scholarly journals AN ARTIFICIAL NEURAL NETWORKS MODEL FOR THE ESTIMATION OF FORMWORK LABOUR / DIRBTINIŲ NEURONINIŲ TINKLŲ MODELIS, KURIO PASKIRTIS – SKAIČIUOTI KLOJINIAMS SKIRTO DARBO APIMTIS

2011 ◽  
Vol 17 (3) ◽  
pp. 340-347 ◽  
Author(s):  
S. Umit Dikmen ◽  
Murat Sonmez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Human Brain ◽  
Field Measurements ◽  
Reinforced Concrete Frame ◽  
Concrete Frame ◽  
Frame Buildings ◽  
Actual Field ◽  
Artificial Neural

Artificial Neural Networks (ANN) is a problem solving technique imitating the basic working principles of the human brain. The formwork labour cost constitutes an important part within the costs of the reinforced concrete frame buildings. This study suggests a method based on artificial neural networks developed for estimating the required manhours for the formwork activity of such buildings. The introduced method has been verified in the study with reference to the test conducted involving two case studies. In all cases, the model produced results reasonably close to actual field measurements. The model is a simple and quick tool for the estimators and planners to aid them in their work. Santrauka Dirbtiniai neuroniniai tinklai (DNT) – tai problemų sprendimo metodas, imituojantis pagrindinius žmogaus smegenų veiklos principus. Statant gelžbetoninius karkasinius pastatus, nemažą sąnaudų dalį sudaro klojinių ruošimas. Šiame tyrime siūlomas dirbtiniais neuroniniais tinklais pagrįstas metodas, kurio paskirtis – apskaičiuoti, kiek žmogaus darbo valandų reikės ruošti klojinius tokiuose pastatuose. Pristatomas metodas tyrimo metu patikrintas remiantis bandymu, susijusiu su dviem atvejo tyrimais. Visais atvejais modelio pateikti rezultatai buvo gana artimi faktiniams matavimams. Modelis – tai paprastas ir greitai naudojamas įrankis, kuris pravers sąmatininkams ir planuotojams.

scholarly journals Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 762
Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Architecture ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Detention Facilities

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.

scholarly journals Artificial Neural Networks and Human Brain: Survey of Improvement Possibilities of Learning

2015 ◽  
Vol 3 ◽  
pp. 228 ◽  
Author(s):  
Aleksejs Zorins ◽  
Peteris Grabusts
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Topology ◽  
Hybrid Methods ◽  
Learning Algorithms ◽  
Real Life ◽  
Real Problem ◽  
Brain Functions ◽  
Artificial Neural

<p class="R-AbstractKeywords">There are numerous applications of Artificial Neural Networks (ANN) at the present time and there are different learning algorithms, topologies, hybrid methods etc. It is strongly believed that ANN is built using human brain’s functioning principles but still ANN is very primitive and tricky way for real problem solving. In the recent years modern neurophysiology advanced to a big extent in understanding human brain functions and structure, however, there is a lack of this knowledge application to real ANN learning algorithms. Each learning algorithm and each network topology should be carefully developed to solve more or less complex problem in real life. One may say that almost each serious application requires its own network topology, algorithm and data pre-processing. This article presents a survey of several ways to improve ANN learning possibilities according to human brain structure and functioning, especially one example of this concept – neuroplasticity – automatic adaptation of ANN topology to problem domain.</p>

scholarly journals New Method for Locker Door with Face Security System Using Backpropagation Algorithm

2018 ◽  
Vol 7 (2.13) ◽  
pp. 402
Author(s):  
Y Yusmartato ◽  
Zulkarnain Lubis ◽  
Solly Arza ◽  
Zulfadli Pelawi ◽  
A Armansah ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Back Propagation ◽  
The Face ◽  
Artificial Neural ◽  
Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.  

