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A Neural Networks portable and Agnostic Implementation Environment for Business Transformation Projects the basic structure

The authors based their Research and Development Project (RDP) on a mixed research method that is mainly based on intelligent neural networks and action research reasoning approach ; where both methods are very similar and resemble to the human brain structure and its way of functioning. The applied research method is founded on a real life case for detecting and processing heuristic algorithms for business transformation patterns using a high level neural network implementation environment. The proposed RDP offers a set of solutions in the form of technical and managerial recommendations, to be used by company's business analysts and engineers to implement intelligent solutions for services- based Business Transformation Projects (BTP). Action Research (AR) is applied mainly in education research that corresponds to the Architecture Transformation Projects (ATP) or BTP's capability to achieve business intelligence objectives, because it inspects and learns from configurable intelligent micro artefacts that are found in the proposed neural networks concept that can be applied to BTPs' implementation phase [4]. The RDP is not influenced by any specific business domain and is considered to be generic and holistic ; where a brain-like neural networks business infrastructure is based on: 1) Neural Networks and Enterprise Architecture's (NNEA) ; and 2) Agnostic Implementation Environment (AIE). This NNEA is based on a reasoning concept that is basically a qualitative research method that manages and qualifies Critical Success Factors (CSF) sets, actions and final solutions for the BTPs support [5]. The RDP's underlined system supports the BTPs in integrating micro artefact scenarios that are in fact a set of interactive atomic service actions. AIE manages atomic services that makes them more advanced and more flexible than the rigid and monolithic micro services proposal.

Computer architecture, Artificial neural networks, Architecture, Biological neural networks, Decision making

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