pattern classification neural networks
Artificial neural networks in pattern recognition: Classification and analysis of proton NMR spectra of human brain tumours
... This thesis has provided an example of the power of neuro-computing techniques as computational tools for detecting subtle or implicit relationships among data with little or no requirement for prior knowledge of the ...
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Encoding candlesticks as images for pattern classification using convolutional neural networks
... Although visualization analysis can benefit decision-makers, many traditional statistical or machine learning methods for predicting currency movements use quantitative mod- els. These methods do not consider ...
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Pattern Recognition Process, Methods and Applications in Artificial Intelligence
... The neural approach applies biological concepts to machines to recognize ...artificial neural networks which is set up by the elicitation of the physiology knowledge of human ...brain. Neural ...
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He_unc_0153M_18474.pdf
... learning neural networks that bring in the state-of-the-art accuracy for pattern recognition (Krizhevsky et ...high classification accuracy with reasonable calculation cost is the main goal of ...
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Development of a deep neural network for automated electromyographic pattern classification
... artificial neural networks (ANNs), which are increasingly used for pattern recognition and classification in the field of machine learning (Russakovsky et ...manual classification, ANNs ...
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Performance Analysis of Various Data Mining Classification Techniques using Neural Networks
... databases, pattern recognition, data recovery, machine learning, parallel, perception and circulated ...as classification scheme and finally clustering techniques. Classification [1] is a best method ...
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Recognition of handwritten Arabic characters
... data points are equally important to the pattern recognition task. Using neural networks. for the task of classification, this would result in further reduction of the data input space[r] ...
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Brain Tumor Classification Using Convolutional Neural Networks
... of neural network. The neural network is mainly used for vector quantization, approximation, data clustering, pattern matching, optimization functions and classification ...The neural ...
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Unified Framework For Deep Learning Based Text Classification
... scale pattern recognition ...document classification & clustering ...text classification include convolutional neural networks (CNN), recurrent neural networks (RNN), ...
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ARTIFICIAL NEURAL NETWORK BASED INTELLIGENT FAULT IDENTIFICATION OF ROTATING MACHINERY
... faults. Neural networks (NN) are suitable for these tasks and have been widely researched as an artificial intelligence tool for machinery fault ...a neural network. The neural network will ...
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Predicting Helicopter Faults by Analyzing the Stability of Vibration Time Series
... sets are non-linearly separable. Instead of each observation being found determin- istically (as in the OR and XOR problems), let each observation have some random error built in. A multi-layer perceptron of appropriate ...
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Inter-Class Angular Loss for Convolutional Neural Networks
... Convolutional Neural Networks (CNNs) have shown great power in various classification tasks and have achieved re- markable results in practical ...deep networks. By minimizing the proposed ...
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Convolutional Neural Networks for Sentence Classification
... Deep learning models have achieved remarkable results in computer vision (Krizhevsky et al., 2012) and speech recognition (Graves et al., 2013) in recent years. Within natural language process- ing, much of the work with ...
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A Novel Compressed Deep Stacking Neural Network Based Classifier for the Monitoring and Classification of Multiple Power Quality Disturbances
... Detection and classification of single and combined power quality disturbances using neural 566. networks[r] ...
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Blind Navigation System using Artificial Intelligence
... We are using CIFAR-10 classification to classify RGB 32x32 pixel images. The reason CIFAR-10 was selected was that it is complex enough to exercise much of TensorFlow's ability to scale to large models. At the ...
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Falcon: A Novel Chinese Short Text Classification Method
... Nowadays, short text classification is the task of automatically assigning pre-defined categories to documents written in natural languages. Several types of text cate- gorization have been studied, each of which ...
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Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation
... recurrent neural networks, two- and three-dimensional feature tensors can also be inputted to the ...convolutional neural network (CNN), the input is a three-dimensional tensor, where the value of ...
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Assessment of Severity Level for Diabetic Macular Oedema Using Machine Learning Algorithms
... Support vector machine is proposed to classify the severity level for different pathologies in the retina. The features are extracted from the raw images using the image processing techniques and fed to the classifier ...
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Implementation of hybrid classification model in distributed systems for network monitoring
... , 512 MB RAM, Linux). We briefly summarize the content of the paper now. In section 2, we introduce the finite Newton method for classification problems. In section 3, we describe how to build the incremental ...
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