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Artículos de revistas sobre el tema "Neural networks (Computer science)"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 29 de julio de 2024

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1

Mijwel, Maad M., Adam Esen, and Aysar Shamil. "Overview of Neural Networks." Babylonian Journal of Machine Learning 2023 (August 11, 2023): 42–45. http://dx.doi.org/10.58496/bjml/2023/008.

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Since it was confirmed and verified that the human nervous system consists of individual cells, which were later called neurons, and it was discovered that these cells connect with each other to form an extensive communication network, a large number of possibilities have been opened for application in multiple disciplines in areas of knowledge. Neural Networks are created to perform tasks such as pattern recognition, classification, regression, and many other functions that serve humans and are an essential component in the field of machine learning and artificial intelligence. In computer sc

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2

Cottrell, G. W. "COMPUTER SCIENCE: New Life for Neural Networks." Science 313, no. 5786 (July 28, 2006): 454–55. http://dx.doi.org/10.1126/science.1129813.

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3

Li, Xiao Guang. "Research on the Development and Applications of Artificial Neural Networks." Applied Mechanics and Materials 556-562 (May 2014): 6011–14. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.6011.

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Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other

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4

Schöneburg, E. "Neural networks hunt computer viruses." Neurocomputing 2, no. 5-6 (July 1991): 243–48. http://dx.doi.org/10.1016/0925-2312(91)90027-9.

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5

Turega, M. A. "Neural Networks." Computer Journal 35, no. 3 (June 1, 1992): 290. http://dx.doi.org/10.1093/comjnl/35.3.290.

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6

Widrow, Bernard, David E. Rumelhart, and Michael A. Lehr. "Neural networks." Communications of the ACM 37, no. 3 (March 1994): 93–105. http://dx.doi.org/10.1145/175247.175257.

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7

Begum, Afsana, Md Masiur Rahman, and Sohana Jahan. "Medical diagnosis using artificial neural networks." Mathematics in Applied Sciences and Engineering 5, no. 2 (June 4, 2024): 149–64. http://dx.doi.org/10.5206/mase/17138.

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Medical diagnosis using Artificial Neural Networks (ANN) and computer-aided diagnosis with deep learning is currently a very active research area in medical science. In recent years, for medical diagnosis, neural network models are broadly considered since they are ideal for recognizing different kinds of diseases including autism, cancer, tumor lung infection, etc. It is evident that early diagnosis of any disease is vital for successful treatment and improved survival rates. In this research, five neural networks, Multilayer neural network (MLNN), Probabilistic neural network (PNN), Learning

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8

Yen, Gary G., and Haiming Lu. "Hierarchical Rank Density Genetic Algorithm for Radial-Basis Function Neural Network Design." International Journal of Computational Intelligence and Applications 03, no. 03 (September 2003): 213–32. http://dx.doi.org/10.1142/s1469026803000975.

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In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A Hierarchical Rank Density Genetic Algorithm (HRDGA) is used to evolve the neural network's topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the confliction between the traini

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9

Cavallaro, Lucia, Ovidiu Bagdasar, Pasquale De Meo, Giacomo Fiumara, and Antonio Liotta. "Artificial neural networks training acceleration through network science strategies." Soft Computing 24, no. 23 (September 9, 2020): 17787–95. http://dx.doi.org/10.1007/s00500-020-05302-y.

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AbstractThe development of deep learning has led to a dramatic increase in the number of applications of artificial intelligence. However, the training of deeper neural networks for stable and accurate models translates into artificial neural networks (ANNs) that become unmanageable as the number of features increases. This work extends our earlier study where we explored the acceleration effects obtained by enforcing, in turn, scale freeness, small worldness, and sparsity during the ANN training process. The efficiency of that approach was confirmed by recent studies (conducted independently)

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10

Kumar, G. Prem, and P. Venkataram. "Network restoration using recurrent neural networks." International Journal of Network Management 8, no. 5 (September 1998): 264–73. http://dx.doi.org/10.1002/(sici)1099-1190(199809/10)8:5<264::aid-nem298>3.0.co;2-o.

