The effect of consanguineous marriage on reading disability based on deep neural networks

For knowledge acquisition and social engagement, reading comprehension is essential. However, 20% or so of younger students have trouble with it. In order to predict the effects of consanguineous marriage on reading handicap and customize adaptive learning experiences, the study proposes an Intellig...

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Published in:	Multimedia tools and applications Vol. 83; no. 17; pp. 51787 - 51807
Main Author:	Talaat, Fatma M.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.05.2024 Springer Nature B.V
Subjects:	Adaptive learning Artificial neural networks Back propagation Back propagation networks Children Computer Communication Networks Computer Science Control theory Data Structures and Information Theory Early warning systems Education Empirical analysis Feedback loops General regression neural networks Knowledge acquisition Machine learning Marriage Multimedia Information Systems Network design Neural networks Reading comprehension Special Purpose and Application-Based Systems Students Consanguineous Marriage Dyslexia Probabilistic Neural Network Orthography
ISSN:	1573-7721, 1380-7501, 1573-7721
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Abstract	For knowledge acquisition and social engagement, reading comprehension is essential. However, 20% or so of younger students have trouble with it. In order to predict the effects of consanguineous marriage on reading handicap and customize adaptive learning experiences, the study proposes an Intelligent Adaptive Learning and Prediction Framework (IALPF). This framework is proposed as a transformative solution that smoothly combines cutting-edge AI approaches. IALPF provides precise predictions and individualized learning pathways by utilizing extensive cognitive profiling, data gathering, and hybrid neural network design. It includes early warning systems, flexible content distribution, and ongoing development based on active learning and feedback loops. The IALPF represents a significant change in education that has wide-ranging effects. We evaluated reading skills among 770 students in a study that included two experimental groups, a control group, and 22 pupils from first-cousin marriages and 21 children of unrelated parents, respectively. Tests were given for word identification and reading comprehension, among other things. The findings showed that children of first cousin parents had a higher chance of reading difficulties than those of parents from other families. The outstanding performance of IALPF, which outperformed conventional techniques like Back Propagation (BP) and General Regression Neural Network (GRNN), was further supported by empirical evaluation. This demonstrates IALPF's success in reinventing personalized learning and predictive analysis, strengthening its potential to improve education in a variety of scenarios. The seamless integration of cutting-edge AI methods into IALPF, which forecasts the effect of consanguineous marriage on reading handicap, is a significant innovation. To set it apart from conventional approaches, this special framework integrates cognitive profile, information gathering, and hybrid neural networks for accurate predictions. The empirical analysis demonstrates the revolutionary potential of IALPF by demonstrating its improved predictive accuracy when compared to Back Propagation (BP) and General Regression Neural Network (GRNN).
AbstractList	For knowledge acquisition and social engagement, reading comprehension is essential. However, 20% or so of younger students have trouble with it. In order to predict the effects of consanguineous marriage on reading handicap and customize adaptive learning experiences, the study proposes an Intelligent Adaptive Learning and Prediction Framework (IALPF). This framework is proposed as a transformative solution that smoothly combines cutting-edge AI approaches. IALPF provides precise predictions and individualized learning pathways by utilizing extensive cognitive profiling, data gathering, and hybrid neural network design. It includes early warning systems, flexible content distribution, and ongoing development based on active learning and feedback loops. The IALPF represents a significant change in education that has wide-ranging effects. We evaluated reading skills among 770 students in a study that included two experimental groups, a control group, and 22 pupils from first-cousin marriages and 21 children of unrelated parents, respectively. Tests were given for word identification and reading comprehension, among other things. The findings showed that children of first cousin parents had a higher chance of reading difficulties than those of parents from other families. The outstanding performance of IALPF, which outperformed conventional techniques like Back Propagation (BP) and General Regression Neural Network (GRNN), was further supported by empirical evaluation. This demonstrates IALPF's success in reinventing personalized learning and predictive analysis, strengthening its potential to improve education in a variety of scenarios. The seamless integration of cutting-edge AI methods into IALPF, which forecasts the effect of consanguineous marriage on reading handicap, is a significant innovation. To set it apart from conventional approaches, this special framework integrates cognitive profile, information gathering, and hybrid neural networks for accurate predictions. The empirical analysis demonstrates the revolutionary potential of IALPF by demonstrating its improved predictive accuracy when compared to Back Propagation (BP) and General Regression Neural Network (GRNN).
Author	Talaat, Fatma M.
Author_xml	– sequence: 1 givenname: Fatma M. orcidid: 0000-0001-6116-2191 surname: Talaat fullname: Talaat, Fatma M. email: fatma.nada@ai.kfs.edu.eg organization: Faculty of Artificial Intelligence, Kafrelsheikh University, Faculty of Computer Science & Engineering, New Mansoura University, Nile Higher Institute for Engineering and Technology
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Cites_doi	10.1007/978-94-017-3492-9_2 10.1016/j.eswa.2023.120135 10.1093/ije/22.3.463 10.1111/j.1469-1809.1977.tb01964.x 10.15390/EB.2019.8032 10.32604/cmc.2022.026547 10.1126/science.2028254 10.1007/s00521-022-08007-59 10.3390/bioengineering10010018 10.1007/s00521-023-08428-w 10.14569/IJACSA.2023.0140442 10.1017/S0021932000016503 10.1007/s00521-023-08678-8 10.1007/s11042-022-13000-0 10.1002/ajmg.1320440102 10.29329/ijpe.2022.426.2 10.3138/jcfs.19.2.229 10.1007/s00500-022-07420-1 10.1007/s00521-023-08619-5 10.1080/10888438.2018.1561698 10.1007/s00521-023-08372-9 10.1023/A:1018727005707 10.1007/s11042-022-12223-5 10.21608/ejai.2023.205554.1008 10.14527/pegegog.2020.034 10.21608/njccs.2022.280047 10.1007/s11042-023-15803-1 10.1007/s00521-023-08809-1 10.1787/9789264130852-en 10.1136/jmg.34.12.1000 10.32604/cmc.2023.039368
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SubjectTerms	Adaptive learning Artificial neural networks Back propagation Back propagation networks Children Computer Communication Networks Computer Science Control theory Data Structures and Information Theory Early warning systems Education Empirical analysis Feedback loops General regression neural networks Knowledge acquisition Machine learning Marriage Multimedia Information Systems Network design Neural networks Reading comprehension Special Purpose and Application-Based Systems Students
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Title	The effect of consanguineous marriage on reading disability based on deep neural networks
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