Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases

Farah Liyana Azizan; Saratha Sathasivam; Nurshazneem Roslan; Ahmad Deedat Ibrahim; Farah Liyana Azizan; Saratha Sathasivam; Nurshazneem Roslan; Ahmad Deedat Ibrahim

doi:10.3934/math.2024153

AIMS Mathematics

2024, Volume 9, Issue 2: 3150-3173. doi: 10.3934/math.2024153

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Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases

1.
School of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia
2.
Centre for Pre-University Studies, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia
3.
Institute of Engineering Mathematics, Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

Received: 24 July 2023 Revised: 10 September 2023 Accepted: 24 September 2023 Published: 03 January 2024
MSC : 03B52, 68T27, 68N17, 68W99

Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect.
- 3SAT,
- Covid-19,
- fuzzy logic system,
- Hopfield neural network,
- logic mining,
- metaheuristic algorithms,
- reverse analysis
Citation: Farah Liyana Azizan, Saratha Sathasivam, Nurshazneem Roslan, Ahmad Deedat Ibrahim. Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases[J]. AIMS Mathematics, 2024, 9(2): 3150-3173. doi: 10.3934/math.2024153

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Abstract

Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect.

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