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11

Oscar, Sánchez Tinoco, Santos Saybe Etasmo, and Islam Arbievich Magomedov. "Convolutional Neural Networks Applied to the Performance of a Coffee Tree." E3S Web of Conferences 537 (2024): 08015. http://dx.doi.org/10.1051/e3sconf/202453708015.

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The use of artificial neural networks has been a significant advancement in the field of computer science, as it supports various fields of study in many traditional sciences. Over the years, numerous models of artificial neural networks have been tested, serving as cornerstones in scientific research. These models have been a starting point for integrating artificial neural networks as support for the detection and analysis processes in situations of risk in food plantations. In this research, the aim is to highlight previous works that have emerged to address the problem of diseases in crops

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12

SIEGELMANN, HAVA T. "ON NIL: THE SOFTWARE CONSTRUCTOR OF NEURAL NETWORKS." Parallel Processing Letters 06, no. 04 (December 1996): 575–82. http://dx.doi.org/10.1142/s0129626496000510.

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Analog recurrent neural networks have attracted much attention lately as powerful tools of automatic learning. However, they are not as popular in industry as should be justified by their usefulness. The lack of any programming tool for networks. and their vague internal representation, leave the networks for the use of experts only. We propose a way to make the neural networks friendly to users by formally defining a high level language, called Neural Information Processing Programming Langage, which is rich enough to express any computer algorithm or rule-based system. We show how to compile

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13

Yashchenko, V. A. "Multidimensional neural growing networks and computer intelligence." Cybernetics and Systems Analysis 30, no. 4 (July 1994): 505–17. http://dx.doi.org/10.1007/bf02366560.

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14

Cerf, Vinton G. "On neural networks." Communications of the ACM 61, no. 7 (June 25, 2018): 7. http://dx.doi.org/10.1145/3224195.

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15

Wong, Eugene. "Stochastic neural networks." Algorithmica 6, no. 1-6 (June 1991): 466–78. http://dx.doi.org/10.1007/bf01759054.

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16

Saiful, Muhammad, Lalu Muhammad Samsu, and Fathurrahman Fathurrahman. "Sistem Deteksi Infeksi COVID-19 Pada Hasil X-Ray Rontgen menggunakan Algoritma Convolutional Neural Network (CNN)." Infotek : Jurnal Informatika dan Teknologi 4, no. 2 (July 31, 2021): 217–27. http://dx.doi.org/10.29408/jit.v4i2.3582.

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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

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17

Xu, Xianfeng, Xinwei Wang, Weilong Luo, Hao Wang, and Yuting Sun. "Efficient Computer-Generated Holography Based on Mixed Linear Convolutional Neural Networks." Applied Sciences 12, no. 9 (April 21, 2022): 4177. http://dx.doi.org/10.3390/app12094177.

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Imaging based on computer-generated holography using traditional methods has the problems of poor quality and long calculation cycles. However, recently, the development of deep learning has provided new ideas for this problem. Here, an efficient computer-generated holography (ECGH) method is proposed for computational holographic imaging. This method can be used for computational holographic imaging based on mixed linear convolutional neural networks (MLCNN). By introducing fully connected layers in the network, the suggested design is more powerful and efficient at information mining and inf

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18

Et. al., K. P. Moholkar,. "Visual Question Answering using Convolutional Neural Networks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 170–75. http://dx.doi.org/10.17762/turcomat.v12i1s.1602.

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The ability of a computer system to be able to understand surroundings and elements and to think like a human being to process the information has always been the major point of focus in the field of Computer Science. One of the ways to achieve this artificial intelligence is Visual Question Answering. Visual Question Answering (VQA) is a trained system which can answer the questions associated to a given image in Natural Language. VQA is a generalized system which can be used in any image-based scenario with adequate training on the relevant data. This is achieved with the help of Neural Netw

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19

Reddy*, M. Venkata Krishna, and Pradeep S. "Envision Foundational of Convolution Neural Network." International Journal of Innovative Technology and Exploring Engineering 10, no. 6 (April 30, 2021): 54–60. http://dx.doi.org/10.35940/ijitee.f8804.0410621.

