Zhi Zhou; Javad Pourqasem; Shadi Sayadmanesh
Abstract
Developing the scale and increasing the data set, make the reliability and availability principal affairs to access process and data achievement. In addition, we face the challenges of handling big data in terms of storage and management. This paper provides the important issues related to the massive ...
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Developing the scale and increasing the data set, make the reliability and availability principal affairs to access process and data achievement. In addition, we face the challenges of handling big data in terms of storage and management. This paper provides the important issues related to the massive storage systems, distributed storage systems, and big data storage mechanisms. Then we present some analysis models utilizing in big data and describe structure of them in details.
Agyan Panda; Bharath Yadlapalli; Zhi Zhou
Abstract
Every year, millions of dollars are lost due to fraudulent credit card transactions. To help fraud investigators, more algorithms are turning to powerful machine learning methodologies. Designing fraud detection algorithms is particularly difficult because to the non-stationary distribution of data, ...
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Every year, millions of dollars are lost due to fraudulent credit card transactions. To help fraud investigators, more algorithms are turning to powerful machine learning methodologies. Designing fraud detection algorithms is particularly difficult because to the non-stationary distribution of data, excessively skewed class distributions, and continuous streams of transactions. At the same time, due to confidentiality considerations, public data is uncommon, leaving many questions unanswered about the best technique for dealing with them. We present some replies from the practitioners in this publication. Un balanced ness, non- stationarity and assessment. Our industrial partner provided us with an actual credit card dataset, which we used to do the analysis. In this project, we attempt to develop and evaluate a model for the imbalanced credit card fraud dataset.
