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Worldwide breast cancer is the most common form of cancer death occurring in 12.6% of women. This paper presents a cost effective approach to classify the normal, malignant and benign tumor using two layer neural network back propagation algorithm. Back propagation algorithm is used to train the neural network. Parallelization techniques speed up the computation process and as a result two layer neural networks outperform the previous work in terms of accuracy. Breast cancer tumor database used for the testing purpose is from the CIA machine learning repository. The highest accuracy of 97.12% is achieved using the two layer neural network back propagation algorithm.

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Business leaders around the world are using emerging technologies to capitalize on data, to create business value and to compete effectively in a digitally driven world. Among them the risk assessment and the risk management, based on the assessment is a process which can be made using the available past historical data and applying Data Analytics. Although it is being implemented in different business domains, it is at a nascent stage. It is further new and emerging in the area of Education. This paper describes such a process followed in an educational institution of an engineering college and the use of data for risk management. Based on the processes followed, the performance of the students is seen to be improving in academic performance, placement, higher education and entrepreneurship. This also provides a good process and framework for taking strategic initiatives which will give long term benefits in the areas like research and outreach activities.

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