Supervised Learning with Artificial Neural Networks

2008 ◽  
pp. 24-40
Author(s):  
Darryl Charles ◽  
Colin Fyfe ◽  
Daniel Livingstone ◽  
Stephen McGlinchey
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Theory And Practice ◽  
Delta Rule ◽  
Neural Learning ◽  
Artificial Neural

In this chapter we will look at supervised learning in more detail, beginning with one of the simplest (and earliest) supervised neural learning algorithms – the Delta Rule. The objectives of this chapter are to provide a solid grounding in the theory and practice of problem solving with artificial neural networks – and an appreciation of some of the challenges and practicalities involved in their use.

Comprehensive Modelling of ANN

2021 ◽  
pp. 18-29
Author(s):  
Meghna Babubhai Patel ◽  
Jagruti N. Patel ◽  
Upasana M. Bhilota
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Human Brain ◽  
Processing System ◽  
Novel Structure ◽  
Pros And Cons ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Networks Structure

An artificial neural network (ANN) is an information processing modelling of the human brain inspired by the way biological nervous systems behave. There are about 100 billion neurons in the human brain. Each neuron has a connection point between 1,000 and 100,000. The key element of this paradigm is the novel structure of the information processing system. In the human brain, information is stored in such a way as to be distributed, and we can extract more than one piece of this information when necessary from our memory in parallel. We are not mistaken when we say that a human brain is made up of thousands of very powerful parallel processors. It is composed of a large number of highly interconnected processing elements (neurons) working in union to solve specific problems. ANN, like people, learns by example. The chapter includes characteristics of artificial neural networks, structure of ANN, elements of artificial neural networks, pros and cons of ANN.

scholarly journals MODELLING MICROWAVE DEVICES USING ARTIFICIAL NEURAL NETWORKS / MIKROBANGŲ ĮTAISŲ MODELIAVIMAS, TAIKANT DIRBTINIŲ NEURONŲ TINKLUS

2012 ◽  
Vol 4 (1) ◽  
pp. 81-84
Author(s):  
Andrius Katkevičius
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Microwave Devices ◽  
Analysis And Synthesis ◽  
Artificial Neural

Artificial neural networks (ANN) have recently gained attention as fast and flexible equipment for modelling and designing microwave devices. The paper reviews the opportunities to use them for undertaking the tasks on the analysis and synthesis. The article focuses on what tasks might be solved using neural networks, what challenges might rise when using artificial neural networks for carrying out tasks on microwave devices and discusses problem-solving techniques for microwave devices with intermittent characteristics. Santrauka Nagrinėjamos dirbtinių neuronų tinklų taikymo galimybės mikrobangų įtaisams tirti. Apžvelgiami eksperimentiniai ir teoriniai darbai. Pateikiama apibendrinta informacija apie uždavinius, sprendžiamus taikant neuronų tinklus, problemas, kylančias dirbtinius neuronų tinklus taikant mikrobangų uždaviniams spręsti, ir problemų sprendimo būdus. Pateikiama bendra neuronų tinklų struktūra ir konkretūs neuronų tinklų naudojimo pavyzdžiai.

Artificial neural networks for source localization in the human brain

1991 ◽  
Vol 4 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Udantha R. Abeyratne ◽  
Yohsuke Kinouchi ◽  
Hideo Oki ◽  
Jun Okada ◽  
Fumio Shichijo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Source Localization ◽  
Artificial Neural

Are you for real? Decoding hyperrealistic AI-generated faces from neural activity

2020 ◽  
Author(s):  
Michoel L Moshel ◽  
Amanda K Robinson ◽  
Thomas A. Carlson ◽  
Tijl Grootswagers
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Brain Imaging ◽  
Neural Activity ◽  
Border Security ◽  
Fake News ◽  
The Real ◽  
Artificial Neural

Can we trust our eyes? Until recently, we rarely had to question whether what we see is indeed what exists, but this is changing. Artificial neural networks can now generate hyperrealistic images that challenge our perception of what is real. This new reality can have significant implications in cybersecurity, counterfeiting, fake news, and border security. We investigated how the human brain encodes and interprets hyperrealistic artificially generated images using behaviour and brain imaging. We found that we could reliably detect AI-generated fake images using neural activity, even though people could not consciously report seeing differences between real and fake images. Understanding this dissociation between brain and behaviour may be key in determining the 'real' in our new reality.

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