scholarly journals Estimation of Waste Mobile Phones in the Philippines using Neural Networks

2018 ◽  
Vol 20 (4) ◽  
pp. 767-772
Keyword(s):  
Neural Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Network Architecture ◽  
The Philippines ◽  
Strategy Formulation ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Mobile Industry ◽  
Hidden Layer

<p>Waste mobile phone is one of the subgroups of e-waste which is defined as discarded electronic products in the Philippine context. This study estimated current and projected quantities of waste mobile phones in the country using feed forward neural network. The neural network architecture used had three layers: (i) input layer, (ii) hidden layer, and (iii) output layer. Seven input factors were fed to the network: (i) population, (ii) literacy rate, (iii) mobile connections, (iv) mobile subscribers, (v) gross domestic product (GDP), (vi) GDP per capita, and (vii) US dollar to Philippine peso exchange rate. These input factors were selected based on the criteria provided in the study by the Groupe Spéciale Mobile Association (GSMA) Intelligence in 2015 on why the Philippines is an innovation hub in mobile industry and the availability of data from the sources. The structure was designed with five hidden layers which consisted of (i) six neurons for layer 1, (ii) five neurons for layer 2, (iii) four neurons for layer 3, (iv) three neurons for layer 4, and (v) two neurons for layer 5. The neural network was designed to initially calculate the sales of mobile phones before estimating waste mobile phone generation. Visual Gene Developer 1.7 Software was used which achieved a sum of squared error of 0.00001. Estimated values were found to be in good agreement with a calculated accuracy of 99%. This study can be used by policy makers as basis for strategy formulation and as guideline and baseline data for establishing a proper management system. Neural network performed better than the traditional linear extrapolation method for forecasting of data.</p>

scholarly journals Comparison of Artificial Neural Network Architecture in Solving Ordinary Differential Equations

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Susmita Mall ◽  
S. Chakraverty
Keyword(s):  
Neural Network ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Network Architecture ◽  
Initial Conditions ◽  
Error Function ◽  
Back Propagation ◽  
Feed Forward Neural Network ◽  
Error Back Propagation ◽  
Hidden Layer

This paper investigates the solution of Ordinary Differential Equations (ODEs) with initial conditions using Regression Based Algorithm (RBA) and compares the results with arbitrary- and regression-based initial weights for different numbers of nodes in hidden layer. Here, we have used feed forward neural network and error back propagation method for minimizing the error function and for the modification of the parameters (weights and biases). Initial weights are taken as combination of random as well as by the proposed regression based model. We present the method for solving a variety of problems and the results are compared. Here, the number of nodes in hidden layer has been fixed according to the degree of polynomial in the regression fitting. For this, the input and output data are fitted first with various degree polynomials using regression analysis and the coefficients involved are taken as initial weights to start with the neural training. Fixing of the hidden nodes depends upon the degree of the polynomial. For the example problems, the analytical results have been compared with neural results with arbitrary and regression based weights with four, five, and six nodes in hidden layer and are found to be in good agreement.

Contributions to Ranking an Ergonomic Workstation, Considering the Human Effort and the Microclimate Parameters, Using Neural Networks

2013 ◽  
Vol 371 ◽  
pp. 812-816 ◽  
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Experimental Results ◽  
Work Place ◽  
Feed Forward Neural Network ◽  
Linear Relationships ◽  
The Neural Network ◽  
Manufacture Industry ◽  
Human Effort ◽  
Hidden Layer

The paper presents a method to use a feed forward neural network in order to rank a working place from the manufacture industry. Neural networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network is simulated with a simple simulator: SSNN. In this paper, we considered as relevant for a work place ranking, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons, 13 neurons in the hidden layer and 1 neuron in the output layer. We present also some experimental results obtained through simulations.

scholarly journals Neural Network Identifiability for a Family of Sigmoidal Nonlinearities

2021 ◽  
Author(s):  
Verner Vlačić ◽  
Helmut Bölcskei
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Uniform Norm ◽  
Feed Forward Neural Network ◽  
The Subject ◽  
Hidden Layer ◽  
Arbitrary Precision ◽  
Necessary And Sufficient ◽  
Fully Connected

