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Deep-learning methods can automatically discover multiple levels of representations from raw data instead of requiring manual engineering.
Evolvable hardware (EH) is a thriving area of research which uses the genetic algorithm (GA) to construct novel circuits without manual engineering.
Although the current performance of the unsupervised solution does not adequately replace manual engineering, we believe once the performance issues are addressed, it could serve as an aide in a semi-supervised solution.
Ontologies have been a hot research topic for the recent decade and have been used for many applications such as information integration, semantic search, knowledge management, etc. Manual engineering of ontologies is a costly process and automatic ontology engineering lacks in precision.
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The process of accreditation by Engineering Accreditation Council Malaysia (EAC) and BEM is purely based on manual of Engineering Programme Accreditation Manual.
These design rules provide a framework for functionally classifying complex natural networks and a manual for engineering networks.
Our approach does not require manual feature engineering or external resources to infer the degree of relevance between a question and documentation.
The results show that the proposed model demonstrates state-of-the-art performance while requiring low computational cost and no manual feature engineering.
However, manual feature engineering from EMR is labor intensive because EMR is complex – it contains temporal, mixed-type and multimodal data packed in irregular episodes.
A conventional analytic framework for phenotyping largely uses a manual knowledge engineering approach or a supervised learning approach where clinical cases are represented by variables encompassing diagnoses, medicinal treatments and laboratory tests, among others.
Thus, attack trends change very fast, making it impossible to keep up with manual signature engineering and an automatic generation of signatures seems to be the only reasonable solution.
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