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1. Bilal, A. Jourabloo, M. Ye, X. Liu, and L. Ren. Do Convolutional Neural Networks Learn Class Hierarchy? IEEE Transactions on Visualization and Computer Graphics, 24(1):152–162, Jan. 2018. 2. M. Carney, B. Webster, I. Alvarado, K. Phillips, N. Howell, J. Griffith, J. Jongejan, A. Pitaru, and A. Chen. Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20. ACM, Honolulu, HI, USA, 2020. 3. A. Karpathy. CS231n Convolutional Neural Networks for Visual Recognition

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20

Chartuni, Andrés, and José Márquez. "Multi-Classifier of DDoS Attacks in Computer Networks Built on Neural Networks." Applied Sciences 11, no. 22 (November 11, 2021): 10609. http://dx.doi.org/10.3390/app112210609.

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The great commitment in different areas of computer science for the study of computer networks used to fulfill specific and major business tasks has generated a need for their maintenance and optimal operability. Distributed denial of service (DDoS) is a frequent threat to computer networks because of its disruption to the services they cause. This disruption results in the instability and/or inoperability of the network. There are different classes of DDoS attacks, each with a different mode of operation, so detecting them has become a difficult task for network monitoring and control systems

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21

Zaharakis, Ioannis D., and Achilles D. Kameas. "Modeling spiking neural networks." Theoretical Computer Science 395, no. 1 (April 2008): 57–76. http://dx.doi.org/10.1016/j.tcs.2007.11.002.

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22

HUANG, WEI, KIN KEUNG LAI, YOSHITERU NAKAMORI, SHOUYANG WANG, and LEAN YU. "NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING." International Journal of Information Technology & Decision Making 06, no. 01 (March 2007): 113–40. http://dx.doi.org/10.1142/s021962200700237x.

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Artificial neural networks (ANNs) have been widely applied to finance and economic forecasting as a powerful modeling technique. By reviewing the related literature, we discuss the input variables, type of neural network models, performance comparisons for the prediction of foreign exchange rates, stock market index and economic growth. Economic fundamentals are important in driving exchange rates, stock market index price and economic growth. Most neural network inputs for exchange rate prediction are univariate, while those for stock market index prices and economic growth predictions are mu

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23

Fogel, D. B., L. J. Fogel, and V. W. Porto. "Evolving neural networks." Biological Cybernetics 63, no. 6 (October 1990): 487–93. http://dx.doi.org/10.1007/bf00199581.

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24

Kovačič, M. "Markovian neural networks." Biological Cybernetics 64, no. 4 (February 1991): 337–42. http://dx.doi.org/10.1007/bf00199598.

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25

Kononenko, I. "Bayesian neural networks." Biological Cybernetics 61, no. 5 (September 1989): 361–70. http://dx.doi.org/10.1007/bf00200801.

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26

Guidotti, Dario. "Verification and Repair of Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15714–15. http://dx.doi.org/10.1609/aaai.v35i18.17854.

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Neural Networks (NNs) are popular machine learning models which have found successful application in many different domains across computer science. However, it is hard to provide any formal guarantee on the behaviour of neural networks and therefore their reliability is still in doubt, especially concerning their deployment in safety and security-critical applications. Verification emerged as a promising solution to address some of these problems. In the following, I will present some of my recent efforts in verifying NNs.

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27

Xue-Feng Jiang, Xue-Feng Jiang, Ken-Cheng Xue-Feng Jiang, and Zhi-De Li Ken-Cheng. "Retinal OCT Image Classification Based on CNN-RNN Unified Neural Networks." 電腦學刊 35, no. 1 (February 2024): 255–66. http://dx.doi.org/10.53106/199115992024023501021.

