principal component analysis artificial neural network
Modelling of some soil physical quality indicators using hybrid algorithm principal component analysis - artificial neural network
... One of the important issues in soil analysis is the evaluation of its derivative properties. Unsupervised methods of multivariate statistics are powerful tools for evaluating derivative properties that help soil ...
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An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis Artificial Neural Network Face Recognition based System
... Principal Component Analysis (PCA) is a method based on information theory concepts where information that best describes a face is derived from the entire face image ...called principal ...
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Human Activity Recognition in Real-Times Environments using Skeleton Joints
... techniques Principal Component Analysis (PCA) with several distance based classifiers and Artificial Neural Network (ANN) respectively with some variants for classify our all ...
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Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network
... pal component analysis (PCA) combined with artificial neural network ...blood analysis results, which demonstrate this method has the ability to extract blood viscosity ...
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Adaptation of multiple regression analysis to identify effective factors of water losses in water distribution systems
... pipe network affecting non-revenue water (NRW) are being actively carried ...statistical analysis techniques such as Artificial Neural Network (ANN) and Principle Component ...
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Prediction of crude protein content in rice grain with canopy spectral reflectance
... by principal component analysis (PCA) method, and the predicted models were built by multiple linear regressions (MLR), artificial neural network (ANN) and partial least squares ...
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Nondestructive identification of tea (Camellia sinensis L ) varieties using FT NIR spectroscopy and pattern recognition
... Discriminant Analysis (LDA) and Artificial Neural Network (ANN) were compared to construct the identification models based on Principal Component Analysis ...of ...
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Segmentation of dust storm areas on Mars images using principal component analysis and neural network
... We adopt a multilayer perceptron (MLP) classifier, which is included in scikit-learn0.18.1 (Python 3.5.1). MLP is a feedforward artificial neural network (NN) that imitates biological neural ...
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Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database
... hybrid neural network (RiHNN) that combines principal component analysis with a feed-forward network to forecast the real-time 1-hour-ahead water depth of inundation at ...
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Performance Analysis of Face Recognition by Principal Component Analysis and Feed Forward Neural Network
... S.N. Kakarwal, received M.E. and B.E. degree in Computer Science and Engineering. She is currently working toward the Ph.D. degree under guidance of Dr. R.R. Deshmukh at Dr. Babasaheb Ambedkar Marathwada Univeristy, ...
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Digit Recognition Using Single Layer Neural Network with Principal Component Analysis
... Artificial Neural Network composed simple neurons connected to each other with its own connection strength whose function is determined by network ...the neural network using ...
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MRI BRAIN IMAGE CLASSIFICATION USING POLYNOMIAL KERNEL PRINCIPAL COMPONENT ANALYSIS WITH NEURAL NETWORK
... ANN is highly interconnected numbers of neurons which are arranged processing into different layers [2]. ANN consists of many layers and each layer contains one or more neuron. A single neuron has many inputs; these ...
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Principal Component Analysis(PCA) with Back Propogation Neural Network(BPNN) for Face Recognition System
... the artificial intelligence (face recognition) which is accomplished by Back Propagation Neural Network ...a Neural and PCA based algorithm for efficient and robust face ...on principal ...
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Title: Evolving Neural Network for Kernel Principal Component Analysis
... , artificial neural networks (ANN ), that tune their parameters without a teacher on the basis of the self-learning paradigm [ 3-5 ], are widely used in solving various problems of Data Mining, Exploratory ...
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Automated Classification of Brain Tumors using Image Pre Processing and Probabilistic Neural Networks
... probabilistic neural network with the computed feature values. Principal component analysis is used for reduction of the dimensionality of the training ...
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Application of multi sensor data fusion based on Principal Component Analysis and Artificial Neural Network for machine tool thermal monitoring
... T he size of the input layer was either fourteen, three or six depending on the model. The hidden layer was made up of ten neurons and one neuron in the output layer. The method of supervised learning using back ...
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Feed Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia
... of principal component analysis (PCA) and artificial neural network (ANN) to pre- dict the air pollutant index (API) within the seven selected Malaysian air monitoring stations ...
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ANALYSIS OF CITIES DATA USING PRINCIPAL COMPONENT INPUTS IN AN ARTIFICIAL NEURAL NETWORK
... Fig. 1 is the proposed Network Architecture with PCA inputs. It has one Input layer, one Hidden Layer, One output layer. Input layer has 6 Neurons for the 6 principal Components. These 6 Inputs are ...
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A Hybrid Approach for Breast Cancer Classification and Diagnosis
... combining artificial intelligent based learning technique with multivariate statistical ...(Principal Component Analysis) and Artificial Neural Network ...
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Gold Price Prediction Based on PCA GA BP Neural Network
... Selecting the first 100 sample as the training sample and the last 10 sample as the test sample. Taking the principal components of the first three days as input data and the output is the closing price of the 4th ...
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