Surface Identification of Top-Down, Bottom-Up, and Cement-Treated Reflective Cracks Using Convolutional Neural Network and Artificial Neural Networks

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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Research on the use of artificial neural networks for the myocardial infarction diagnosis

Information Technology and Nanotechnology ◽

10.18287/1613-0073-2019-2416-158-164 ◽

2019 ◽

pp. 158-164

Author(s):

P I Katkov ◽

N S Davydov ◽

A G Khramov ◽

A N Nikonorov

Keyword(s):

Neural Network ◽

Myocardial Infarction ◽

Neural Networks ◽

Artificial Neural Networks ◽

Convolutional Neural Network ◽

The Neural Network ◽

University Of Technology ◽

Artificial Neural ◽

Leave One Out

In this paper, the use of artificial neural networks for the myocardial infarction diagnosis is investigated. For the analysis, 169 ECG records were taken from the database of the Massachusetts University of Technology, of which 80 correspond to healthy patients and 89 correspond to patients who have a myocardial infarction. Each signal has been preprocessed. The result of preprocessing each signal is a common segment consisting of 1000 samples. To detect myocardial infarction, a convolutional neural network consisting of two convolutional layers was used. For accuracy of the neural network leave-one-out crossvalidation was used. The best results of the experiments are obtained with the neural network for leads V1, V2, AVF.

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Speech Command Recognition using Artificial Neural Networks

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.4.2.358 ◽

2020 ◽

Vol 4 (2) ◽

pp. 73

Author(s):

Sushan Poudel ◽

Dr. R Anuradha

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Convolutional Neural Network ◽

Recurrent Neural Network ◽

Network Models ◽

Recognition System ◽

Neural Network Models ◽

Artificial Neural

Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Google’s TensorFlow and AIY teams is used to implement different Neural Network models which include Convolutional Neural Network and Recurrent Neural Network combined with Convolutional Neural Network. The combination of Convolutional and Recurrent Neural Network outperforms Convolutional Neural Network alone by 8% and achieved 96.66% accuracy for 20 labels.

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Sistem Deteksi Infeksi COVID-19 Pada Hasil X-Ray Rontgen menggunakan Algoritma Convolutional Neural Network (CNN)

Infotek : Jurnal Informatika dan Teknologi ◽

10.29408/jit.v4i2.3582 ◽

2021 ◽

Vol 4 (2) ◽

pp. 217-227

Author(s):

Muhammad Saiful ◽

◽

Lalu Muhammad Samsu ◽

Fathurrahman Fathurrahman ◽

◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Computer Vision ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Architecture ◽

Infection Detection ◽

Learning Techniques ◽

Artificial Neural

The development of the world's technology is growing rapidly, especially in the field of health in the form of detection tools of various objects, including disease objects. The technology in point is part of artificial intelligence that is able to recognize a set of imagery and classify automatically with deep learning techniques. One of the deep learning networks widely used is convolutional neural network with computer vision technology. One of the problems with computer vision that is still developing is object detection as a useful technology to recognize objects in the image as if humans knew the object of the image. In this case, a computer machine is trained in learning using artificial neural networks. One of the sub types of artificial neural networks that are able to handle computer vision problems is by using deep learning techniques with convolutional neural network algorithms. The purpose of this research is to find out how to design the system, the network architecture used for COVID-19 infection detection. The system cannot perform detection of other objects. The results of COVID-19 infection detection with convolutional neural network algorithm show unlimited accuracy value that ranges from 60-99%.

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Convolutional Neural Network for Image Recognition

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39061 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1619-1624

Author(s):

Ankith I

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Pattern Recognition ◽

Artificial Neural Networks ◽

Convolutional Neural Network ◽

Image Recognition ◽

Computational Models ◽

Simplified Approach ◽

Artificial Neural

Abstract: Recent developments in the field of machine learning have changed the way it operates for ever, especially with the rise of Artificial Neural Networks (ANN). There is no doubt that these biologically inspired computational models are capable of performing far better than previous forms of artificial intelligence in common machine learning tasks as compared to their previous versions. There are several different forms of artificial neural networks (ANNs), but one of the most impressive is the convolutional neural network (CNN). CNN's have been extensively used for solving difficult pattern recognition tasks using images. With their simple yet precise architecture, they offer a simplified approach to getting started with ANNs. The goal of this paper is to provide a brief introduction to CNN. It discusses the latest papers and newly formed techniques in order to develop these absolutely brilliant models of image recognition. This introduction assumes that you already have a basic understanding of ANNs and machine learning. Keywords: Pattern recognition, artificial neural networks, machine learning, image analysing.

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Convolutional Neural Network

Encyclopedia of Information Science and Technology, Fifth Edition - Advances in Information Quality and Management ◽

10.4018/978-1-7998-3479-3.ch002 ◽

2021 ◽

pp. 12-26

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽

10.3390/biom11040500 ◽

2021 ◽

Vol 11 (4) ◽

pp. 500

Author(s):

László Keresztes ◽

Evelin Szögi ◽

Bálint Varga ◽

Viktor Farkas ◽

András Perczel ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector ◽

User Needs ◽

Computational Technique ◽

Linear Support Vector Machine ◽

Β Sheets ◽

Artificial Neural ◽

Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

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Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽

10.3390/met11010018 ◽

2020 ◽

Vol 11 (1) ◽

pp. 18

Author(s):

Rahel Jedamski ◽

Jérémy Epp

Keyword(s):

Neural Network ◽

Neural Networks ◽

Regression Analysis ◽

Artificial Neural Networks ◽

Linear Regression ◽

Linear Regression Analysis ◽

Case Hardening ◽

Artificial Neural ◽

Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽

10.3390/s21010047 ◽

2020 ◽

Vol 21 (1) ◽

pp. 47

Author(s):

Vasyl Teslyuk ◽

Artem Kazarian ◽

Natalia Kryvinska ◽

Ivan Tsmots

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Input Data ◽

Optimization Criterion ◽

Fuzzy Input ◽

Different Types ◽

Smart House ◽

Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

Volume 6B: Energy ◽

10.1115/imece2015-51723 ◽

2015 ◽

Cited By ~ 1

Author(s):

M. A. Rafe Biswas ◽

Melvin D. Robinson

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Fuel Cell ◽

Direct Methanol Fuel Cell ◽

Cell Voltage ◽

Operating Conditions ◽

Direct Methanol ◽

Artificial Neural ◽

Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

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