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Journal articles on the topic 'Artificail neural networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

CVS, Rajesh, and Nadikoppula Pardhasaradhi. "Analysis of Artificial Neural-Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 418–28. http://dx.doi.org/10.31142/ijtsrd18482.

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2

Sarwar, Abid. "Diagnosis of hyperglycemia using Artificial Neural Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 606–10. http://dx.doi.org/10.31142/ijtsrd7045.

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3

CVS, Rajesh, and M. Padmanabham. "Basics and Features of Artificial Neural Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (2018): 1065–69. http://dx.doi.org/10.31142/ijtsrd9578.

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4

Parks, Allen D. "Characterizing Computation in Artificial Neural Networks by their Diclique Covers and Forman-Ricci Curvatures." European Journal of Engineering Research and Science 5, no. 2 (2020): 171–77. http://dx.doi.org/10.24018/ejers.2020.5.2.1689.

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The relationships between the structural topology of artificial neural networks, their computational flow, and their performance is not well understood. Consequently, a unifying mathematical framework that describes computational performance in terms of their underlying structure does not exist. This paper makes a modest contribution to understanding the structure-computational flow relationship in artificial neural networks from the perspective of the dicliques that cover the structure of an artificial neural network and the Forman-Ricci curvature of an artificial neural network’s connections

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5

O., Sheeba, Jithin George, Rajin P. K., Nisha Thomas, and Thomas George. "Glaucoma Detection Using Artificial Neural Network." International Journal of Engineering and Technology 6, no. 2 (2014): 158–61. http://dx.doi.org/10.7763/ijet.2014.v6.687.

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6

Demiralay, Raziye. "Estimating of student success with artificial neural networks." New Trends and Issues Proceedings on Humanities and Social Sciences 03, no. 07 (2017): 21–27. http://dx.doi.org/10.18844/prosoc.v2i7.1980.

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7

Klyuchko, O. M. "APPLICATION OF ARTIFICIAL NEURAL NETWORKS METHOD IN BIOTECHNOLOGY." Biotechnologia Acta 10, no. 4 (2017): 5–13. http://dx.doi.org/10.15407/biotech10.04.005.

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8

Jung, Jisoo, and Ji Won Yoon. "Author Identification Using Artificial Neural Network." Journal of the Korea Institute of Information Security and Cryptology 26, no. 5 (2016): 1191–99. http://dx.doi.org/10.13089/jkiisc.2016.26.5.1191.

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9

Al-Abaid, Shaimaa Abbas. "Artificial Neural Network Based Image Encryption Technique." Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (2020): 1184–89. http://dx.doi.org/10.5373/jardcs/v12sp3/20201365.

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10

Al-Rawi, Kamal R., and Consuelo Gonzalo. "Adaptive Pointing Theory (APT) Artificial Neural Network." International Journal of Computer and Communication Engineering 3, no. 3 (2014): 212–15. http://dx.doi.org/10.7763/ijcce.2014.v3.322.

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11

Gupta, Sakshi. "Concrete Mix Design Using Artificial Neural Network." Journal on Today's Ideas-Tomorrow's Technologies 1, no. 1 (2013): 29–43. http://dx.doi.org/10.15415/jotitt.2013.11003.

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12

KONOVALOV, S. "FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS." Digital Technologies 26 (2019): 36–46. http://dx.doi.org/10.33243/2313-7010-26-36-46.

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In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid e

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13

Li, Xiao Hu, Feng Xu, Jin Hua Zhang, and Su Nan Wang. "A New Small-World Neural Network with its Performance on Fault Tolerance." Advanced Materials Research 629 (December 2012): 719–24. http://dx.doi.org/10.4028/www.scientific.net/amr.629.719.

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Many artificial neural networks are the simple simulation of brain neural network’s architecture and function. However, how to rebuild new artificial neural network which architecture is similar to biological neural networks is worth studying. In this study, a new multilayer feedforward small-world neural network is presented using the results form research on complex network. Firstly, a new multilayer feedforward small-world neural network which relies on the rewiring probability heavily is built up on the basis of the construction ideology of Watts-Strogatz networks model and community struc

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14

Shih, Yi-Fan, Yu-Ren Wang, Shih-Shian Wei, and Chin-Wen Chen. "Improving Non-Destructive Test Results Using Artificial Neural NetworksImproving Non-Destructive Test Results Using Artificial Neural Networks." International Journal of Machine Learning and Computing 5, no. 6 (2015): 480–83. http://dx.doi.org/10.18178/ijmlc.2015.5.6.557.

