Bronchial asthma is one of the most common chronic diseases of childhood and considered as a major health problem globally. The irregularity in meteorological factors has become a primary cause of health severity for the individuals suffering from asthma. In the presented research, a dew-cloud assisted cyber-physical system (CPS) is proposed to analyze the correlation between the meteorological and health parameters of the individuals. The work is primarily focused on determining the health adversity caused by the irregular scale of meteorological factors in real-time. IoT-assisted smart sensors are utilized to capture ubiquitous information from indoor environment that make a vital impact on the health of the individual directly or indirectly. The data is analyzed over the cyber-space to quantify the probable irregular health events by utilizing the data classification efficiency of Weighted-Naïve Bayes modeling technique. Moreover, the relationship between meteorological and health parameters is estimated by utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) and calculate a unifying factor over the temporal scale. To validate the monitoring performance, the proposed model is implemented in the four schools of Jalandhar, India. The experimental evaluation of the proposed model acknowledges the performance efficiency through several statistical approaches. Furthermore, the comparative analysis is evaluated with state-of-the-art decision-making algorithms that demonstrate the effectiveness of the proposed solution for the targeted application.

支气管哮喘是儿童最常见的慢性疾病之一，被认为是全球范围内的主要健康问题。气象因素的不规则性已经成为患有哮喘的个体造成健康严重程度的主要原因。在提出的研究中，提出了一种露云辅助的网络物理系统（CPS），以分析个体的气象和健康参数之间的相关性。这项工作主要集中在实时确定由气象因素的不规则规模引起的健康逆境。物联网辅助的智能传感器可用于捕获室内环境中无处不在的信息，这些信息对个人的健康产生直接或间接的重大影响。利用权重朴素贝叶斯建模技术的数据分类效率，对网络空间中的数据进行分析，以量化可能的不规则健康事件。此外，通过利用自适应神经模糊推理系统（ANFIS）估算气象和健康参数之间的关系，并计算时间尺度上的统一因子。为了验证监控性能，建议的模型在印度Jalandhar的四所学校中实施。对提出的模型的实验评估通过几种统计方法确认了性能效率。此外，使用最先进的决策算法对比较分析进行评估，该算法可证明所提出的解决方案针对目标应用的有效性。