning
physicochemical features
tabular data classification
attention mechanism
Online Access:https://www.mdpi.com/2076-3417/15/13/7262
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items