Knowledge and experience engineering techniques are becoming increasingly useful and popular components of hybrid integrated systems used to solve complex real-life problems in different disciplines. These techniques offer features such as learning from experience, handling noisy and incomplete data, helping with decision making, and predicting capabilities. In this article, we present a number of different applications of a multidomain knowledge representation structure called decisional DNA that can be implemented and shared for the exploitation of embedded knowledge within different technologies.

In this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicates to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing devices. It is constructed in a similar fashion of how DNA formed: built up by four essential elements. As the DNA produces phenotypes, the Neural Knowledge DNA carries information and knowledge via its four essential interrelated elements, namely, Networks, Experiences, States, and Actions; which store the detail of the artificial neural networks for training and reusing such knowledge. The novelty of this approach is that it uses previous decisional experience to collect and expand intelligence for future decision making formalized support. The experience based collective computational techniques of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) are used to develop aforesaid decisional sustenance. Together with artificial neural networks and reinforcement learning, the proposed Neural Knowledge DNA is used to catch knowledge of a very simple maze problem, and the results show that our Neural Knowledge DNA is a very promising knowledge representation approach for artificial neural network-based intelligent systems.

As a reliable cardiovascular system, ECG has been widely used in the detection of heart rhythms. This paper presents an attention-based ResNet processing of ECG data. Respectively are MIT and PTB Diagnostics datasets. The model is prominent in both data sets. And the accuracy in MIT datasets is 96.2%. And in PTB datasets the accuracy is 99.6%.