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<p>Computer-aided diagnosis of retinopathy is a hot research topic in the field of medical image classification, where optical coherence tomography (OCT) is an important basis for the diagnosis of ophthalmic diseases. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, two publicly available retinal OCT image datasets are integrated and screened. Then,

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28

Armenta, Marco, and Pierre-Marc Jodoin. "The Representation Theory of Neural Networks." Mathematics 9, no. 24 (December 13, 2021): 3216. http://dx.doi.org/10.3390/math9243216.

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In this work, we show that neural networks can be represented via the mathematical theory of quiver representations. More specifically, we prove that a neural network is a quiver representation with activation functions, a mathematical object that we represent using a network quiver. Furthermore, we show that network quivers gently adapt to common neural network concepts such as fully connected layers, convolution operations, residual connections, batch normalization, pooling operations and even randomly wired neural networks. We show that this mathematical representation is by no means an app

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29

Hou, Xiaohui, Lei Huang, and Xuefei Li. "An Effective Method to Evaluate the Scientific Research Projects." Foundations of Computing and Decision Sciences 39, no. 3 (July 1, 2014): 175–88. http://dx.doi.org/10.2478/fcds-2014-0010.

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Abstract The evaluation of the scientific research projects is an important procedure before the scientific research projects are approved. The BP neural network and linear neural network are adopted to evaluate the scientific research projects in this paper. The evaluation index system with 12 indexes is set up. The basic principle of the neural network is analyzed and then the BP neural network and linear neural network models are constructed and the output error function of the neural networks is introduced. The Matlab software is applied to set the parameters and calculate the neural netwo

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30

Zhou, Zhi-Hua. "Rule extraction: Using neural networks or for neural networks?" Journal of Computer Science and Technology 19, no. 2 (March 2004): 249–53. http://dx.doi.org/10.1007/bf02944803.

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31

Kovalnogov, Vladislav N., Ruslan V. Fedorov, Denis A. Demidov, Malyoshina A. Malyoshina, Theodore E. Simos, Spyridon D. Mourtas, and Vasilios N. Katsikis. "Computing quaternion matrix pseudoinverse with zeroing neural networks." AIMS Mathematics 8, no. 10 (2023): 22875–95. http://dx.doi.org/10.3934/math.20231164.

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<abstract><p>In recent years, it has become essential to compute the time-varying quaternion (TVQ) matrix Moore-Penrose inverse (MP-inverse or pseudoinverse) to solve time-varying issues in a range of disciplines, including engineering, physics and computer science. This study examines the problem of computing the TVQ matrix MP-inverse using the zeroing neural network (ZNN) approach, which is nowadays considered a cutting edge technique. As a consequence, three new ZNN models are introduced for computing the TVQ matrix MP-inverse in the literature for the first time. Particularly,

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32

HAYASHI, YOICHI. "NEURAL NETWORK RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW." International Journal of Computational Intelligence and Applications 12, no. 04 (December 2013): 1340006. http://dx.doi.org/10.1142/s1469026813400063.

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This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situ

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33

Tridgell, Stephen, Martin Kumm, Martin Hardieck, David Boland, Duncan Moss, Peter Zipf, and Philip H. W. Leong. "Unrolling Ternary Neural Networks." ACM Transactions on Reconfigurable Technology and Systems 12, no. 4 (November 27, 2019): 1–23. http://dx.doi.org/10.1145/3359983.

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34

Asakawa, Kazuo, and Hideyuki Takagi. "Neural networks in Japan." Communications of the ACM 37, no. 3 (March 1994): 106–12. http://dx.doi.org/10.1145/175247.175258.

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35

Ma, Gang-Feng, Xu-Hua Yang, Yue Tong, and Yanbo Zhou. "Graph neural networks for preference social recommendation." PeerJ Computer Science 9 (May 19, 2023): e1393. http://dx.doi.org/10.7717/peerj-cs.1393.

