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Artificial Neural Networks (ANNs) can be described as mapping of non-linear structures which are based on the functioning of the human brain. ANN simulates the intuitive way of thinking. It thus differs from the conventional computing methods in the sense that apart from replacing or speeding up the computations of human brain, it focuses on the organization and interconnection of the computing elements. However, the simulation of neural network is very tedious, costly and gross thereby making the whole process challenging. Yet ANN continues to possess its attractive nature in computing due to several reasons. Firstly, it allows computation of complex mathematical problems in a very simple. Secondly, it has a self-organizing nature which makes it feasible to imitate decision making capability of human brain easily. Thirdly, it has vivid properties such as inherent parallelism, distributed storage, robustness, non linear nature, tolerance to failure, handling of fuzzy information and their in and the generalization ability. Thus, ANN being of prime importance in the era of technological advancements, this paper aims at providing an in-depth understanding of the neural network, its modern-day applications and various issues that are needed to be dealt with while we use this technology. |
