NEURAL NETWORK-2020


Artificial neural networks or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules

Approximation of Fractal Interpolation using Artificial Neural Network
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A new fractal interpolation method called NNA ( Neural Network Algorithm) based on IFS is proposed to interpolate the self-affine signals with the expected interpolation error. Experiments on the theoretical data and real field seismic data show that the proposed

River Flood Prediction Using a Long Short-Term Memory Recurrent Neural Network
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Page 1. River Flood Prediction Using a Long Short- Term Memory Recurrent Neural Network Andrew T. White Kristopher D. White Christopher R. Hain Jonathan L. Case4 1University of Alabama in Huntsville/NASA MSFC SPoRT 2NWS/NASA MSFC SPoRT 3NASA MSFC SPoRT

WATER EVAPORATION ALGORITHM WITH PROBABILISTIC NEURAL NETWORK FOR SOLVING CLASSIFICATION PROBLEMS
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Classification is a crucial step in data mining as it facilitates decision-making in many areas of human activity, such as scientific endeavors, marketing campaigns, biomedical research and industrial applications. The probabilistic neural network (PNN) is widely utilized to solve

Identifying Earth-impacting asteroids using an artificial neural network
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By means of a fully connected artificial neural network , we identified asteroids with the potential to impact Earth. The resulting instrument, named the Hazardous Object Identifier (HOI), was trained on the basis of an artificial set of known impactors which were generated

Deep ReLU Neural Network Expression Rates for Data-to-QoI Maps in Bayesian PDE Inversion
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For Bayesian inverse problems with input-to-response maps given by well-posed partial differential equations (PDEs) and subject to uncertain parametric or function space input, we establish (under rather weak conditions on the forward , input-to-response maps) the

Neural Network Theory
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In these notes, we study a mathematical structure called neural networks. These objects have recently received much attention and have become a central concept in modern machine learning. Historically, however, they were motivated by the functionality of the

Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network
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Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore

Radial basis function neural network for 2 satisfiability programming
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Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There

Recurrent neural network based speech recognition using MATLAB
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The purpose of this paper is to design an efficient recurrent neural network (RNN) based speech recognition system using software with long short-term memory (LSTM). The design process involves speech acquisition, pre-processing, feature extraction, training and pattern

A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing.
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Phishing attacks are one of the slanting cyber-attacks that apply socially engineered messages that are imparted to individuals from expert hackers going for tricking clients to uncover their delicate data, the most mainstream correspondence channel to those

Hybrid teaching learning artificial neural network for city-level electrical load prediction
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Dear editor, Rapid economic growth, accompanied by structural changes, strongly affects the global trend of electrical energy consumption. Short-term electrical load forecasting plays a significant role in efficient energy management, fault diagnosis, and power system

Training Convolutional Neural Network for Sketch Recognition on Large-Scale Dataset
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With the rapid development of computer vision technology, increasingly more focus has been put on image recognition. More specifically, a sketch is an important hand-drawn image that is garnering increased attention. Moreover, as handheld devices such as tablets

Decomposition of color wavelet with higher order statistical texture and convolutional neural network features set based classification of colorectal polyps from
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Gastrointestinal cancer is one of the leading causes of death across the world. The gastrointestinal polyps are considered as the precursors of developing this malignant cancer. In order to condense the probability of cancer, early detection and removal of

Golden chip free Tro detection leveraging probabilistic neural network with genetic algorithm applied in the training phase
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Dear editor, Hardware Tro has become a serious threat to the trustworthiness of critical applications . Among all existing detection approaches, golden chip-free Tro detection approaches have attracted wide attention in . Recently, various supervised learning

Application of Artificial Neural Network and Climate Indices to Drought Forecasting in South-Central Vietnam
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Widespread negative consequences of droughts related to climate indices in Vietnam have motivated many studies integrating those indices to predict the onset of drought in the region. This study aims to examine the capacity of eight climate Pacific Ocean indices as

A Schmitt Trigger Based Oscillatory Neural Network for Reservoir Computing
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With the increase in communication bandwidth and frequency, the development level of communication technology is also constantly developing. The scale of the Internet of Things (IoT) has shifted from single point-to-point communication to mesh communication between

Adaptive neural network control for marine surface vehicles platoon with input saturation and output constraints
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This paper addresses the decentralized problem for marine surface vessels (MSVs) in the presence of unknown unmodeled nonlinear dynamics, time-varying external disturbances and input saturations. First, platoon formation is proceeded by using line-of-sight (LOS)

Neural network learning of the Russian genitive of negation: optionality and structure sensitivity
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A number of recent studies have investigated the ability of language models (specifically, neural network language models without syntactic supervision) to capture syntactic dependencies. In this paper, we contribute to this line of work and investigate the neural

A flexible technique to select objects via convolutional neural network in VR space
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Most studies on the selection techniques of projection-based VR systems are dependent on users wearing complex or expensive input devices, however there are lack of more convenient selection techniques. In this paper, we propose a flexible 3D selection technique

Optimum Design of Liquified Natural Gas Bi-lobe Tanks using Finite Element, Genetic Algorithm and Neural Network
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A comprehensive set of ten artificial neural networks is developed to suggest optimal dimensions of type CBi-lobe tanks used in the shipping of liquefied natural gas. Multi- objective optimization technique considering the maximum capacity and minimum cost of