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JAIS (Journal of Applied Intelligent System)
Published by Universitas Dian Nuswantoro
ISSN : 25020493     EISSN : 25029401     DOI : -
Core Subject :
Journal of Applied Intelligent System (JAIS) is published by LPPM Universitas Dian Nuswantoro Semarang in collaboration with CORIS and IndoCEISS, that focuses on research in Intelligent System. Topics of interest include, but are not limited to: Biometric, image processing, computer vision, knowledge discovery in database, information retrieval, computational intelligence, fuzzy logic, signal processing, speech recognition, speech synthesis, natural language processing, data mining, adaptive game AI.
Arjuna Subject : -
Articles 1 Documents
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Search results for , issue "Vol. 4 No. 2 (2019): Journal of Applied Intelligent System" : 1 Documents clear
Broad Learning System for Investigating Corrosion Inhibition Efficiency of Heterocyclic Compounds Akrom, Muhamad; Prabowo, Wahyu Aji Eko
(JAIS) Journal of Applied Intelligent System Vol. 4 No. 2 (2019): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v4i2.12487

Abstract

This study explores the use of Broad Learning Systems (BLS) to predict the corrosion inhibition efficiency (CIE) of heterocyclic compounds, addressing limitations of deep neural networks (DNNs) such as vanishing gradients and computational inefficiency. BLS prioritizes network width over depth, enabling faster learning and improved generalization. Trained on quantum chemical properties (QCPs) of 192 heterocyclic compounds, BLS outperformed multilayer perceptron neural networks (MLPNN) and random forest (RF) models, achieving lower mean absolute error (MAE: 1.41), root mean square error (RMSE: 1.79), and higher R² (0.993). Predicted CIE values for quinoxaline derivatives (95.39% and 94.05%) aligned closely with experimental data. This study demonstrates the potential of BLS as an efficient, accurate, and scalable approach for predicting corrosion inhibition capabilities, contributing to advancements in corrosion science and environmentally friendly solutions. Keywords - machine learning, broad learning system, neural network, corrosion.

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