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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Mohd Hanafi Ahmad Hijazi
Universiti Malaysia Sabah
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
Artificial speech detection using image-based features and random forest classifier Choon Beng Tan; Mohd Hanafi Ahmad Hijazi; Frazier Kok; Mohd Saberi Mohamad; Puteri Nor Ellyza Nohuddin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp161-172

Abstract

The ASVspoof 2015 Challenge was one of the efforts of the research community in the field of speech processing to foster the development of generalized countermeasures against spoofing attacks. However, most countermeasures submitted to the ASVspoof 2015 Challenge failed to detect the S10 attack effectively, the only attack that was generated using the waveform concatenation approach. Hence, more informative features are needed to detect previously unseen spoofing attacks. This paper presents an approach that uses data transformation techniques to engineer image-based features together with random forest classifier to detect artificial speech. The objectives are two-fold: (i) to extract image-based features from the melfrequency cepstral coefficients representation of the speech signal and (ii) to compare the performance of using the extracted features and Random Forest to determine the authenticity of voices with the existing approaches. An audio-to-image transformation technique was used to engineer new features in classifying genuine and spoof voices. An experiment was conducted to find the appropriate combination of the engineered features and classifier. Experimental results showed that the proposed approach was able to detect speech synthesis and voice conversion attacks effectively, with an equal error rate of 0.10% and accuracy of 99.93%.
Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images Stefanus Kieu Tao Hwa; Abdullah Bade; Mohd Hanafi Ahmad Hijazi; Mohammad Saffree Jeffree
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp713-720

Abstract

Tuberculosis (TB) is a disease that causes death if not treated early. Ensemble deep learning can aid early TB detection. Previous work trained the ensemble classifiers on images with similar features only. An ensemble requires a diversity of errors to perform well, which is achieved using either different classification techniques or feature sets. This paper focuses on the latter, where TB detection using deep learning and contrast-enhanced canny edge detected (CEED-Canny) x-ray images is presented. The CEED-Canny was utilized to produce edge detected images of the lung x-ray. Two types of features were generated; the first was extracted from the Enhanced x-ray images, while the second from the Edge detected images. The proposed variation of features increased the diversity of errors of the base classifiers and improved the TB detection. The proposed ensemble method produced a comparable accuracy of 93.59%, sensitivity of 92.31% and specificity of 94.87% with previous work.
Ensemble deep learning for tuberculosis detection using chest X-Ray and canny edge detected images Stefanus Kieu Tao Hwa; Mohd Hanafi Ahmad Hijazi; Abdullah Bade; Razali Yaakob; Mohammad Saffree Jeffree
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (333.062 KB) | DOI: 10.11591/ijai.v8.i4.pp429-435

Abstract

Tuberculosis (TB) is a disease caused by Mycobacterium Tuberculosis. Detection of TB at an early stage reduces mortality. Early stage TB is usually diagnosed using chest x-ray inspection. Since TB and lung cancer mimic each other, it is a challenge for the radiologist to avoid misdiagnosis. This paper presents an ensemble deep learning for TB detection using chest x-ray and Canny edge detected images. This method introduces a new type of feature for the TB detection classifiers, thereby increasing the diversity of errors of the base classifiers. The first set of features were extracted from the original x-ray images, while the second set of features were extracted from the edge detected image. To evaluate the proposed approach, two publicly available datasets were used. The results show that the proposed ensemble method produced the best accuracy of 89.77%, sensitivity of 90.91% and specificity of 88.64%. This indicates that using different types of features extracted from different types of images can improve the detection rate.
Co-Authors Abdullah Bade Abdullah Bade Chin Kim On Ching Fui Fui Choon Beng Tan Chung Seng Kheau Emmanuel Steward Tindik Frans Coenen Frazier Kok Hairulnizam Mahdin Haviluddin Haviluddin Leong Qi Yang Mohammad Saffree Jeffree Mohammad Saffree Jeffree Mohd Saberi Mohamad Mohd Suhairi Md Suhaimin Mohd Suhairi Md Suhaimin Puteri Nor Ellyza Nohuddin Rayner Alfred Razali Yaakob Stefanus Kieu Tao Hwa Stefanus Kieu Tao Hwa Toh Yin Wei