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Journal articles on the topic 'Artificial neural networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

N, Vikram. "Artificial Neural Networks." International Journal of Research Publication and Reviews 4, no. 4 (2023): 4308–9. http://dx.doi.org/10.55248/gengpi.4.423.37858.

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2

Partridge, Derek, Sarah Rae, and Wen Jia Wang. "Artificial Neural Networks." Journal of the Royal Society of Medicine 92, no. 7 (1999): 385. http://dx.doi.org/10.1177/014107689909200723.

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3

Moore, K. L. "Artificial neural networks." IEEE Potentials 11, no. 1 (1992): 23–28. http://dx.doi.org/10.1109/45.127697.

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4

Dalton, J., and A. Deshmane. "Artificial neural networks." IEEE Potentials 10, no. 2 (1991): 33–36. http://dx.doi.org/10.1109/45.84097.

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5

Yoon, Youngohc, and Lynn Peterson. "Artificial neural networks." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 23, no. 1 (1992): 55–57. http://dx.doi.org/10.1145/134347.134362.

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6

MAKHOUL, JOHN. "Artificial Neural Networks." INVESTIGATIVE RADIOLOGY 25, no. 6 (1990): 748–50. http://dx.doi.org/10.1097/00004424-199006000-00027.

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7

Watt, R. C., E. S. Maslana, M. J. Navabi, and K. C. Mylrea. "ARTIFICIAL NEURAL NETWORKS." Anesthesiology 77, Supplement (1992): A506. http://dx.doi.org/10.1097/00000542-199209001-00506.

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8

Allinson, N. M. "Artificial Neural Networks." Electronics & Communications Engineering Journal 2, no. 6 (1990): 249. http://dx.doi.org/10.1049/ecej:19900051.

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9

Drew, Philip J., and John R. T. Monson. "Artificial neural networks." Surgery 127, no. 1 (2000): 3–11. http://dx.doi.org/10.1067/msy.2000.102173.

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10

McGuire, Tim. "Artificial neural networks." Computer Audit Update 1997, no. 7 (1997): 25–29. http://dx.doi.org/10.1016/s0960-2593(97)84495-3.

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11

Fulcher, John. "Artificial neural networks." Computer Standards & Interfaces 16, no. 3 (1994): 183–84. http://dx.doi.org/10.1016/0920-5489(94)90010-8.

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12

Buyse, Marc, and Pascal Piedbois. "Artificial neural networks." Lancet 350, no. 9085 (1997): 1175. http://dx.doi.org/10.1016/s0140-6736(05)63819-6.

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13

Drew, Philip, Leonardo Bottaci, Graeme S. Duthie, and John RT Monson. "Artificial neural networks." Lancet 350, no. 9085 (1997): 1175–76. http://dx.doi.org/10.1016/s0140-6736(05)63820-2.

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14

Piuri, Vincenzo, and Cesare Alippi. "Artificial neural networks." Journal of Systems Architecture 44, no. 8 (1998): 565–67. http://dx.doi.org/10.1016/s1383-7621(97)00063-5.

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15

Roy, Asim. "Artificial neural networks." ACM SIGKDD Explorations Newsletter 1, no. 2 (2000): 33–38. http://dx.doi.org/10.1145/846183.846192.

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16

Griffith, John. "Artificial Neural Networks:." Medical Decision Making 20, no. 2 (2000): 243–44. http://dx.doi.org/10.1177/0272989x0002000210.

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17

Hopfield, J. J. "Artificial neural networks." IEEE Circuits and Devices Magazine 4, no. 5 (1988): 3–10. http://dx.doi.org/10.1109/101.8118.

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18

Dayhoff, Judith E., and James M. DeLeo. "Artificial neural networks." Cancer 91, S8 (2001): 1615–35. http://dx.doi.org/10.1002/1097-0142(20010415)91:8+<1615::aid-cncr1175>3.0.co;2-l.

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19

Raol, Jitendra R., and Sunilkumar S. Mankame. "Artificial neural networks." Resonance 1, no. 2 (1996): 47–54. http://dx.doi.org/10.1007/bf02835699.

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20

Wang, Jun. "Artificial neural networks versus natural neural networks." Decision Support Systems 11, no. 5 (1994): 415–29. http://dx.doi.org/10.1016/0167-9236(94)90016-7.

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21

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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22

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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23

Qahtan, Helal, Ekram Osman, and Ebrahim Alhamidi. "Transmission Line Protection Using Artificial Neural Networks." International Journal of Science and Research (IJSR) 11, no. 11 (2022): 1404–8. http://dx.doi.org/10.21275/mr221123175925.

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24

Prithvi, P., and T. Kishore Kumar. "Speech Emotion Recognition using Artificial Neural Networks." International Journal of Scientific Engineering and Research 4, no. 5 (2016): 8–10. https://doi.org/10.70729/ijser15784.

