Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AI26WQDID" target="_blank" >RIV/00216208:11320/23:I26WQDID - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/22:ETHX9L5G

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141870329&doi=10.1007%2fs00521-022-07999-4&partnerID=40&md5=9e10e768ed74b7844d9bc9e538258b69" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141870329&doi=10.1007%2fs00521-022-07999-4&partnerID=40&md5=9e10e768ed74b7844d9bc9e538258b69</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-022-07999-4" target="_blank" >10.1007/s00521-022-07999-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Popis výsledku v původním jazyce

"Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature."

Název v anglickém jazyce

Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels

Popis výsledku anglicky

"Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature."

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

"Neural Computing and Applications"

ISSN

0941-0643

e-ISSN

—

Svazek periodika

35

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

6065-6077

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85141870329