Alharbi Mohammad F
Department of Health Informatics, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia.
Comput Biol Med. 2025 Jan;184:109471. doi: 10.1016/j.compbiomed.2024.109471. Epub 2024 Nov 30.
Severe gastrointestinal infections and watery diseases like cholera are still a major worldwide medical concern in the developing nations. A mathematical system contains some necessary dynamics based on the cholera spread to investigate the influence of public health education movements along with treatment and vaccination as control policies in restraining the infection. The cholera disease system with public health mediations divide the density of human population into seven categories based on the status of diseases, who are susceptible, educated, vaccinated, quarantined, infected, treated and removed individuals along with the aquatic bacteria population. The motive of current research is to present the numerical performances of the cholera disease system with public health mediations by using a stochastic computing process based on the Bayesian regularization neural network. A data is constructed by using a conventional Adam scheme that reduces the mean square error by distributing the data into training, validation and testing with some reasonable percentages. Twenty-five neurons, and sigmoid fitness function are used in the stochastic process to solve the model. The accuracy is justified by using comparison of the results, absolute error around 10-06 to 10-08 and some statistical operator performances.
严重的胃肠道感染和诸如霍乱之类的水媒疾病在发展中国家仍然是全球主要的医学关注点。一个数学系统包含基于霍乱传播的一些必要动态，以研究公共卫生教育运动以及作为控制政策的治疗和疫苗接种在抑制感染方面的影响。带有公共卫生调解的霍乱疾病系统根据疾病状况将人口密度分为七类，即易感人群、受过教育者、接种疫苗者、隔离者、感染者、接受治疗者和康复者以及水生细菌种群。当前研究的目的是通过基于贝叶斯正则化神经网络的随机计算过程，展示带有公共卫生调解的霍乱疾病系统的数值性能。使用传统的亚当算法构建数据，该算法通过将数据按合理百分比分配到训练、验证和测试中，来降低均方误差。随机过程中使用二十五个神经元和 sigmoid 适应度函数来求解模型。通过结果比较、绝对误差在 10 - 06 到 10 - 08 之间以及一些统计算子性能来证明准确性。
