April'19
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The IUP Journal of Computer Sciences
ISSN: 0973-9904
A 'peer reviewed' journal indexed on Cabell's Directory,
and also distributed by EBSCO and Proquest Database
It is a quarterly journal that publishes research papers on state-of-the-art Computing techniques and Encryption techniques; Computational problems; Artificial Intelligence; Cloud Computing; Databases; Algorithms and data structures; Cybernetics; Firewall techniques; Chip designing, Logic circuits and Software engineering; Programming techniques; Computer architecture; Neural networks, Machine Learning etc.
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Cloud Computing for MATLAB and R Users: Part II – Transferring Data and Cloud Storage
Cloud-based computing has tremendous potential for academia in general and researchers in particular. The ability to store, read and manipulate large dataset is increasingly important to be on the cutting-edge of research. While most researchers have extensive programming skills, they generally are not computer scientists. A number of cloud-based, virtual work spaces have been developed which can bring enormous computing advantages over any desktop or laptop. However, the instructions to get up and running in the cloud are often overwhelming, and require a significant investment of time. There is an initial steep learning curve associated with developing a high performance computing environment that several times becomes an obstacle, preventing many from benefitting the most from using the cloud. This paper seeks to demystify the process and get the reader up and running very quickly. Once this initial process is over and the reader is proficient on these issues, performing computational chores that would take days on a desktop can be done in minutes. We next show the way the readers can implement their research using two well-known computer programs, MATLAB and R. Part II provides an in-depth instruction on using Amazon Web Services (AWS) for transferring data and cloud storage.
Cloud Computing for MATLAB and R Users: Part III – Multicore Virtual Machine Parallel
Computing Environment
Part III narrates instructions on harnessing the power of multicore virtual machine parallel computing environments, suitable for large dataset and heavy computational needs. Also, presents guidance to use the storage cloud (S3) along with computing cloud based on Amazon Elastic Compute Cloud (EC2).
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