Comparing the Technologies Used For the Application of Artificial Neural Networks (Published)
In the past few years, artificial neural networks (ANNs) have become a practice wherever it is necessary to solve problems of forecasting, classification, or control. ANNs are intuitively attractive because they are based on a primitive biological model of nervous systems. In the future, the development of such neurobiological models may lead to the creation of truly thinking computers. The areas of application of neural networks are very diverse – these are text and speech recognition, semantic search, expert systems, and decision support systems, prediction of stock prices, security systems, text analysis, etc. Based on the wide application of artificial neural networks, the application of numerous and diverse technologies can be inferred. In this research paper, a comparative approach towards the above-mentioned technologies will be undertaken in order to identify the optimal technology for each problem area in accordance with their respective advantages and disadvantages.
Keywords: Applications, Artificial Neural Networks, Modules