With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable.
Artificial neural network are the structures consisted of interconnected adaptive simple processing elements that can perform massive parallel computation for data processing and knowledge representation.
One researcher (author PM) carried out all data collection and kinematic data processing without knowledge of subjects' gene status.
Artificial neural networks (ANN) mimic the function of the human brain and are capable of performing massively parallel computations for data processing and knowledge representation.
Moreover, increasing data-intensification poses new demands for more efficient organizational data processing and effective knowledge utilization capabilities.
That is, when future companies become more software-intensive, they will need more efficient internal data processing and effective knowledge utilization capabilities (incorporating data science).
Both have been integrated in our intelligent vehicle to develop cutting-edge applications to cope with perception difficulties, data processing algorithms, expert knowledge, and decision-making.
Similarly, cluster deduplication and redundancy elimination algorithms that remove duplicated data for efficient data processing and useful knowledge discovery are primarily post-data collection methods [12, 13, 14, 15].
The results show that the proposed approach is promising and may help to improve the concept review and evaluation efficiency; facilitate data processing using design knowledge; and enhance the reliability of concept selection decisions.
Data collection and preparation, storage management, data processing, data analysis, and knowledge presentation would generate new insights to support decision-making and business intelligent operation.