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25

Yashchenko, V. O. "Artificial brain. Biological and artificial neural networks, advantages, disadvantages, and prospects for development." Mathematical machines and systems 2 (2023): 3–17. http://dx.doi.org/10.34121/1028-9763-2023-2-3-17.

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The article analyzes the problem of developing artificial neural networks within the framework of creating an artificial brain. The structure and functions of the biological brain are considered. The brain performs many functions such as controlling the organism, coordinating movements, processing information, memory, thinking, attention, and regulating emotional states, and consists of billions of neurons interconnected by a multitude of connections in a biological neural network. The structure and functions of biological neural networks are discussed, and their advantages and disadvantages a

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26

Matveeva, Nataliya. "ARTIFICIAL NEURAL NETWORKS IN MEDICAL DIAGNOSIS." System technologies 2, no. 133 (2021): 33–41. http://dx.doi.org/10.34185/1562-9945-2-133-2021-05.

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Artificial neural networks are finding many uses in the medical diagnosis application. The article examines cases of renopathy in type 2 diabetes. Data are symptoms of disease. The multilayer perceptron networks (MLP) is used as a classifier to distinguish between a sick and a healthy person. The results of applying artificial neural networks for diagnose renopathy based on selected symptoms show the network's ability to recognize to recognize diseases corresponding to human symptoms. Various parameters, structures and learning algorithms of neural networks were tested in the modeling process.

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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

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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29

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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30

Еськов, В. М., М. А. Филатов, Г. В. Газя, and Н. Ф. Стратан. "Artificial Intellect with Artificial Neural Networks." Успехи кибернетики / Russian Journal of Cybernetics, no. 3 (October 11, 2021): 44–52. http://dx.doi.org/10.51790/2712-9942-2021-2-3-6.

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В настоящее время не существует единого определения искусственного интеллекта. Требуется такая классификация задач, которые должны решать системы искусственного интеллекта. В сообщении дана классификация задач при использовании искусственных нейросетей (в виде получения субъективно и объективно новой информации). Показаны преимущества таких нейросетей (неалгоритмизируемые задачи) и показан класс систем (третьего типа — биосистем), которые принципиально не могут изучаться в рамках статистики (и всей науки). Для изучения таких биосистем (с уникальными выборками) предлагается использовать искусст

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31

Basu, Abhirup, Pinaki Bisaws, Sarmi Ghosh, and Debarshi Datta. "Reconfigurable Artificial Neural Networks." International Journal of Computer Applications 179, no. 6 (2017): 5–8. http://dx.doi.org/10.5120/ijca2017915961.

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32

Chen, Tin-Chih, Cheng-Li Liu, and Hong-Dar Lin. "Advanced Artificial Neural Networks." Algorithms 11, no. 7 (2018): 102. http://dx.doi.org/10.3390/a11070102.

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33

Xin Yao. "Evolving artificial neural networks." Proceedings of the IEEE 87, no. 9 (1999): 1423–47. http://dx.doi.org/10.1109/5.784219.

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34

YAO, XIN. "EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS." International Journal of Neural Systems 04, no. 03 (1993): 203–22. http://dx.doi.org/10.1142/s0129065793000171.

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Evolutionary artificial neural networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search procedures such as genetic algorithms (GAs). This paper distinguishes among three levels of evolution in EANNs, i.e. the evolution of connection weights, architectures and learning rules. It first reviews each kind of evolution in detail and then analyses major issues related to each kind of evolution. It is shown in the paper that although there is a lot of work on the evolution of connection weights and architectures, research on the evolution of lea

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35

Boutsinas, B., and M. N. Vrahatis. "Artificial nonmonotonic neural networks." Artificial Intelligence 132, no. 1 (2001): 1–38. http://dx.doi.org/10.1016/s0004-3702(01)00126-6.

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36

Yashchenko, V. O. "Neural-like growing networks in the development of general intelligence. Neural-like growing networks (P. II)." Mathematical machines and systems 1 (2023): 3–29. http://dx.doi.org/10.34121/1028-9763-2023-1-3-29.

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This article is devoted to the development of general artificial intelligence (AGI) based on a new type of neural networks – “neural-like growing networks”. It consists of two parts. The first one was published in N4, 2022, and describes an artificial neural-like element (artificial neuron) in terms of its functionality, which is as close as possible to a biological neuron. An artificial neural-like element is the main element in building neural-like growing networks. The second part deals with the structures and functions of artificial and natural neural networks. The paper proposes a new app

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37

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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38

Parks, Allen D. "Characterizing Computation in Artificial Neural Networks by their Diclique Covers and Forman-Ricci Curvatures." European Journal of Engineering and Technology Research 5, no. 2 (2020): 171–77. http://dx.doi.org/10.24018/ejeng.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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39

Aggarwal, R., and Yonghua Song. "Artificial neural networks in power systems. Part 2: Types of artificial neural networks." Power Engineering Journal 12, no. 1 (1998): 41–47. http://dx.doi.org/10.1049/pe:19980110.

