Muhammad Shoaib, Rafia Tabassum, Muhammad Asif Zahoor Raja, Kottakkaran Sooppy Nisar
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引用次数: 0
Abstract
Background
There are a variety of lethal infectious diseases that are seriously affecting people's lives worldwide, particularly in developing countries. Hepatitis B, a fatal liver disease, is a contagious disease spreading globally. In this paper, a new hybrid approach of feed forward neural networks is considered to investigate aspects of the SEACTR (susceptible, exposed, acutely infected, chronically infected, treated, and recovered) transmission model of hepatitis B virus disease (HBVD). The combination of genetic algorithms and sequential quadratic programming, namely CGASQP, is applied, where genetic algorithm (GA) is used as the main optimization algorithm and sequential quadratic programming (SQP) is used as a fast-searching algorithm to fine-tune the outcomes obtained by GA. Considering the nature of HBVD, the whole population is divided into six compartments. An activation function based on mean square errors (MSEs) is constructed for the best performance of CGASQP using proposed model.
Results
The solution's confidence is boosted through comparative analysis with reference to the Adam numerical approach. The results revealed that approximated results of CGASQP overlapped the reference approach up to 3–9 decimal places. The convergence, resilience, and stability characteristics are explored through mean absolute deviation (MAD), Theil’s coefficient (TIC), and root mean square error (RMSE), as well as minimum, semi-interquartile range, and median values with respect to time for the nonlinear proposed model. Most of these values lie around 10−10–10−4 for all classes of the model.
Conclusion
The results are extremely encouraging and indicate that the CGASQP framework is very effective and highly feasible for implementation. In addition to excellent reliability and level of precision, the developed CGASQP technique also stands out for its simplicity, wider applicability, and flexibility.
期刊介绍：
Beni-Suef University Journal of Basic and Applied Sciences (BJBAS) is a peer-reviewed, open-access journal. This journal welcomes submissions of original research, literature reviews, and editorials in its respected fields of fundamental science, applied science (with a particular focus on the fields of applied nanotechnology and biotechnology), medical sciences, pharmaceutical sciences, and engineering. The multidisciplinary aspects of the journal encourage global collaboration between researchers in multiple fields and provide cross-disciplinary dissemination of findings.
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