Presented a Framework of Computational Modeling to Identify the Patient Admission Scheduling Problem in the Healthcare System.

Operating room scheduling is a prominent study topic due to its complexity and significance. The increasing number of technical operating room scheduling articles produced each year calls for another evaluation of the literature to enable academics to respond to new trends more quickly. The mathematical application of a model for the patient admission scheduling issue with stochastic arrivals and departures is the subject of this study.

An efficient approach to diagnose brain tumors through deep CNN.

Background And Objective: Brain tumors are among the most common complications with debilitating or even death potential. Timely detection of brain tumors particularly at an early stage can lead to successful treatment of the patients. In this regard, numerous diagnosis methods have been proposed, among which deep convolutional neural networks (deep CNN) method based on brain MRI images has drawn huge attention.

A hierarchical detection method in external communication for self-driving vehicles based on TDMA.

Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs).