Wheelchair Control Based on Facial Gesture Recognition

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among diﬀerent machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Diﬀerent methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant diﬀerence among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among diﬀerent methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.13.16931 ◽

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.

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Artificial Neural Network What-If Theory

International Journal of Information Systems and Social Change ◽

10.4018/ijissc.2015100104 ◽

2015 ◽

Vol 6 (4) ◽

pp. 52-81 ◽

Cited By ~ 4

Author(s):

Paolo Massimo Buscema ◽

William J Tastle

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Pattern Recognition ◽

Artificial Neural Network ◽

Data Sets ◽

Data Set ◽

Innovative Solutions ◽

Artificial Neural

Data sets collected independently using the same variables can be compared using a new artificial neural network called Artificial neural network What If Theory, AWIT. Given a data set that is deemed the standard reference for some object, i.e. a flower, industry, disease, or galaxy, other data sets can be compared against it to identify its proximity to the standard. Thus, data that might not lend itself well to traditional methods of analysis could identify new perspectives or views of the data and thus, potentially new perceptions of novel and innovative solutions. This method comes out of the field of artificial intelligence, particularly artificial neural networks, and utilizes both machine learning and pattern recognition to display an innovative analysis.

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Evaluation of machine learning applied to the realignment of hierarchies for image segmentation

10.5753/sibgrapi.est.2019.8311 ◽

2019 ◽

Author(s):

Milena Menezes Adão ◽

Silvio Jamil F. Guimarães ◽

Zenilton K. G. Patrocı́nio Jr.

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Image Segmentation ◽

Random Forest ◽

Ground Truth ◽

Experimental Results ◽

Hierarchical Segmentation ◽

Segmentation Methods ◽

Artificial Neural

A hierarchical image segmentation is a set of image segmentations at different detail levels. However, objects can be located at different scales due to their size differences or to their distinct distances from the camera. In literature, many works have been developed to improve hierarchical image segmentation results. One possible solution is to realign the hierarchy such that every region containing an object (or its parts) is at the same level. In this work, we have explored the use of random forest and artificial neural network as regressors models to predict score values for regions belonging to a hierarchy of partitions, which are used to realign it. We have also proposed a new score calculation witch considering all user-defined segmentations that exist in the ground-truth. Experimental results are presented for two different hierarchical segmentation methods. Moreover, an analysis of the adoption of different combination of mid-level features to describe regions and different architectures from random forest and artificial neural network to train regressors models. Experimental results have point out that the use of new proposed score was able to improve final segmentation results.

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Artificial Neural Network What-If Theory

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch001 ◽

2020 ◽

pp. 1-29

Author(s):

Paolo Massimo Buscema ◽

William J. Tastle

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Pattern Recognition ◽

Artificial Neural Network ◽

Data Sets ◽

Data Set ◽

Innovative Solutions ◽

Artificial Neural

Data sets collected independently using the same variables can be compared using a new artificial neural network called Artificial neural network What If Theory, AWIT. Given a data set that is deemed the standard reference for some object, i.e. a flower, industry, disease, or galaxy, other data sets can be compared against it to identify its proximity to the standard. Thus, data that might not lend itself well to traditional methods of analysis could identify new perspectives or views of the data and thus, potentially new perceptions of novel and innovative solutions. This method comes out of the field of artificial intelligence, particularly artificial neural networks, and utilizes both machine learning and pattern recognition to display an innovative analysis.

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Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

Journal of Marine Science and Engineering ◽

10.3390/jmse8100766 ◽

2020 ◽

Vol 8 (10) ◽

pp. 766

Author(s):

Dohan Oh ◽

Julia Race ◽

Selda Oterkus ◽

Bonguk Koo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Machine Learning Techniques ◽

Burst Pressure ◽

Comparison Results ◽

Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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Determination of thermodynamic state variables of liquids from its microscopic structure using an artificial neural network

Soft Matter ◽

10.1039/d0sm02127j ◽

2020 ◽

Author(s):

Ulices Que-Salinas ◽

Pedro Ezequiel Ramirez-Gonzalez ◽

Alexis Torres-Carbajal

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Distribution Function ◽

Microscopic Structure ◽

Machine Learning Method ◽

Learning Method ◽

State Variables ◽

Thermodynamic State

In this work we implement a machine learning method to predict the thermodynamic state of a liquid using only its microscopic structure provided by the radial distribution function (RDF). The...

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Artificial Neural Network Simulation of Energetic Performance for Sorption Thermal Energy Storage Reactors

Energies ◽

10.3390/en14113294 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3294

Author(s):

Carla Delmarre ◽

Marie-Anne Resmond ◽

Frédéric Kuznik ◽

Christian Obrecht ◽

Bao Chen ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Energy Storage ◽

Thermal Energy ◽

Temperature Difference ◽

Thermal Energy Storage ◽

Heat Storage ◽

Experimental Results ◽

Physical Models ◽

Artificial Neural

Sorption thermal heat storage is a promising solution to improve the development of renewable energies and to promote a rational use of energy both for industry and households. These systems store thermal energy through physico-chemical sorption/desorption reactions that are also termed hydration/dehydration. Their introduction to the market requires to assess their energy performances, usually analysed by numerical simulation of the overall system. To address this, physical models are commonly developed and used. However, simulation based on such models are time-consuming which does not allow their use for yearly simulations. Artificial neural network (ANN)-based models, which are known for their computational efficiency, may overcome this issue. Therefore, the main objective of this study is to investigate the use of an ANN model to simulate a sorption heat storage system, instead of using a physical model. The neural network is trained using experimental results in order to evaluate this approach on actual systems. By using a recurrent neural network (RNN) and the Deep Learning Toolbox in MATLAB, a good accuracy is reached, and the predicted results are close to the experimental results. The root mean squared error for the prediction of the temperature difference during the thermal energy storage process is less than 3K for both hydration and dehydration, the maximal temperature difference being, respectively, about 90K and 40K.

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