AbstractThis paper addresses the following question of neural network identifiability: Does the input–output map realized by a feed-forward neural network with respect to a given nonlinearity uniquely specify the network architecture, weights, and biases? The existing literature on the subject (Sussman in Neural Netw 5(4):589–593, 1992; Albertini et al. in Artificial neural networks for speech and vision, 1993; Fefferman in Rev Mat Iberoam 10(3):507–555, 1994) suggests that the answer should be yes, up to certain symmetries induced by the nonlinearity, and provided that the networks under consideration satisfy certain “genericity conditions.” The results in Sussman (1992) and Albertini et al. (1993) apply to networks with a single hidden layer and in Fefferman (1994) the networks need to be fully connected. In an effort to answer the identifiability question in greater generality, we derive necessary genericity conditions for the identifiability of neural networks of arbitrary depth and connectivity with an arbitrary nonlinearity. Moreover, we construct a family of nonlinearities for which these genericity conditions are minimal, i.e., both necessary and sufficient. This family is large enough to approximate many commonly encountered nonlinearities to within arbitrary precision in the uniform norm.

scholarly journals Particle Swarm Optimization to Obtain Weights in Neural Network

MATEMATIKA ◽  
2019 ◽  
Vol 35 (3) ◽  
Author(s):  
Budi Warsito ◽  
Hasbi Yasin ◽  
Alan Prahutama
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Hidden Layer

This research discusses the use of a class of heuristic optimization to obtain the weights in neural network model for time series prediction. In this case, Feed Forward Neural Network (FFNN) was chosen as the class of network architecture. The heuristic algorithm determined to obtain the weights in network was Particle Swarm Optimization (PSO). It is a non-gradient optimization technique. This method was used for optimizing the connection weights of network. The lags used as the input were selected based on the strong relationship with the current. The eight architectures were conducted to improve the accuracy of the neural network model. In each architecture, we repeated the running thirty times to get the statistics of mean and variance. The comparison of the performance of various architectures based on the minimum MSE and the stability of the results is presented in this paper. The optimal number of neurons in hidden layer was determined by these criteria. The proposed procedure was applied in air pollution data, i.e. Solid Particulate Matter (SPM). The results showed that the proposed procedure gave promising results in terms of prediction accuracy. A few neurons in hidden layer are strongly recommended in choosing the optimal architecture.

scholarly journals Deep Autoencoder-Based Image Compression using Multi-Layer Perceptrons

2020 ◽  
Vol 9 (6) ◽  
pp. 1-6
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Backpropagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Proposed Model ◽  
Hidden Layer ◽  
Preprocessing Technique ◽  
Block Sizes ◽  
Normalization Process

The Artificial Neural Network is one of the heavily used alternatives for solving complex problems in machine learning and deep learning. In this research, a deep autoencoder-based multi-layer feed-forward neural network has been proposed to achieve image compression. The proposed neural network splits down a large image into small blocks and each block applies the normalization process as the preprocessing technique. Since this is an autoencoder-based neural network, each normalized block of pixels has been initialized as the input and the output of the neural network. The training process of the proposed network has been done for various block sizes and different saving percentages of various kinds of images by using the backpropagation algorithm. The output of the middle-hidden layer will be the compressed representation for each block of the image. The proposed model has been implemented using Python, Keras, and Tensorflow backend.

scholarly journals Substitution for lost one-hour means of the geomagnetic elements for the first half of the 20-th century at the Hurbanovo Geomagnetic Observatory by means of neural networks

2013 ◽  
Vol 43 (2) ◽  
pp. 125-140
Author(s):  
Fridrich Valach ◽  
Magdaléna Váczyová ◽  
Peter Dolinský ◽  
Melinda Vajkai
Keyword(s):  
Neural Network ◽  
Model Data ◽  
Geomagnetic Observatory ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Data Gaps ◽  
Geomagnetic Data ◽  
Annual Means ◽  
Hidden Layer ◽  
Long Acting

