One of the mosquito-borne pandemic viral infections is Dengue which is mostly transmitted to humans by the Aedes agypti or female Aedes albopictis mosquitoes. The dengue disease expansion is mainly due to the different factors such as climate change, socioeconomic factors, viral evolution, globalization, etc. The unavailability of certain antiviral therapy and specific vaccine increases the risk of the dengue disease spreading even further. This arises the need for a novel technique that overcomes the complexities associated with dengue disease prediction such as low reporting level, misclassification, and incompatible disease monitoring framework. This paper mainly overcomes the above-mentioned problems by integrating the Internet of Things (IoT), fog-cloud, and deep learning techniques for efficient dengue monitoring. A compatible disease monitoring framework is formed via the IoT devices and the reports are effectively created and transferred to the healthcare facilities via the fog-cloud model. The misdiagnosis error is overcome in this paper using the novel Hybrid Convolutional Neural Network (CNN) with Tanh Long and Short Term Memory (TLSTM) based Adaptive Teaching Learning Based Optimization (ATLBO) algorithm. The ATLBO optimized CNN-TLSTM architecture mainly analyzes the dengue-related parameters such as Soft Bleeding, Muscle Pain, Joint Pain, Skin rash, Fever, Water Site, Carbon Dioxide, Water Site Humidity, Water Site Temperature, etc. for an efficient clinical decision making and timely disease diagnosis. The experimental results are conducted using a real-time dataset and its performance is validated using various performance metrics. When compared in terms of different statistical parameters such as accuracy, f-measure, mean square error, and reliability, the proposed method offers superior results in the case of dengue disease detection than other existing methods. The ATLBO optimized hybrid CNN-TLSTM shows an accuracy of 96.9%, a precision of 95.7%, recall of 96.8%, and F-measure of 96.2% which is relatively high when compared to the existing techniques. The proposed model is capable of identifying the patients in a certain geographical region and preventing the disease emergency via immediate disease diagnosis and alerting the healthcare officials to offer the stipulated services.

蚊子传播的大流行性病毒感染之一是登革热，它主要通过埃及伊蚊或雌性白纹伊蚊传播给人类。登革热的蔓延主要受气候变化、社会经济因素、病毒进化、全球化等不同因素的影响，某些抗病毒疗法和特异性疫苗的不可用增加了登革热进一步传播的风险。这就需要一种新技术来克服与登革热疾病预测相关的复杂性，例如低报告水平、错误分类和不兼容的疾病监测框架。本文主要通过集成物联网 (IoT)、雾云和深度学习技术来克服上述问题，以实现高效的登革热监测。通过物联网设备形成兼容的疾病监测框架，并通过雾云模型有效地创建报告并传输到医疗机构。本文使用 Tan 的新型混合卷积神经网络 (CNN) 克服了误诊错误H基于长短期记忆 (TLSTM) 的自适应教学学习优化 (ATLBO) 算法。ATLBO 优化的 CNN-TLSTM 架构主要分析登革热相关参数，如软出血、肌肉痛、关节痛、皮疹、发热、水位、二氧化碳、水位湿度、水位温度等，以实现高效的临床决策和及时的疾病诊断。实验结果是使用实时数据集进行的，其性能使用各种性能指标进行了验证。当根据不同的统计参数（如准确性、f 度量、均方误差和可靠性）进行比较时，所提出的方法在登革热检测的情况下提供了比其他现有方法更好的结果。ATLBO 优化的混合 CNN-TLSTM 显示准确率为 96.9%，精度为 95.7%，召回率为 96.8%，F-measure 为 96.2%，与现有技术相比相对较高。所提出的模型能够识别特定地理区域的患者，并通过即时疾病诊断预防疾病紧急情况，并提醒医疗官员提供规定的服务。