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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Megat Syahirul Amin Megat Ali
Universiti Teknologi MARA
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
Classification of hand gestures from forearm electromyogram signatures from support vector machine Diaa Albitar; R. Jailani; Megat Syahirul Amin Megat Ali; Anwar P. P. Abdul Majeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp260-268

Abstract

Robotic prosthetics is increasingly adopted as an enabling technology for amputees. These are vital not only for activities of daily living but to display expression and affection. A vital element to this system is an intelligent model that can identify signatures from the remaining limb that can be mapped to specific effector movements. Therefore, the study proposes the use of forearm electromyogram to classify between different types of hand gestures; fingers spread, wave out, wave in, fist, double tap, and relaxed state. Data are acquired from 32 subjects using Myo armband. Initially, a total of 248 time-and frequency-domain features are extracted from the eightchannel device. Neighborhood component analysis has reduced them to a total of fourteen features. A hand gesture classification model based on electromyogram signal has been successfully developed using support vector machine with overall accuracy of 97.4% for training, and 88.0% for testing.
Electrocardiogram profiling of myocardial infarction history using MLP and HMLP networks Fatin Syahirah Ab Gani; Mohd Khairi Nordin; Ahmad Ihsan Mohd Yassin; Idnin Pasya Ibrahim; Megat Syahirul Amin Megat Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 1: January 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i1.pp183-190

Abstract

Narrowing of coronary arteries caused by cholesterol deposits deprives heart tissues of oxygen. In prolonged conditions, these will result in myocardium infarction. The presence of damage tissues modifies the normal sinus rhythm and this can be detected using electrocardiogram (ECG). Hence, this paper characterized history of myocardial infarction from survivors using QRS power ratio features from the ECG. Subsequent profiling is performed using multilayered perceptron (MLP) and hybrid multilayered perceptron (HMLP) networks. ECG with history of anterior and inferior infarctions, along with healthy controls is obtained from PTB Diagnostic ECG Database. The signal is initially pre-processed and the power ratio features are extracted for low- and mid-frequency components. The features are then used as input vector to the MLP and HMLP networks. The optimized MLP has attained accuracies of 99.2% for training and 98.0% for testing. Meanwhile, the optimized HMLP managed to achieve accuracies of 99.4% for training and 97.8% for testing. Despite the similarities in network performance, MLP provides a better alternative due to the reduced computational requirements by as much as 30%.
NARX-based water quality index model of Air Busuk River using chemical parameter measurements Muhammad Ierfan Hasnan; Azhar Jaffar; Norashikin M. Thamrin; Mohamad Farid Misnan; Ahmad Ihsan Mohd Yassin; Megat Syahirul Amin Megat Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i3.pp1663-1673

Abstract

Water quality plays a major role in issues related to public health and marine life. Hence, monitoring river for contaminations is vital for ensuring safe and sustainable water resources. Conventional method for assessing water quality index is costly as it requires considerable amount of time and laboratory resources. Therefore, this study proposes a water quality index model based on artificial neural network. A six-year data for Air Busuk River is obtained from the Department of Environment. Dissolved oxygen, biological oxygen demand, and ammoniacal nitrogen has shown high correlation with water quality index. The water quality index model is then developed based on these parameters, employing the non-linear autoregressive with exogeneous input structure. Generally, the model which is based on three chemical parameters has shown satisfactory performance with overall regression of 0.8767 and passed the correlation function tests. The model offers a potentially efficient method for assessing water quality with cost-saving benefits for government agencies and monitoring authorities.
Co-Authors Ahmad Ihsan Mohd Yassin Aisyah Hartini Jahidin Aisyah Hartini Jahidin Anwar P. P. Abdul Majeed Azhar Jaffar Diaa Albitar Dyg Suzana Awang Fatin Syahirah Ab Gani Idnin Pasya Ibrahim Mohamad Farid Misnan Mohammad Farid Saaid Mohd Hanafi Mat Som Mohd Khairi Nordin Mohd Nasir Taib Muhammad Asraf H. Muhammad Asraf Hairuddin Muhammad Ierfan Hasnan Muhammad Ikmal Hafiz Razali Noor Hidayah Ros Azamin Nooritawati Md Tahir Norashikin M. Thamrin R. Jailani Wan Nurazwin Syazwani Rahimi