Abstract-Cloud computing is a type of parallel and distributed system consisting of a co llect ion of interconnected and virtual computers. With the increasing demand and benefits of cloud computing infrastructure, different co mputing can be performed on cloud environment. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorith ms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimizat ion algorith m for load balancing compared with different scheduling algorith ms has been proposed. Ant Co lony Optimization (ACO) is random optimization search approach that will be used for allocating the incoming jobs to the virtual mach ines. The main contribution of our work is to balance the system load while t rying to min imizing the make span of a given tasks set. The load balancing factor, related to the job fin ishing rate, is proposed to make the job fin ishing rate at different resource being similar and the ability of the load balancing will be improved. The proposed scheduling strategy was simu lated using Cloudsim toolkit package. Experimental results showed that, the proposed algorith m outperformed scheduling algorith ms that are based on the basic ACO or Modified Ant Colony Optimization (MACO).
Cardiovascular diseases (CVDs) are the most critical heart diseases. Accurate analytics for real-time heart disease is significant. This paper sought to develop a smart healthcare framework (SHDML) by using deep and machine learning techniques based on optimization stochastic gradient descent (SGD) to predict the presence of heart disease. The SHDML framework consists of two stage, the first stage of SHDML is able to monitor the heart beat rate condition of a patient. The SHDML framework to monitor patients in real-time has been developed using an ATmega32 Microcontroller to determine heartbeat rate per minute pulse rate sensors. The developed SHDML framework is able to broadcast the acquired sensor data to a Firebase Cloud database every 20 seconds. The smart application is infectious in regard to displaying the sensor data. The second stage of SHDML has been used in medical decision support systems to predict and diagnose heart diseases. Deep or machine learning techniques were ported to the smart application to analyze user data and predict CVDs in real-time. Two different methods of deep and machine learning techniques were checked for their performances. The deep and machine learning techniques were trained and tested using widely used open-access dataset. The proposed SHDML framework had very good performance with an accuracy of 0.99, sensitivity of 0.94, specificity of 0.85, and F1-score of 0.87.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.