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

A modified reverse-based analysis logic mining model with Weighted Random 2 Satisfiability logic in Discrete Hopfield Neural Network and multi-objective training of Modified Niched Genetic Algorithm DOI
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 240, P. 122307 - 122307

Published: Oct. 28, 2023

Language: Английский

Citations

44
Dual optimization approach in discrete Hopfield neural network DOI
Yueling Guo, Nur Ezlin Zamri, Mohd Shareduwan Mohd Kasihmuddin

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 164, P. 111929 - 111929

Published: July 7, 2024

Language: Английский

Citations

6
Higher order Weighted Random <i>k</i> Satisfiability ($k = 1, 3$) in Discrete Hopfield Neural Network DOI Creative Commons
Xiaoyan Liu, Mohd Shareduwan Mohd Kasihmuddin, Nur Ezlin Zamri

et al.

AIMS Mathematics, Journal Year: 2025, Volume and Issue: 10(1), P. 159 - 194

Published: Jan. 1, 2025

<p>Researchers have explored various non-systematic satisfiability approaches to enhance the interpretability of Discrete Hopfield Neural Networks. A flexible framework for has been developed investigate diverse logical structures across dimensions and improved lack neuron variation. However, logic phase this approach tends overlook distribution characteristics literal states, ratio negative literals not mentioned with higher-order clauses. In paper, we propose a new named Weighted Random $k$ Satisfiability ($k = 1, 3$), which implements in The proposed logic, integrated into Network, established structure by incorporating during phase. This enhancement increased network's storage capacity, improving its ability handle complex, high-dimensional problems. advanced was evaluated learning metrics. When values were $r 0.2$, 0.4, 0.6, 0.8, demonstrated potential better performances smaller errors. Furthermore, performance positive impact on management synaptic weights. results indicated that optimal global minimum solutions are achieved when set 0.8$. Compared state-of-the-art structures, novel more significant achieving solutions, particularly terms literals.</p>

Language: Английский

Citations

0
Hierarchical grid-constrained fusion network for image stitching DOI Creative Commons
Yongqin Zhang,

Ruan Bai-yao,

Lei Du

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2025, Volume and Issue: 37(3)

Published: April 4, 2025

Language: Английский

Citations

0
Logic mining method via hybrid discrete hopfield neural network DOI
Yueling Guo, Mohd Shareduwan Mohd Kasihmuddin, Nur Ezlin Zamri

et al.

Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111200 - 111200

Published: May 1, 2025

Language: Английский

Citations

0
Data mining algorithm in the identification of accounting fraud by smart city information technology DOI Creative Commons
Xinyi Zheng, Mohamad Ali Abdul Hamid,

Y. Hou

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e30048 - e30048

Published: April 27, 2024

The identification of accounting fraud is an important measure to safeguard the interests stakeholders and ensure long-term development company. current traditional methods for identifying rely on manual review judgment, lacking objectivity accuracy. In order improve accuracy identification, efficiency objectivity, this article combines smart city information technology conduct in-depth research data mining algorithms identification. This first provides a brief overview cities technology, then introduces basic theory finally implements through k-means clustering algorithm. divided into k clusters, abnormal clusters are identified by checking characteristics attributes each cluster. Compared with rule-based pattern based methods, approach can more flexibly adapt different types forms fraud, discover unknown patterns fraud. experiment, used electronic collection, analysis, retrieval systems websites Shanghai Stock Exchange Shenzhen collect 641 annual reports financial from 62 listed companies that engaged in statement 84 were not reported have 2012 2021 as test samples. results tested analyzed several aspects, including number misjudgments, misjudgment rate, ROC curve. final show compared comprehensive rate has decreased 3 %. conclusion indicates identify help audit effectiveness.

Language: Английский

Citations

2
Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability DOI Creative Commons
Nurul Atiqah Romli, Nur Fariha Syaqina Zulkepli, Mohd Shareduwan Mohd Kasihmuddin

et al.

