scholarly journals Discovering Customer Paths from Location Data with Process Mining

2020 ◽  
Vol 3 (1) ◽  
pp. 139-145
Author(s):  
Onur Dogan
Keyword(s):  
Machine Learning ◽  
System Performance ◽  
Process Mining ◽  
Tracking Data ◽  
Human Behaviors ◽  
Location Data ◽  
Machine Learning Methods ◽  
Visit Duration ◽  
Individual Tracking ◽  
Visual Results

customer paths can be used for several purposes, such as understanding customer needs, defining bottlenecks, improving system performance. Two of the principal difficulties depend on discovering customer paths due to dynamic human behaviors and collecting reliable tracking data. Although machine learning methods have contributed to individual tracking, they have complex iterations and problems to produce understandable visual results. Process mining is a methodology that can rapidly create process flows and graphical representations. In this study, customer flows are created with process mining in a supermarket. The differences between the paths of customers purchased and non-purchased are discussed. The results show that both groups have almost similar visit duration, which is 87.5 minutes for purchased customers and 86.6 minutes for non-purchased customers. However, the duration of aisles is relatively small in non-purchased customer flows because customers aim to return or change the item instead of buying.

scholarly journals Email Spam Detection using Ensemble Methods

2019 ◽  
Vol 8 (3) ◽  
pp. 4148-4153
Keyword(s):  
Machine Learning ◽  
System Performance ◽  
Research Work ◽  
Ensemble Methods ◽  
Spam Detection ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Tree Algorithms ◽  
Email Spam ◽  
F Measure

The swiftly growth of spam email has escalated the need to upgrade the existing spam detection and filtration methods. There is the existence of several machine learning methods for the classification and detection of email spam but these lacks in some cases. In this research work ensemble methods are adapted to detect the email spam. The machine learning methods of Multinomial Naïve Bayes and J48 Decision Tree algorithms are considered and ensembled. The considered ensemble methods are bagging and boosting. The experimentation is conducted on the dataset of CSDMC2010 Spam corpus. The results for the considered dataset are evaluated using individual classifiers, bagging, and boosting ensemble approaches. The system performance is accessed in terms of precision, recall, f-measure, and accuracy. The experimental outcomes indicates the distinguish results for the detection of email spam using ensemble methods.

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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