scholarly journals Recognition of Human Facial Expressions Using DCT-DWT and Artificial Neural Network

2021 ◽  
pp. 2090-2098
Author(s):  
Wasan. Maddah Alaluosi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Facial Expressions ◽  
Emotional State ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Discrete Wavelet ◽  
Three Stages ◽  
Artificial Neural ◽  
Facial Images

Facial expressions are a term that expresses a group of movements of the facial fore muscles that is related to one's own human emotions. Human–computer interaction (HCI) has been considered as one of the most attractive and fastest-growing fields. Adding emotional expression’s recognition to expect the users’ feelings and emotional state can drastically improves HCI. This paper aims to demonstrate the three most important facial expressions (happiness, sadness, and surprise). It contains three stages; first, the preprocessing stage was performed to enhance the facial images. Second, the feature extraction stage depended on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) methods. Third, the recognition stage was applied using an artificial neural network, known as Back Propagation Neural Network (BPNN), on database images from Cohen-Kanade. The method was shown to be very efficient, where the total rate of recognition of the three facial expressions was 92.9%.

scholarly journals Pengenalan Citra Wajah Dengan Menggunakan Transformasi Wavelet Diskrit dan Jaringan Saraf Tiruan Back-Propagation

2010 ◽  
Vol 1 (2) ◽  
pp. 691
Author(s):  
Suhendry Effendy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Input Image ◽  
Discrete Wavelet ◽  
Web Camera ◽  
Artificial Neural ◽  
Facial Images

This paper discusses the facial image recognition system using Discrete Wavelet Transform and back-propagation artificial neural network. Discrete Wavelet Transform processes the input image to obtain the essential features found on the face image. These features are then classified using an back-propagation artificial neural network for the input image to be identified. Testing the system using facial images in AT & T Database of Faces of 400 images comprising 40 facial images of individuals and web-camera catches as many as 100 images of 10 individuals. The accuracy of level of recognition on AT & T Database of Faces reaches 93.5%, while the accuracy of level of recognition on a web-camera capture images up to 96%. Testing is also done on image of AT & T Database of Faces with given noise. Apparently the noise in the image does not give meaningful effect on the level of recognition accuracy. 

Application of Neural Network in Urban Land Use Suitability Evaluation

2011 ◽  
Vol 474-476 ◽  
pp. 681-686
Author(s):  
Xiao Rui Zhang ◽  
Gang Chen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Urban Land ◽  
Urban Land Use ◽  
Suitability Evaluation ◽  
Land Use Suitability ◽  
Artificial Neural

Urban land use suitability evaluation is the basic work of urban land use planning and management. The evaluation method is a core in urban land use suitability evaluation. Traditional urban land use suitability evaluation methods are GIS-based methods which often can not get satisfactory results for the complex nonlinear urban land use system. Artificial neural network is a frontier theory of complex non-linearity scientific and artificial intelligence science. It is a new method to evaluate urban land use suitability. This paper took the land use suitability evaluation of Hefei city as an example, building a back propagation neural network with 8 neurous of input layer, 5 neurons of hide layer and 3 neurons of output layer. The analysis shows: the high suitability area is 682.27 km2in Hefei city, being about 8.73% of the total study area; the middle suitability area is 5965.76 km2, or about 76.33% of the total area and the low suitability area is 1167.35 km2, or about 14.94% of the total area. The results reflect the actual situation in Hefei city. The study shows that the back propagation neural network model can overcome the shortcomings of traditional evaluation methods. It means that artificial neural network is suitable for urban land use suitability evaluation. This reflects that artificial neural network has great academic value and application prospect in urban land use suitability evaluation. It also reflects that this study can provide a new idea and method for urban land use suitability evaluation.

scholarly journals RANCANG BANGUN APLIKASI PENGENALAN WAJAH UNTUK PASSWORD FILE DENGAN METODE JARINGAN SYARAF TIRUAN

2019 ◽  
Vol 21 (1) ◽  
pp. 11-21
Author(s):  
Genrawan Hoendarto ◽  
Vicni Iskandar
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Data Security ◽  
Facial Recognition ◽  
Back Propagation ◽  
Computer Users ◽  
Artificial Neural ◽  
Access Rights ◽  
Facial Images

Data security for computer users is increasingly becoming a concern because it is increasingly vulnerable to illegal access even though the file has been protected with a password. This is possible with the increasing number of applications aimed at hacking owner protection. Artificial neural network that was appointed in this study is one part of computer vision, which in this study is intended to make computers able to "see" through a webcam and recognize that face has access rights to the selected file. So that computers can distinguish facial images, it needs to be trained by applying the back propagation method. The reason for choosing facial recognition is because each person has a different face, so that it can be a more effective security key than conventional methods of making or accessing files that are on a computer.

scholarly journals Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhou Yang ◽  
Unsong Pak ◽  
Cholu Kwon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Sampling Method ◽  
Back Propagation ◽  
Simulation Method ◽  
Back Propagation Neural Network ◽  
Element Analysis ◽  
Analysis Methods ◽  
Artificial Neural

This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.

