[PDF] Top 20 Analysis of Tanzanian Energy Demand using Artificial Neural Network and Multiple Linear Regression
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Analysis of Tanzanian Energy Demand using Artificial Neural Network and Multiple Linear Regression
... economic, energy and environment indicators models’ results are presented in this section to show the comparison of the predicted values against actual values for the purpose of determining the best indicators for ... See full document
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Comparative Analysis of Multiple Linear Regression and Artificial Neural Network for Predicting Friction and Wear of Automotive Brake Pads Produced from Palm Kernel Shell
... pad using multiple linear regression was carried out by ...composite using multiple regression analysis (MRA) and artificial neural network was ... See full document
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A REVIEW ON DIMENSIONALITY REDUCTION USING COPULA APPROACH IN DATA MINING
... classification using a comparative study of four different classifiers, multiple linear regression (MLR), artificial neural network (ANN), k-nearest neighbor (k-NN), and ... See full document
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Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America
... for analysis of both climate variabil- ity and climate ...of artificial neural networks (ANNs) and multiple linear re- gression (MLR) by principal components to estimate rainfall in ... See full document
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Risk Prediction Assessment In Life Insurance Company Through Dimensionality
... these analysis process has been automated for faster ...Component Analysis (PCA) , Linear Discriminant Analysis (LDA), Correlation-Based Feature Selection (CFS), ...like Artificial ... See full document
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QSAR Studies of Breast Carcinoma using Artificial Neural Network, Bayesian Classifier and Multiple Linear Regression
... Instances 195 195 195 Result analysis of various parameters of three robust machine learning tools i.e. ANN, Bayesian classifier and MLR, it is clearly evident that these tools have similar robustness in ... See full document
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... statistical analysis and regression step-by-step using Microsoft ...of artificial neural network with multiple linear regression in prediction soil pore size ... See full document
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Hole Cleaning Prediction in Foam Drilling Using Artificial Neural Network and Multiple Linear Regression
... the regression coefficients, are unknown and are to be ...fitted regression line. The deviation of a particular point from the regression line (its predicted value) is called the residual ...the ... See full document
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Multiple linear regression and neural network for electric load forecasting
... and demand controllers could ensure that they would be enough supply of electricity to cope with increasing demands (Mastorocostas et ...forecast energy usage by using various methods from classical ... See full document
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Modeling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis
... One of the most important factors in manufacturing costs is energy consumption. Specifically, the power consumed during machining determines the energy consumption. Depending on the specific cutting ... See full document
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Corrosion Inhibition Efficiency of Thiophene Derivatives on Mild Steel: A QSAR Model
... inhibitor using quantum chemical calculation the correlation was found to be good between the theoretical and experimental corrosion inhibition ...as multiple linear regression (MLR), partial ... See full document
12
Evolutionary optimization of classifiers and features for single-trial EEG Discrimination
... single-trial limb laterality discrimination, and that the optimal EEG channels differ much between subjects. It should be noted that this study has focused on compar- ing classifiers rather than maximizing prediction ... See full document
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The Development of an Alternative Method for the Sovereign Credit Rating System Based on Adaptive Neuro Fuzzy Inference System
... by using stepwise regression analysis; logistic regression analysis, artificial neural network and ANFIS model were implemented in order to determine the ... See full document
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Land Titling: A Sine Qua Non For Enhancing Property Taxation
... feed-forward neural network architectures optimized with Levenberg- Marquardt back-propagation with transig activation function in hidden and output layers in predicting monthly river water ...the ... See full document
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Traffic Flow Condition Classification for Short Sections Using Single Microwave Sensor
... 3.2. Support Vector Machine as a Classifier. Another inno- vative supervised pattern classifier technique SVM was first proposed by Vapnik in 1995 [36]. The formulation applied by SVM embodies the Structural Risk ... See full document
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Neural Network based Software Effort Estimation: A Survey
... (Artificial Neural Network) has the ability to discover relationships between the dependent and independent ...biological neural networks are Neurons (nodes) and Synapses ...effort ... See full document
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Forecasting nitrate concentration in groundwater using artificial neural network and linear regression models
... groundwater using regression and neural ...utilized artificial neural networks to predict the pesticide and nitrate contamination in rural private ...used regression and ... See full document
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Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes
... SVM is a data mining tool and is a supervised classification technique. This method can be used for prediction when the outcome variable is binary. SVM constructs multi-dimensional hyper-planes separating the two classes ... See full document
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Design and analysis of a multivariate regression model using artificial neural network
... themes, neural network is a method used to develop and design for a regression ...of artificial neural network (ANN) as a soft computation tool for determining the multivariable ... See full document
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Prediction of Tanzanian Energy Demand using Support Vector Machine for Regression (SVR)
... the energy indicators model had a lower value of ...to energy indicators ...predicted energy demand is relatively very small for the polynomial-SVR kernel as illustrated in ...the ... See full document
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