No abstract

In this work, the mobile health services (MHS) approach has been introduced to encourage locals with different educational backgrounds. This work intends to minimize personal interaction hours between patients and doctors in a real-time healthcare environment. The increasing number of pilgrims to Saudi Arabia (SA) demands such an arrangement for the benefit of both people and service provider authorities. Especially dealing with the patients visiting at the time of Ramadan is going to be a challenging task for the authorities and healthcare service providers if some kind of virus spreads in the Kingdom. The recent Corona virus threat is making most of the people panic and almost all the countries in the world are feeling the heat to tackle such a scenario. Due to a famous pilgrim destination, dealing the visitor's flow is always a challenging task. Therefore, the proposed MHS uses the latest applications of neural networks (NN), artificial intelligence (AI), bigdata (BD) and predictive data analytics (PDA) for improving the performance of healthcare operations. At the initial stage of this research, the risk prediction and mitigation process of various events have seen an accuracy of 95 %. Applications of AI and BD are being extensively used to upgrade the patient records and information at a faster rate to enhance the overall performance of healthcare services.

In this work, an uncertainty prediction method for the home environment is proposed using the IoT devices (sensors) for predicting uncertainties using place-based approach. A neural network (NN) based smart communication system was implemented to test the results obtained from placebased approach using the inputs from sensors linked with internet of things (IoT). In general, there are so many smart systems for home automation is available for alerting the owners using IoT, but they can communicate only after an accident happens. But it is always better to predict a hazard before it happens is very important for a safe home environment due to the presence of kids and pet animals at home in the absence of parents and guardians. Therefore, in this work, the uncertainty prediction component (UPC) using place-based approach helps to make suitable prediction decisions and plays a vital role to predict uncertain events at the smart home environment. A comparison of different classifiers like multi-layer perceptrons (MLP), Bayesian Networks (BN), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) is made to understand the accuracy of the obtained results using the proposed approach. The results obtained in this method shows that place-based approach is providing far better results as compared to the global approach with respect to training and testing time as well. Almost a difference of 10 times is seen with respect to the computing times, which is a good improvement to predict uncertainties at a faster rate.