AIMS Mathematics, Journal Year: 2024, Volume and Issue: 9(8), P. 22321 - 22365

Published: Jan. 1, 2024

<p>Evaluating behavioral patterns through logic mining within a given dataset has become primary focus in current research. Unfortunately, there are several weaknesses the research regarding models, including an uncertainty of attribute selected model, random distribution negative literals logical structure, non-optimal computation best logic, and generation overfitting solutions. Motivated by these limitations, novel model incorporating mechanism to control literal systematic Satisfiability, namely Weighted Systematic 2 Satisfiability Discrete Hopfield Neural Network, is proposed as structure represent behavior dataset. For we used ratio <italic>r</italic> structures prevent solutions optimize synaptic weight values. A new computational approach considering both true false classification values learning system was applied this work preserve significant Additionally, unsupervised techniques such Topological Data Analysis were ensure reliability attributes model. The comparative experiments models utilizing 20 repository real-life datasets conducted from repositories assess their efficiency. Following results, dominated all metrics for average rank. ranks each metric Accuracy (7.95), Sensitivity (7.55), Specificity (7.93), Negative Predictive Value (7.50), Mathews Correlation Coefficient (7.85). Numerical results in-depth analysis demonstrated that consistently produced optimal induced represented performance study.</p>

Language: Английский

Citations

2
Optimizing Artificial Neural Network Learning Using Improved Reinforcement Learning in Artificial Bee Colony Algorithm DOI Creative Commons
Taninnuch Lamjiak, Booncharoen Sirinaovakul,

Siriwan Kornthongnimit

et al.

Applied Computational Intelligence and Soft Computing, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

Artificial neural networks (ANNs) are widely used machine learning techniques with applications in various fields. Heuristic search optimization methods typically to minimize the loss function ANNs. However, these can lead network become stuck local optima, limiting performance. To overcome this challenge, study introduces an improved approach, improvement of reinforcement artificial bee colony (improved R‐ABC) algorithm, enhance process for The proposed method aims limitations heuristic and improve efficiency weight adjustment This new approach enhances discovery phase traditional R‐ABC by including parameters neighboring food sources, augmenting capabilities finding optimal solution. performance was compared ANNs utilizing backpropagation stochastic gradient descent (SGD) Adam optimizers, as well other swarm intelligence (SI) such particle (PSO) R‐ABC. results showed that both PSO continuously solutions across all benchmark datasets. In iris dataset, SI approaches consistently achieved F 1‐scores exceeding 0.94, outperforming SGD Adam. For datasets, generally outperformed methods. indicate when is applied ANNs, it outperforms optimization, especially size expands. Although faster execution times TensorFlow, suggests using model accuracy efficiency. Advanced increase ability obtain solutions. Enhanced algorithms significantly ANN training efficiency, complex high‐dimensional

Language: Английский

Citations

2
Binary Ant Colony Optimization Algorithm in Learning Random Satisfiability Logic for Discrete Hopfield Neural Network DOI
Yuan Gao, Mohd Shareduwan Mohd Kasihmuddin,

Ju Chen

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112192 - 112192

Published: Sept. 1, 2024

Language: Английский

Citations

2
A novel Hybrid Exhaustive Search and data preparation technique with multi-objective Discrete Hopfield Neural Network DOI Creative Commons
Alyaa Alway, Nur Ezlin Zamri, Mohd. Asyraf Mansor

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 9, P. 100354 - 100354

Published: Nov. 2, 2023

The primary objective in building predictive analytics models is to achieve optimal accuracy with real datasets. limitations of existing lie their storage capacity, which hinders the progress generating high accuracy. When a model capacity limited, it may struggle process large datasets and encounter underfitting issues, preventing from capturing complexities data. Hence, this paper addresses these challenges by introducing novel approach analytics, focusing on expanding Discrete Hopfield Neural Network (DHNN). First, employs satisfiability logic represent attributes dataset DHNN. This representation enables establish connection between neurons attributes, enabling efficient information processing. Second, introduce multi–objective DHNN, key innovation that enhances model's capacity. In context, training algorithm named Hybrid Exhaustive Search developed optimize DHNN's phase. Third, introduces new data preparation techniques, including feature selection method for identifying best–induced logic. explains extracts relationships. Finally, proposed evaluated based four reputable metrics variety primarily collected UCI Repository. performance compared three models. Through extensive experiments rigorous evaluation, can outperform all demonstrating effectiveness expanded techniques employed.

Language: Английский

Citations

5