Improving Obsolescence Detection Accuracy using Recurrent Neural Networks
Manasvi Gurnaney1, Shubhangi Neware2
1Manasvi Gurnaney, Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India.
2Dr. Shubhangi Neware, Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India.
Manuscript received on 08 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 08 July 2019 | PP: 277-281 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H10760688S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Forecasting a product’s obsolescence depends on a multitude of factors which can be both technical and non-technical aspects of the product under study. The predictions are usually an approximate of the obsolescence and might not reflect the true nature of the product. Thus, researchers from various fields including market research, technology, public perception and others unite together in order to device a model which can be used for efficient obsolescence detection of products. In this paper, we propose an algorithm for effective obsolescence detection with the help of integrated datasets and a recurrent neural network (RNN). The RNN is used so that the effectiveness of prediction can be improved, and it is found that RNN is better when compared with other standard prediction classifiers
Keywords: Obsolescence, Recurrent Neural Network, Perception, Prediction
Scope of the Article: Artificial Intelligence