Acta Scientific Medical Sciences (ASMS)(ISSN: 2582-0931)

Research Article Volume 5 Issue 6

Comprehensive Application of the Theory of Fuzzy Logic and Neural Networks to Predict the Demand for Drugs

Ramiz Alekperov*

Professor, Department of Computer Engineering, Odlar Yurdu University, Baku, Azerbaijan

*Corresponding Author: Ramiz Alekperov, Professor, Department of Computer Engineering, Odlar Yurdu University, Baku, Azerbaijan.

Received: April 26, 2021; Published: May 15, 2021

Abstract

  Determining the need for medicines and medical supplies is directly related to the characteristics of their products, their actual consumption, and the identification of patterns of changes in demand for them. This article discusses the use of fuzzy logic and a neural network to predict the demand for pharmaceutical products in a distributed network, in conditions of insufficient information, a large assortment, and the influence of risk factors. A comprehensive approach to solving forecasting problems is proposed using: the theory of fuzzy logic - when forecasting emerging and unmet needs and a neural network - if there is a lot of retrospective information about the actual sale of drugs. A method for fuzzy classification of drug demand using ABC and XYZ analysis is described. Using this approach to solve the problems of forecasting demand allows you to get statistics and experience. The general algorithm, mathematical interpretation, and examples of forecasting the demand for pharmaceutical products in the face of uncertainty of information are given, and the general structure of the system for forecasting the demand for drugs is described. A fragment of the program code for predicting the demand for drugs based on neural networks for cases with sufficient sales statistics is presented.

Keywords: Demand Forecasting; Pharmaceutical Market; Fuzzy Classification; Neural Networks; ABC Analysis; XYZ Analysis, Cross-analysis

References

  1. Особенности спроса на фармацевтическом рынке [Osobennosti sprosa na farmatsevticheskom rynke].
  2. Tichonov EYe. “Методы прогнозирования в условиях рынка [Metody prognozirovaniya v usloviyakh rynka]: uchebnoye posobiye”. Nevinnomyssk (2006): 221.
  3. “How to Use ABC Analysis for Inventory Management (and the Added Value of XYZ Analysis)”.
  4. “ABC XYZ Inventory Management”.
  5. Zadeh Lotfi A. “Fuzzy Logic, Neural Networks, and Soft Computing”. Communications of the ACM3 (1994): 77-84.
  6. Aliev RA., et al. “Soft Computing and its Applications in Business and Economics”. Springer-Verlag Berlin Heidelberg (2004).
  7. Takagi Т and Sugeno М. “Fuzzy identification of systems and its applications to modeling and control”. IEEE Transactions on Systems, Man and Cybernetics 15 (1985): 116-132.
  8. Beale MH., et al. “Neural Network ToolboxTM User’s Guide”. Natick: The MathWorks, Inc., (2014): 435.
  9. Jonas S. “Mathematica Neural Networks, Train and Analyze Neural Networks to fit your Data”. Wolfram Research, Inc Illinois, USA (2005): 406.
  10. Khaykin S. “Нейронные сети[Neyronnyye seti]: Polnyy kurs: Perevod s angl”. Moskva (2008): 1103.
  11. Alekperov RB and Iskanderli IT. “Application of Neural Networks for Segmentation of Catering Services Market Within the Overall System of Consumer Market on the Model of Restaurant Business with the Aim to Advance the Efficiency of Marketing Policy”. 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing 896 (2019): 905-913.
  12. Alekperov RB and Ibrahimova KA. “Neural Network Modeling and Estimation of the Effectiveness of the Financing Policy Impact on the Socio-Economic Development of the Socio-Educational System”. 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing 896 (2019): 754-759.
  13. Oglu ARB and Kizi IIT. “A Method for Forecasting the Demand for Pharmaceutical Products in a Distributed Pharmacy Network Based on an Integrated Approach Using Fuzzy Logic and Neural Networks”. In: Kahraman C., Cevik Onar S., Oztaysi B., Sari I., Cebi S., Tolga A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing 1197 (2021).

Citation

Citation: Ramiz Alekperov. “Comprehensive Application of the Theory of Fuzzy Logic and Neural Networks to Predict the Demand for Drugs”.Acta Scientific Medical Sciences 5.6 (2021): 74-82.

Copyright

Copyright: © 2021 Ramiz Alekperov. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate30%
Acceptance to publication20-30 days
Impact Factor1.403

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