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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Mohd Suhaimi Sulaiman
Universiti Teknologi MARA
Computer Science & IT Engineering

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Gait cycle prediction model based on gait kinematic using machine learning technique for assistive rehabilitation device Che Ani Adi Izhar; Z. Hussain; M. I. F. Maruzuki; Mohd Suhaimi Sulaiman; A. A. Abd. Rahim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp752-763

Abstract

The gait cycle prediction model is critical for controlling assistive rehabilitation equipment like orthosis. The human gait model has recently used statistical models, but the dynamic properties of human physiology limit the current approach. Current human gait cycle prediction models need detailed kinematic and kinetic data of the human body as input parameters, and measuring them requires special instruments, making them difficult to use in real-world applications. In our study, three separate machine learning algorithms were used to create a human gait model: Gaussian process regression, support vector machine, and decision tree. The algorithm used to create the model's input parameters are height, weight, hip and knee angle, and ground reaction force (GRF). For better gait cycle model prediction, the models produced were enhanced by incorporating different sliding window data. The best gait period prediction model was DT with sliding window data (t−3), which had a root mean square error of 3.3018 and the R-squared (R-Value) of 0.97. The projection model focused on hip and knee angle and GRF was a feasible solution to controlling assistive rehabilitation devices during the gait cycle.
Artificial neural network modeling for predicting the quality of water in the Sabak Bernam River Faqihah Affandi; Mohamad Faizal Abd Rahman; Adi Izhar Che Ani; Mohd Suhaimi Sulaiman
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1616-1623

Abstract

Water quality prediction is aided by environmental monitoring, ecological sustainability, and aquaculture. Traditional prediction approaches capture the nonlinearity and non-stationarity of water quality well. Due to their rapid progress, artificial neural networks (ANNs) have become a hotspot in water quality prediction in recent years. ANNs are utilised in this study to predict water quality using soft computing techniques. The feedforward network and the standard back-propagation method of Levenberg-Marquardt and scaled conjugate gradient learning algorithm were employed in this research. One hidden layer has been recommended for the modelling, with the number of hidden neurons set at 3, 24, and 49. For this analysis, six different testing percentages were used, and the output data can be categorised as '0' for clean water and '1' for polluted water. From the results, it can be shown that the most optimised model was from the model of trainlm with a testing percentage of 18% and with 3 number of neurons. This most optimised model obtains an accuracy of 91.7%, the best validation performance of 0.073346 with 24 epochs, and having a receiver operating characteristic (ROC) curve that is closer to the true positive rate compared to other samples.
Development of an optical pH measurement system based on colorimetric effect Puteri Nur Syahirah Mohamed Mustafa; Aiman Shahmi Azam; Mohd Suhaimi Sulaiman; Ahmad Fairuz Omar; Mohamad Faizal Abd Rahman
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp728-735

Abstract

This paper proposed an optical pH measurement system developed to measure the changes of pH levels based on colorimetric reactions with phenol red reagent. The optical sensing was achieved through the implementation of a pair of light emitting diode (LED) and photodiode. The detection mechanism was based on different absorbance of light intensity at pH values of 2.36, 8.32, 9.08 and 12.83. The data processing method was carried out using LabVIEW software and interfaced with NI USB DAQ 6008. The measured voltage showed a good correlation in relation to the pH level with R2 equals to 0.9624. This relationship was used as a calibration curve in the final system testing. The final measurement of pH showed a good agreement between the actual and measured values with an error of less than 5%, thus indicating the reliability of the proposed system.
Classification of healthy and white root disease infected rubber trees based on relative permittivity and capacitance input properties using LM and SCG artificial neural network Mohd Suhaimi Sulaiman; Zuraidi Saad
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp222-228

Abstract

White root disease is one of the most serious diseases in rubber plantation in Malaysia that originally infects on the root surface of the rubber tree. So, prevention is important compared to treatment. The classification system proposed in the research had the ability of detecting the disease by classifying between healthy rubber trees and white root disease infected rubber trees. 600 samples of latex from healthy rubber trees and white root disease infected rubber trees were taken from the RRIM station in Kota Tinggi, Johor. These samples were measured based on its relative permittivity and capacitance. All of the measurement inputs from the experiment were tested using statistical analysis. These measurement input were then went through the process of classification in ANN to generate the optimized models by using LM and SCG algorithm. There were four optimized models selected from the classification process. The accuracy from the selected most optimized models were greater than 70%. The selected most optimized models were then used to classify between healthy trees and white root infected trees based on single input categories. 
Co-Authors A. A. Abd. Rahim Adi Izhar Che Ani Ahmad Fairuz Omar Aiman Shahmi Azam Che Ani Adi Izhar Faqihah Affandi M. I. F. Maruzuki Mohamad Faizal Abd Rahman Mohamad Faizal Abd Rahman Puteri Nur Syahirah Mohamed Mustafa Z. Hussain Zuraidi Saad