Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3312
Title: Mobile Base Solution for Individuals with Limited Knowledge About Cars
Authors: Nammunige, H
Chamuditha, T
Udara, S
Athapaththu, D
Gamage, A
Gamage, N
Keywords: Mobile Base Solution
Individuals
Limited Knowledge
Cars
Issue Date: 9-Dec-2022
Publisher: IEEE
Citation: H. Nammunige, T. Chamuditha, S. Udara, D. Athapaththu, A. Gamage and N. Gamage, "Mobile Base Solution for Individuals with Limited Knowledge About Cars," 2022 4th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 2022, pp. 276-281, doi: 10.1109/ICAC57685.2022.10025183.
Series/Report no.: 2022 4th International Conference on Advancements in Computing (ICAC);
Abstract: Different modes of transportation were discovered by our ancestors from ancient times. Currently, the majority of people choose to purchase a personal automobile for transport needs. However, the vast majority of people are not automobile industry experts. As a result, the majority of people have trouble when recognizing cars. Due to numerous variations of a single vehicle model, even an expert has trouble correctly identifying a certain car model. People must take into account a number of factors before purchasing a specific automobile. Some of crucial factors are service costs and future market prices. Ordinary people require the assistance of a professional when estimating the market price of a car and calculating the cost of servicing a car. Accidents can also occur at any time when driving a car often. In similar circumstances, consumers require the assistance of an insurance agent or a technician to estimate the cost of damage repair. In this study, we provide a way for non-automotive experts to use their smartphones to identify car models, forecast future market prices, determine and forecast servicing costs, and estimate minor damage repair costs. This paper demonstrates how we accomplished aforementioned tasks using YOLO V4, Multiple Linear Regression, Random Forest Classifier and Faster RCNN.
URI: https://rda.sliit.lk/handle/123456789/3312
ISBN: 979-8-3503-9809-0
Appears in Collections:4th International Conference on Advancements in Computing (ICAC) | 2022
Department of Information Technology
Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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