Management of V.U.C.A. (Volatility, Uncertainty, Complexity and Ambiguity) Using Machine Learning Techniques in Industry 4.0 Paradigm

2019 ◽  
pp. 1-24 ◽  
Author(s):  
Bhagyashree Mohanta ◽  
Pragyan Nanda ◽  
Srikanta Patnaik
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Machine Learning Techniques ◽  
Learning Techniques

Machine learning techniques applied to industrial engineering: a multi criteria approach

2019 ◽  
Author(s):  
Fabio De Felice ◽  
Marta Travaglioni ◽  
Giuseppina Piscitelli ◽  
Raffaele Cioffi ◽  
Antonella Petrillo
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Technological Development ◽  
Predictive Maintenance ◽  
Industrial Engineering ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The World ◽  
The Subject ◽  
To Receive

With the Industry 4.0 (I4.0) beginning, the world is witnessing an important technological development. The success of I4.0 is linked to the implementation of enabling technologies, including Machine Learning, which focuses on the machines’ ability to receive a series of data and learn on their own. The present research aims to systematically analyze the existing literature on the subject in various aspects, including publication year, authors, scientific sector, country, institution and keywords. Understanding and analyzing the existing literature on Machine Learning applied to predictive maintenance is preparatory to recommend policy on the subject.

scholarly journals Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry

2021 ◽  
Vol 13 (8) ◽  
pp. 4120
Author(s):  
Hail Jung ◽  
Jinsu Jeon ◽  
Dahui Choi ◽  
Jung-Ywn Park
Keyword(s):  
Machine Learning ◽  
Injection Molding ◽  
Industry 4.0 ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sustainable Growth ◽  
Machine Learning Techniques ◽  
Quality Prediction ◽  
Learning Techniques ◽  
Prediction Problems

With sustainable growth highlighted as a key to success in Industry 4.0, manufacturing companies attempt to optimize production efficiency. In this study, we investigated whether machine learning has explanatory power for quality prediction problems in the injection molding industry. One concern in the injection molding industry is how to predict, and what affects, the quality of the molding products. While this is a large concern, prior studies have not yet examined such issues especially using machine learning techniques. The objective of this article, therefore, is to utilize several machine learning algorithms to test and compare their performances in quality prediction. Using several machine learning algorithms such as tree-based algorithms, regression-based algorithms, and autoencoder, we confirmed that machine learning models capture the complex relationship and that autoencoder outperforms comparing accuracy, precision, recall, and F1-score. Feature importance tests also revealed that temperature and time are influential factors that affect the quality. These findings have strong implications for enhancing sustainability in the injection molding industry. Sustainable management in Industry 4.0 requires adapting artificial intelligence techniques. In this manner, this article may be helpful for businesses that are considering the significance of machine learning algorithms in their manufacturing processes.

scholarly journals SSL-VFC4.5: An approach to adapt Very Fast C4.5 classification algorithm to deal with semi-supervised learning

2021 ◽  
Author(s):  
Carlos Eduardo Nass ◽  
Agustín Alejandro Ortíz Díaz ◽  
Fabiano Baldo
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Real Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Training Time ◽  
Predicting Performance ◽  
Learning Techniques

The growing popularity of audio and video streaming, industry 4.0 and IoT (Internet of Things) technologies contribute to the fast augment of the generation of various types of data. Therefore, to analyze these data for decision-making, supervised machine learning techniques need to be fast while keeping a suitable predicting performance even in many real-life scenarios where labeled data are expensive and hard to be gotten. To overcome this problem, this work proposes an adaptation to the Very Fast C4.5 (VFC4.5) algorithm implementing on it a semi-supervised impurity metric presented in the literature. The results pointed out that this adaptation can slightly increase the accuracy of the VFC4.5 when the datasets have the presence of a very few amount of labeled instances, but it increases the training time, especially when the number of labeled instances in the datasets increase.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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