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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Vehicle Damage Level Estimator For Claiming Insurance Using AI |
| Authors Name: | Sneha M , Velmurugam M , Shabikha Fiza I , Shobika Sri S , Umamaheshwari P |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_219012
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| Published Paper Id: | IJNRD2404564 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | At present, under the guidance of the new generation of information technology, the rapid accumulation of data, the continuous improvement of computing power, the continuous optimization of algorithm models, and the rapid rise of multi-scene applications have made profound changes in the development environment of artificial intelligence. Following a car accident, an insurance company must assess the level of damage to each vehicle to decide on the compensation paid to the insurance customer. This assessment is usually performed by manual inspection, which is costly and time-consuming. Automatic car damage assessment using image data is an under-addressed problem highly relevant to the insurance industry. There are many ways to claim insurance for the damaged vehicle but it may not be accurate all the time and it also takes long time for processing and providing insurance and detect the cost for the only given dataset. The claiming process will take long time. Only able to predict the cost for the damage. Based on the demand of automobile insurance claims and intelligent transportation, combined with abundant basic data and advanced machine vision algorithm, an intelligent damage determination system of ‘Artificial Intelligence + Vehicle Insurance’ is constructed. This system is used for detecting the exact cost for the damage occurred in an accident so it is helpful to avoid loss of cost for the insurance companies. Gathering data from the user and predicting the exact damage that have occurred . We solve this problem using Convolutional Neural Network (CNN) algorithm using Open Source Computer Vision Library (Open CV) and deep learning which are mainly used for image processing, video capture and analysis with Python Flask framework. This project first introduces the functions of the intelligent damage assessment system. Secondly, it discusses the realization path of each functional module in detail, and finally puts forward the vision for the future. |
| Keywords: | Damage Detection, Artificial Intelligence, Deep Learning, Convolutional Neural Network, Prediction |
| Cite Article: | "Vehicle Damage Level Estimator For Claiming Insurance Using AI", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.f523-f528, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404564.pdf |
| Downloads: | 00026 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404564 Registration ID: 219012 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: f523-f528 Country: Coimbatore, TamilNadu, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404564 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404564 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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