Advances in Computer and Communication

Downloads: 18319 Total View: 180028
Frequency: bimonthly ISSN Online: 2767-2875 CODEN: ACCDC3

In Shortly about Neural Networks

Siniša Franjić1,*, Dario Galić2

1Independent Researcher, Osijek, Croatia.

2Faculty of Dental Medicine and Health, Osijek, Croatia.

*Corresponding author: Siniša Franjić

Published: August 3,2021


A neural network is a collection of neurons that are interconnected and interactive through signal processing operations. The traditional term “neural network” refers to a biological neural network, i.e., a network of biological neurons. The modern meaning of this term also includes artificial neural networks, built of artificial neurons or nodes. Machine learning includes adaptive mechanisms that allow computers to learn from experience, learn by example and by analogy. Learning opportunities can improve the performance of an intelligent system over time. One of the most popular approaches to machine learning is artificial neural networks. An artificial neural network consists of several very simple and interconnected processors, called neurons, which are based on modeling biological neurons in the brain. Neurons are connected by calculated connections that pass signals from one neuron to another. Each connection has a numerical weight associated with it. Weights are the basis of long-term memory in artificial neural networks. They express strength or importance for each neuron input. An artificial neural network “learns” through repeated adjustments of these weights.


[1] Dinov, I. D. (2018). “Data Science and Predictive Analytics—Biomedical and Health Applications using R”, Springer Nature Switzerland AG, Cham, Switzerland, p. 383.

[2] Khan, S., Rahmani, H., Ali Shah, S. A., Bennamoun, M. (2018). “A Guide to Convolutional Neural Networks for Computer Vision”, Morgan & Claypool Publishers, Williston, USA, pp. 31-33, 43-44, 1.

[3] Ringel, M. (2018). “Digital Healing—People, Information, Healthcare”, Routledge, Taylor & Francis Group, New York, USA, p. 43.

[4] Haddadin, S. and Knobbe, D. (2020). “Robotics and Artificial Intelligence—The Present and Future Visions” in Ebers, M., Navas, S. (eds.): “Algorithms and Law”, Cambridge University Press, Cambridge, UK, pp. 20-23.

[5] Astakhova, T. and Astakhov, V. (2009). “Brain Model of Text Animation as a Data Mining Strategy” in Astakhov, V. (ed.): “Biomedical Informatics”, Humana Press, Springer Science+Business Media, LLC, New York, USA,    p. 198.

[6] Cassidy, J. W. (2020). “Applications of Machine Learning in Drug Discovery I: Target Discovery and Small Molecule Drug Design” in Cassidy, J. W., Taylor, B. (eds.): “Artificial Intelligence in Oncology Drug Discovery and Development”, IntechOpen, London, UK, p. 20.

[7] Fatima, A., Tariq, A., Akhtar, M., and Zahid, H. (2019). “Segmentation of Chest Radiographs for Tuberculosis Screening Using Kernel Mapping and Graph Cuts” in Khan, F., Jan, M. A., Alam, M. (eds.): “Applications of Intelligent Technologies in Healthcare”, Springer Nature Switzerland AG, Cham, Switzerland, p. 26.

[8] Fillard, J. P. (2016). “Brain vs Computer—The Challenge of the Century”, World Scientific Publishing Co. Pte. Ltd, Singapore, Singapore, pp. 139-140.

How to cite this paper

In Shortly about Neural Networks

How to cite this paper: Siniša Franjić, Dario Galić. (2021) In Shortly about Neural Networks. Advances in Computer and Communication2(1), 15-19.