Application of Neural Networks in Solving Business Problems

Author(s): Chetti Akshay, DV Kannika and Prof Sunetra Chatterjee

Abstract:

The article discusses the application of neural networks in business and financial fields, focusing on management, marketing, and decision making. The article explains that neural networks are capable of modeling the relationships present in data collections, which makes them useful for data mining and increasing business intelligence. The article highlights the benefits of neural networks, including their ability to detect patterns that human eyes fail to perceive, and to recognize relationships and patterns in a given set of similar data. The two main types of neural networks discussed are the multilayered feedforward neural network (MFNN), used for prediction and classification problems, and the self-organizing map (SOM), used for clustering data. The architecture of MFNN and the different activation functions that can be used in the network are also explained. The article further states that artificial neural networks are based on a mathematical model that facilitates information processing and learns from data by adjusting synaptic connections. The article concludes that neural networks are special computing systems that resemble the mesh-like interconnected elements (called neurons) present in the brain, and they have emerged as one of the fastest-growing areas in artificial intelligence.

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