neural network training techniques
Brain Disease Detection From EEGS: Comparing Spiking and Recurrent Neural Networks for Non-stationary Time Series Classification
... Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures ...
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Powdery Mildew Disease Identification in Pachaikodi Variety of Betel Vine Plants Using Histogram and Neural Network Based Digital Imaging Techniques
... propagation neural network based techniques were used to input and output data set of betel vine ...propagation neural network and evaluate its performance. Trained neural ...
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NeuDetect: A neural network data mining system for wireless network intrusion detection
... In this thesis, we proposed and implemented a wireless intrusion detection system: NeuDetect, which uses Neural Network techniques to detect anomalous wireless packets by using training[r] ...
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USE OF GENETIC SVM FOR ECG ARRHYTHMIA CLASSIFICATION
... feedforward neural network has been hampered by the lack of training algorithm which reliably finds a nearly globally optimal set of ...a neural network classifier of PVC beats whose ...
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Verification and validation of neural networks: a sampling of research in progress
... best neural network architecture for a specific ...different neural network solutions exist that are capable of approximating classification, linear, or non- linear functions to varying ...
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Efficient Subsampling for Training Complex Language Models
... subsampling techniques for training large multi-class classifiers such as maxi- mum entropy language models and neural network language ...the training instances in the binary setting ...
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Modeling of System of Systems via Data Analytics Case for Big Data in SoS 1
... and techniques considered while trying to generate the best solar irradiance prediction ...model. Techniques discussed included dataset sanitation, training input parameter selection, model ...
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Optimized Face Recognition Technique based on PCA and RBF Neural Network
... less training time than BP algorithm and other classification ...the training time of ...the training time of ...the training time of 59.32 sec. Comparing the recognition rate and ...
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A Predictive Model for Corrosion Inhibition of Mild Steel by Thiophene and Its Derivatives Using Artificial Neural Network
... experimental techniques to predict inhibitor candidates for corrosion ...the neural network can effectively generalize correct responses that only broadly resemble the data in the training ...
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Comprehensible credit scoring models using rule extraction from support vector machines.
... positional techniques directly extract rules which relate the inputs and outputs of the ...for neural networks has been extensively researched ...compositional techniques typically use the trained ...
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A Review: Evaluating the Parametric Optimization of Electrical Discharge Machining (EDM) by Using & Comparing Artificial Neural Network (ANN) and Genetic Algorithm (GA)
... Multiperceptron neural network models were developed using Neuro Solutions ...the network is optimized with ...computing techniques. Artificial neural network (ANN) with back ...
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RSI RANGE DETERMINATION USING CUBICAL DISTANCE CLASSIFICATION
... for training the network, and other sets of data are used to test the ...Various techniques used in this experiment are creating the pattern files, normalizing the files, training the ...
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Improving The Fault Prediction In Oo Systems Using ANN With Firefly Algorithm
... various techniques for fault prediction which depends on historical ...these techniques are logistic regression by Basili et ...1988, neural networks by Khoshgoftaar and Lanning 1995 effective for ...
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Robust Exponential Stability of Periodic Solutions for Static Recurrent Neural Networks with Delays
... biological neural network system, the human brain is often in periodic or chaotic state, so it is very important to research the periodic oscillation and chaos phenomenon of neural network, ...
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A NOVEL TWO DIMENSIONAL SPECTRAL/SPATIAL HYBRID CODE FOR OPTICAL CODE DIVISION MULTIPLE ACCESS SYSTEM
... An ear recognition method is proposed by Revaud et al. [18], which uses the Scale-Invariant Features Transform (SIFT) method to detect features and create descriptors for them. These features are extracted for the model ...
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A Technical Review on Intrusion Detection System
... Artificial neural networks (ANN) were developed as generalizations of mathematical models of biological nervous ...in neural network. The main processing elements of neural networks are called ...
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Would two-stage scoring models alleviate bank exposure to bad debt?
... There are a number of opportunities for further work. This in- cludes the application of additional techniques and their possible combination into integrated models with larger sample sizes. In particular, gene ...
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A Unique Approach to Epilepsy Classification from EEG Signals Using Dimensionality Reduction and Neural Networks
... incorporating techniques such as Fuzzy Mutual Information (FMI), Independent Component Analysis (ICA), Linear Graph Embedding (LGE), Linear Discriminant Analysis (LDA) and Variation- al Bayesian Matrix ...
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New Approaches for Image Compression Using Neural Network
... coding network is trained with the small 8 × 8 blocks of image and ...the neural network is trying to determine the updated weights and biases in each step to minimize the systems ...
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Face Recognition using Rectangular Feature
... In this paper, we generalize the things that are useful for the face detection and recognition based on the rectangular feature. The rectangular feature is used for the face detection purpose and feature extraction. PCA ...
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