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40

Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan, and Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance." Journal of Computing Research and Innovation 7, no. 1 (2022): 29–38. http://dx.doi.org/10.24191/jcrinn.v7i1.264.

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Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the

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41

VENHER, Evheniy, and Valentyn NIKULCHA. "FEATURES OF THE USE OF ARTIFICIAL NEURAL NETWORKS IN DIGITAL MARKETING." Herald of Khmelnytskyi National University. Economic sciences 316, no. 2 (2023): 312–18. http://dx.doi.org/10.31891/2307-5740-2023-316-2-49.

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This article examines the possibilities and advantages of using artificial neural networks in digital marketing, describes different types of neural networks. It was determined that neural networks are complex structures created from artificial neurons that can accept multiple inputs to obtain a single output. Artificial neural networks are a subsystem of machine learning, which in turn is a subsystem of artificial intelligence. In the processes of creating content for “sales funnels” and optimizing it to generate quality traffic, neural networks have long proven themselves as a worthy replace

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42

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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43

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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44

Çelik, Şenol. "MODELING AVOCADO PRODUCTION IN MEXICO WITH ARTIFICIAL NEURAL NETWORKS." Engineering and Technology Journal 07, no. 10 (2022): 1605–9. http://dx.doi.org/10.47191/etj/v7i10.08.

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An Artificial Neural Network (ANN) model was created in this research to estimate and predict the amount of avocado production in Mexico. In the development of the ANN model, the years that are time variable were used as the input parameter, and the avocado production amount (tons) was used as the output parameter. The research data includes avocado production in Mexico for 1961-2020 period. Mean Squared Error (MSE) and Mean Absolut Error (MAE) statistics were calculated using hyperbolic tangent activation function to determine the appropriate model. ANN model is a network architecture with 12

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45

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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46

Baranova, A., A. Astafiev, and E. Korchagin. "Artificial neural networks and translation." Bulletin of Science and Practice, no. 6 (June 15, 2017): 349–52. https://doi.org/10.5281/zenodo.808888.

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Relevance of the issue under study is enough high to worry and speculate about it, because technologies now are reaching the step, there it was not expected to see about 5–6 years ago. The purpose of the article is to understand, is it possible in the near future to make machine translator based Artificial Neural Network (ANN) able to remove live translators. The leading approach to the study is to compare statistical translation, translation by neural network and translation by professional live translator, to see how high quality of translation by machine interpreter. The article considers t

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47

Müller, V., and D. Nelles. "Artificial neural networks as static equivalent networks." IEE Proceedings - Generation, Transmission and Distribution 152, no. 1 (2005): 61. http://dx.doi.org/10.1049/ip-gtd:20040827.

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48

Aryan Rose, Aryan Rose. "How do Artificial Neural Networks Work." Journal of Advances in Science and Technology 20, no. 1 (2024): 172–77. http://dx.doi.org/10.29070/ttrkmm98.

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Artificial Neural Networks (ANNs) are computer models inspired by the structure and operation ofthe human brain. They comprise of linked nodes, called neurons, grouped in layers. Information passesacross these neurons, and each connection between neurons is connected with a weight denoting itssignificance. The network's learning process includes altering these weights depending on input data toincrease its capacity to generate correct predictions or classifications. During training, the networkcompares its output to the intended output, computes the error, and then applies optimization methods

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49

Kujawa, Sebastian, and Gniewko Niedbała. "Artificial Neural Networks in Agriculture." Agriculture 11, no. 6 (2021): 497. http://dx.doi.org/10.3390/agriculture11060497.

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Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple processing operations take place. The spectrum of neural networks application is very wide, and it also includes agriculture. Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management. Examples of their applications include: forecasting of production effects in agriculture on the

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50

De Groff, Dolores, and Perambur Neelakanta. "Faster Convergent Artificial Neural Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 1 (2018): 7126–32. http://dx.doi.org/10.24297/ijct.v17i1.7106.

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Proposed in this paper is a novel fast-convergence algorithm applied to neural networks (ANNs) with a learning rate based on the eigenvalues of the associated Hessian matrix of the input data. That is, the learning rate applied to the backpropagation algorithm changes dynamically with the input data used for training. The best choice of learning rate to converge to an accurate value quickly is derived. This newly proposed fast-convergence algorithm is applied to a traditional multilayer ANN architecture with feed-forward and backpropagation techniques. The proposed strategy is applied to vario

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