Abstract
Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies.
Highlights
Artificial Intelligence (AI) refers to the “intelligence” provided by software included in some machines [1]
In this work we focus on Artificial Neural Networks (ANN), that have been used to solve a great variety of problems that are difficult to solve using other techniques [17]
We focus on the Feedforward artificial neural network, there are some other such as Elman Neural Network or the Jordan Neural Network, interesting depending on the type of problem to be solved
Summary
Artificial Intelligence (AI) refers to the “intelligence” provided by software included in some machines [1] It is a field of study commonly defined as the design of intelligent agents, which perceives their environment and takes actions that maximize their possibility of success [2]. In this work we focus on Artificial Neural Networks (ANN), that have been used to solve a great variety of problems that are difficult to solve using other techniques [17]. They can be defined as statistical learning models inspired by biological neural networks. We focus on the Feedforward artificial neural network, there are some other such as Elman Neural Network or the Jordan Neural Network, interesting depending on the type of problem to be solved
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Interactive Multimedia and Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
