scholarly journals An Effective Method to Evaluate the Scientific Research Projects

2014 ◽  
Vol 39 (3) ◽  
pp. 175-188
Author(s):  
Xiaohui Hou ◽  
Lei Huang ◽  
Xuefei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Regression ◽  
Bp Neural Network ◽  
Error Function ◽  
Scientific Research ◽  
Research Projects ◽  
Neural Network Models ◽  
The Neural Networks ◽  
Linear Neural Network

Abstract The evaluation of the scientific research projects is an important procedure before the scientific research projects are approved. The BP neural network and linear neural network are adopted to evaluate the scientific research projects in this paper. The evaluation index system with 12 indexes is set up. The basic principle of the neural network is analyzed and then the BP neural network and linear neural network models are constructed and the output error function of the neural networks is introduced. The Matlab software is applied to set the parameters and calculate the neural networks. By computing a real-world example, the evaluation results of the scientific research projects are obtained and the results of the BP neural network, linear neural network and linear regression forecasting are compared. The analysis shows that the BP neural network has higher efficiency than the linear neural network and linear regression forecasting in the evaluation of the scientific research projects problem. The method proposed in this paper is an effective method to evaluate the scientific research projects.

Modified Tartagli Method for Calculation of Jominy Hardenability Curve

2008 ◽  
Vol 575-578 ◽  
pp. 892-897 ◽  
Author(s):  
Wojciech Sitek ◽  
Jacek Trzaska ◽  
Leszek Adam Dobrzański
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Chemical Composition ◽  
Network Models ◽  
Steel Grade ◽  
Regression Coefficients ◽  
Neural Network Models ◽  
The Neural Network ◽  
The Neural Networks ◽  
Hardenability Curve

Basing on the experimental results of the hardenability investigations, which employed Jominy method, the model of the neural networks was developed and fully verified experimentally. The model makes it possible to obtain Jominy hardenability curves basing on the steel chemical composition. The modified hardenability curves calculation method is presented in the paper, initially developed by Tartaglia, Eldis, and Geissler, later extended by T. Inoue. The method makes use of the similarity of the Jominy curve to the hyperbolic secant function. The empirical formulae proposed by the authors make calculation of the hardenability curve possible basing on the chemical composition of the steel. However, regression coefficients characteristic for the particular steel grade must be known. Replacing some formulae by the neural network models is proposed in the paper.

Comprehensive Introduction to Neural Networks

2021 ◽  
pp. 24-48
Author(s):  
Rajesh Sai K. ◽  
Veneela Adapa ◽  
Hari Kishan Kondaveeti
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Human Brain ◽  
Network Models ◽  
Neuron Models ◽  
Neural Network Models ◽  
The Neural Networks ◽  
Biological Neuron ◽  
Comprehensive Introduction

Unknowingly, artificial intelligence (AI) has become an inevitable part of our lives. In this chapter, the authors discuss how the neural networks, a sub-part of AI, changed the way we analyse things. In this chapter, the advent of neural networks, inspiration from the human brain, simplification models of biological neuron models are discussed. Later, a detailed overview of various neural network models, their strengths, limitations, applications, and challenges are presented in detail.

scholarly journals Accurate Prediction of Concentration Changes in Ozone as an Air Pollutant by Multiple Linear Regression and Artificial Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 356 ◽  
Author(s):  
Svajone Bekesiene ◽  
Ieva Meidute-Kavaliauskiene ◽  
Vaida Vasiliauskiene
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Network Models ◽  
Air Pollutant ◽  
Neural Network Models ◽  
Ozone Concentrations ◽  
Concentration Changes ◽  
Artificial Neural

This study considers the usage of multilinear regression and artificial neural network modelling to forecast ozone concentrations with regard to weather-related indicators (wind speed, wind direction, relative humidity and temperature). Initial data were obtained by measuring the meteorological parameters using the PC Radio Weather Station. Ozone concentrations near high-voltage lines were measured using RS1003 and at a 220 m distance using ML9811. Neural network models such as the multilayer perceptron and radial basis function neural networks were constructed. The prognostic capacities of the designed models were assessed by comparing the result data by way of the square of the coefficient of multiple correlations (R2) and mean square error (MSE) values. The number of hidden neurons was optimised by decreasing an error function that recorded the number of units in the hidden layers to the precision of the expanded networks. The neural software IBM SPSS 26v was used for artificial neural network (ANN) modelling. The study demonstrated that the linear regression modelling approach was lacking in its capacity to predict the investigated ozone concentrations by used parameters, whereas the use of an ANN offered more precise outcomes. The conducted tests’ results established the strength of the designed artificial neural network models with irrelevant differences between detected and forecasted data.

scholarly journals A Novel Low-Bit Quantization Strategy for Compressing Deep Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xin Long ◽  
XiangRong Zeng ◽  
Zongcheng Ben ◽  
Dianle Zhou ◽  
Maojun Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Network Models ◽  
Memory Consumption ◽  
Neural Network Models ◽  
The Neural Network ◽  
The Neural Networks ◽  
Novel Strategy

