ANNETT-O: an ontology for describing artificial neural network evaluation, topology and training

The article deals with the modelling of Artificial Neural Networks as an information transmission system to optimize their computational complexity. The analysis of existing theoretical approaches to optimizing the structure and training of neural networks is carried out. In the process of constructing the model, the well-known problem of isolating a deterministic signal on the background of noise and adapting it to solving the problem of assigning an input implementation to a certain cluster is considered. A layer of neurons is considered as an information transformer with a kernel for solving a certain class of problems: orthogonal transformation, matched filtering, and nonlinear transformation for recognizing the input implementation with a given accuracy. Based on the analysis of the proposed model, it is concluded that it is possible to reduce the number of neurons in the layers of neural network and to reduce the number of features for training the classifier.

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Artificial neural network evaluation of cement-bonded particle board produced from red iron wood (Lophira alata) sawdust and palm kernel shell residues

Case Studies in Construction Materials ◽

10.1016/j.cscm.2018.e00185 ◽

2018 ◽

Vol 9 ◽

pp. e00185 ◽

Cited By ~ 6

Author(s):

Olumoyewa D. Atoyebi ◽

Temitope F. Awolusi ◽

Iyinoluwa E.E. Davies

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Palm Kernel ◽

Particle Board ◽

Palm Kernel Shell ◽

Artificial Neural ◽

Network Evaluation

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Prediction of Call Drops in GSM Network using Artificial Neural Network

Jurnal Teknologi dan Sistem Komputer ◽

10.14710/jtsiskom.7.1.2019.38-46 ◽

2019 ◽

Vol 7 (1) ◽

pp. 38-46

Author(s):

Olaonipekun Oluwafemi Erunkulu ◽

Elizabeth Nnonye Onwuka ◽

Okechukwu Ugweje ◽

Lukman Adewale Ajao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mobile System ◽

Neural Network Approach ◽

Gsm Network ◽

Artificial Neural ◽

And Training ◽

Service Call ◽

Artificial Neural Network Approach

Global System for Mobile communication is a digital mobile system that is widely used in the world. Over the years, the number of subscribers has tremendously increased, the quality of service (Call Drop Rate) became an issue to consider as many subscribers were not satisfied with the services rendered. In this paper, we present the Artificial Neural Network approach to predict call drop during an initiated call. GSM parameters data for the prediction were acquired using TEMS Investigations software. The measurements were carried out over a period of three months. Post analysis and training of the parameters was done using the Artificial Neural Network to have an output of “0” for no-drop calls and “1” for drop calls. The developed model has an accuracy of 87.5% prediction of drop call. The developed model is both useful to operators and end users for optimizing the network.

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