Neural networks as a tool for modeling of biological systems

• Autorzy: Tadeusiewicz R.

Identyfikatory

DOI

10.1515/bams-2015-0021

• Języki publikacji: EN

Abstrakty

EN

Neural networks become very popular as a tool for modeling of numerous systems, including technological, economical, sociological, psychological, and even political ones. On the contrary, neural networks are models of neural structures and neural processes observed in a real brain. However, for modeling of real neural structures and real neural processes occurring in a living brain, neural networks are too simplified and too primitive. Nevertheless, neural networks can be used for modeling the behavior of many biological systems and structures. Such models are not useful for explanation, taking into account the biological systems and processes, but can be very useful for the analysis of such system behavior, including the prognosis of future results of selected activities (e.g. the prognosis of results of different therapies for modeled illnesses). In this paper, selected examples of such models and their applications are presented.

Słowa kluczowe

EN

modeling of biological systems; neural networks

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; De Gruyter
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2015
• Tom: Vol. 11, no. 3
• Strony: 135--144
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Tadeusiewicz R.

• Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Mickiewicza 30 Ave., 30-059 Krakow, Poland

Bibliografia

• 1. Tadeusiewicz R, Chaki R, Chaki N. Exploring neural networks with C#. Boca Raton: CRC Press, Taylor & Francis Group, 2014.
• 2. Wu JJ, Zhang Y. ECG identification based on neural networks. 11th Int Comput Conf Wavelet Active Media Technol Inf Process IEEE 2014:92–6.
• 3. Shen W, Yang F, Mu W, Yang C, Yang X, Tian J. Automatic localization of vertebrae based on convolutional neural networks. Med Imaging 2015 Image Process SPIE. Proc SPIE Prog Biomed Optics Imaging 9413, 2015, 94132E (6 pp.).
• 4. De A, Guo C. An image segmentation method based on the fusion of vector quantization and edge detection with applications to medical image processing. Int J Mach Learn Cybernet 2014;5:543–51.
• 5. Morra L, Delsanto S, Lamberti F. Methods for neural-networkbased segmentation of magnetic resonance images. In: Akay M, editor. Handbook of neural engineering. Chapter 10. Piscataway, NJ, USA: IEEE Press, 2007:173–92.
• 6. Li G, Liu T, Li T, Inoue Y, Yi J. Neural network-based gait assessment using measurements of a wearable sensor system. Proc 2014 IEEE Int Conf Robot Biomimet 2014:1673–78.
• 7. Azzerboni B, Ipsale M, La Foresta F, Morabito FC. Neural networks and time-frequency analysis of surface electromyographic signals for muscle cerebral control. In: Akay M, editor. Handbook of neural engineering. Chapter 7. Piscataway, NJ, USA: IEEE Press, 2007:131–55.
• 8. Inbarani HH, Nizar Banu PK, Azar AT. Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Comput Appl 2014;25:793–806.
• 9. Tedesco M, Frega M, Pastorino L, Massobrio P, Martinoia S. 3D engineered neural networks coupled to micro-electrode based devices: a new experimental model for neurophysiological applications. XVIII AISEM Annu Conf Proc IEEE 2015:4–6.
• 10. Kawaguchi M, Ishii N, Jimbo T. Analog learning neural network using two-stage mode by multiple and sample hold. Int J Soft Innov 2014;2:61–72.
• 11. Beheshti Z, Shamsuddin SM, Beheshti E, Yuhaniz SS. Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis. Soft Comput 2014;18:2253–70.
• 12. Gutierrez A. The PSO algorithm and the diagnosis of multiple sclerosis using artificial neural networks. Proc 2014 Annu Global Online Conf Inf Comput Technol IEEE Comput Soc 2014:5–10.
• 13. Harikumar R, Vinoth Kumar B. Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Int J Imaging Syst Technol 2015;25:33–40.
• 14. Utomo CP, Kardiana A, Yuliwulandari R. Breast cancer diagnosis using artificial neural networks with extreme learning techniques. Int J Adv Res Artif Intell 2014;3:10–4.
• 15. Zaman NA, Rahman WE, Jumaat AK, Yasiran SS. Classification of breast abnormalities using artificial neural network. Int Conf Math Eng Ind Appl 2014:1660–7.
• 16. Hamedi M, Salleh SH, Noor AM, Mohammad Rezazadeh I. Neural network-based three-class motor imagery classification using time-domain features for BCI applications. 2014 IEEE Region 10 Symp 2014:204–7.
• 17. Slim MA, Abdelkrim A, Benrejeb M. Handwriting velocity modeling by sigmoid neural networks with Bayesian regularization. Int Conf Elect Sci Technol Maghreb Tunis 2014:7–12.
• 18. Vincent I, Kwon KR, Lee SH, Seok Moon KS. Acute lymphoid leukemia classification using two-step neural network classifier. 21st Korea Japan Joint Workshop Front Comput Vision 2015:123–7.
• 19. Gao X, Huang T, Wang Z, Xiao M. Exploiting a modified gray model in back propagation neural networks for enhanced forecasting. Cognit Comput 2014;6:331–7.
• 20. Wu TH, Pang GK, Kwong EW. Predicting systolic blood pressure using machine learning. 7th Int Conf Inf Automat Sustain IEEE 2014:1–6.
• 21. Lin CC, Chan HH, Huang CY, Yang NS. Predictive models for preoperative diagnosis of rotator cuff tear: a comparison study of two methods between logistic regression and artificial neural network. Appl Mech Mater 2014;595:263–8.
• 22. Hu J, Hou ZG, Chen YX, Kasabov N, Scott N. EEG-based classification of upper-limb ADL using SNN for active robotic rehabilitation. 5th IEEE RAS EMBS Int Conf Biomed Robot Biomechatron 2014:409–14.
• 23. Korovin EN, Trukhachev AS, Fursova EA. Neural-network modelling choice of treatment tactics for patients with chronic heart failure against operated acquired heart diseases. Syst Anal Control Biomed Syst 2014;13:916–20.
• 24. Ning Y, Han LL, Xiao ZR, Liu BG. Force feedback time prediction based on neural network of MIS Robot with time delay. Proc 2014 IEEE Int Conf Robot Biomimet 2014:2703–8.