Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

Amjad, Ammar; Khan, Lal; Chang, Hsien-Tsung

doi:10.7717/peerj-cs.1053

Cited by 2 publications

(1 citation statement)

References 82 publications

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“…Therefore, an automatic affordable, accessible approach is required for migraine classification and prediction. Fortunately, machine learning algorithms show state-of-the-art performance in tasks such as text classification 12 – 15 speech recognition 16 – 19 , and many health-related automatic disease classifications and predictions 20 , 21 .…”

Section: Introductionmentioning

confidence: 99%

Migraine headache (MH) classification using machine learning methods with data augmentation

Khan,

Shahreen,

Qazi

et al. 2024

Sci Rep

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Migraine headache, a prevalent and intricate neurovascular disease, presents significant challenges in its clinical identification. Existing techniques that use subjective pain intensity measures are insufficiently accurate to make a reliable diagnosis. Even though headaches are a common condition with poor diagnostic specificity, they have a significant negative influence on the brain, body, and general human function. In this era of deeply intertwined health and technology, machine learning (ML) has emerged as a crucial force in transforming every aspect of healthcare, utilizing advanced facilities ML has shown groundbreaking achievements related to developing classification and automatic predictors. With this, deep learning models, in particular, have proven effective in solving complex problems spanning computer vision and data analytics. Consequently, the integration of ML in healthcare has become vital, especially in developing countries where limited medical resources and lack of awareness prevail, the urgent need to forecast and categorize migraines using artificial intelligence (AI) becomes even more crucial. By training these models on a publicly available dataset, with and without data augmentation. This study focuses on leveraging state-of-the-art ML algorithms, including support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DST), and deep neural networks (DNN), to predict and classify various types of migraines. The proposed models with data augmentations were trained to classify seven various types of migraine. The proposed models with data augmentations were trained to classify seven various types of migraine. The revealed results show that DNN, SVM, KNN, DST, and RF achieved an accuracy of 99.66%, 94.60%, 97.10%, 88.20%, and 98.50% respectively with data augmentation highlighting the transformative potential of AI in enhancing migraine diagnosis.

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Section: Introductionmentioning

confidence: 99%

Migraine headache (MH) classification using machine learning methods with data augmentation

Khan,

Shahreen,

Qazi

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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Performance evaluation of lung sounds classification using deep learning under variable parameters

Wang,

Sun

2024

EURASIP J. Adv. Signal Process.

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It is desired to apply deep learning models (DLMs) to assist physicians in distinguishing abnormal/normal lung sounds as quickly as possible. The performance of DLMs depends on feature-related and model-related parameters heavily. In this paper, the relationship between performance and feature-related parameters of a DLM, i.e., convolutional neural network (CNN) is analyzed through experiments. ICBHI 2017 is selected as the lung sounds dataset. The sensitivity analysis of classification performance of the DLM on three parameters, i.e., the length of lung sounds frame, overlap percentage (OP) of successive frames and feature type, is performed. An augmented and balanced dataset is acquired by the way of white noise addition, time stretching and pitch shifting. The spectrogram and mel frequency cepstrum coefficients of lung sounds are used as features to the CNN, respectively. The results of training and test show that there exists significant difference on performance among various parameter combinations. The parameter OP is performance sensitive. The higher OP, the better performance. It is concluded that for fixed sampling frequency 8 kHz, frame size 128, OP 75% and spectrogram feature is optimum under which the performance is relatively better and no extra computation or storage resources are required.

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Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

Cited by 2 publications

References 82 publications

Migraine headache (MH) classification using machine learning methods with data augmentation

Migraine headache (MH) classification using machine learning methods with data augmentation

Performance evaluation of lung sounds classification using deep learning under variable parameters

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