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Social recommendation aims to improve the performance of recommendation systems with additional social network information. In the state of art, there are two major problems in applying graph neural networks (GNNs) to social recommendation: (i) Social network is connected through social relationships, not item preferences, i.e., there may be connected users with completely different preferences, and (ii) the user representation of current graph neural network layer of social network and user-item interaction network is the output of the mixed user representation of the previous layer, which ca

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36

Purushothaman, G., and N. B. Karayiannis. "Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks." IEEE Transactions on Neural Networks 8, no. 3 (May 1997): 679–93. http://dx.doi.org/10.1109/72.572106.

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37

Qian, Wei, and Yijie Wang. "Analyzing E-Commerce Market Data Using Deep Learning Techniques to Predict Industry Trends." Journal of Organizational and End User Computing 36, no. 1 (April 9, 2024): 1–22. http://dx.doi.org/10.4018/joeuc.342093.

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Faced with challenges in sales predicting research, this article combines the capabilities of deep learning algorithms in handling complex tasks and unstructured data. Through analyzing consumer behavior, it selects factors influencing sales, including images, prices and discounts, and historical sales, as input variables for the model. Three different types of neural network models-fully connected neural networks, convolutional neural networks, and recurrent neural networks-are employed to process structured data, image data, and sales sequence data, respectively. This forms a deep neural net

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38

Gupta, Rajiv. "Research Paper on Artificial Intelligence." International Journal of Engineering and Computer Science 12, no. 02 (February 18, 2023): 25654–0656. http://dx.doi.org/10.18535/ijecs/v12i02.4720.

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This branch of computer science is concerned with making computers behave like humans. Artificial intelligence includes game playing, expert systems, neural networks, natural language, and robotics. Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and

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39

Aiken, William, Hyoungshick Kim, Simon Woo, and Jungwoo Ryoo. "Neural network laundering: Removing black-box backdoor watermarks from deep neural networks." Computers & Security 106 (July 2021): 102277. http://dx.doi.org/10.1016/j.cose.2021.102277.

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40

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 (November 28, 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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41

Brito Carcaño, Jesús E., Stéphane Cuenat, Belal Ahmad, Patrick Sandoz, Raphaël Couturier, Guillaume Laurent, and Maxime Jacquot. "Digital holographic microscopy applied to 3D computer microvision by using deep neural networks." EPJ Web of Conferences 287 (2023): 13011. http://dx.doi.org/10.1051/epjconf/202328713011.

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Deep neural networks are increasingly applied in many branches of applied science such as computer vision and image processing by increasing performances of instruments. Different deep architectures such as convolutional neural networks or Vision Transformers can be used in advanced coherent imaging techniques such as digital holography to extract various metrics such as autofocusing reconstruction distance or 3D position determination in order to target automated microscopy or real-time phase image restitution. Deep neural networks can be trained with both datasets simulated and experimental

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42

Abrar, Muhammad Fauzan, and Vina Ayumi. "Aircraft Recognition in Remote Sensing Images Based on Artificial Neural Networks." Journal of Computer Science and Engineering (JCSE) 4, no. 2 (November 20, 2023): 97–112. http://dx.doi.org/10.36596/jcse.v4i2.381.

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Computer Vision (CV) is a field of Artificial Intelligence (AI) that enables computers and systems to obtain data from images, recordings and other visual information sources. Image Recognition, a subcategory of Computer Vision, addresses a bunch of strategies for perceiving and taking apart pictures to engage the automation of a specific task. It is fit for perceiving places, people, objects and various types of parts inside an image, and reaching deductions from them by analyzing them. With these kinds of utilities it is a no-brainer that Computer Vision has its use cases in the military wor

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43

Siegelmann, Hava T., and Eduardo D. Sontag. "Analog computation via neural networks." Theoretical Computer Science 131, no. 2 (September 1994): 331–60. http://dx.doi.org/10.1016/0304-3975(94)90178-3.

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44

Neruda, M., and R. Neruda. "To contemplate quantitative and qualitative water features by neural networks method." Plant, Soil and Environment 48, No. 7 (December 21, 2011): 322–26. http://dx.doi.org/10.17221/4375-pse.

