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- 저자 : ( Hamoud Alshammari ) (검색결과 2 건)
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Big IoT Healthcare Data Analytics Framework Based on Fog and Cloud Computing
( Hamoud Alshammari ),( Sameh Abd El-ghany ),( Abdulaziz Shehab ) 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.6
Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.
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Developing Cloud Computing Time Slot-availability Predictions Using an Artificial Neural Network
Alanazi Rayan,Muhammad Ashfaq khan,Fawaz Alhazemi,Hamoud Alshammari,Yunmook Nah 대한전자공학회 2020 IEIE Transactions on Smart Processing & Computing Vol.9 No.1
Over the last decade, cloud computing has exponentially transformed the ways of computing. In spite of its various advantages, cloud computing suffers from several challenges that affect performance. Two of the fundamental challenges are power consumption and dynamic resource scaling. An efficient resource allocation strategy could help cloud computing to improve overall performance and operational costs. In this paper, we design a novel approach to available time slot prediction in a data node, based on an artificial neural network (ANN), which predicts the time at which the required resources will be available. We conducted experiments on several nodes, obtaining up to 98%, and outperforming state-of-the-art available time slot prediction approaches. We claim that available time–slot prediction for cloud computing based on an ANN will lead to optimum resource allocation and to minimizing energy consumed while maintaining the essential performance level.
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