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The Impact of Real Big Data on our Future and Risk Identification

Al-Shouiliy, Khaldoon
http://orcid.org/0000-0002-6446-2090
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593267375537873

Abstract Details

2020, PhD, University of Cincinnati, Engineering and Applied Science: Computer Science and Engineering.
In recent days, revolution of the internet and the telecommunication with new smart devices have made big data booming fame. Tons of data are stored for each one of us in different servers like Facebook, Tweeter, and other apps. However, if collecting data looks as an easy task, then classifying and processing big data is a difficult challenge that ought to be addressed among many issues. Taxonomy is a mechanism of data labels that can perform economic and effective analysis of associated values. In term of processing and classifying big data, most of the large companies are busy trying to find out the best and easy way to do that. While they succeeded in some respects they are still struggling on other sides. Moreover, some of those technology companies like Microsoft produce Azure cloud service that can help in term of big data (storage, process, and develop). Amazon also have their own too. In this work we used Azure cloud. This work is to focus on this important field where big data is used for health care in order to diagnose diseases before they occur. One of the major academic work on health data (Breast cancer) could be divided into two big objectives like statistic work and analytical work, with an emphasis on predictive analytics as it is emerging as a transformative tool that can enable more proactive and preventative treatment option. Moreover, in this work, we create a new platform that works perfectly with big data to create our model and then the sub-aim is to compare our results with other researchers. Later on, in this research we focus on pandemic of COVID-19 wand we used a dataset from internet to read it and analysis to understand and predict whether the patient going to release, isolate or decease. The second aim of this research is to focus on the economy datasets. Loans have been continuously growing up to trillions of dollars in the US, which makes it difficult for the lenders to study every case individually. Borrowers tend to acquire more loans and lenders are willing to increase their profitability. In the era of big data, it would be beneficial for lenders to use modern technology such as machine learning algorithms to analyze and predict customers’ creditworthy. We are continuously creating new platform that this big data can fit. We also compare our model results with other works, and its shows that our results are better than other existing works. Then, another hypothesis is to focus on the drawback of big data. One of the biggest drawbacks of such datasets is an imbalance representation of samples from different categories. In such a case, the classifiers and deep learning techniques are not capable of handling issues like these. A majority of existing works tend to overlook these issues. Typical data balancing methods in the literature resort to data resampling whether it is under sampling a majority class of samples or oversampling the minority class of samples. In this work, we focus on the minority sample and ignore the majority ones. "
Dharma Agrawal, D.Sc. (Committee Chair)
Rui Dai, Ph.D. (Committee Member)
Chia Han, Ph.D. (Committee Member)
Wen-Ben Jone, Ph.D. (Committee Member)
Haider K. Raad, Ph.D. (Committee Member)
116 p.
Computer Science
AzureML; Jungle forest; P2P; SMOTE-3D; Big data; Breast Cancer

Recommended Citations

Citations

  • Al-Shouiliy, K. (2020). The Impact of Real Big Data on our Future and Risk Identification [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593267375537873

    APA Style (7th edition)

  • Al-Shouiliy, Khaldoon. The Impact of Real Big Data on our Future and Risk Identification. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593267375537873.

    MLA Style (8th edition)

  • Al-Shouiliy, Khaldoon. "The Impact of Real Big Data on our Future and Risk Identification." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593267375537873

    Chicago Manual of Style (17th edition)

ucin1593267375537873
388
© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.