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All Journal International Journal of Advances in Applied Sciences Civil Engineering Journal
Hassan Mohamed
Software Engineering Department, Universiti Tenaga Nasional, Malaysia
Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Modelling soil deposition predictions on solar photovoltaic panels using ANN under Malaysia’s meteorological condition Suhaimi, Muhammad Aiman Amin Muhammad; Dahlan, Nofri Yenita; Asman, Saidatul Habsah; Rajasekar, Natarajan; Mohamed, Hassan
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp796-805

Abstract

Solar photovoltaic (PV) panels performance is influenced by various external factors such as precipitation, wind angle, ambient temperature, wind speed, transient irradiation, and soil deposition. Soiling accumulation on panels poses a significant challenge to PV power generation. This paper presents the development of an artificial neural network (ANN)-based soil deposition prediction model for PV systems. Conducted at a Malaysian solar farm over three months, the research utilized power output data from the inverter as model output and meteorological data as input variables. The model employed the Levenberg-Marquardt backpropagation method with Tansig and Purline activation functions. Performance assessment via statistical comparison of experimental and simulated results revealed a coefficient of determination (R2) value of 0.68073 for the ANN architecture of 5 input layers, 30 hidden layers, and 1 output layer (5-30-1). Sensitivity analysis highlighted relative humidity and wind direction as the most influential parameters affecting PV soiling rate. The developed ANN model, combined with sensitivity analysis, serves as a robust foundation for enhancing the efficiency of smart sensors in PV module cleaning systems.
An Automated Framework for Benthic Habitat Classification and Segmentation Based on Deep Learning Algorithms Mohamed, Hassan; Nadaoka , Kazuo
Civil Engineering Journal Vol. 12 No. 2 (2026): February
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2026-012-02-022

Abstract

Although benthic habitats represent some of the largest, most diverse, and productive ecosystems on Earth with great environmental, and economical value, they are increasingly threatened and declining in many locations worldwide. Every year, numerous underwater images are collected for monitoring these habitats. Still, the manual labelling process remains tedious and time-consuming, creating a huge gap between data collection and extraction of meaningful information. In this study, an automated framework is proposed for single-label classification and semantic segmentation of benthic habitats using convolutional neural networks (CNNs). The framework integrates and evaluates various pre-trained CNNs, bagging of features (BOF), color spaces, and texture descriptors for benthic habitat classification. Furthermore, the classified images served as training and validation samples to assess the semantic segmentation performance of pre-trained CNNs with different architectures (e.g., ResNet-50, AlexNet, Xception, etc.). Both high- and low-quality underwater images of benthic habitats collected from six diverse study areas located off Australia and Japan were used to evaluate the proposed framework. The analysis revealed that the ResNet-50 FC1000 combined with BOF, color space, and texture attributes yielded the highest automatic classification accuracy. Moreover, the ResNet-50 network outperformed all the tested networks for automatic semantic segmentation of benthic habitats. Overall, the presented framework enhanced the automation of benthic habitat classification and semantic segmentation processes.
Co-Authors Asman, Saidatul Habsah Dahlan, Nofri Yenita Nadaoka , Kazuo Rajasekar, Natarajan Suhaimi, Muhammad Aiman Amin Muhammad