Prediction of infection and death ratio of CoVID-19 virus in Turkey by using artificial neural network (ANN)

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Andaç Batur Çolak
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Death Rate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
World Health ◽  
Feed Forward Back Propagation ◽  
The World ◽  
Artificial Neural ◽  
Health Organization

Background: For the first time in December 2019 as reported in the Whuan city of China COVID-19 deadly virus, spread rapidly around the world and the first cases were seen in Turkey on March 11, 2020. On the same day, a pandemic was declared by the World Health Organization due to the rapid spread of the disease throughout the world. Methods: In this study, a multilayered perception feed-forward back propagation neural network has been designed for predicting the spread and mortality rate of COVID-19 virus in Turkey. COVID-19 data from six different countries were used in the design of the artificial neural network, which has 15 neurons in its hidden layer. 70% of these optimized data were used for training, 20% for validation and 10% for testing. Results: The resulting simulation results, COVID-19 virus in Turkey between 20 and 37 days showed the fastest to rise. The number of cases for the 20th day was predicted to be 13.845 and the 51st day for the 37th day. Conclusion: As for the death rate, it was predicted that a rapid rise on the 20th day would start and a slowdown around the 43rd day and progress towards the zero case point. The death rate for the 20th day was predicted to be 170 and the 43rd day for the 1.960s.

Predicting Flow Stress of Four Types of Microalloyed Forging Steel at High Temperature by Artificial Neural Network Model

2017 ◽  
Vol 729 ◽  
pp. 75-79
Author(s):  
Hu Sen Jiang ◽  
Jin Wang ◽  
Li Hua Li ◽  
Hai Tao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Constitutive Model ◽  
Relative Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Artificial Neural ◽  
Input Variables

Artificial neural network (ANN) gets a lot of applications in predicting flow stress of steels at high temperature. However, few studies have been devoted to simultaneously predict flow stress of several steels by ANN. The purpose of this paper is to determine the effect of ANN on simultaneously predicting flow stress of several steels. Based on the results of previous compression experiments of four types of microalloyed forging steel, using the mass percentage of major chemical composition of the steels, such as as C, Mn, Si and V, and deformation temperature, strain rate and strain as input variables, a three-layers back propagation neural network was established as the constitutive model for them. Standard statistical methods were employed to quantitatively measure the accuracy of predicted results by the model. The calculated correlation coefficient and the average relative error absolute value between the predicted values by the model and experimental values were 0.9982 and 2.4181%, respectively. In addition, the relative error between the two kinds of values was calculated, and for more than 89% samples, the relative error was within ± 5%. The results show that the developed constitutive model can predict the flow stress of the four types of microalloyed forging steel accurately and simultaneously.

scholarly journals Design of an Artificial Neural Network (BPNN) to Predict the Content of Silicon Oxide (SiO2) based on the Values of the Rock Main Oxides: Glass Factory Feed Case Study

2022 ◽  
Vol 11 (02) ◽  
pp. 41-44
Author(s):  
Hamed Nazerian ◽  
Adel Shirazy ◽  
Aref Shirazi ◽  
Ardeshir Hezarkhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Silicon Oxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Artificial Neural Network Algorithm

Artificial neural network (ANN) is one of the practical methods for prediction in various sciences. In this study, which was carried out on Glass and Crystal Factory in Isfahan, the amount of silica purification used in industry has been investigated according to its analyses. In this discussion, according to the artificial neural network algorithm back propagation neural network (BPNN), the amount of silica (SiO2) was predicted according to rock main oxides in chemical analysis. These studies can be used as a criterion for estimating the purity for use in the factory due to the high accuracy obtained.

