An artificial neural network approach for the language learning model

Sabir, Zulqurnain; Ben Said, Salem; Al-Mdallal, Qasem

doi:10.1038/s41598-023-50219-9

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…Each node on the neural network can process information and output it to other nodes, which receive it and output it again until all the work of the neural network is completed, and finally output the result. 47 Tingting Lee’s team through artificial neural network analysis of the available data in hospital information system, a prediction model was established, and it was found that the use of fall-related nursing assessment, anti-psychotics and diuretics might be the related factors of patients’ falls. 48 Decision tree is an analysis method to judge the feasibility on the basis of the known probability of the occurrence of various situations.…”

Section: Discussionmentioning

confidence: 99%

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Zhang,

Ge,

Xia

et al. 2024

JMDH

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Backgrounds With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers. Aim To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward. Methods Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords. Results According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing , author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement. Conclusion Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline’s development, but it also introduces a slew of new issues that need researchers to address.

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Section: Discussionmentioning

confidence: 99%

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Zhang,

Ge,

Xia

et al. 2024

JMDH

View full text Add to dashboard Cite

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Numerical Performance of the Fractional Direct Spreading Cholera Disease Model: An Artificial Neural Network Approach

Malik

2024

Fractal Fract

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The current investigation examines the numerical performance of the fractional-order endemic disease model based on the direct spreading of cholera by applying the neuro-computing Bayesian regularization (BR) neural network process. The purpose is to present the numerical solutions of the fractional-order model, which provides more precise solutions as compared to the integer-order one. Real values based on the parameters can be obtained and one can achieve better results by utilizing these values. The mathematical form of the fractional direct spreading cholera disease is categorized as susceptible, infected, treatment, and recovered, which represents a nonlinear model. The construction of the dataset is performed through the implicit Runge–Kutta method, which is used to lessen the mean square error by taking 74% of the data for training, while 8% is used for both validation and testing. Twenty-two neurons and the log-sigmoid fitness function in the hidden layer are used in the stochastic neural network process. The optimization of BR is performed in order to solve the direct spreading cholera disease problem. The accuracy of the stochastic process is authenticated through the valuation of the outputs, whereas the negligible calculated absolute error values demonstrate the approach’s correctness. Furthermore, the statistical operator performance establishes the reliability of the proposed scheme.

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Artificial neural networks for stability analysis and simulation of delayed rabies spread models

Shafqat,

Alsaadi

2024

MATH

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<p>Rabies remains a significant public health challenge, particularly in areas with substantial dog populations, necessitating a deeper understanding of its transmission dynamics for effective control strategies. This study addressed the complexity of rabies spread by integrating two critical delay effects—vaccination efficacy and incubation duration—into a delay differential equations model, capturing more realistic infection patterns between dogs and humans. To explore the multifaceted drivers of transmission, we applied a novel framework using piecewise derivatives that incorporated singular and non-singular kernels, allowing for nuanced insights into crossover dynamics. The existence and uniqueness of solutions was demonstrated using fixed-point theory within the context of piecewise derivatives and integrals. We employed a piecewise numerical scheme grounded in Newton interpolation polynomials to approximate solutions tailored to handle singular and non-singular kernels. Additionally, we leveraged artificial neural networks to split the dataset into training, testing, and validation sets, conducting an in-depth analysis across these subsets. This approach aimed to expand our understanding of rabies transmission, illustrating the potential of advanced mathematical tools and machine learning in epidemiological modeling.</p>

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An artificial neural network approach for the language learning model

Cited by 3 publications

References 53 publications

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Numerical Performance of the Fractional Direct Spreading Cholera Disease Model: An Artificial Neural Network Approach

Artificial neural networks for stability analysis and simulation of delayed rabies spread models

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