Artificial Neural Network (ANN) is a vital subset of machine learning that helps computer scientists in their work on complex tasks, such as, strategizing, making predictions, and recognizing trends. Artificial neural network is not like other machine learning algorithms that crunch numbers or organize data; it is an algorithm that learns from experience and repeated tasks performed by its users. An artificial neural network is also known as neural network. ANN is a computational model based on the functions and structure of biological neural networks. Information that runs through the network affects the structure of the artificial neural network due to the fact that a neural network learns or changes based on the input and output. For instance, Under Armour has been using IBM’s Watson for its health tracking application records. The app helps in tracking consumer data that includes nutrition, workouts, and sleep data, derived from wearable, third-party apps, and manually entered data. It then plugs in these variables to create custom-tailored content for users.
Key features of ANN, such as, predicting consumer behavior and sales forecasting are expected to drive the artificial neural network market during the forecast period. Rapid digitization is anticipated to boost the deployment of artificial neural network platforms. Furthermore, an extensively used application of artificial neural networks is in the field of predictive analytics. Artificial neural network helps marketers predict the outcome of a campaign by recognizing the trends from previous marketing campaigns. While neural networks have available for a while, it is mainly the recent emergence of Big Data that has made this technology extremely useful in the field of marketing.
The global artificial neural network market can be segmented based on type, architecture, application, enterprise size, industry, and geography. In terms of type, the market can be classified into single-layer neural network, multi-layer feed forward neural network, temporal neural network, self-organizing neural network, and others. The multi-layer feed forward neural network segment is expected to dominate the artificial neural network market during the forecast period. Based on architecture, the artificial neural network market can be divided into feed forward networks, feedback networks, and lateral networks. The feed forward network is anticipated to dominate the artificial neural network market during the forecast period owing to the fact that it has more than one hidden layer. This hidden layer helps to approximate continuous function. On the basis of application, the artificial neural network can be segmented into classifications, pattern matching, pattern completion, optimization, data mining, function approximation, and time series modelling. The pattern matching segment is anticipated to witness a significant growth rate during the forecast period due to the fact that it this segment it aims to produce a pattern that is associated with given input. Based on enterprise size, the feed forward network market can be bifurcated into small and medium enterprises and large enterprises. On the basis of industry, the artificial neural network market can be classified into marketing, social media, healthcare, and others. The marketing segment is expected to dominate the feed forward network market during the forecast period as feed forward network helps in sales forecasting.
