ANN modeling
Ann Modeling For Predicting Car Travel Time using Bus As Probe
... traffic modeling methods combining proper transportation domain knowledge (such as traffic flow theories or principles) and advanced machine learning and optimization ...
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PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent
... In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for ...
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Biological hydrogen production from synthetic wastewater by an anaerobic migrating blanket reactor: Artificial neural network (ANN) modeling
... the ANN ability to data classification and ...the ANN models have been used for the environmental engineering fields, such as biological treatment of wastewater, membrane filtration, pollution adsorption, ...
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ANN Based Modeling for Prediction of Evaporation in Reservoirs (RESEARCH NOTE)
... perception ANN and linear regressions based modeling techniques are performing better when all four parameters are used as input for model building for the prediction of ...the ANN modeling is ...
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Using artificial neural network to monitor and predict induction motor bearing (IMB) failure
... using ANN for IMB failure prediction ...of ANN modeling, two networks were tested; Feedforward Neural Network (FFNN) and Elman Network for the performance of training, validation and testing with ...
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Steel Re-Bars In-line Treatment Process Optimization by ANN-DOE Modeling
... V. ANN-D OE M ODELING F OR TMT S EQUENCES Rolling processes are very complex and its performance is influenced by different ...variables. ANN modeling was performed to predict symptoms of Re-bars ...
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Predicting India Volatility Index: An Application of Artificial Neural Network
... (ANN) modeling technique has been employed to forecast the upwards or downwards movement in next trading day's volatility using India VIX (a volatility index based on the NIFTY Index Option prices) based ...
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Artificial Neural Network Modeling of Job Satisfaction: A Case Study of ICT, Federal University of Agriculture, Makurdi
... and modeling of job satisfaction of the Information and Communications Technology (ICT) Directorate workers of Federal University of Agriculture, Makurdi was investigated in this ...(ANN) modeling ...
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A COMPUTATIONAL HYBRID MODEL WITH TWO LEVEL CLASSIFICATION USING SVM AND NEURAL NETWORK FOR PREDICTING THE DIABETES DISEASE
... in ANN modeling via cross validation training [18] to train and validate model based on five-fold cross validation (5-FCV) approach with two different data ...the ANN model is trained and tested with ...
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Time series modeling and designing of artifical neural network (ANN) for revenue forecasting
... 3 Due to their flexibility, neural networks lack a systematic procedure for model building. Therefore obtaining a reliable neural model involves selecting a large number of parameters experimentally through trial and ...
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Developing an artificial neural network for modeling and prediction of temporal structure and spectral composition of environmental noise in cities
... of modeling and prediction a very complex and non-linear problem, to which we may apply a powerful tool of data mining —artificial neural ...environmental modeling and prediction (Chelani et ...
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FACIAL ANIMATION AND MULTIPLE HIERARCHICAL MODELS FOR AGE AND GENDER PREDICT THE FEED FORWARD PROPAGATION AND CASCADE PROPAGATION
... and modeling the distinctive features of human faces that contribute most toward face recognition are some of the challenges faced by computer vision and psychophysics ...
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Chemometric Approach for Mechanical Properties Prediction during the Electromagnetic Casting Process
... re fl ects in obtaining a better quality of ingots compared to conventional continuous casting process. 58) The obtained structure is finer and more uniform over the cross section, segregation of alloying elements and ...
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Application of Artificial Neural Network for Modeling the Flash Land Dimensions in the Forging Dies
... the modeling of many complex processes and systems shows that this is not always the case or improvements are negligibly small [10] to ...training ANN. Then ANN can likewise be trained though the ...
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Frequency domain fatigue analysis of dynamically sensitive structures
... Chapters 7 and 8 study the effect of non-Gaussianality. Extensive computer m od elling and Artificial Neural Networks (ANN) have been applied to study the effect of non-Gaussianality. Analysis of WEG, HWP and ...
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ANN Laser Hardening Quality Modeling Using Geometrical and Punctual Characterizing Approaches
... After performing the laser hardening process on 4340 steel during which the parameters of laser power, beam scanning speed, initial hardness and surface roughness were considered and for which the testing strategy was ...
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← Return to Article Details MODELING AND OPTIMIZATION OF IN SYRINGE MAGNET STIRRING ASSISTED-DISPERSIVE LIQUIDLIQUID MICROEXTRACTION METHOD FOR EXTRACTION OF CADMIUM FROM FOOD SAMPLES BY ARTIFICIAL NEURAL NETWORK AND GENETIC ALGORITHM
... and modeling. The advantages of ANN are that the mathematical description of the phenomena involved in the process is not required; less time is required for model development than the traditional ...
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Different Methods for Predicting and Optimizing Weld Bead Geometry with Mathematical Modeling and ANN Technique
... Abstract Bead geometry plays very important role in predicting the quality of weld as cooling rate of the weld depends on the height and bead width, also bead geometry determines it’s residual stresses and distortion. ...
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Supervised learning methods in modeling of CD4+ T cell heterogeneity
... stochastic modeling simula- ...from modeling the pleiotropic and highly dynamic regulation of CD4+ T cell differentiation has guided experimentation to elucidate underlying regulatory mechanisms, identify ...
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Concrete Mix Design Using Artificial Neural Network
... Back propagation method of training of ANN has been done. From a desired output, the network learns from many inputs. It is a supervised learning method, and is a generalization of the delta rule. The learning ...
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