Výsledky vyhledávání - "Duong Thu Hang Pham"

A Novel Intelligent System for Detection of Type 2 Diabetes with Modified Loss Function and Regularization

Autor: P. W. C. Prasad, Duong Thu Hang Pham, G. C. Mallika, Abeer Alsadoon, Salma Abdullah, Tran Quoc Vinh Nguyen, Ha Thi Mai

Publikováno v: Programming and Computer Software. 47:402-414

Type 2 Diabetes (T2DM) makes up about 90% of diabetes cases, as well as tough restriction on continuous monitoring and detecting become one of key aspects in T2DM. This research aims to develop an ensemble of several machine learning and deep learnin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e3414333df7464449dfd2b7faacf7d77
https://doi.org/10.1134/s0361768821050054

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A novel solution of enhanced loss function using deep learning in sleep stage classification: predict and diagnose patients with sleep disorders

Autor: Duong Thu Hang Pham, Ereena Rajbhandari, Indra Seher, Abeer Alsadoon, Tran Quoc Vinh Nguyen, P. W. C. Prasad

Publikováno v: Multimedia Tools and Applications. 80:11607-11630

Sleep stage classification is important to accurately predict and diagnose patients with sleep disorders. Though various deep learning approaches have been implemented to classify sleep classes, these consist limitations that impact the accuracy and

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::056d48d2d0d0a103f49de1347b23a8a9
https://doi.org/10.1007/s11042-020-10199-8

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A novel approach for early prediction of sudden cardiac death (SCD) using hybrid deep learning

Autor: Duong Thu Hang Pham, P. W. C. Prasad, Abeer Alsadoon, Tran Quoc Vinh Nguyen, Nedhal A. Al-Saiyd, Rabin Kaspal

Publikováno v: Multimedia Tools and Applications. 80:8063-8090

Importance of early prediction of Sudden Cardiac Deaths (SCD) has been rising as a large percentage of mortality of patients with cardiovascular diseases. Various deep learning methodologies has been developed to predict the onset of SCDs, Their key

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::53b452baec8994e4721e935829cd3485
https://doi.org/10.1007/s11042-020-10150-x

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Generative Adversarial Network (GAN) and Enhanced Root Mean Square Error (ERMSE): Deep Learning for Stock Price Movement Prediction

Autor: Salma Abdullah, P.W.C. Prasad, Abeer Alsadoon, Duong Thu Hang Pham, Tran Quoc Vinh Nguyen, Tarik A. Rashid, Ashok Kumar

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c1462a174742dca0cb18f620ddbe1dd

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A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction

Autor: Duong Thu Hang Pham, Kiran Sharma, Abeer Alsadoon, P. W. C. Prasad, Thair Al-Dala'in, Tran Quoc Vinh Nguyen

Publikováno v: Computer methods and programs in biomedicine. 197

Background and aim deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and impro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::722ca7bba043cae107a2b99f431c2545
https://pubmed.ncbi.nlm.nih.gov/32957061

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Is Higher Order Mutant Harder to Kill Than First Order Mutant? An Experimental Study

Autor: Duong-Thu-Hang Pham, Quang Vu Nguyen

Publikováno v: Intelligent Information and Database Systems ISBN: 9783319754161
ACIIDS (1)

This paper considers the problem whether higher order mutant is harder to kill than first order mutant or not. Higher order mutation testing has been proposed to overcome the limitations of traditional mutation testing (also called first order mutati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f1200df118eb41bbc919e87140652492
https://doi.org/10.1007/978-3-319-75417-8_62

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