feature and propagation
Application of Feature Selective Validation to Propagation Path Loss Models for Wireless Cellular Networks
... wave propagation impairments varies from one environment to ...The Feature Selective Validation (FSV) method of validating data will therefore be applied to measured and path loss predictions by the three ...
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Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation
... We describe a Bayesian method for group feature selection in linear regression problems. The method is based on a generalized version of the standard spike-and-slab prior distribution which is often used for ...
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Incremental Affinity Propagation Clustering with Feature Selection
... Affinity Propagation (AP) is a clustering algorithm based on the concept of message ...for feature selection, which help to reduce execution time and also increase the ...
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Cross Breed Biometric Fusion at Feature Level Using Back Propagation Algorithm
... In our research, fusion of tongue biometric and speech biometric has taken place. We have used feature extraction techniques MFCC and FFT to improve/increase the accuracy rate. The FAR i.e. false acceptance rate ...
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Feature Extracting Gait to acknowledge illness shisusen algorithms exploitation Multilayered Back Propagation
... the feature extraction and have choice technologies, section four contains numerous} classification ways mistreatment various machine learning techniques and section 5 contains the experiment results and ...
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GPU-based multiple back propagation for big data problems
... without feature selection after which feature selection is performed and the selected features were classified using MBP; starting from the highest ranked 200 features, a stepwise increment of 200 features ...
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Tumor Disease Multiclass Prediction using Biomolecular Gene Expression Data by Signal Processing and Computational Intelligence Techniques
... Efficient feature extraction and computational method development is indispensible for the ...a feature extraction method by Discrete Cosine Transform (DCT) and discrete wavelet transform (DWT) has been ...
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II. REVIEW OF RELATED LITERATURE A. Image Acquisition
... the feature vectors. Those feature sets are the input on the back-propagation neural ...the feature matching method can achieve up to ...
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An Efficient Food Image Classification By Inception-V3 Based Cnns
... Back Propagation Neural Network (BPNN) is used to classify and recognize the fruit image samples, using three different types of feature sets, viz, color, texture, combination of both color and texture ...
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Determination of Corrosion Types from Electrochemical Noise by Artificial Neural Networks
... A feature vector is extracted from each EN data ...Back Propagation (BP) and the Support Vector Machine (SVM), are constructed by training feature ...
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Hybrid Techniques for Arabic Letter Recognition
... three feature extraction techniques: Yule-Walker spectrum feature, Walsh spectrum feature and Mel Frequency Cepstral ...Back Propagation Neural Network (FFBPNN) is ...
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On the Chain Length and Rate of Ozone Depletion in the Main Stratospheric Cycles
... chain propagation reactions is a basic new feature of stratospheric chain process, which was not considered till now at description of the chain stratospheric ...
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Wavelet based Multi Class image classification using Neural Network
... In the proposed image classification system we have introduced new approach using Haar wavelet decomposition and Back Propagation Neural Network. We used the correlation coefficient, mean and standard deviation ...
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Feature dependencies as change propagators:an exploratory study of software product lines
... Software change and change propagation. Many research work have explored the understanding of software change and the impact of those changes on specific software proper- ties [8, 21, 44, 45]. Most of these ...
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Systematic Cell Design of Three-Input XOR/XNOR with Energy Efficiency
... In the end, new high performance three- input XOR/XNOR circuits with less power consumption and delay during SCDM. The new circuits enjoy higher driving capability, transistor density, noise immunity with low- voltage ...
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Face Recognition Using Neural Networks
... Back Propagation Network (BPN) and Radial Basis Function Network (RBF) ...Back propagation can train multilayer feed-forward networks with differentiable transfer functions to perform function ...
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Classifying 3D objects in LiDAR point clouds with a back-propagation neural network
... labels is used to train a BPNN to recognize five types of outdoor objects, namely walls, poles, pedestrians, trees, and bushes. The BPNN model has 5 input neurons, 20 hidden- layer neurons, and 5 output neurons. After ...
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Face Recognition Using Principal Component Analysis
... Abstract— Face recognition is one of the most relevant applications of image analysis. It’s an efficient task (true challenge) to build an automated system with equal human ability to face recognised. Face is a complex ...
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Assamese Digit Recognition with Feed Forward Neural Network
... Richa Sharma, Arun Jain, Ritika Sharma and Jyoti Wadhwa, in the year 2013, proposed a character and digits recognition system for English language [9]. In their experiment they have used primitive morphological ...
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Flower Grain Image Classification Using Supervised Classification Algorithm
... texture feature. In order to extract the color feature we used HSV color model and for texture feature used GLCM (Gray level co-occurrence ...back propagation algorithm to classify our pollen ...
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