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Applications of Neural Networks to Biomedical Image Processing
Published in Yu Hen Hu, Jenq-Neng Hwang, Handbook of Neural Network Signal Processing, 2018
Tülay Adali, Yue Wang, Huai Li
The decision making support by a CAD system addresses the problem of mapping a knowledge database given a finite set of data examples. The mapping function can therefore be interpreted as a quantitative representation of the knowledge about the mass lesions contained in the database [50]. Instead of mapping the whole data set using a single complex network, it is more practical to design a set of simple class subnets with local mixture clusters, each of which represents a specific region of the knowledge space. Inspired by the principle of divide-and-conquer in applied statistics, probabilistic modular neural networks (PMNNs) have become increasingly popular in machine learning research [16, 22, 50, 53, 56, 57]. This section presents PMNN applications to the problem of mapping from a feature database for mass detection.
A multi-level modelling and fidelity evaluation method of digital twins for creating smart production equipment in Industry 4.0
Published in International Journal of Production Research, 2023
Chao Zhang, Jingjing Li, Guanghui Zhou, Qian Huang, Min Zhang, Yifan Zhi, Zhibo Wei
Knowledge space modelling aims to construct a dynamic knowledge base and a set of continuously updated knowledge models that act as the brain of the equipment to handle various production problems in physical or virtual space. Here, the construction of dynamic knowledge base could be founded in our previous work (Zhang et al. 2017), and the construction of knowledge models is as follows. A knowledge model is constructed through mining and analysing real-world data/knowledge from physical space and simulation data/knowledge from cyber space with machine learning algorithms. Each knowledge model could provide the decision-making support for a specific production application scene, e.g. tool wear monitoring (Hassan, Sadek, and Attia 2021), machining quality prediction (Li, Zhao, and Zhang 2019). To this end, a general method and procedure of knowledge modelling is summarised from the current related works (Qi, Jiang, and Scott 2012; Zhang 2020a), which includes two procedures, namely offline modelling and online application, as shown in Figure 5.
Toward a Design Theory of User-Centered Score Mechanics for Gamified Competency Development
Published in Information Systems Management, 2023
Martin Böckle, Markus Bick, Jasminko Novak
Knowledge space theory (KST) helps to explain this issue through the competency performance approach, which originated in KST and was extended by the work of Korossy (1997, 1999). In general, a knowledge space describes a “mathematical structure consisting of all the knowledge states within a certain domain that a person may be in,” whereas a particular “knowledge state represents the subset of tasks of the domain that a person is capable of accomplishing” (Ley & Albert, 2003, p. 1503). Within these subsets of tasks, the dependencies allow assumptions to be made about the end-user’s capabilities (e.g., the accomplishment of task x indicates that the end-user is also able to accomplish task y). Tasks are therefore defined as performance outcomes, while competencies are viewed as the knowledge, skills and abilities necessary to accomplish the given task.