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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
S. Noorjannah Ibrahim
International Islamic University Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Classification of Normal and Crackles Respiratory Sounds into Healthy and Lung Cancer Groups N. Abdul Malik; W. Idris; T. S. Gunawan; R. F. Olanrewaju; S. Noorjannah Ibrahim
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (729.457 KB) | DOI: 10.11591/ijece.v8i3.pp1530-1538

Abstract

Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer.
Web based Water Turbidity Monitoring and Automated Filtration System: IoT Application in Water Management S. Noorjannah Ibrahim; A. L. Asnawi; N. Abdul Malik; N. F. Mohd Azmin; A. Z. Jusoh; F. N. Mohd Isa
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (463.625 KB) | DOI: 10.11591/ijece.v8i4.pp2503-2511

Abstract

Water supplied to residential areas is prone to contaminants due to pipe residues and silt, and therefore resulted in cloudiness, unfavorable taste, and odor in water. Turbidity, a measure of water cloudiness, is one of the important factors for assessing water quality. This paper proposes a low-cost turbidity system based on a light detection unit to measure the cloudiness in water. The automated system uses Intel Galileo 2 as the microprocessor and a server for a web-based monitoring system. The turbidity detection unit consists of a Light Dependent Resistor (LDR) and a Light Emitting Diode (LED) inside a polyvinyl chloride (PVC) pipe. Turbidity readings were recorded for two different positionings; 90° and 180° between the detector (LDR) and the incident light (LED). Once the turbidity level reached a threshold level, the system will trigger the filtration process to clean the water. The voltage output captured from the designed system versus total suspended solid (TSS) in sample water is graphed and analyzed in two different conditions; in total darkness and in the present of ambient light. This paper also discusses and compares the results from the above-mentioned conditions when the system is submerged in still and flowing water. It was found that the trends of the plotted graph decline when the total suspended solid increased for both 90° and 180° detector turbidimeter in all conditions which imitate the trends of a commercial turbidimeter. By taking the consideration of the above findings, the design can be recommended for a low-cost real-time web-based monitoring system of the water quality in an IOT environment.
Design a compact CPW monopole antenna on rubber substrate for ISM band application Nazmus Sakib; S. Noorjannah Ibrahim; M. M. Hasan Mahfuz; S. Yasmin Mohamad
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 3: June 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i3.18769

Abstract

One of the most challenging works on compact antenna design is to maintain the flexibility orientation. This paper demonstrates a coplanar waveguide (CPW) fed monopole antenna with rubber substrate at 2.45 GHz center frequency for ISM band application. The proposed antenna attained the realized gain at 4.06 dB with the radiation efficiency around 90% at peak value and the bandwidth of 541.5 MHz. The antenna was designed using the CPW structure. CST microwave studio applied to design the proposed antenna simulation. The main purposed of this study is to improve the antenna performances specially the bandwidth, gain, and radiation efficiency. Moreover, another aim of that antenna design is to reduce the antenna size and thickness upon the existing related design with rubber substrate.
Investigation of lower limb’s muscles activity during performance of salat between two age groups N. Abdul Malik; Z. Wahid; A. F. Zulkipili; S. Noorjannah Ibrahim; T. S. Gunawan; Sheroz Khan
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 2: May 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i2.pp608-617

Abstract

Muscles play an important role in the movement of limbs. They undergo contraction to straighten or to bend a joint for the limbs to move. There are many factors that can affect muscle activity. Age could be one of the possible factors affecting muscle activity. The purpose of this study was to investigate the lower limb’s muscles activity during performance of salat between two age groups. The lower limb’s muscles investigated were Gastrocnemius (GAS), Biceps Femoris (BF), Tibialis Anterior (TA) and Rectus Femoris (RF). The postures involve are standing, bowing, prostrating and sitting. The electromyography (EMG) signals of the muscles were measured using the technique of surface EMG (sEMG). The signals were acquired by using Delsys Bagnoli™ Desktop sEMG system and EMGworks®. Ten healthy subjects from two age groups were recruited in this study. The first group consists of five males aged between 20 to 29 while the second group consists of five males aged above 40. The raw EMG signals acquired were analyzed and the EMG envelopes were developed using MATLAB. The averaged RMS values of EMG for each muscle were also calculated. Analysis of variance (ANOVA) of the EMGs was obtained by using F-test. Further investigation of the variance was performed by using Tukey comparison. From the results, the most active muscle during the performance of salat is BF while the less active muscle is GAS for both age groups. The statistical result show that there is no difference in the muscle activity pattern between the two age groups but there is significant difference among the muscles investigated.
Classification of ECG signals for detection of arrhythmia and congestive heart failure based on continuous wavelet transform and deep neural networks Rashidah Funke Olanrewaju; S. Noorjannah Ibrahim; Ani Liza Asnawi; Hunain Altaf
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1520-1528

Abstract

According to World Health Organization (WHO) report an estimated 17.9 million lives are being lost each year due to cardiovascular diseases (CVDs) and is the top contributor to the death causes. 80% of the cardiovascular cases include heart attacks and strokes. This work is an effort to accurately predict the common heart diseases such as arrhythmia (ARR) and congestive heart failure (CHF) along with the normal sinus rhythm (NSR) based on the integrated model developed using continuous wavelet transform (CWT) and deep neural networks. The proposed method used in this research analyses the time-frequency features of an electrocardiogram (ECG) signal by first converting the 1D ECG signals to the 2D Scalogram images and subsequently the 2D images are being used as an input to the 2D deep neural network model-AlexNet. The reason behind converting the ECG signals to 2D images is that it is easier to extract deep features from images rather than from the raw data for training purposes in AlexNet. The dataset used for this research was obtained from Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH normal sinus rhythm database and Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. In this work, we have identified the best fit parameters for the AlexNet model that could successfully predict the common heart diseases with an accuracy of 98.7%. This work is also being compared with the recent research done in the field of ECG Classification for detection of heart conditions and proves to be an effective technique for the classification.
Co-Authors A. F. Zulkipili A. L. Asnawi A. Z. Jusoh Ani Liza Asnawi F. N. Mohd Isa Hunain Altaf M. M. Hasan Mahfuz N. Abdul Malik N. F. Mohd Azmin Nazmus Sakib R. F. Olanrewaju Rashidah Funke Olanrewaju S. Yasmin Mohamad Sheroz Khan T. S. Gunawan W. Idris Z. Wahid