Best neural simultaneous approximation

Almurieb, Hawraa Abbas; Bhaya, Eman Samir

doi:10.11591/ijeecs.v20.i3.pp1584-1590

Cited by 5 publications

(5 citation statements)

References 30 publications

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“…Calculations have shown that in the short term (from the startup implementation stage), graphs 1, 2, and 3 coincide with an accuracy of δ = 10 -3 . Thus, the practical recommendations for using Volterra network modeling obtained in relation to the model (16)(17)(18)(19) can be applied in studying the effectiveness of innovative startup projects (sups) with a turbulent environment.…”

Section: Simulation Resultsmentioning

confidence: 99%

Development of a model for evaluating the effectiveness of innovative startups based on information cycles and using neural networks

Morozov

Kolomiiets²,

Olga³

2021

IJEECS

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An integrated approach to the creation and development of innovative startup projects in the field of information technology is considered. To conduct research, the authors proposed a model of information cycles of startup projects based on the creation of an information model of such projects. At the same time there are dynamic processes of changes in the parameters of the model, which are turbulent in nature and require the use of tools and methods of artificial intelligence for research. The key areas of knowledge of such influence are defined. The mathematical model of processes of management of development of IT startups on the basis of creation and development of a difficult IT product, taking into account influences of environments of the project is constructed, the basic characteristics are allocated and parameters are defined. To do this, the construction of predictive models is proposed to be carried out by modified Demarc trends, the method of self-organization and the neural network. The modeling of the main objective functions of the mathematical model of these processes is performed. The analysis of the received results is carried out and the conclusions are made.

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Section: Simulation Resultsmentioning

confidence: 99%

Development of a model for evaluating the effectiveness of innovative startups based on information cycles and using neural networks

Morozov

Kolomiiets²,

Olga³

2021

IJEECS

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“…These dense layers [10], [11], on the other hand, can only learn patterns that fall within their input feature space, while each convolution filter is capable of learning patterns locally (kernel space or region of interest) [12]- [14]. This implies the possibility of fragmenting input images into edges and textures foreasy learning; they are also more useful for classification rather than global patterns [15].…”

Section: Methods 21 Dense Layersmentioning

confidence: 99%

Classification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application

Ahmed

Hazry

Hussain³

2023

IJ-AI

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The classification of semantic segmentation-based unmanned aerial vehicle (UAV) application based on the datasets used in this work and the necessary data preprocessing steps for the optimization and implementation of the models are also involved. The optimization of the various models was done using the evaluation metrics and loss functions because deep neural networks (DNNs) are just about writing a cost function and its subsequent optimization. convolutional neural network (CNN) is a common type of artificial neural network (ANN) that has found application in numerous tasks, such as image and video recognition, image classification, recommender systems, financial time series, medical image analysis, and natural language processing. CNN is developed to automatically and adaptively learn spatial feature hierarchies via backpropagation using numerous building blocks, such as pooling, convolution, and fully connected layers. The result of identification was excellent. The image segmentation was detected and comprehend the actual components of an image down to the pixel level. The result created an entire image segmentation masks with instances using the new label editor in the label box.

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“…In [6,7,10,13] studied best trigonometric approximation using continuous function. In [8,9,11] studied the approximation using many types of neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…, 𝑇 𝑛,𝑠 𝐶 2 𝑒 −𝐶 3 𝑛 12Eman S. Bhaya and Sara Saleh Mahdi , Journal of Al-Qadisiyah for Computer Science and Mathematics Vol.Thus,(7) leads to a network 𝑉(𝑓) with 𝑛 𝑠+1 neurons such that: ‖𝑓(𝑥) − 𝑉(𝑓, 𝑥)‖ 𝑝 ≤ 𝐶𝑛 −𝑠 ∑ ‖𝐷 𝑗 𝑓(𝑥)‖ 𝑝…”

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confidence: 99%

Trigonometric Approximation and 2π-Periodic Neural Network Approximation

Bhay,

Mahdi

2023

J. Al-Qadisiyah Comp. Sci. Math.

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Many articles studied best trigonometric approximation and many researchers worked on the neural network approximation, but no one related the best trigonometric approximation to neural network approximation. We define trigonometric activation function, then we use it to obtain neural network, which we use it as a best approximation for functions in spaces for . That what we shall introduce in our work have.

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Best neural simultaneous approximation

Cited by 5 publications

References 30 publications

Development of a model for evaluating the effectiveness of innovative startups based on information cycles and using neural networks

Development of a model for evaluating the effectiveness of innovative startups based on information cycles and using neural networks

Classification of semantic segmentation using fully convolutional networks based unmanned aerial vehicle application

Trigonometric Approximation and 2π-Periodic Neural Network Approximation

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