scholarly journals Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition

2021 ◽  
Vol 7 (2) ◽  
pp. 149
Author(s):  
Lady Silk Moonlight ◽  
Fiqqih Faizah ◽  
Yuyun Suprapto ◽  
Nyaris Pambudiyatno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Face Image ◽  
Self Organizing Map ◽  
Training Process ◽  
Face Images ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers. Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning 

scholarly journals Self-Organizing and Error Driven (SOED) artificial neural network for smarter classifications

2017 ◽  
Vol 4 (4) ◽  
pp. 282-304 ◽  
Author(s):  
Ruholla Jafari-Marandi ◽  
Mojtaba Khanzadeh ◽  
Brian K. Smith ◽  
Linkan Bian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Styles ◽  
Health Care Systems ◽  
Self Organizing Map ◽  
Care Systems ◽  
Classification Tasks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Abstract Classification tasks are an integral part of science, industry, business, and health care systems; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this paper, motivated by learning styles in human brains, ANN's shortcomings are assuaged and its prediction power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. The proposed method, which we name Self-Organizing Error-Driven (SOED) Artificial Neural Network, shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five different datasets. Highlights A synthesis of MLP and SOM is presented for tackling classification challenges. The superiority of SOED over MLP in addressing 5 classification tasks is presented. SOED is compared with other states of the art techniques such as DT, KNN, and SVM. It is shown that SOED is a more accurate and reliable in comparison with MLP. It is shown SOED is more accurate, reliable and transparent in comparison with MLP.

scholarly journals Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19

2021 ◽  
Vol 5 (2) ◽  
pp. 109-118
Author(s):  
Euis Saraswati ◽  
Yuyun Umaidah ◽  
Apriade Voutama
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Artificial Neural Network ◽  
Text Mining ◽  
Ann Model ◽  
Rapid Spread ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
The Government ◽  
Artificial Neural Network Ann

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.

scholarly journals Modeling of daily runoff from a small agricultural watershed using artificial neural network with resampling techniques

2014 ◽  
Vol 17 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Gurjeet Singh ◽  
Rabindra K. Panda ◽  
Marc Lamers
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Agricultural Watershed ◽  
Training Data ◽  
Optimum Number ◽  
Training Process ◽  
Data Set ◽  
Reported Study ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The reported study was undertaken in a small agricultural watershed, namely, Kapgari in Eastern India having a drainage area of 973 ha. The watershed was subdivided into three sub-watersheds on the basis of drainage network and land topography. An attempt was made to relate the continuously monitored runoff data from the sub-watersheds and the whole-watershed with the rainfall and temperature data using the artificial neural network (ANN) technique. The reported study also evaluated the bias in the prediction of daily runoff with shorter length of training data set using different resampling techniques with the ANN modeling. A 10-fold cross-validation (CV) technique was used to find the optimum number of hidden neurons in the hidden layer and to avoid neural network over-fitting during the training process for shorter length of data. The results illustrated that the ANN models developed with shorter length of training data set avoid neural network over-fitting during the training process, using a 10-fold CV method. Moreover, the biasness was investigated using the bootstrap resampling technique based ANN (BANN) for short length of training data set. In comparison with the 10-fold CV technique, the BANN is more efficient in solving the problems of the over-fitting and under-fitting during training of models for shorter length of data set.

Applications of Artificial Neural Network for the Prediction of Pool Boiling Curves

2002 ◽  
Author(s):  
Guanghui Su ◽  
K. Fukuda ◽  
K. Morita
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pool Boiling ◽  
Convergence Criterion ◽  
Training Process ◽  
Transient Heating ◽  
Wall Superheat ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Best Fit

Artificial neural network (ANN) has the advantage that the best-fit correlations of experimental data will no longer be necessary for predicting unknowns from the known parameters. The ANN was applied to predict the pool boiling curves in this paper. The database of experimentel data presented by Berenson, Dhuga et al., and Bui and Dhir etc. were used in the analysis. The database is subdivided in two subsets. The first subset is used to train the network and the second one is used to test the network after the training process. The input parameters of the ANN are: wall superheat ΔTw, surface roughness, steady/transient heating/transient cooling, subcooling, Surface inclination and pressure. The output parameter is heat flux q. The proposed methodology allows us to achieve the accuracy that satisfies the user’s convergence criterion and it is suitable for pool boiling curve data processing.

scholarly journals Implementation of Autonomous Control System of The Chain Wheel Robot Using the Backpropagation Artificial Neural Network (ANN) Methods

2020 ◽  
Vol 5 (8) ◽  
pp. 1230-1235
Author(s):  
Nur Rachman Supadmana Muda ◽  
Nugraha Gumilar ◽  
R.Djoko Andreas. Navalino ◽  
Tirton. N ◽  
M.Iman Hidayat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Robot Control ◽  
Back Propagation ◽  
Robot System ◽  
Output Data ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research is to implement the Artificial Neural Network (ANN) method in combat robots so it can be directed to shoot targets well. The robot control system uses remote control and autonomous. In the autonomous robot system, ANN back propagation method is applied, where the weight value variable depends on ultrasonic sensor, GPS and camera. The microcontroller system will process automatically depending on the sensor input. Output data is used to direct the robot to the target, tracking and shooting. Robot is used chain wheel systems and weapons that used pistol types. The riffle is mounted on the robot can be moved mechanically azimuth and the elevation towards the target then triggered mechanically by the riffle through the activation of data relays from the microcontroller. Thus, the backpropagation method can be applied to robots so it can be functioned autonomously.

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