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An application deals with calibration of neural model and Fourier series model for Ploučnice catchment. This approach has an advantage, that the network choice is independent of other example&rsquo;s parameters. Each networks, and their variants (different units and hidden layer number) can be connected in as a&nbsp;black box and tested independently. A&nbsp;Stuttgart neural simulator SNNS and a&nbsp;multiagent hybrid system Bang2 developed in Institute of Computer Science, AS CR have been used for testing. A&nbsp;perceptron network has been constructed, which was trained b

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45

MAINZER, KLAUS. "CELLULAR NEURAL NETWORKS AND VISUAL COMPUTING." International Journal of Bifurcation and Chaos 13, no. 01 (January 2003): 1–6. http://dx.doi.org/10.1142/s0218127403006534.

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Brain-like information processing has become a challenge to modern computer science and chip technology. The CNN (Cellular Neural Network) Universal Chip is the first fully programmable industrial-sized brain-like stored-program dynamic array computer which dates back to an invention of Leon O. Chua and Lin Yang in Berkeley in 1988. Since then, many papers have been written on the mathematical foundations and technical applications of CNN chips. They are already used to model artificial, physical, chemical, as well as living biological systems. CNN is now a new computing paradigm of interdisci

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46

Kramer, M. A. "Autoassociative neural networks." Computers & Chemical Engineering 16, no. 4 (April 1992): 313–28. http://dx.doi.org/10.1016/0098-1354(92)80051-a.

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47

Humphries, Mark D. "Dynamical networks: Finding, measuring, and tracking neural population activity using network science." Network Neuroscience 1, no. 4 (December 2017): 324–38. http://dx.doi.org/10.1162/netn_a_00020.

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Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can v

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48

Uteuliyeva, Malika, Abylay Zhumekenov, Rustem Takhanov, Zhenisbek Assylbekov, Alejandro J. Castro, and Olzhas Kabdolov. "Fourier neural networks: A comparative study." Intelligent Data Analysis 24, no. 5 (September 30, 2020): 1107–20. http://dx.doi.org/10.3233/ida-195050.

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We review neural network architectures which were motivated by Fourier series and integrals and which are referred to as Fourier neural networks. These networks are empirically evaluated in synthetic and real-world tasks. Neither of them outperforms the standard neural network with sigmoid activation function in the real-world tasks. All neural networks, both Fourier and the standard one, empirically demonstrate lower approximation error than the truncated Fourier series when it comes to approximation of a known function of multiple variables.

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49

Sovin, K. "Simulation of neural network for determination of technical state of internal state engine (ICE)." Sel'skohozjajstvennaja tehnika: obsluzhivanie i remont (Agricultural Machinery: Service and Repair), no. 1 (January 1, 2020): 74–78. http://dx.doi.org/10.33920/sel-10-2001-08.

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The article considers Artificial Intelligence (AI), which is science and technology, where the idea of modeling the processes of human thinking by using the capabilities of the computer is laid down. Machine learning, which can be imagined as a set of certain algorithms and methods that allow computers to be trained to obtain specific conclusions for the subject matter on the basis of available data. Neural networks are controlled by complex mathematical functions and solve problems of increased complexity using mathematical models built on analytical or other methods. Efficient neural network

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50

Gafurov, Artur M., and Oleg P. Yermolayev. "Automatic Gully Detection: Neural Networks and Computer Vision." Remote Sensing 12, no. 11 (May 28, 2020): 1743. http://dx.doi.org/10.3390/rs12111743.

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Transition from manual (visual) interpretation to fully automated gully detection is an important task for quantitative assessment of modern gully erosion, especially when it comes to large mapping areas. Existing approaches to semi-automated gully detection are based on either object-oriented selection based on multispectral images or gully selection based on a probabilistic model obtained using digital elevation models (DEMs). These approaches cannot be used for the assessment of gully erosion on the territory of the European part of Russia most affected by gully erosion due to the lack of n

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