Determination of Internal Forces Using Artificial Neural Networks

Pandelea, Alexandrina Elena; Budescu, Mihai; Soveja, Lucian; Solonaru, Maria

doi:10.4028/www.scientific.net/aef.21.151

Cited by 6 publications

(8 citation statements)

References 3 publications

(1 reference statement)

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“…Artificial neuronal network (ANN) is a computer science method that represents the working system of human brain by simulating the learning process. ANN can solve digital image processing problems in identification, classification, authentication, optimization, diagnostics and approximation [1]. ANN is the best recognition method, compared to fuzzy and other hybrid techniques [2].…”

Section: B Artificial Neural Network (Ann)mentioning

confidence: 99%

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

Moonlight

Faizah

Suprapto

et al. 2021

JISEBI

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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

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Section: B Artificial Neural Network (Ann)mentioning

confidence: 99%

“…(24) c) Weights and Bias Updates (1) For each unit of yk output (from the 1 st unit to the m-unit), the bias and weight (j = 0, ..., p; k = 1, ...., m) are corrected so that the new bias and weights become (25):…”

Section: ∆ =mentioning

confidence: 99%

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

Moonlight

Faizah

Suprapto

et al. 2021

JISEBI

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“…Satellite photographs can be calibrated, medical images can be clarified and faded pictures can be enhanced through image processing. Numeric information can also be translated into visual images by image processing that can be edited, animated, filtered and enhanced in order to show the association previously not apparent [6]. Analysis of image involves collection of data from digital images in form of measurements that can be transformed and analysed.…”

Section: Literature Review 21 Image Processingmentioning

confidence: 99%

“…The internal attack is a result of a misconfigured, malicious router, faulty and compromised inside a network domain. A temporary network is formed by a collection of wireless mobile hosts which forms finally the network in ad-hoc without the need to include a stand alone infrastructure or centralized administration [6]. Self organization and self-configuring are the characteristics of the mobile multi-hop ad-hoc network where network structure is subjected to dynamic changes as a result of node mobility.…”

Section: Network Configurationmentioning

confidence: 99%

Optimized image processing and clustering to mitigate security threats in mobile ad hoc network

Alameri¹,

Mutar²,

Onaizah

et al. 2020

TELKOMNIKA

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Since there are provisions of many attributes that are not possible or difficult to follow by networks conventionally, mobile ad-hoc networks are extensively deployed. This application starts through the defense sectors, the sensory node presents in the hostile territories down to the gadgets for congestion communication in traffic by general transportation when travelling for adequate provision of infrastructure during disaster recovery. As a lot of importance related to (mobile ad hoc network) MANET application, one important factor in ad-hoc networks is security. Using image processing for securing MANET is the area of focus of this research. Therefore, in this article, the security threats are assessed and representative proposals are summarized in ad-hoc network's context. The study reviewed the current situation of the art for original to security provision called mobile ad hoc network for wireless networking. The threats to security are recognized while the present solution is observed. The study additionally summarized education erudite, talks on general issues and future instructions are recognized. Also, in this study, the forecast weighted clustering algorithm (FWCA) is employed as a cluster head over weighted clustering algorithm (WCA) is examined as quality in cluster-based routing, service is highly significant with MANET.

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“…Using this type of method, the researchers can scan the image from the geometrical patterns and the size of the image. Combined with the generalization, it includes invariance with a parallel position and can increase the average recognition rate to 90.70% [20].…”

Section: A Related Workmentioning

confidence: 99%

Visual Recognition to Identify Helmet on Motorcycle Rider Using Convolutional Neural Network

Alexander

Dwantara

Naufal

et al. 2020

CommIT (Communication and Information Technology) Journal

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The amount of motorcycle accidents is increasing each year. The main reason is that the riders do not wear a helmet. The research aims to minimize the accident by training the machine learning using the IBM Watson Studio. It trains the data about “wearing helmet” and “not wearing helmet”. The used method is Convolutional Neural Network (CNN). About 170 image datasets are used. CNN is conducted on the input image using a kernel or filter. The filter will multiply its values with the overlapping values of the image while also sliding and adding them all to produce a single value for each of them until the entire images have passed and finished. After CNN method is done, the researchers can classify the images by using supervised learning. It can identify whether the rider is wearing a helmet or not simply by scanning a picture on the street. The result shows high accuracy of 92.87%. The method can be used to minimize the percentage of motorcycle accidents caused by not wearing a helmet.

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Determination of Internal Forces Using Artificial Neural Networks

Cited by 6 publications

References 3 publications

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

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

Optimized image processing and clustering to mitigate security threats in mobile ad hoc network

Visual Recognition to Identify Helmet on Motorcycle Rider Using Convolutional Neural Network

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