Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

0.444

P-Index

This Author published in this journals

All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)

Shafishuhaza Sahlan

Universiti Teknologi Malaysia

Author-ID : 2392718

Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document

Claim Missing Document

Articles

Neural Network Model Development with Soft Computing Techniques for Membrane Filtration Process Zakariah Yusuf; Norhaliza Abdul Wahab; Shafishuhaza Sahlan
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IW-PSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO.

Modeling of submerged membrane filtration processes using recurrent artificial neural networks Zakariah Yusof; Norhaliza Abdul Wahab; Syahira Ibrahim; Shafishuhaza Sahlan; Mashitah Che Razali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

The modeling of membrane filtration processes is a challenging task because it involves many interactions from both biological and physical operational behavior. Membrane fouling behaviour in filtration processes is complex and hard to understand, and to derive a robust model is almost not possible. Therefore, it is the aim of this paper to study the potential of time series neural network based dynamic model for a submerged membrane filtration process. The developed model that represent the dynamic behavior of filtration process is later used in control design of the membrane filtration processes. In order to obtain the dynamic behaviour of permeate flux and transmembrane pressure (TMP), a random step was applied to the suction pump. A recurrent neural network (RNN) structure was employed to perform as the dynamic models of a filtration process, based on nonlinear auto-regressive with exogenous input (NARX) model structure. These models are compared with the linear auto-regressive with exogenous input (ARX) model. The performance of the models were evaluated in terms of %R2, mean square error (MSE,) and a mean absolute deviation (MAD). For filtration control performance, a proportional integral derivative (PID) controller was implemented. The results showed that the RNN-NARX structure able to model the dynamic behavior of the filtration process under normal conditions in short range of the filtration process. The developed model can also be a reliable assistant for two different control strategies development in filtration processes.

An assessment of stingless beehive climate impact using multivariate recurrent neural networks Noor Hafizah Khairul Anuar; Mohd Amri Md Yunus; Muhammad Ariff Baharudin; Sallehuddin Ibrahim; Shafishuhaza Sahlan; Mahdi Faramarzi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2030-2039

A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation.

Co-Authors Mahdi Faramarzi Mashitah Che Razali Mohd Amri Md Yunus Muhammad Ariff Baharudin Noor Hafizah Khairul Anuar Norhaliza Abdul Wahab Sallehuddin Ibrahim Syahira Ibrahim Zakariah Yusof Zakariah Yusuf

Title

Found 3 Documents
Search

Abstract

Abstract

Abstract

Title Search

Found 3 Documents Search

Abstract

Abstract

Abstract

Title

Found 3 Documents
Search