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All Journal The Indonesian Journal of Computer Science
Mulongo, Yves
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Computer Science & IT Electrical & Electronics Engineering Engineering

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An integrated Machine Learning Model for Manufacturing industry Mulongo, Yves
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i1.3021

Abstract

The purpose of this article is to examine the process of developing services based on machine learning technologies in a manufacturing environment context. Over the past decade, there has been a resurgence of interest in technology, with prominent technology business organisations and several start-ups incorporating it into their operations. The technology's widespread applications capacity for obtaining a competitive advantage, combined with the prospect of task automation for increased operational efficiency, renders it a significant resource for large corporations. Yet, the rate at which most of manufacturing firms are implementing Machine Learning services into their operations is very insignificant. Technical expertise has dominated study in this field, with minimal participation from other areas. Thus, the goal of this article is to identify the development benefits of Machine Learning services and to provide a process model for their deployment, based on service development and value theory.
Application of Artificial Neural Network into Manufacturing Processes Mnkandla, Ernest; Mulongo, Yves
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3022

Abstract

The neural network model is an advanced and effective tool aims at simulating the manufacturing operations. An important number of researchers have utilized artificial neural network (ANN) to optimising multiple response metrics in manufacturing applications. In the majority of situations, the use of ANN enables the prediction of the mechanical and physical properties of manufacturing goods based on provided technical data. To this end, the deployment of ANN in manufacturing sector is tremendously significant in terms of cost and material resource savings. Thus, Artificial neural network as a key component regarding the optimization of the manufacturing processes.
Co-Authors Mnkandla, Ernest