Breast Cancer Dataset Classification using Neural Network
Priyanka Verma1, Ayush Agrawal2

1Priyanka Verma, Dept. of Computer Science Engineering, Sagar Institute of Science & Technology, Bhopal, India.
2Ayush Agrawal, Dept. of Computer Science Engineering, Sagar Institute of Science & Technology, Bhopal, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 3476-3480 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9522088619/2019©BEIESP | DOI: 10.35940/ijeat.F9522.088619
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Abstract: Agile Software Development Approach is the buzzword for the Organizations working with Traditional Software Development Approach. Scrum is one of the most vital frameworks used when working towards Agile Software Development projects. Material Management is one of the salient processes of the Supply Chain Management in transforming inputs into outputs. Extreme Programming is more technical in nature and Scrum is a People focused Management approach. This scrum framework is customizable according to the project teams and it is adopted widely to effectively manage software projects. Scrum framework fits into the groove for delivering quality product at a faster pace with minimal continuous feature delivery at frequent intervals. The minimal Marketable feature is delivered at the end of each Sprint. A Sprint is an iterative activity which ends up at regular intervals. But, the way of customizing the process and giving a minimal feature delivery at the end of each Sprint in the projects act as a major challenge. Other challenges resulted in the team collaboration and the involvement of the team members in the project and in integrating the minimal marketable feature. Syncing with the product owner and the other stake holders, and participation in the sprint planning were some of the issues that the team faced. The success of the entire team in implementing the Agile Software Development approach in the Enterprise Resource Planning Material Management Section is discussed.
Keywords: Machine Learning, Classification, CNN, RNN, Neurons