Mel-Frequency Cepstral Coefficient Features Based on Standard Deviation and Principal Component Analysis for Language Identification Systems

Albadr, Musatafa Abbas Abbood; Tiun, Sabrina; Ayob, Masri; Mohammed, Manal; AL‐Dhief, Fahad Taha

doi:10.1007/s12559-021-09914-w

Cited by 26 publications

(10 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These evaluation measurements are accuracy, precision, specificity, sensitivity, negative prediction value (NPV), F-measure, and G-mean. Further, these measurements have been calculated as shown in ( 1)-( 7) [34]- [36].…”

Section: Resultsmentioning

confidence: 99%

Convolutional neural network for the detection of coronavirus based on X-ray images

Ahmed

Alsemawi

Mutar

et al. 2022

IJEECS

View full text Add to dashboard Cite

Nowadays, <span lang="EN-US">the coronavirus disease (COVID-19) is considered an ongoing pandemic that spread quickly in most countries around the world. The COVID-19 causes severe acute respiratory syndrome. Moreover, the technique of chest computed tomography (CT) is a method used in the detection of COVID-19. However, the CT method consumes more time and higher-cost as compared with chest X-ray images. Therefore, this paper presents convolutional neural network (CNN) algorithm in the detection of COVID-19 by using X-ray images. In this method, we have used a balanced image database for the normal (healthy) and COVID-19 subjects. The total number of image database is 188 samples (94 healthy samples and 94 COVID-19 samples). Furthermore, there are several evaluation measurements are used to evaluate the proposed model such as accuracy, precision, specificity, sensitivity, F-measure, G-mean, and others. According to the experimental results, the proposed model obtains 98.68% accuracy, 100% precision, and 100% specificity. Besides, the proposed model achieves 97.37%, 98.67%, and 98.68% for sensitivity, F-measure, and G-mean, respectively. The performance of the proposed model by using CNN algorithm shows promising results in the detection of COVID-19. Also, it has outperformed all its comparatives in terms of detection accuracy.</span>

show abstract

Section: Resultsmentioning

confidence: 99%

Convolutional neural network for the detection of coronavirus based on X-ray images

Ahmed

Alsemawi

Mutar

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…The output weights are calculated based on discovering the least-squares solution ( 19 , 20 ). ELM is preferred by researchers as it is superior to traditional Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) ( 21 , 22 ) specifically in: (1) preventing overfitting, (2) its implementation on multi and binary classifications, and (3) its similar kernel-based capability SVM and working with a Neural Network (NN) structure. These factors make the ELM more efficient in accomplishing a better learning performance.…”

Section: Related Workmentioning

confidence: 99%

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Albadr

Ayob

Tiun

et al. 2022

Front. Public Health

Self Cite

View full text Add to dashboard Cite

Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.

show abstract

“…At present, the commonly used speech feature extraction methods include linear prediction cepstral coefficient extraction method [ 32 ], linear predictive cepstral coefficient (LPCC), and Mel frequency cepstral coefficient extraction method [ 33 ], and Mel frequency cepstral coefficient (MFCC). MFCC was proposed by Stevens, Volkman, and Newman in 1937.…”

Section: Video Captioning Modelmentioning

confidence: 99%

Research on Video Captioning Based on Multifeature Fusion

Zhao

Guo

Chen

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Aiming at the problems that the existing video captioning models pay attention to incomplete information and the generation of expression text is not accurate enough, a video captioning model that integrates image, audio, and motion optical flow is proposed. A variety of large-scale dataset pretraining models are used to extract video frame features, motion information, audio features, and video sequence features. An embedded layer structure based on self-attention mechanism is designed to embed single-mode features and learn single-mode feature parameters. Then, two schemes of joint representation and cooperative representation are used to fuse the multimodal features of the feature vectors output by the embedded layer, so that the model can pay attention to different targets in the video and their interactive relationships, which effectively improves the performance of the video captioning model. The experiment is carried out on large datasets MSR-VTT and LSMDC. Under the metrics BLEU4, METEOR, ROUGEL, and CIDEr, the MSR-VTT benchmark dataset obtained scores of 0.443, 0.327, 0.619, and 0.521, respectively. The result shows that the proposed method can effectively improve the performance of the video captioning model, and the evaluation indexes are improved compared with comparison models.

show abstract

Mel-Frequency Cepstral Coefficient Features Based on Standard Deviation and Principal Component Analysis for Language Identification Systems

Cited by 26 publications

References 34 publications

Convolutional neural network for the detection of coronavirus based on X-ray images

Convolutional neural network for the detection of coronavirus based on X-ray images

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Research on Video Captioning Based on Multifeature Fusion

Contact Info

Product

Resources

About