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15

Abdul Khader Jilani, Saudagar, and Syed Abdul Sattar. "JPEG Image Compression Using FPGA with Artificial Neural Networks." International Journal of Engineering and Technology 2, no. 3 (2010): 252–57. http://dx.doi.org/10.7763/ijet.2010.v2.129.

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16

Fernández, Ana Isabel Velasco, Ricardo José Rejas Muslera, Juan Padilla Fernández-Vega, and María Isabel Cepeda González. "Bankruptcy Prediction Models: A Bet on Artificial Neural Networks." Global Journal For Research Analysis 3, no. 2 (2012): 48–50. http://dx.doi.org/10.15373/22778160/february2014/16.

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17

Jeong, Yeongsang, and Sungshin Kim. "A Study of Arrow Performance using Artificial Neural Network." Journal of Korean Institute of Intelligent Systems 24, no. 5 (2014): 548–53. http://dx.doi.org/10.5391/jkiis.2014.24.5.548.

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18

Singh, Vikash, Samreen Bano, and Anand Kumar Yadav Dr Sabih Ahmad. "Feasibility of Artificial Neural Network in Civil Engineering." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 724–28. http://dx.doi.org/10.31142/ijtsrd22985.

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19

Zainal, Azavitra. "pH Neutralization Plant Optimization Using Artificial Neural Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1466–72. http://dx.doi.org/10.5373/jardcs/v12sp4/20201625.

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20

Fahd, Syed Muhammed. "Artificial Neural Network Model for Friction Stir Processing." International Journal of Engineering Research 3, no. 6 (2014): 396–97. http://dx.doi.org/10.17950/ijer/v3s6/606.

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21

Tun, Myat Thida. "Alphabet Recognition System Based on Artifical Neural Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 469–73. http://dx.doi.org/10.31142/ijtsrd15854.

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22

Taiwo Amoo, Oseni, and Bloodless Dzwairo. "Trend analysis and artificial neural networks forecasting for rainfall prediction." Environmental Economics 7, no. 4 (2016): 149–60. http://dx.doi.org/10.21511/ee.07(4-1).2016.07.

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The growing severe damage and sustained nature of the recent drought in some parts of the globe have resulted in the need to conduct studies relating to rainfall forecasting and effective integrated water resources management. This research examines and analyzes the use and ability of artificial neural networks (ANNs) in forecasting future trends of rainfall indices for Mkomazi Basin, South Africa. The approach used the theory of back propagation neural networks, after which a model was developed to predict the future rainfall occurrence using an environmental fed variable for closing up. Once

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23

Poonsilp, Kittiya. "Imaging Analysis of Steel Rod Shrapnel Using Artificial Neural Networks." International Journal of Machine Learning and Computing 10, no. 5 (2020): 707–13. http://dx.doi.org/10.18178/ijmlc.2020.10.5.994.

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24

T., Lakshmi Praveena. "AD-CANN: ASD Detection Using Cooccurrences based Artificial Neural Networks." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (2020): 579–87. http://dx.doi.org/10.5373/jardcs/v12i3/20201226.

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25

Teh, Chee Siong, Ming Leong Yii, and Chwen Jen Chen. "Dimensional Reduction and Data Visualization Using Hybrid Artificial Neural Networks." International Journal of Machine Learning and Computing 5, no. 5 (2015): 420–25. http://dx.doi.org/10.7763/ijmlc.2015.v5.545.

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26

Dinary, M., Abou-Hashema M. El-Sayed, Abdel Badie Sharkawy, and G. Abouelmagd. "A Control Scheme for Industrial Robots Using Artificial Neural Networks." International Journal of Materials, Mechanics and Manufacturing 3, no. 2 (2015): 80–85. http://dx.doi.org/10.7763/ijmmm.2015.v3.171.

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27

Walczak, Steven. "Artificial Neural Network Research in Online Social Networks." International Journal of Virtual Communities and Social Networking 10, no. 4 (2018): 1–15. http://dx.doi.org/10.4018/ijvcsn.2018100101.

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Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the

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28

Ermakov, B. S. "THE IMPACT OF ARTIFICIAL NEURAL NETWORK’S STRUCTURE ON ITS EFFICIENCY FOR FINANCIAL INDICATORS FORECASTING." System analysis and logistics 2, no. 28 (2021): 44–51. http://dx.doi.org/10.31799/2077-5687-2021-2-44-51.

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The article investigates the influence of artificial neural network’s structure on the results, with example of multlayer perceptron for forecasting some of the financial indicators. Multiple tests were made with various networks structures: different numbers of hidden layers and different numbers of neurons in these layers. Based on tests results, the increase of network’s size is effective to a certain extent, but at some point the further size increase is unreasonable. Also, the test results demonstrate that overfitting problem for multilayer perceptron is not as crucial as for the other ma

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29

Kim, Sang-Ho, Jae-Hwan Ryu, Byeong-Hyeon Lee, and Deok-Hwan Kim. "Human Identification using EMG Signal based Artificial Neural Network." Journal of the Institute of Electronics and Information Engineers 53, no. 4 (2016): 142–48. http://dx.doi.org/10.5573/ieie.2016.53.4.142.

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30

Ziemke, Tom. "Radar Image Segmentation Using Self-Adapting Recurrent Networks." International Journal of Neural Systems 08, no. 01 (1997): 47–54. http://dx.doi.org/10.1142/s0129065797000070.

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This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn t

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31

Tun, Myat Thida. "Myanmar Alphabet Recognition System Based on Artificial Neural Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 1343–48. http://dx.doi.org/10.31142/ijtsrd17054.

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32

Gökgöz, Fazil, and Fahrettin Filiz. "Electricity price forecasting in Turkey with artificial neural network models." Investment Management and Financial Innovations 13, no. 3 (2016): 150–58. http://dx.doi.org/10.21511/imfi.13(3-1).2016.01.

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The electricity market has experienced significant changes towards deregulation with the aim of improving economic efficiency. The electricity pricing is a major consideration for consumers and generation companies in deregulated electric markets, so that offering the right price for electricity has become more important. Various methods and ideas have been tried for electricity price forecasting. Artificial neural networks have received much attention with its nonlinear property and many papers have reported successful experiments with them. This paper introduces artificial neural network mod

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33

IATAN, Iuliana. "DEALING THE NONLINEARITY ASSOCIATED WITH THE DATA USING ARTIFICIAL NEURAL NETWORKS." Review of the Air Force Academy 15, no. 2 (2017): 15–22. http://dx.doi.org/10.19062/1842-9238.2017.15.2.2.

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34

Mustafa, Hassan M. H., Mohamed Abdulrahman, Shahinaz Mahmoud, Ayesha Sohail, and Nosipho Dladlu. "On Enhancement of Reading Brain Performance Using Artificial Neural Networks’ Modeling." International Journal of Signal Processing Systems 4, no. 3 (2016): 177–84. http://dx.doi.org/10.18178/ijsps.4.3.177-184.

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35

Debunov, Leonid. "Modeling company's financial sustainability with the use of artificial neural networks." Economy and forecasting 2019, no. 3 (2019): 76–93. http://dx.doi.org/10.15407/econforecast2019.03.076.

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36

Çelik, Ufuk, Nilüfer Yurtay, and Ziynet Yılmaz. "Migraine diagnosis by using artificial neural networks and decision tree techniques." AJIT-e: Online Academic Journal of Information Technology 5, no. 14 (2014): 79–90. http://dx.doi.org/10.5824/1309-1581.2014.1.005.x.

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37

Devi, Kharibam Jilenkumari, and Khelchandra Thongam. "A Survey of Automatic Speaker Recognition System Using Artificial Neural Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 453–56. http://dx.doi.org/10.5373/jardcs/v11sp10/20192832.

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38

JORGENSEN, THOMAS D., BARRY P. HAYNES, and CHARLOTTE C. F. NORLUND. "PRUNING ARTIFICIAL NEURAL NETWORKS USING NEURAL COMPLEXITY MEASURES." International Journal of Neural Systems 18, no. 05 (2008): 389–403. http://dx.doi.org/10.1142/s012906570800166x.

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This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the

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39

Jiang, Yiming, Chenguang Yang, Shi-lu Dai, and Beibei Ren. "Deterministic learning enhanced neutral network control of unmanned helicopter." International Journal of Advanced Robotic Systems 13, no. 6 (2016): 172988141667111. http://dx.doi.org/10.1177/1729881416671118.

