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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Noreha Abdul Malik
International Islamic University Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Distinctive features for normal and crackles respiratory sounds using cepstral coefficients N. H. Mohd Johari; Noreha Abdul Malik; K. A. Sidek
Bulletin of Electrical Engineering and Informatics Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.387 KB) | DOI: 10.11591/eei.v8i3.1517

Abstract

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.
Distinctive features for normal and crackles respiratory sounds using cepstral coefficients N. H. Mohd Johari; Noreha Abdul Malik; K. A. Sidek
Bulletin of Electrical Engineering and Informatics Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.387 KB) | DOI: 10.11591/eei.v8i3.1517

Abstract

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.
Distinctive features for normal and crackles respiratory sounds using cepstral coefficients N. H. Mohd Johari; Noreha Abdul Malik; K. A. Sidek
Bulletin of Electrical Engineering and Informatics Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (587.387 KB) | DOI: 10.11591/eei.v8i3.1517

Abstract

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.
Penetration Testing using Kali Linux: SQL Injection, XSS, Wordpres, and WPA2 Attacks Teddy Surya Gunawan; Muhammad Kasim Lim; Mira Kartiwi; Noreha Abdul Malik; Nanang Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp729-737

Abstract

Nowadays, computers, smart phones, smart watches, printers, projectors, washing machines, fridges, and other mobile devices connected to Internet are exposed to various threats and exploits. Of the various attacks, SQL injection, cross site scripting, Wordpress, and WPA2 attack were the most popular security attacks and will be further investigated in this paper. Kali Linux provides a great platform and medium in learning various types of exploits and peneteration testing. All the simulated attack will be conducted using Kali Linux installed on virtual machine in a compuer with Intel Core i5 and 8 GB RAM, while the victim’s machine is the host computer which run Windows 10 version 1709. Results showed that the attacks launched both on web and firewall were conducted successfully.
Co-Authors K. A. Sidek Mira Kartiwi Muhammad Kasim Lim N. H. Mohd Johari Nanang Ismail Teddy Surya Gunawan