The increase in sophistication of neural network models in recent years has exponentially expanded memory consumption and computational cost, thereby hindering their applications on ASIC, FPGA, and other mobile devices. Therefore, compressing and accelerating the neural networks are necessary. In this study, we introduce a novel strategy to train low-bit networks with weights and activations quantized by several bits and address two corresponding fundamental issues. One is to approximate activations through low-bit discretization for decreasing network computational cost and dot-product memory. The other is to specify weight quantization and update mechanism for discrete weights to avoid gradient mismatch. With quantized low-bit weights and activations, the costly full-precision operation will be replaced by shift operation. We evaluate the proposed method on common datasets, and results show that this method can dramatically compress the neural network with slight accuracy loss.

scholarly journals Utilizing Artificial Neural Network for Load Prediction Caused by Fluid Sloshing in Tanks

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hossein Goudarzvand Chegini ◽  
Gholamreza Zarepour
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Harmonic Excitation ◽  
Load Prediction ◽  
Neural Network Models ◽  
Fluid Sloshing ◽  
Particle Hydrodynamics ◽  
Feed Forward Neural Networks ◽  
The Neural Networks

In this research, neural network models were used to predict the action of sloshing phenomena in a tank containing fluid under harmonic excitation. A new methodology is proposed in this analysis to test and simulate fluid sloshing behavior in the tank. The sloshing behavior was first modeled using the smooth particle hydrodynamics (SPH) method. The backpropagation of the error algorithm was then used to apply the two multilayer feed-forward neural networks and the recurrent neural network. The findings of the SPH process are employed in the training and testing of neural networks. Input neural network data include the tank position, velocity, and acceleration, neural output data, and fluid sloshing curve wave position. The findings of the neural networks were correlated with the experimental evidence provided in the literature. The findings revealed that neural networks can be used to predict fluid sloshing.

FEEDFORWARD NEURAL NETWORK MODELS FOR HANDLING CLASS OVERLAP AND CLASS IMBALANCE

2005 ◽  
Vol 15 (05) ◽  
pp. 323-338 ◽  
Author(s):  
RALF KRETZSCHMAR ◽  
NICOLAOS B. KARAYIANNIS ◽  
FRITZ EGGIMANN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Error Function ◽  
Class Imbalance ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Neural Network Models ◽  
Real World Data ◽  
Special Case

This paper proposes a framework for training feedforward neural network models capable of handling class overlap and imbalance by minimizing an error function that compensates for such imperfections of the training set. A special case of the proposed error function can be used for training variance-controlled neural networks (VCNNs), which are developed to handle class overlap by minimizing an error function involving the class-specific variance (CSV) computed at their outputs. Another special case of the proposed error function can be used for training class-balancing neural networks (CBNNs), which are developed to handle class imbalance by relying on class-specific correction (CSC). VCNNs and CBNNs are compared with conventional feedforward neural networks (FFNNs), quantum neural networks (QNNs), and resampling techniques. The properties of VCNNs and CBNNs are illustrated by experiments on artificial data. Various experiments involving real-world data reveal the advantages offered by VCNNs and CBNNs in the presence of class overlap and class imbalance.

scholarly journals COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING ELECTRICITY CONSUMPTION

2020 ◽  
Vol 16 (2) ◽  
pp. 135-140
Author(s):  
Tyas Setiyorini ◽  
Frieyadie Frieyadie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Regression ◽  
Electricity Consumption ◽  
Nonlinear Problems ◽  
Complex Data ◽  
The Neural Network ◽  
The Neural Networks ◽  
Linear Regressions ◽  
Better Than

Electricity has a major role in humans that is very necessary for daily life. Forecasting of electricity consumption can guide the government's strategy for the use and development of energy in the future. But the complex and non-linear electricity consumption dataset is a challenge. Traditional time series models in such as linear regression are unable to solve nonlinear and complex data-related problems. While neural networks can overcome the problems of nonlinear and complex data relationships. This was proven in the experiments in this study. Experiments carried out with linear regressions and neural networks on the electricity consumption dataset A and the electricity consumption dataset B. Then the RMSE results are compared on the linear regressions and neural networks of the two datasets. On the electricity consumption dataset, A obtained by RMSE of 0.032 used the linear regression, and RMSE of 0.015 used the neural network. On the electricity consumption, dataset B obtained by RMSE of 0.488 used the linear regression, and RMSE of 0.466 used the neural network. The use of neural networks shows a smaller RMSE value compared to the use of linear regressions. This shows that neural networks can overcome nonlinear problems in the electricity consumption dataset A and the electricity consumption dataset B. So that the neural networks are afforded to improve performance better than linear regressions.  This study to prove that there is a nonlinear relationship in the electricity consumption dataset used in this study, and compare which performance is better between using linear regression and neural networks.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

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