Etch proximity correction through machine-learning-driven etch bias model

Type 2 Diabetes mellitus is a serious metabolic disorder that is prevailing worldwide at an alarming rate. Medical dataset often suffers from the problem of missing data and outliers. However, handling of missing data with traditional mean based imputing may lead towards a bias model and return unpredictable outcome. Making complex models by combining multiple classifiers as well as some other methods could increase the accuracy which again is a time-consuming approach and requires heavy computation capability which significantly increases the deployment cost. The proposed research is to design a model to classify the data using class wise imputation technique and outlier handling. Performance of the proposed model is evaluated on nine machine learning classifiers and compared with traditional approaches like simple mean, median, and linear regression. Experimental results show the superiority of the proposed model in terms of classification accuracy and model complexity. The accuracy achieved by the proposed approach is 88.01%, which is highest as compared to the previous studies. The proposed research work is presented to improve accuracy, scalability and overall performance of the classification in the medical dataset, which ultimately proves to be a lifesaver if the diagnosis is achieved efficiently at an early stage.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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