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All Journal International Journal of Advances in Intelligent Informatics Indonesian Journal of Electrical Engineering and Computer Science
Khalid Isa
Universiti Tun Hussein Onn Malaysia
Computer Science & IT

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Cable fault classification in ADSL copper access network using machine learning Nurul Bashirah Ghazali; Dang Fillatina Hashim; Fauziahanim Che Seman; Khalid Isa; Khairun Nidzam Ramli; Zuhairiah Zainal Abidin; Saizalmursidi Md Mustam; Mohammed Al Haek
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.488

Abstract

Asymmetrical Digital Subscriber Line (ADSL) is the technology widely deployed worldwide, but its performance may be limited with respect to its intrinsic. The nature of the copper cable causes it to be more susceptible to signal degradation and faulty line. Common ADSL line faults are short-wired fault, open-wired fault, bridge taps, and uneven pair. However, ADSL technology is still one of the most established networks, and users in the suburban area still depend on the technology to access the internet service. This paper discussed and compared a machine learning algorithm based on Decision Trees (J48), K-Nearest Neighbor, Multi-level Perceptron, Naïve Bayes, Random Forest, and Sequential Minimal Optimization (SMO) for ADSL line impairment that affects the line operation performance concerning their percentage of accuracy. Resulting from classifications done using algorithms as mentioned above, the random forest algorithm gives the highest overall accuracy for the ADSL line impairment dataset. The best algorithm for classifying DSL line impairment is chosen based on the highest accuracy percentage. The accomplishment classification of fault type in the ADSL copper access network project may benefit the telecommunication network provider by remotely assessing the network condition rather than on-site.
Voiced and unvoiced separation in malay speech using zero crossing rate and energy Rafizah Mohd Hanifa; Khalid Isa; Shamsul Mohamad; Shaharil Mohd Shah; Shelena Soosay Nathan; Rosni Ramle; Mazniha Berahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp775-780

Abstract

This paper contributes to the literature on voice-recognition in the context of non-English language. Specifically, it aims to validate the techniques used to present the basic characteristics of speech, viz. voiced and unvoiced, that need to be evaluated when analysing speech signals. Zero Crossing Rate (ZCR) and Short Time Energy (STE) are used in this paper to perform signal pre-processing of continuous Malay speech to separate the voiced and unvoiced parts. The study is based on non-real time data which was developed from a collection of audio speeches. The signal is assessed using ZCR and STE for comparison purposes. The results revealed that ZCR are low for voiced part and high for unvoiced part whereas the STE is high for voiced part and low for unvoiced part. Thus, these two techniques can be used effectively for separating voiced and unvoiced for continuous Malay speech.
Speaker ethnic identification for continuous speech in malay language using pitch and MFCC Rafizah Mohd Hanifa; Khalid Isa; Shamsul Mohamad
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.pp207-214

Abstract

Voice recognition has evolved exponentially over the years. The purpose of voice recognition or sometimes called speaker identification, is to identify the person who is speaking. This can be done by extracting features of speech that differ between individuals due to physiology (shape and size of the mouth and throat) and also behavioral patterns (pitch, accent and style of speaking). This paper explains an approach of voice recognition to identify the ethnicity of Malaysian people. Pitch and 13 Mel-Frequency Cepstrum Coefficients (MFCCs) are extracted from 52 recorded continuous speech in Malay for use as features to train the classifiers using Tree, Naïve Bayes, Nearest Neighbors and Support Vector Machine (SVM) and another 10 recorded speeches are used for testing. The results reveal that the use of a combination of pitch and 13 coefficients for features extraction and training the data using SVM provide better accuracy (57.7%) than the use of only 13 coefficients (53.8%).
Co-Authors Dang Fillatina Hashim Fauziahanim Che Seman Khairun Nidzam Ramli Mazniha Berahim Mohammed Al Haek Nurul Bashirah Ghazali Rafizah Mohd Hanifa Rosni Ramle Saizalmursidi Md Mustam Shaharil Mohd Shah Shamsul Mohamad Shelena Soosay Nathan Zuhairiah Zainal Abidin