An Efficient AlexNet Deep Learning Architecture for Automatic Diagnosis of Cardio-Vascular Diseases in Healthcare System

Fog computing gives various kinds of properties over the internet which allows utilizing different types of services from industries. In these cloud architectures, the key bottleneck is their restricted scalability and therefore incapacity to meet the requirements of centralized computing environments focused on the Internet of Things (IoT). The vital clarification for this is that inertness touchy applications, for example, wellbeing observing and observation frameworks currently need processing over a lot of information (Big Data) moved to concentrated data set and from data set to cloud server farms which prompts drop in execution of such frame works. Fog and edge computing latest paradigms offer revolutionary technologies by taking user services closer and delivering low latency and energy-productive information preparing arrangements contrasted with cloud areas. However the latest fog models have several drawbacks and concentrate on either outcome precision or it may down the time of response but not under narrow perspective. The proposed novel system called ABFog which integrating with edge computing devices in deep learning and it is useful to analyze Heart disease automatically in real-time application. Fog computing is enabled in cloud framework to utilize the Fog Bus which is used to convey the accuracy to present the proposed model. ABFog is useful to provide best quality of service in various configuration modes or to predict accuracy as needed to assort in different situations and for various users prerequisites.