scholarly journals Artificial Neural Network Modeling for Drug Dialyzability Prediction

2013 ◽  
Vol 16 (5) ◽  
pp. 665 ◽  
Author(s):  
Kahina Daheb ◽  
Mark L. Lipman ◽  
Patrice Hildgen ◽  
Julie J Roy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Protein Binding ◽  
Plasma Protein Binding ◽  
Plasma Protein ◽  
Neural Network Modeling ◽  
Ann Model ◽  
Drug Concentrations ◽  
Drug Removal ◽  
Artificial Neural

Purpose. The purpose of this study was to develop an artificial neural network (ANN) model to predict drug removal during dialysis based on drug properties and dialysis conditions. Nine antihypertensive drugs were chosen as model for this study. Methods. Drugs were dissolved in a physiologic buffer and dialysed in vitro in different dialysis conditions (UFRmin/UFRmax, with/without BSA). Samples were taken at regular intervals and frozen at -20ºC until analysis. Extraction methods were developed for drugs that were dialysed with BSA in the buffer.  Drug concentrations were quantified by high performance liquid chromatography (HPLC) or mass spectrometry (LC/MS/MS). Dialysis clearances (CLDs) were calculated using the obtained drug concentrations.  An ANOVA with Scheffe’s pairwise adjustments was performed on the collected data in order to investigate the impact of drug plasma protein binding and ultrafiltration rate (UFR) on CLD. The software Neurosolutions® was used to build ANNs that would be able to predict drug CLD (output). The inputs consisted of dialysis UFR and the herein drug properties: molecular weight (MW), logD and plasma protein binding. Results. Observed CLDs were very high for the majority of the drugs studied. The addition of BSA in the physiologic buffer statistically significantly decreased CLD for carvedilol (p= 0.002) and labetalol (p<0.001), but made no significant difference for atenolol (p= 0.100). In contrast, varying UFR does not significantly affect CLD (p>0.025). Multiple ANNs were built and compared, the best model was a Jordan and Elman network which showed learning stability and good predictive results (MSEtesting = 129). Conclusion. In this study, we have developed an ANN-model which is able to predict drug removal during dialysis. Since experimental determination of all existing drug CLDs is not realistic, ANNs represent a promising tool for the prediction of drug CLD using drug properties and dialysis conditions. 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.

scholarly journals Modeling SMEs’ Trust in the Implementation of Industry 4.0 using Kansei Engineering and Artificial Neural Network: Food and Beverage SMEs Context

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Mirwan Ushada ◽  
Titis Wijayanto ◽  
Fitri Trapsilawati ◽  
Tsuyoshi Okayama
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Industry 4.0 ◽  
Appropriate Technology ◽  
Preference Ranking ◽  
Neural Network Modeling ◽  
Kansei Engineering ◽  
Ann Model ◽  
Food And Beverage ◽  
Artificial Neural

Trust is an important aspect for policy makers in recommending the implementation of Industry 4.0 in food and beverage small and medium-sized enterprises (SMEs). SMEs’ trust in the implementation of Industry 4.0 is defined as the  level of belief in applying appropriate technology for Industry 4.0 based on their knowledge, familiarity, agreement and preference. Trust is a complex construct involving several Kansei words, or human mentality parameters. Artificial neural network modeling was utilized to model SMEs’ trust in implementation of Industry 4.0. The research objectives were: 1) to analyze the trust of SMEs in the implementation of Industry 4.0 using Kansei Engineering; 2) to model the trust of SMEs in the implementation of Industry 4.0 using an artificial neural network (ANN). A questionnaire was developed using Kansei words that were generated from adjectives to represent human mentality parameters, which were stimulated by visual samples of Industry 4.0 technology. The questionnaires were distributed among 190 respondents from the three large islands of Indonesia. The data were recapitulated for training, validating and testing the ANN model based on the backpropagation supervised learning method. The output was classification of trust as ‘distrust’, ‘trust’ or ‘overtrust’. The research results indicated that the SMEs’ trust was influenced by education, knowledge, familiarity, benefit, preference ranking and verbal components.

scholarly journals Artificial Neural Network Modeling of MoS2 Supercapacitor for Predicative Synthesis

2019 ◽  
Vol 9 (2) ◽  
pp. 554-560
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Neural Network Modeling ◽  
Ann Model ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Artificial Neural ◽  
Forward Calculation ◽  
Artificial Neural Network Ann

Energy storage systems are fundamental to the activity of intensity frameworks. They guarantee coherence of vitality supply and improve the dependability of the framework. The first area is centered on various energy storage frameworks, considering capacity limit, voltage and current proportions, and energy accessibility. Among the energy storage devices, supercapacitor is widely used because it is a high-limit capacitor with capacitance esteem a large amount than different capacitors. In the supercapacitor we have used MoS2 material synthesized with various Electrolytes. In perspective on the above mentioned, we report an Artificial Neural Network (ANN) strategy to achieve the predictable results. Levenberg- Marquardt feed-forward calculation prepares the neural network. We measure the exhibition of the ANN model with respect to mean square error (MSE) and the relationship coefficient between anticipated yield and yield given by the system. Results confirm the stability of supercapacitor over the other energy storage devices. To show such kind of conduct, we give Synthesis technique, Electrolyte, Cycle Life as an info esteems and Specific limit as yield esteem. For the amalgamation technique info esteem we have taken both compound and physical strategies by normalizing it. The practiced ANN demonstrating confirmations a higher number of concealed neuron design showing ideal execution as respects to expectation exactness

