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Related Experiment Video

Updated: Jul 16, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

IoT-Based Monitoring and Prediction of Water Quality Using Swin-Transformer and Depthwise Separable Convolutional

Navala Rani Gunti1, Kodukula Subrahmanyam1

  • 1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.

Water Environment Research : a Research Publication of the Water Environment Federation
|July 15, 2026
PubMed
Summary

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Testing Water Quality01:14

Testing Water Quality

When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...

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Finite-Element-Informed Pyramid Neural Network With Draco Lizard Optimizer for Accurate and Efficient Water Contamination Classification.

Water environment research : a research publication of the Water Environment Federation·2026
See all related articles

This study introduces an advanced deep learning model for accurate water quality forecasting using IoT sensors. The system achieves high accuracy in predicting water quality index and class, addressing a key environmental challenge.

Area of Science:

  • Environmental Science
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Water quality degradation is a significant global environmental issue.
  • Intelligent monitoring and prediction systems are crucial for managing water resources.
  • Real-time data acquisition and analysis are essential for effective water quality assessment.

Purpose of the Study:

  • To develop an advanced deep learning model for accurate water quality index (WQI) and water quality class (WQC) forecasting.
  • To integrate Internet of Things (IoT) sensors for real-time water quality parameter measurement.
  • To enhance data quality and model efficiency through advanced preprocessing and feature selection techniques.

Main Methods:

  • Utilized IoT sensors (turbidity, temperature, pH, TDS) for real-time data collection.
Keywords:
IoT‐based frameworkdeep learningfeature selectionprediction accuracyreal‐time analysiswater quality monitoring

Related Experiment Videos

Last Updated: Jul 16, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

  • Applied a quality-aware fuzzy min-max neural network (QAFMMN) for data preprocessing and noise reduction.
  • Employed the Addax optimization algorithm and Nadam optimizer (AOA-NO) for efficient feature selection.
  • Developed a Swin-Transformer-depthwise separable convolutional neural network (ST-DSCNN) trained with a circulatory system-based optimization method (CSBO).

    • Main Results:

    • Achieved an R-squared (R²) score of 0.999, indicating excellent model fit.
    • Obtained a Mean Absolute Error (MAE) of 0.008, demonstrating high prediction accuracy.
    • Reached an overall water quality classification accuracy of 99.96%, showcasing the model's effectiveness.

    Conclusions:

    • The proposed IoT-based deep learning model significantly improves water quality monitoring and prediction accuracy.
    • The combination of advanced DL techniques and optimization algorithms offers a computationally efficient and highly effective solution.
    • This research provides a robust framework for intelligent water quality management systems.