A Hybrid GA-ANN Model to Predict the Response of Heavy Metal Ion Sensor-Reference-Cited by-同舟云学术

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A Hybrid GA-ANN Model to Predict the Response of Heavy Metal Ion Sensor

Published:2023-10-12 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE)
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Author:

Landolsi Kamel¹,Jacem Wannassi²,Hamza Kahri²

Affiliation:

1. University of Monastir,Laboratory of Heterocyclic Chemistry, Natural Products and Reactivity (LR11ES39), Faculty of Science of Monastir,Monastir,Tunisia.1

2. University of Monastir,Faculty of Sciences of Monastir,Department of Chemistry,Monastir,Tunisia

Funder

Ministry of Higher Education and Scientific Research of Tunisia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10346234/10346249/10346429.pdf?arnumber=10346429

Reference16 articles.

1. Nanomaterials application in electrochemical detection of heavy metals

2. Chemical Sensors for Heavy Metals/Toxin Detection

3. Sensor based on redox conjugated poly(para-phenylene) for the simultaneous detection of dopamine, ascorbic acid, and uric acid in human serum sample

4. Electroanalytical application of molecular imprinted polyaniline matrix for dapsone determination in real pharmaceutical samples

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1. Sensing Volatile Pollutants with Spin-Coated Films Made of Pillar[5]arene Derivatives and Data Validation via Artificial Neural Networks;ACS Applied Materials & Interfaces;2024-06-05

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