Abstract The existence of long-acting observatories by itself does not guarantee that their historical magnetograms are available or complete. In the archive of the Hurbanovo Geomagnetic Observatory (acronym HRB; geographical coordinates 47.86 ◦ N, 18.19 ◦ E), records of the geomagnetic field made on photo paper covering the period between the two World Wars were found for which the values of the baselines are unknown. We studied if a feed-forward neural network with one hidden layer can be used to supplement one-hour means of the geomagnetic elements D, H and Z of observatory HRB, using for this purpose the geomagnetic data of observatories Potsdam, Seddin and Niemegk (all of them being referenced to Niemegk). We focused our interest on the first half of the 20-th century. The neural-network model for element D proved to be applicable to substitute for the lost data of the magnetic declination at observatory HRB; however, the usability of the model for both elements H and Z turned out to be limited to a few years close to beginning or end of data gaps. Further we supplemented the time series of annual means of geomagnetic elements D, H and Z at observatory HRB with the model data.

CLASSIFICATION OF CIRRHOSIS FROM B-SCAN IMAGES USING PYRAMID NEURAL NETWORK

2005 ◽  
Vol 05 (04) ◽  
pp. 457-470 ◽  
Author(s):  
YAN SUN ◽  
JIANMING LU ◽  
TAKASHI YAHAGI
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Lower Layer ◽  
Discrete Wavelet ◽  
The Neural Network ◽  
The Common ◽  
Hidden Layer ◽  
B Scan Images ◽  
Compressed Data

This paper proposes a system applying a pyramid neural network for classifying the hepatic parenchymal diseases in ultrasonic B-scan texture. The conventional multilayer neural network emphasizing on the data carried by the last hidden layer has the drawback of not fully utilizing the information carried by the input data. A pyramid network can solve the problem successfully. To solve the common problem of neural network, which is time-consuming in computation, FDWT (Fast Discrete Wavelet Transform) is a key technique used during preprocessing to cut down the size of patterns feed to the network. The B-scan patterns are wavelet transformed, and then the compressed data is fed into a pyramid neural network to diagnose the type of cirrhotic diseases. The performance of the proposed system and that of a system based on the conventional multilayer network architecture is compared. The result shows that compared to the conventional 3-layer neural network, the performance of the proposed pyramid neural network is improved by effectively utilizing the lower layer of the neural network.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

scholarly journals A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Qi ◽  
Yanan Zhao ◽  
Yufang Huang ◽  
Yang Wang ◽  
Wei Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Phones ◽  
Mean Value ◽  
Wheat Cultivars ◽  
N Content ◽  
Color Parameters ◽  
The Neural Network ◽  
The Mean ◽  
Leaf N Content ◽  
Leaf N

AbstractThe accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.

scholarly journals Graph neural networks for an accurate and interpretable prediction of the properties of polycrystalline materials

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Minyi Dai ◽  
Mehmet F. Demirel ◽  
Yingyu Liang ◽  
Jia-Mian Hu
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Polycrystalline Materials ◽  
Feed Forward Neural Network ◽  
Microscopic Interactions ◽  
Physical Interactions ◽  
Graph Neural Networks ◽  
Individual Grains ◽  
Physical Features ◽  
Machine Learning Models

AbstractVarious machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macroscopic material properties. Here, we develop a graph neural network (GNN) model for obtaining an embedding of polycrystalline microstructure which incorporates not only the physical features of individual grains but also their interactions. The embedding is then linked to the target property using a feed-forward neural network. Using the magnetostriction of polycrystalline Tb0.3Dy0.7Fe2 alloys as an example, we show that a single GNN model with fixed network architecture and hyperparameters allows for a low prediction error of ~10% over a group of remarkably different microstructures as well as quantifying the importance of each feature in each grain of a microstructure to its magnetostriction. Such a microstructure-graph-based GNN model, therefore, enables an accurate and interpretable prediction of the properties of polycrystalline materials.

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