Investigating data preprocessing methods for circuit complexity models

Prasad, P. W. C.; Beg, Azam

doi:10.1016/j.eswa.2007.09.052

Expert Systems with Applications

2009

DOI: 10.1016/j.eswa.2007.09.052

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Investigating data preprocessing methods for circuit complexity models

P. W. C. Prasad

Azam Beg

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Cited by 11 publications

References 19 publications

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PerfPred: A web‐based tool for exploring computer architecture design space

Beg

Ibrahim

2009

Comp Applic In Engineering

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This paper presents a Web-based tool for predicting the performance of computer systems. The tool can be used for teaching how the hardware configurations (processor microarchitecture, memory hierarchy, etc.) and/or software (benchmark program) characteristics can affect the system throughput. Use of the proposed tool in computer architecture classes has demonstrated its effectiveness in improving the students' understanding of the related topics.ß

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PerfPred: A web‐based tool for exploring computer architecture design space

Beg

Ibrahim

2009

Comp Applic In Engineering

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Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA

Heddam

2014

Environ Sci Pollut Res

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In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling.

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Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review

Rajaee

Khani

2020

Chemometrics and Intelligent Laboratory Systems

184

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Investigating data preprocessing methods for circuit complexity models

Cited by 11 publications

References 19 publications

PerfPred: A web‐based tool for exploring computer architecture design space

PerfPred: A web‐based tool for exploring computer architecture design space

Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA

Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review

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