Predictive performance of “highly complex” artificial neural networks

Performance Modeling ◽

Predictive Performance ◽

Mathematical Tool ◽

Effluent Water ◽

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Modeling Semiarid River–Aquifer Systems with Bayesian Networks and Artificial Neural Networks

Mathematics ◽

10.3390/math10010107 ◽

2021 ◽

Vol 10 (1) ◽

pp. 107

Author(s):

Ana D. Maldonado ◽

María Morales ◽

Francisco Navarro ◽

Francisco Sánchez-Martos ◽

Pedro A. Aguilera

Keyword(s):

Neural Networks ◽

Bayesian Networks ◽

Structure Learning ◽

Computational Cost ◽

Predictive Performance ◽

Water Infiltration ◽

Groundwater Temperature ◽

Aquifer Systems ◽

In semiarid areas, precipitations usually appear in the form of big and brief floods, which affect the aquifer through water infiltration, causing groundwater temperature changes. These changes may have an impact on the physical, chemical and biological processes of the aquifer and, thus, modeling the groundwater temperature variations associated with stormy precipitation episodes is essential, especially since this kind of precipitation is becoming increasingly frequent in semiarid regions. In this paper, we compare the predictive performance of two popular tools in statistics and machine learning, namely Bayesian networks (BNs) and artificial neural networks (ANNs), in modeling groundwater temperature variation associated with precipitation events. More specifically, we trained a total of 2145 ANNs with different node configurations, from one to five layers. On the other hand, we trained three different BNs using different structure learning algorithms. We conclude that, while both tools are equivalent in terms of accuracy for predicting groundwater temperature drops, the computational cost associated with the estimation of Bayesian networks is significantly lower, and the resulting BN models are more versatile and allow a more detailed analysis.

Diagnostic Cost Reduction Using Artificial Neural Networks

Clinical Technologies ◽

10.4018/978-1-60960-561-2.ch614 ◽

2011 ◽

pp. 1812-1830

Author(s):

Steven Walczak ◽

Bradley B. Brimhall ◽

Jerry B. Lefkowitz

Keyword(s):

Neural Networks ◽

Clinical Laboratory ◽

Predictive Performance ◽

Diagnostic Model ◽

Medical Problems ◽

Ann Models ◽

Artificial Neural ◽

Diagnostic Capabilities ◽

Performance Results

Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly to diagnose with respect to direct costs, including pulmonary embolism (PE). Advanced decision-making tools such as artificial neural networks (ANNs) improve diagnostic capabilities for these problematic medical conditions. The research in this chapter develops a backpropagation trained ANN diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for a PE, with 15% suffering a confirmed PE, are collected and used to evaluate various ANN models’ performance. Results indicate that using ANN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, significantly improving both overall positive predictive and negative predictive performance.

From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic Programming

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/863874 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Aleksander Mendyk ◽

Sinan Güres ◽

Renata Jachowicz ◽

Jakub Szlęk ◽

Sebastian Polak ◽

...

Keyword(s):

Neural Networks ◽

Genetic Programming ◽

Mean Squared Error ◽

Predictive Performance ◽

Drug Dissolution ◽

Robust Model ◽

Original Dataset ◽

Artificial Neural ◽

Simple Equations

The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling ofQversus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations’ parameters. Two inputs were found important for the drug dissolution:dandt. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling ofQversusdandtresulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs’ black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.

Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System

Sustainability ◽

10.3390/su9081382 ◽

2017 ◽

Vol 9 (8) ◽

pp. 1382 ◽

Cited By ~ 8

Author(s):

Wahiba Yaïci ◽

Michela Longo ◽

Evgueniy Entchev ◽

Federica Foiadelli

Keyword(s):

Neural Networks ◽

Solar Energy ◽

Simulation Study ◽

Predictive Performance ◽

Energy System ◽

Diagnostic Cost Reduction Using Artificial Neural Networks

Healthcare Information Systems and Informatics ◽

10.4018/978-1-59904-690-7.ch006 ◽

2011 ◽

pp. 108-130

Author(s):

Steven Walczak ◽

Bradley B. Brimhall ◽

Jerry B. Lefkowitz

Keyword(s):

Neural Networks ◽

Clinical Laboratory ◽

Predictive Performance ◽

Diagnostic Model ◽

Medical Problems ◽

Ann Models ◽

Artificial Neural ◽

Diagnostic Capabilities ◽

Performance Results

Patients face a multitude of diseases, trauma, and related medical problems that are difficult and costly to diagnose with respect to direct costs, including pulmonary embolism (PE). Advanced decision-making tools such as artificial neural networks (ANNs) improve diagnostic capabilities for these problematic medical conditions. The research in this chapter develops a backpropagation trained ANN diagnostic model to predict the occurrence of PE. Laboratory database values for 292 patients who were determined to be at risk for a PE, with 15% suffering a confirmed PE, are collected and used to evaluate various ANN models’ performance. Results indicate that using ANN diagnostic models enables the leveraging of knowledge gained from standard clinical laboratory tests, significantly improving both overall positive predictive and negative predictive performance.

Predictive performance of a wastewater source heat pump using artificial neural networks

Building Services Engineering Research and Technology ◽

10.1177/0143624414547966 ◽

2014 ◽

Vol 36 (3) ◽

pp. 331-342 ◽

Cited By ~ 1

Author(s):

Chao Shen ◽

Liangcheng Yang ◽

Xinlei Wang ◽

Yiqiang Jiang ◽

Yang Yao

Keyword(s):

Neural Networks ◽

Heat Pump ◽

Predictive Performance ◽

Application of Counter-propagation Artificial Neural Networks in Prediction of Topiramate Concentration in Patients with Epilepsy

Journal of Pharmacy & Pharmaceutical Sciences ◽

10.18433/j33031 ◽

2015 ◽

Vol 18 (5) ◽

pp. 856 ◽

Cited By ~ 3

Author(s):

Marija Jovanovic ◽

Dragoslav Sokić ◽

Iztok Grabnar ◽

Tomaž Vovk ◽

Milica Prostran ◽

...

Keyword(s):

Neural Networks ◽

Relative Error ◽

High Performance ◽

Predictive Performance ◽

Serum Levels ◽

Relative Importance ◽

Statistical Parameters ◽

Epileptic Patients ◽

Purpose: The application of artificial neural networks in the pharmaceutical sciences is broad, ranging from drug discovery to clinical pharmacy. In this study, we explored the applicability of counter-propagation artificial neural networks (CPANNs), combined with genetic algorithm (GA) for prediction of topiramate (TPM) serum levels based on identified factors important for its prediction. Methods: The study was performed on 118 TPM measurements obtained from 78 adult epileptic patients. Patients were on stable TPM dosing regimen for at least 7 days; therefore, steady-state was assumed. TPM serum concentration was determined by high performance liquid chromatography with fluorescence detection. The influence of demographic, biochemical parameters and therapy characteristics of the patients on TPM levels were tested. Data analysis was performed by CPANNs. GA was used for optimal CPANN parameters, variable selection and adjustment of relative importance. Results: Data for training included 88 measured TPM concentrations, while remaining were used for validation. Among all factors tested, TPM dose, renal function (eGFR) and carbamazepine dose significantly influenced TPM level and their relative importance were 0.7500, 0.2813, 0.0625, respectively. Relative error and root mean squared relative error (%) and their corresponding 95% confidence intervals for training set were 2.14 [(-2.41) - 6.70] and 21.5 [18.5 - 24.1]; and for test set were -6.21 [(-21.2) - 8.77] and 39.9 [31.7 - 46.7], respectively. Conclusions: Statistical parameters showed acceptable predictive performance. Results indicate the feasibility of CPANNs combined with GA to predict TPM concentrations and to adjust relative importance of identified variability factors in population of adult epileptic patients. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.