Shuo He^1,∗, Jianbin Zheng^1,†, Jiale Lin^2,‡, Tao Tang^1,§, Jintao Zhao^1,¶, Hongbao Liu^1,ll

Computer Systems Science and Engineering, Vol.35, No.3, pp. 141-149, 2020, DOI:10.32604/csse.2020.35.141

Abstract Nowadays, many payment service providers use the discounts and other marketing strategies to promote their products. This also raises the issue of people who deliberately take advantage of such promotions to reap financial benefits. These people are known as ‘scalper parties’ or ‘econnoisseurs’ which can constitute an underground industry. In this paper, we show how to use machine learning to assist in identifying abnormal scalper transactions. Moreover, we introduce the basic methods of Decision Tree and Boosting Tree, and show how these classification methods can be applied in the detection of abnormal transactions. In addition,… More >

Daniel J. Sadowski¹, Chandy S. Ellimoottil², Ajay Tejwani³, Alex Gorbonos¹

Canadian Journal of Urology, Vol.20, No.6, pp. 7015-7020, 2013

Abstract Introduction: Proton therapy (PT) for prostate cancer is an expensive treatment with limited evidence of benefit over conventional radiotherapy. We sought to study whether online information on PT for prostate cancer was balanced and whether the website source influenced the content presented.
Materials and methods: We applied a systematic search process to identify 270 weblinks associated with PT for prostate cancer, categorized the websites by source, and filtered the results to 50 websites using predetermined criteria. We then used a customized version of the DISCERN instrument, a validated tool for assessing the quality of consumer health information,… More >

H.S. Fadewar¹, S.B. Jagtap¹, G.N.Shinde²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.9, No.1, pp. 57-66, 2009, DOI:10.3970/icces.2009.009.057

Abstract Data Mining (DM) techniques are well-known for providing flexible and efficient analytical tools for data processing. In this paper, we propose intelligent data control system design and specifications as an example of DM application in marketing data processing. E-Marketing businesses are using Data Mining to identify patterns in customers' buying behavior; identify profitable customer segments; increase marketing return rates; prevent loss of valuable customers; estimate credit risk; identify fraudulent activity and much more. More >