Modification of BJT using Artificial Neural Network and implemented it on FPGA

2015 ◽  
Vol 3 (1) ◽  
pp. 55-69
Author(s):  
Hassan Fahad Khazal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Second Stage ◽  
Feed Forward Back Propagation ◽  
Input Signals ◽  
Third Stage ◽  
Field Programmable ◽  
Three Stages ◽  
Artificial Neural

       In this research the performance of the BJT has been improved using the "Feed Forward – Back Propagation Artificial Neural Network" (FFBPANN). The use of this type of networks led to improve the pre specified functions, by widening its bandwidth, improving its sensitivity to the minimum and maximum values of input signals, and reduce the effect of the rise of the temperature on its performance. The improvement done on the type "npn" of the  code "2N2222A /ZTX". The execution of this work passed through three stages using various types of computer's programs. The first step have been done using the "Orcad Pspice" program, the second stage; the collected data from the first stage have been introduced as the input data of the "FFBPANN" that represented using "MATLAB R2013b" and the third stage have been done using the (ISE, Project navigator (P.14.2)) in order to apply the results of second stage on the "Field Programmable Gate Array" chip (FPGA).

Performance Prediction of Mechanical Draft Wet Cooling Tower Using Artificial Neural Network

2014 ◽  
Vol 1070-1072 ◽  
pp. 1994-1997
Author(s):  
Zhe Tian Xu ◽  
Jia Chen Mao ◽  
Yi Qun Pan ◽  
Zhi Zhong Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cooling Tower ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Prediction Approach

This paper proposed a prediction approach for the performance of the mechanical draft wet cooling tower based on artificial neural network (ANN). The inlet water temperature, the ambient wet bulb temperature and the ratio of water to air mass flow rate in the cooling tower were selected as the input parameters of a four-layer back propagation neural network (BPNN) to predict the temperature of the water at the tower outlet. After the test of the available data set, the BPNN results in a correlation coefficient of 0.9 between the predicted and experimental values. Thus the prediction performance is good and such prediction approach proves to be feasible and effective.

scholarly journals Optimalisasi Model Artificial Neural Network Menggunakan Certainty Factor (C-ANN) Untuk Pemetaan Kerawanan Tanah Longsor Skala Semi-Detil di DAS Bendo, Kabupaten Banyuwangi

2021 ◽  
Vol 35 (1) ◽  
pp. 1
Author(s):  
Syamsul Bachri ◽  
Kresno Sastro Bangun Utomo ◽  
Sumarmi Sumarmi ◽  
Mohammad Naufal Fathoni ◽  
Yulius Eka Aldianto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Landslide Hazard ◽  
Susceptibility Mapping ◽  
Ann Model ◽  
Certainty Factor ◽  
Artificial Neural ◽  
Factor C

Kerawanan longsor di DAS Bendo termasuk dalam kerawanan kelas sedang hingga tinggi. Sampai dengan saat ini, pemetaan rawan longsor di DAS Bendo baru dilakukan pada  skala pemetaan 1:250.000. Penelitian ini bertujuan untuk melakukan pemodelan pemetaan kerawanan longsor di DAS Bendo pada skala semi-detil. Metode yang digunakan dalam penelitian ini adalah optimalisasi model artificial neural network menggunakan certainty factor (C-ANN). Peta kerawanan dibangun berdasarkan faktor pengontrol tanah longsor yang berkorelasi positif terhadap kejadian longsor menggunakan Certainty Factor. Sedangkan pemodelan prediksi kerawanan menggunakan model ANN, khususnya arsitektur BPNN (back-propagation neural network). Hasil pemodelan menunjukkan bahwa model C-ANN (7 variabel independen) memiliki nilai AUC (0,916) lebih tinggi daripada model ANN (0,778). Faktor redundansi data, multikolinieritas data, dan proporsi kejadian longsor terhadap cakupan wilayah penelitian mengakibatkan ketidakpastian dalam data variabel independen. Melalui penelitian ini ditemukan hasil bahwa kondisi kerawanan longsor di DAS Bendo masuk kategori tinggi, khususnya pada lereng atas Gunung Ijen, Rante, dan Merapi. Landslide disaster in DAS Bendo is categorized as moderate to highly susceptible. Until today, landslide hazard mapping in DAS Bendo has been carried out with a scale 1:250.000. This study aimed to model landslide susceptibility mapping on a semi-detailed scale. The method used in this research was the integration of the Certainty Factor with Artificial Neural Network models (C-ANN).The development of susceptibility mapping based on factors that positively correlate to landslide events using Certainty Factor. While the susceptibility prediction model using the ANN model, specifically the BPNN (back-propagation neural network) architecture. Modelling results show that the C-ANN model (7 independent variables) has an AUC value (0.916) higher than the ANN model (0.778). Data redundancy factors, multicollinearity of data, and the proportion of landslide events to the study area's coverage resulted in uncertainty in the independent variable data. This research found that the Landslide hazard in the Bendo Watershed is in the high category, especially on the upper slopes of Mount Ijen, Rante, and Merapi.

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