The Use of LPC and Wavelet Transform for Influenza Disease Modeling

Computational and Mathematical Methods in Medicine

Al‐Qawasmi

Balamesh

et al. 2019

Self Cite

Speech parameters may include perturbation measurements, spectral and cepstral modeling, and pathological effects of some diseases, like influenza, that affect the vocal tract. The verification task is a very good process to discriminate between different types of voice disorder. This study investigated the modeling of influenza’s pathological effects on the speech signals of the Arabic vowels “A” and “O.” For feature extraction, linear prediction coding (LPC) of discrete wavelet transform (DWT) subsignals denoted by LPCW was used. k-Nearest neighbor (KNN) and support vector machine (SVM) classifiers were used for classification. To study the pathological effects of influenza on the vowel “A” and vowel “O,” power spectral density (PSD) and spectrogram were illustrated, where the PSD of “A” and “O” was repressed as a result of the pathological effects. The obtained results showed that the verification parameters achieved for the vowel “A” were better than those for vowel “O” for both KNN and SVM for an average. The receiver operating characteristic curve was used for interpretation. The modeling by the speech utterances as words was also investigated. We can claim that the speech utterances as words could model the influenza disease with a good quality of the verification parameters with slightly less performance than the vowels “A” as speech utterances. A comparison with state-of-the-art method was made. The best results were achieved by the LPCW method.

Section: Resultsmentioning

confidence: 99%

“…is method is denoted by LPCW and was used by the rst author in [5]. Second part: the feature extraction vector is sent to the classi ers KNN and SVM for classi cation.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Use of Arabic Vowels to Model the Pathological Effect of Influenza Disease by Wavelets

Computational and Mathematical Methods in Medicine

Al‐Qawasmi

Balamesh

et al. 2019

Self Cite

“…In our system, we implement PNN as the main classifier. PNN was used for pattern recognition applications as signature recognition [34] and speech recognition [35]. To use different classification methods using classification learner apps in the MATLAB software is implemented for comparison.…”

Section: Experiments 4: Classification Methodsmentioning

confidence: 99%

The Study of Recognition Methods Based on Wavelet Transform for Melanoma Detection

Almarei

2020

JERR

Self Cite

Skin cancer is one of the most cancers occurring in the world. Malignant melanoma is the most skin cancer type causing death around the world. Melanoma could be treated 100% if they are detected at earlier stages. In this paper, various melanoma detection systems were reviewed according to the year of publishing. All reviewed papers were based on feature extraction methods using wavelet transform (WT) in its two versions: Discrete wavelet transform (DWT), and wavelet packet transform (WPT) for melanoma recognition. Our methodology that was based on the WPT feature extraction and probabilistic neural network (PNN) was used for comparison. The ISIC database was used for differentiating between malignant (1110 images) and benign (1110 image) tumors. A (75% training /25% testing) verification system was applied. Many experiments were conducted using different parameters for each experiment. The support vector machine classifier (SVM) was the most common classifier combined with various types of wavelet features that have appeared in many kinds of literature during the last two decades, which achieved relatively the best accuracy ranged between [76% -98.29%]. In this paper, our combination method of the WPT and entropy was proposed and evaluated. Several experiments were conducted for testing. A Almarei and Daqrouq; JERR, 11(3): 46-61, 2020; Article no.JERR.55316 47 comparison manner was used for discussion of the investigation. The proposed method was an excellent detection method for melanoma regarding the complexity, where no preprocessing stage was conducted. Original Research Article

Simulation and Analysis with Wavelet Transform Technique and the Vibration Characteristics for Early Revealing of Cracks in Structures

Abu‐Hamdeh

Mathematical Problems in Engineering

Mebarek‐Oudina

2021

Self Cite

Implementation of improved instruments is used to detect damage in an accurate manner and fully analyze its characteristics. An aluminum beam has been used in this work to identify cracks by using a vibration technique. The simulation of frequency response feature was conducted using a finite element model to provide average measures of intensities of vibration. Two forms of wavelet packet transform (WPT) entropies Shannon and log energy were applied to identify the position, width, and size of the crack. The results showed that with an increase in crack depth, the amplitude also increased at certain crack sizes and for all crack positions. For two crack depths of 1.6 mm and 0.16 mm having the same crack size and position 12 mm and 60 mm, respectively, a 4.5% increase in amplitude was observed at a crack depth of 1.6 mm. Moreover, the amplitude varied inversely with the position. A 12.6% increase in amplitude was observed at a crack depth of 1.6 mm rather than 0.16 mm, while both depths occurred at the same crack position (75 mm) and size (20 mm). Experimental validation was performed on a cantilever beam with one crack. The maximum absolute error found was 7.5% for the crack position and 9.1% for the crack size. With the increase in crack depth, the obtained results decrease the stiffness of a beam in a single crack case.