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Paper Title

Empowering Water Treatment Through Convolutional Neural Network Classification

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Registration ID: IJNRD_219374

Published ID: IJNRD2404747

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Keywords

Water Treatment, CNN, Image Analysis, Water Quality, Machine Learning for Environmental Applications, Deep Learning Classification, VGG19

Abstract

With significant development of Internet of Things and substantial advancements in sensors, researchers can now readily obtain photos of the water and use them to understand what is happening in the ecosystem. In essence, expanding data size and category helps address issues related to water contamination. In this research, we concentrate on categorizing water photos into subcategories of clean and contaminated water in order to provide real-time feedback of an IoT-based water pollution monitoring system. Water picture categorization is difficult as collected images have large intra-class and minimal inter-class differences. Motivated by the capacity to derive very distinctive characteristics from Convolutional Neural Networks (CNNs), We wish to construct an attention neural network for the classification of gathered water photographs that appropriately encodes channel-wise and multi-layer characteristics in order to accomplish feature representation augmentation. Before building a local and global hierarchical attention neural network, we propose the VGG 19 model with a channel-wise attention gate structure. We carried out comparative experiments with a water surface image dataset from many publications, proving the effectiveness of the proposed attention neural network for classifying water photos. We integrated the proposed neural network as an essential part of an image-based water pollution monitoring system, allowing users to monitor water pollution breaches in real time and take timely corrective action.

How To Cite (APA)

Chowdam Vinaya Sri , Ayushmann Arya Kumar , & Suman kumar (April-2024). Empowering Water Treatment Through Convolutional Neural Network Classification. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h400-h415. https://ijnrd.org/papers/IJNRD2404747.pdf

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Other Publication Details

Paper Reg. ID: IJNRD_219374

Published Paper Id: IJNRD2404747

Research Area: Engineering

Author Type: Indian Author

Country: Greater Noida , Uttar Pradesh , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404747.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404747

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