scholarly journals Light GBM Machine Learning Algorithm to Online Click Fraud Detection

2019 ◽  
pp. 1-12
Author(s):  
Elena-Adriana MINASTIREANU ◽  
Gabriela MESNITA
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Online Advertising ◽  
Fraud Detection ◽  
Online Marketing ◽  
Machine Learning Algorithm ◽  
Advertising Industry ◽  
Click Fraud ◽  
Web Advertising ◽  
Critical Issues

In the current web advertising activities, the fraud increases the number of risks for online marketing, advertising industry and e-business. The click fraud is considered one of the most critical issues in online advertising. Even if the online advertisers make permanent efforts to improve the traffic filtering techniques, they are still looking for the best protection methods to detect click frauds.

scholarly journals Transaction Fraud Detection (Anomaly detection) using Machine Learning

2021 ◽  
pp. 361-363
Author(s):  
Prof. Viresh Vanarote ◽  
Omkar Gaykar ◽  
Sarfaraz Saudagar ◽  
Naresh Bulbule ◽  
Tushar Funde
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Anomaly Detection ◽  
Online Shopping ◽  
Learning Algorithm ◽  
Fraud Detection ◽  
Machine Learning Algorithm ◽  
Behavior Profiles

Now a day’s online shopping crazy thing for peoples. As well as many people uses Online transaction for many purposes. Transaction fraud is growing seriously. Therefore, the study on fraud detection is interesting and significant and we can say necessary. An important way of detecting fraud is to extract the behavior profiles (BPs) of users based on their historical transaction records, and then to verify if an incoming transaction is a fraud or not in view of Their BPs. Also we apply SVM, Adaboost, and Neural Network machine learning algorithm to see which one is giving the best result.

Model for Credit Card Fraud Detection using Machine Learning Algorithm

2021 ◽  
Author(s):  
Prabhat Singh ◽  
Vishesh Chauhan ◽  
Shivam Singh ◽  
Priya Agarwal ◽  
Shrey Agrawal
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Learning Algorithm ◽  
Fraud Detection ◽  
Machine Learning Algorithm ◽  
Credit Card Fraud

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

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