# Recurrent neural network

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the imageID and textInfoID. In order to solve this, I crawled the image data online, manually labeled health index on thousands of them, and applied data augmentation and CNN to construct a health index for each restaurant. I also applied a Recurrent Neural Network, more specifically LSTM, on the review text, which is around 5 gigabytes, to compute sentiment score for each restaurant. With the aid of cloud computing tool Spark, we were able to finish the task very efficiently. The final visualization

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AND PERFORMANCE ANALYSIS OF AND GATE USING SYNAPTIC INPUTS FOR NEURAL NETWORK APPLICATION [1]S.SOUNDARYA [2] VAMSHI.G [3] SOUNDARAJAN.M [4] RAMKUMAR.A [1] [2] [3] Students, ece department, saveetha school of engineering, Chennai [4] Asst.prof, ece department, Saveetha School of engineering, Chennai ABSTRACT Logic gates are one of the main constituents to design and integrate as a chip. With advent of vision and method in neural network, the intricacy can be clear alongside this knowledge to a remarkable

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function, their numbers and intersection value determines accuracy of the tool and its range of operation. If the membership function covers poles values from 0 to 4 then the maximum value for pole is 4 and the least value is 0 and same thing happens for the constants. Each unit reduces an order of two to order of one. If a transfer function of higher order is needed to be reduced the operation is repeated several times. For example an order 8 to 2 function reduction will reduce the 8 poles into

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One is of pendulum angle (θ) and cart position (x). Proper control structure is required to provide balance for the inverted pendulum. In literature, several conventional control methods [1]-[3] and soft computing methods like fuzzy logic and neural networks [4]-[6] has been successfully implemented to control pendulum angle or cart position. But at a time controlling two degree of freedom is a difficult task and is successfully handled in [7]. Soft computing techniques uses simple rules to model

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Classification is a principle technique in hyperspectral images (HSI) analysis, where a label is assigned to each pixel based on its characteristics. Applying machine learning techniques to these datasets need special consideration, since the hyperspectral images are typically represented by features vectors of extremely high dimensions. A robust HSI classification requires a prudent combination of deep feature extractor and powerful classifier. In the last one decade, extensive classification methods

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and V are the network parameters associated with xt and ht−1 respectively. φ is a nonlinear function which we can choose from several alternatives. Some common choices are sigmoid, ReLu, tanh etc. Two of most important differences between RNN and feed forward neural networks are time ordered input and network parameter sharing. We can see from figure 3 that input is feeding to the network in different time steps, not at once like feed forward case. However the parameters the network learns throughout

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traders are developed using Keras, a high level neural network toolkit wrapped up by Tensorflow. The data is generated from Bristol Stock Exchange developed by Cliff[21] which is an Automated market. This generated data is then trained and tested using deep learning neural network traders. This research also gives a comprehensive knowledge of Neural Networks especially Multi Layer Perceptron Neural Network, LSTM (Long Short-Term Memory) Recurrent Neural Network, and BSE. Moreover, it also

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the performance analysis of various neural networks (NN) for short term price forecasting. Several NN models are trained and tested on the half-hourly data from Australian Energy Market and their performances have been compared. Overall findings suggest that the value of mean absolute percentage Error (MAPE) in the case of 3-Layered cascaded neural network (CNN) is better than other proposed models. Keywords— Short term price forecasting, Cascaded Neural Network,

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Neural Networks in Finance 2600 Words By Maria L. Vicente University of Hawaiʻi at Hilo QBA 362 Fall 2016 Introduction Predictions are an important component of determining the financial progress of a business. Business decisions rely on forecasting techniques to predict things such as price movements or overall success in markets. In the attempt to forecast market predictions, it must be assumed that future occurrences may be partly based on present and past data (Abu-Mostafa

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A growing expansion of wireless systems and applications operating at centimetre-and millimetre-wave frequencies motivates the development of effective and affordably-priced technologies for manufacturing cm- and mm-wave components like antennas, filters, directional couplers, etc. Advances in technology associated with signal processing, R.F. components and batteries have stimulated more innovative applications in wireless that has in turn stimulate for extensive research to find new solutions to

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