Understanding the Dynamics--Activity Relationship in Metalloproteases: Ideas for New Inhibition Strategies

Petreuş, Tudor; C, Cotrutz; Neamtu, M.; Buruiana, E.C.; Sîrbu, Paul Dan; Neamţu, Andrei

doi:10.1109/atequal.2010.17

Search citation statements

Order By: Relevance

Paper Sections

Select...

The Multi Layer Perceptron Artificial1

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2012

Publication Types

Select...

Article1

Book1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural Network before applying PCA is [41- [5][6][7][8][9][10][11][12][13][14][15]  It contains 41 input neurons for each input feature in the KDD dataset  Five hidden neurons were selected based on trial and error method  Fifteen output neurons to determine normal or attack types. for all j=1 to num_Hidden, where Tj is corresponding target output.…”

Section: The Multi Layer Perceptron Artificialmentioning

confidence: 99%

See 1 more Smart Citation

Feature Optimization and Performance Improvement of a Multiclass Intrusion Detection System using PCA and ANN

Varma¹,

Kumari²

2012

IJCA

View full text Add to dashboard Cite

There are several bottle necks in the process of high speed intrusion detection, of which large dimensionality is one of the major problem. We have employed the Principal Component Analysis (PCA) algorithm to handle this problem, through which we have improved the performance of the Artificial Neural Network (ANN) classifier for intrusion detection. With the help of PCA we were able to identify the top 15 out of 41 features among the feature set of KDD cup 1999 data set, and noticed an improvement of over 62% in the training time of ANN. The Multi Layer Perceptron Neural Network improved the accuracy even after the feature reduction.

show abstract

Section: The Multi Layer Perceptron Artificialmentioning

confidence: 99%

“…In a paper by T.Petreus et.al. [5] PCA was used to extract essential features in the field of bio-informatics. In a study [6] the authors used PCA to detect hidden software features for malware analysis.…”

Section: Introductionmentioning

confidence: 99%