Estimation of HbA1c for DMT2 risk prediction on the Mexican population based in Artificial Neural Networks DOI Creative Commons
Alexis Alonso-Bastida,

Marisol Cervantes-Bobadilla,

Dolores Azucena Salazar-Piña

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2023, Volume and Issue: 36(1), P. 101905 - 101905

Published: Dec. 31, 2023

In this paper, the main objective is to estimate percentage of glycosylated hemoglobin through an easily accessible computational platform risk generating type 2 diabetes mellitus in Mexican population. The estimation tool developed artificial neural network model, which was trained and validated according a population sample 1120 people between 18 59 years old. model inputs were gender, age, body mass index, waist circumference, weekly food consumption, family history, whether person suffers from any chronic degenerative disease other than T2DM. We used as output, estimated dynamic glucose model. results present coefficient determination 99%, demonstrating acceptable performance aid for health personnel, seeks generate first approximation glycemic status those communities with high marginalization index prevention strategies.

Language: Английский

Synergizing intelligence and knowledge discovery: Hybrid black hole algorithm for optimizing discrete Hopfield neural network with negative based systematic satisfiability DOI Creative Commons
Nur ‘Afifah Rusdi, Nur Ezlin Zamri, Mohd Shareduwan Mohd Kasihmuddin

et al.

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(11), P. 29820 - 29882

Published: Jan. 1, 2024

<p>The current systematic logical rules in the Discrete Hopfield Neural Network encounter significant challenges, including repetitive final neuron states that lead to issue of overfitting. Furthermore, neglect impact on appearance negative literals within structure, and most recent efforts have primarily focused improving learning capabilities network, which could potentially limit its overall efficiency. To tackle limitation, we introduced a Negative Based Higher Order Systematic Logic imposing restriction clauses. Additionally, Hybrid Black Hole Algorithm was proposed retrieval phase optimize states. This ensured optimized achieved maximum diversity reach global minima solutions with lowest similarity index, thereby enhancing performance network. The results illustrated model can achieve up 10,000 diversified an average index 0.09. findings indicated are optimal configurations. findings, development new SAT implementation algorithm DHNN multi-objective functions result updated high diversity, attainment solutions, produces low index. Consequently, this be extended for logic mining applications classification tasks. will enhance high-quality induced logic, is effective knowledge extraction.</p>

Language: Английский

Citations

0
Interpretable neural network classification model using first-order logic rules DOI Creative Commons
Hu Tuo, Zuqiang Meng,

Zihao Shi

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 614, P. 128840 - 128840

Published: Nov. 8, 2024

Language: Английский

Citations

0
Variational learning to rank for Test Case Prioritization via prioritizing metric inspired differentiable loss DOI
Peng Tang, Junfeng Wang, Mingxing Liu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 141, P. 109776 - 109776

Published: Dec. 12, 2024

Language: Английский

Citations

0
An efficient method to build music generative model by controlling both general and local note characteristics DOI Creative Commons

Thinh Do Quang,

Trang Hoang

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2023, Volume and Issue: 35(9), P. 101761 - 101761

Published: Sept. 20, 2023

It has been shown that since the rapid development of entertainment industry, music generation become a focused research topic. Numerous methods for creating music, or musical notes specifically have announced, each with distinct characteristics and advantages. These usually concentrated on these two aspects: overall harmony whole score link between adjacent notes, which this referred respectively as general local aspects. This study proposes model combined is capable deriving benefits from both aspects, hence good quality in terms quantitative qualitative evaluations. Various results based those discussed judged efficient enhancing well future opportunities. The value Average Pitch Interval (API) achieved remarkable 1.43, along note range 12.145; while subjective aspect, survey participants gave 6.81 generated yet only about 70% them can distinguish genuine pieces music.

Language: Английский

Citations

0
Estimation of HbA1c for DMT2 risk prediction on the Mexican population based in Artificial Neural Networks DOI Creative Commons
Alexis Alonso-Bastida,

Marisol Cervantes-Bobadilla,

Dolores Azucena Salazar-Piña

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2023, Volume and Issue: 36(1), P. 101905 - 101905

Published: Dec. 31, 2023

In this paper, the main objective is to estimate percentage of glycosylated hemoglobin through an easily accessible computational platform risk generating type 2 diabetes mellitus in Mexican population. The estimation tool developed artificial neural network model, which was trained and validated according a population sample 1120 people between 18 59 years old. model inputs were gender, age, body mass index, waist circumference, weekly food consumption, family history, whether person suffers from any chronic degenerative disease other than T2DM. We used as output, estimated dynamic glucose model. results present coefficient determination 99%, demonstrating acceptable performance aid for health personnel, seeks generate first approximation glycemic status those communities with high marginalization index prevention strategies.

Language: Английский

Citations

0