scholarly journals In-process Tool Wear Prediction System Based on Machine Learning Techniques and Force Analysis

Procedia CIRP ◽  
2018 ◽  
Vol 77 ◽  
pp. 501-504 ◽  
Author(s):  
A. Gouarir ◽  
G. Martínez-Arellano ◽  
G. Terrazas ◽  
P. Benardos ◽  
S. Ratchev
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Machine Learning Techniques ◽  
Prediction System ◽  
Force Analysis ◽  
Wear Prediction ◽  
Learning Techniques ◽  
Tool Wear Prediction

scholarly journals Development of a Simulation Prediction System Using Statistical Machine Learning Techniques

2016 ◽  
Vol 5 (11) ◽  
pp. 593-606
Author(s):  
Ki Yong Lee ◽  
YoonJae Shin ◽  
YeonJeong Choe ◽  
SeonJeong Kim ◽  
Young-Kyoon Suh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Prediction System ◽  
Statistical Machine Learning ◽  
Learning Techniques

scholarly journals An Efficient Ad-Click Prediction System using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 1269-1272
Keyword(s):  
Machine Learning ◽  
Online Advertising ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Prediction System ◽  
Advertising Industry ◽  
Python Language ◽  
Internet Users ◽  
Learning Techniques ◽  
Machine Learning Model

Ad-click prediction is a learning problem that is highly related to the multi-billion-dollar ad- promoting the online advertising industry. As the number of internet users in India reached 525 million in 2019, online advertising companies are trying to influence internet usage users for promoting their business. Machine learning is a technique in which systems getting to act without any explicit programming. Currently, machine learning is pervasive today and we can use machine learning models in every research field. The accuracy of the ad-click prediction system impacts business revenue, so it is very important to build a prediction system with fewer false positives and false negatives.in this paper, we proposed an ad-click prediction system based on machine learning techniques. The dataset is taken from Kaggle. The dataset contains nine features. The goal of the model is to evaluate the probability of an online user to click on a given ad. We built a machine learning model based on these features. We applied a voting classifier on the dataset and achieved an accuracy of 98%.We used python language for implementation.

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