scholarly journals A hybrid and effective learning approach for Click Fraud detection

2021 ◽  
Vol 3 ◽  
pp. 100016
Author(s):  
Thejas G.S. ◽  
Surya Dheeshjith ◽  
S.S. Iyengar ◽  
N.R. Sunitha ◽  
Prajwal Badrinath
2019 ◽  
Vol 33 (15) ◽  
pp. 1950150 ◽  
Author(s):  
Lijiao Pan ◽  
Shibiao Mu ◽  
Yingyan Wang

A user click fraud detection method based on Top-Rank-k frequent pattern mining algorithm is presented to solve the click fraud problem appearing in current online advertising. Firstly, this method combines the click frequency of event samples, calculates the real evaluation score of click stream, and the click stream density function and evaluation score expression under multi-dimensional variables, and further obtains the time complexity of the next user’s click fraud process. Secondly, according to the Top-Rank-k frequent pattern, the process of click fraud detection algorithm is designed, and the click fraud user is analyzed and obtained. The results show that this method has good efficiency and correctness, and is superior to other similar algorithms.


Author(s):  
Haitao Xu ◽  
Daiping Liu ◽  
Aaron Koehl ◽  
Haining Wang ◽  
Angelos Stavrou
Keyword(s):  

Author(s):  
Riwa Mouawi ◽  
Imad H. Elhajj ◽  
Ali Chehab ◽  
Ayman Kayssi
Keyword(s):  

2021 ◽  
Vol 13 (2) ◽  
pp. 62-76
Author(s):  
Muhammad Hafeez

From the beginning of 21st century, the leaning stratigies have been changed from traditional to information and communication based. A critical review of published articles about blended and traditional leaning stratigies has been conducted to highlight the importance and significance of both learning stratigies. Thirty-six (36) research articles published in various databases in various disciplines have been selected for review.  The review of literature showed that in most of the studies, the blended learning strategy proved to be more effective learning strategy against the traditional lecture method. From thirty-six published articles reviewed, twenty-five studies showed a statistically more significance value in blended learning approach for academic achievement, critical and creative skills in various disciplines. So, on the basis of this study, it is strongly recommended that blended learning strategy must be applied to achieve high academic and professional results.    


2021 ◽  
Vol 13 (2) ◽  
pp. 1314-1321
Author(s):  
Ahmad Faizi ◽  
Djoko Saryono ◽  
Muakibatul Hasanah ◽  
Nurcha sanah

Learning efficiency highly relies on the implemented learning approach. The Madurese language (BM) learning is a social situation that stores cultural diversity reflected from students’ background. Meanwhile, culturally responsive learning facilitates effective learning that accommodates students’ cultural differences. This study investigates students’ knowledge and cultural experiences in a classroom, primarily those related to Probolinggo society’s local culture, using descriptive qualitative approach. The data were obtained through observation and interview with some Madurese language teachers. The data, in the form of excerpts, were analyzed using direct interpretation technique. The findings are associated with social, moral, and art cultural knowledge and experience related to local culture during the Madurese language learning. Various differences have been observed between students who are speaking Madurese and other languages. Their distinctive knowledge and experiences induce different opinion, behavior, and attitude, along with perspective toward art, in the class. Integrating students’ local culture related experiences present learning independence.


2020 ◽  
Author(s):  
Pengtao Xie ◽  
Xingchen Zhao

Learning by ignoring, which identifies less important things and excludes them from the learning process, is an effective learning technique in human learning. There has been psychological studies showing that learning to ignore certain things is a powerful tool for helping people focus. We are interested in investigating whether this powerful learning technique can be borrowed from humans to improve the learning abilities of machines. We propose a novel learning approach called learning by ignoring (LBI). Our approach automatically identifies pretraining data examples that have large domain shift from the target distribution by learning an ignoring variable for each example and excludes them from the pretraining process. We propose a three-level optimization framework to formulate LBI which involves three stages of learning: pretraining by minimizing the losses weighed by ignoring variables; finetuning; updating the ignoring variables by minimizing the validation loss. We develop an efficient algorithm to solve the LBI problem. Experiments on various datasets demonstrate the effectiveness of our method.


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