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Fusion: Practice and Applications
Volume 3 , Issue 1, PP: 70-90 , 2021 | Cite this article as | XML | Html |PDF

Title

Deep Neural Network-based Fusion and Natural Language Processing in Additive Manufacturing for Customer Satisfaction

Authors Names :   Abedallah Z. Abualkishik   1 *     Rasha Almajed   2  

1  Affiliation :  American University in the Emirates, Dubai, UAE

    Email :  abedallah.abualkishik@aue.ae


2  Affiliation :  American University in the Emirates, Dubai, UAE

    Email :  rasha.almajed@aue.ae



Doi   :   https://doi.org/10.54216/FPA.030105

Received: November 11, 2020 Revised: January 05, 2020 Accepted: March 19 2021

Abstract :

Modern Machine learning fusion approaches tend to extract features depending on two techniques (hand-crafted feature and representation learning). Hand-crafted features can waste time and are not sufficient for downstream tasks. Unlike representation learning, we automatically learn features with minimum time and effort and are suitable for downstream tasks. In our paper, we provide work on graph neural network methods with details on classical graph embedding approaches and the different methods in neural graph networks such as graph filtering, graph pooling, and the learning parameter for graph following each technique with a general framework or mathematical proof for customer satisfaction. To satisfy customer's feel, this research employs NLP techniques. We describe the adversarial attacks and defenses on graph representation approaches. Also, advanced application of neural graph networks is reviewed, such as combinational optimization, learning program representation, physical system modeling, and natural language processing. Finally, the challenges in geometric neural networks and future research work have been introduced.

Keywords :

Machine learning; neural graph networks; graph filtering; graph pooling; optimization; fusion based on NLP; customer satisfaction.

 

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Cite this Article as :
Abedallah Z. Abualkishik , Rasha Almajed, Deep Neural Network-based Fusion and Natural Language Processing in Additive Manufacturing for Customer Satisfaction, Fusion: Practice and Applications, Vol. 3 , No. 1 , (2021) : 70-90 (Doi   :  https://doi.org/10.54216/FPA.030105)