NORMA eResearch @NCI Library

Prioritization Of Mobile Car Service Unit Placements Using Neural Networks

Sahasrabuddhe, Abhijit (2021) Prioritization Of Mobile Car Service Unit Placements Using Neural Networks. Masters thesis, Dublin, National College of Ireland.

[thumbnail of Master of Science]
PDF (Master of Science)
Download (5MB) | Preview
[thumbnail of Configuration manual]
PDF (Configuration manual)
Download (2MB) | Preview


Artificial intelligence has been widely utilized in a variety of corporate fields in recent years, including design, marketing, customer service, inventory management, and stock prediction. The goal of this research is to see how neural networks, specifically Resnet50 may be utilized to improve automobile brand marketing and customer support. The most prevalent brands in vehicle parking lots will be detected using neural networks, which will aid in determining where to locate a mobile car servicing unit in order to optimize its use while also achieving car brand promotion. As part of this research four case studies are carried out using modified RseNet50 model using different input data as normal data, gray scale, selected images( Front/Rear view ) and Limited classes. Model run on normal colored data yielded best result with accuracy 94.84% and compared with previous studies carried on same data set.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence
H Social Sciences > HF Commerce > Marketing
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 10 Mar 2023 15:41
Last Modified: 10 Mar 2023 15:41

Actions (login required)

View Item View Item