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In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid wit

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40

Sultana, Zakia, Md Ashikur Rahman Khan, and Nusrat Jahan. "Early Breast Cancer Detection Utilizing Artificial Neural Network." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 18 (March 18, 2021): 32–42. http://dx.doi.org/10.37394/23208.2021.18.4.

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Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs

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41

Akdeniz, Esra, Erol Egrioglu, Eren Bas, and Ufuk Yolcu. "An ARMA Type Pi-Sigma Artificial Neural Network for Nonlinear Time Series Forecasting." Journal of Artificial Intelligence and Soft Computing Research 8, no. 2 (2018): 121–32. http://dx.doi.org/10.1515/jaiscr-2018-0009.

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Abstract Real-life time series have complex and non-linear structures. Artificial Neural Networks have been frequently used in the literature to analyze non-linear time series. High order artificial neural networks, in view of other artificial neural network types, are more adaptable to the data because of their expandable model order. In this paper, a new recurrent architecture for Pi-Sigma artificial neural networks is proposed. A learning algorithm based on particle swarm optimization is also used as a tool for the training of the proposed neural network. The proposed new high order artific

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42

Kartini, Unit Three, and Chao Rong Chen. "K-NN Decomposition Artificial Neural Network Models for Global Solar." International Journal of Computer and Electrical Engineering 9, no. 1 (2017): 341–59. http://dx.doi.org/10.17706/ijcee.2017.9.1.351-359.

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43

Malisuwan, S., N. Suriyakrai, and N. Madan. "Radio Spectrum Valuation by Applying the Artificial Neural Network Model." Journal of Advances in Computer Networks 4, no. 1 (2016): 19–23. http://dx.doi.org/10.18178/jacn.2016.4.1.197.

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44

Venkatesan, Murugan, S. Gokul, and Dr R. Indra Gandhi. "Artificial Neural Network Based Automated Escalating Tools for Crises Navigation." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 350–54. http://dx.doi.org/10.31142/ijtsrd10900.

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45

Bui, Ngoc Tam, and Hiroshi Hasegawa. "Training Artificial Neural Network Using Modification of Differential Evolution Algorithm." International Journal of Machine Learning and Computing 5, no. 1 (2015): 1–6. http://dx.doi.org/10.7763/ijmlc.2015.v5.473.

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46

Sagala, Noviyanti, Cynthia Hayat, and Frahselia Tandipuang. "Identification of fat-soluble vitamins deficiency using artificial neural network." Jurnal Teknologi dan Sistem Komputer 8, no. 1 (2019): 6–11. http://dx.doi.org/10.14710/jtsiskom.8.1.2020.6-11.

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The fat-soluble vitamins (A, D, E, K) deficiency remain frequent universally and may have consequential adverse resultants and causing slow appearance symptoms gradually and intensify over time. The vitamin deficiency detection requires an experienced physician to notice the symptoms and to review a blood test’s result (high-priced). This research aims to create an early detection system of fat-soluble vitamin deficiency using artificial neural network Back-propagation. The method was implemented by converting deficiency symptoms data into training data to be used to produce a weight of ANN an

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47

Dhumale, Dr R. B., Anshuman Namdeo, Sanket Narharshettiwar, and Rushikesh Nagare. "Hardware Implementation of Artificial Neural Network for Buck Boost Converter." Journal of Advanced Research in Dynamical and Control Systems 11, no. 9 (2019): 32–39. http://dx.doi.org/10.5373/jardcs/v11i9/20192831.

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48

Gupta, Saroj Kumar, M. V. Jagannatha Reddy, and A. Nanda Kumar. "Possibilistic Clustering Adaptive Smoothing Bilateral Filter Using Artificial Neural Network." International Journal of Engineering and Technology 2, no. 6 (2010): 499–503. http://dx.doi.org/10.7763/ijet.2010.v2.171.

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49

Faek, Fatima K. "Digital Modulation Classification Using Wavelet Transform and Artificial Neural Network." Journal of Zankoy Sulaimani - Part A 13, no. 1 (2009): 59–70. http://dx.doi.org/10.17656/jzs.10211.

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50

Yang, Judy X., Lily D. Li, and Mohammad G. Rasul. "A Conceptual Artificial Neural Network Model in Warehouse Receiving Management." International Journal of Machine Learning and Computing 11, no. 2 (2021): 130–36. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1025.

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The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel valu

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