ESTIMATING THE RUNNING COSTS OF COMMERCIAL BUILDINGS: ARTIFICIAL NEURAL NETWORK MODELING

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Devindi Geekiyanage ◽  
Thanuja Ramachandra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Life Cycle Cost ◽  
Prediction Accuracy ◽  
Building Design ◽  
Commercial Buildings ◽  
Neural Network Modeling ◽  
Ann Model ◽  
Design Variables ◽  
Artificial Neural

Running costs of a building is a substantial share of its total life-cycle cost (LCC) and it ranges between 70-80% in commercial buildings. Despite its significant contribution to LCC, investors and construction industry practitioners tend to mostly rely on construction cost exclusively. Though the early stage estimation of running costs is limited due to the unavailability of historical cost data, several efforts have been taken to estimate the running costs of buildings using different cost estimation techniques. However, the prediction accuracy of those models is still challenged due to less quality and amount of data employed. This study, therefore, developed an artificial neural network (ANN) model for running costs estimation of commercial buildings with the use of building design variables. The study was quantitively approached and running costs data together with 13 building design variables were collected from 35 commercial buildings. The ANN model developed resulted in a 96.6% perfect correlation between the running cost and building design variables. The testing and validation of the model developed indicate that there is greater prediction accuracy. These findings will enable industry practitioners to make informed cost decisions on implications of running costs in commercial buildings at its early stages, eliminating excessive costs to be incurred during the operational phase.

scholarly journals Estimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anum Shafiq ◽  
Andaç Batur Çolak ◽  
Tabassum Naz Sindhu ◽  
Qasem M. Al-Mdallal ◽  
T. Abdeljawad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Generation ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Multi Layer Perceptron ◽  
Ann Model ◽  
Feed Forward ◽  
Williamson Fluid ◽  
Artificial Neural

AbstractIn current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg–Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.

scholarly journals Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

Fermentation ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 71
Author(s):  
Sahar Safarian ◽  
Seyed Mohammad Ebrahimi Saryazdi ◽  
Runar Unnthorsson ◽  
Christiaan Richter
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comprehensive Evaluation ◽  
Biomass Gasification ◽  
Proximate Analysis ◽  
Neural Network Modeling ◽  
Operating Parameters ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R2) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogen-content are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the smhydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the smhydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on smhydrogen.

scholarly journals Biodegradation of Naphthalene Using Glass Beads Roller Bioreactor: Application of Artificial Neural Network Modeling

2021 ◽  
Author(s):  
Sinan J. Mohammed ◽  
yasmen mustafa ◽  
Mohanad S. Jabbar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Specific Growth ◽  
Glass Beads ◽  
Neural Network Modeling ◽  
Ann Model ◽  
Biodegradation Rate ◽  
Waste Sludge ◽  
Artificial Neural ◽  
Naphthalene Biodegradation

Abstract A roller bioreactor containing inert glass beads was employed to enhance naphthalene biodegradation in an aqueous solution. Mixed culture of microorganisms was isolated from sewage waste sludge and adopted for naphthalene biodegradation. The biodegradation of 300mg/L naphthalene in the bioreactor with no glass beads proceeded slowly until depletion after seven days. In the presence of glass beads, the biodegradation rate was faster; it depleted after four days. The biodegradation rate of naphthalene was equal to 1.99 mgL-1 hr-1 for bioreactor with no beads, while it is equal to 5.42, and 5.54 mgL-1 hr-1 for bioreactor with 40%load, 6mm size and 50% load, 5mm size of glass beads, respectively. For 500mg/L naphthalene, nine days on the bioreactor with no glass beads and five days on glass beads bioreactors were required to complete depletion. The biodegradation rate is equal to 2.33, 7.29, and 7.85 mg/L-1hr-1 for bioreactors with no glass beads, 40% load with 6mm, and 50% load with 5mm glass beads, respectively. The specific growth rate was increased in the bioreactor with glass beads; it represents 0.031, 0.050, and 0.054 hr−1 for 300mg/L and 0.043, 0.061, and 0.065 hr−1 for 500mg/L respectively for the previously mentioned conditions. An artificial neural network was used to model naphthalene dissolution and biodegradation. A correlation coefficient of 99.2% and 98.3% were obtained between the experimental and predicted output values for dissolution and biodegradation, respectively, indicating that the ANN model could efficiently predict the experimental results. Time represents the most influential parameter on the dissolution and biodegradation treatment.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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