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- W2884842191 abstract "Abstract Ontological nodes and training material, in any case, contain however a little part of the quickly developing volume of unstructured contents accessible across training datasets ranging from the greater part of heterogeneous learning. Recovery of these assets is constrained to keyword search, which is in a far-reaching way incognizant in regards to the normal sequential semantic connection in which the query is often requested and all the more essentially for more importantly for development of a perception about a learned concept through the sequential arrangement of information. Numerous core topics covered in several referential sources when no two are precisely similar, with one frequently more suitable when seen in a connection of an ontological relational modalities of semantic web and online training of network based on continuous streaming database; while other on giving major insights of practical implementation. The objective of this work is to present an evolutionary neurodynamic framework to mine such process based learning validated by the visual recording & extraction of neural weights through neurodynamic neural experiences when going through the referenced content. The motivation of this behavioral modeling of autonomous machine’s reaction working as the feedback of the learning content set forth above hinges on the principal capacity to gather a structure of a curricular graph automatically from the training datasets. As it empowers the acknowledgment of: 1) Sharing of neural experiences with the help of structurally sequenced networked topology of such large sample of training datasets resources - with the idea of dynamic analysis and linking of key resources in the mechanism with which the learner develops a general concept about the subject which can be latter re-used or shared with other user's and thus facilitating the qualitative evaluation of the ontological nodes content and its contribution in facilitating deep grasp of the data from feedback loop. 2) Obtaining information driven ontological knowledge base from the curricular relations inside and crosswise over other learning resources and 3) Generating customized, focused curricula, utilizing the tremendous assets of heterogeneous modalities on the training datasets." @default.
- W2884842191 created "2018-08-03" @default.
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- W2884842191 date "2018-01-01" @default.
- W2884842191 modified "2023-09-26" @default.
- W2884842191 title "Differed Restructuring of Neural Connectome Using Evolutionary Neurodynamic Algorithm for Improved M2M Online Learning" @default.
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- W2884842191 doi "https://doi.org/10.1016/j.procs.2018.07.037" @default.
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