Analysis of Human Brain NMR Spectra in Vivo Using Artificial Neural Networks

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.

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

Prosiding Seminar Nasional Riset Information Science (SENARIS) ◽

10.30645/senaris.v1i0.82 ◽

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%.

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In Silico Prediction of siRNA Ionizable-Lipid Nanoparticles in vivo Efficacy: Machine Learning Modeling Based on Formulation and Molecular Descriptors

10.20944/preprints202108.0254.v1 ◽

2021 ◽

Author(s):

Abdelkader A Metwally ◽

Amira A Nayel ◽

Rania M Hathout

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

In Silico ◽

Molecular Descriptors ◽

Lipid Nanoparticles ◽

Data Set ◽

In Vivo Efficacy ◽

Artificial Neural

In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable yet never achieved before. This study aims to computationally predict siRNA nanoparticles in vivo efficacy, which saves time and resources. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log(dose) with R2val = 0.86 – 0.89 and 0.75 – 80 for validation sets one and two respectively. Artificial neural networks resulted in the best R2val for both validation sets. For predictions that have high bias, improvement of R2val from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.

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

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2015vol3.165 ◽

2015 ◽

Vol 3 ◽

pp. 228 ◽

Cited By ~ 6

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>

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Predicting the Cardioactivity of Tivela stultorum clam with Digoxin Using Artificial Neural Networks

10.17488/rmib.381.15 ◽

2017 ◽

Author(s):

◽

D. Flores

Keyword(s):

Neural Networks ◽

Heart Rate ◽

Artificial Neural Networks ◽

High Performance ◽

Digoxin Concentration ◽

Computational Method ◽

Specific Concentration ◽

Filling Time ◽

Artificial Neural

Artificial neural networks (ANN) are a computational method that has been widely used to solve complex problems and carry out predictions on nonlinear systems. Multilayer perceptron artificial neural networks were used to predict the physiological response that would be obtained by adding a specific concentration of digoxin to Tivela stultorum hearts, this organism is a model for testing cardiac drugs that pretends to be used in humans. The MLPANN inputs were weight, volume, length, and width of the heart, digoxin concentration and volume used for diluting digoxin, and maximum contraction, minimum contraction, filling time, and heart rate before adding digoxin, and the outputs were the maximum contraction, minimum contraction, filling time, and heart rate that would be obtained after adding digoxin to the heart. ANNs were trained, validated, and tested with the results obtained from the in vivo experiments. To choose the optimal network, the smallest square mean error value was used. Perceptrons obtained a high performance and correlation between predicted and calculated values, except in the case of the filling time output. Accurate predictions of the T. stultorum clams cardioactivity were obtained when a specific concentration of digoxin was added using ANNs with one hidden layer; this could be useful as a tool to facilitate laboratory experiments to test digoxin effects.

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