Nishonov, Akhram
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Name Variants
A.K. Nishanov
Nishanov A.K.
Нишанов А.Х.
А.Х. Нишанов;Nishanov, A. Kh
Nishanov, A. Kh.
Nishanov, A. X.
Nishanov, Akhram Khasanovich
Nishanov, A.Kh
Nishanov, A.Kh.
Nishanov, A. Kh
Nishanov A.K.
Нишанов А.Х.
А.Х. Нишанов;Nishanov, A. Kh
Nishanov, A. Kh.
Nishanov, A. X.
Nishanov, Akhram Khasanovich
Nishanov, A.Kh
Nishanov, A.Kh.
Nishanov, A. Kh
Job Title
Doctor of Technical Sciences, Professor
Email Address
nishanov_akram@mail.ru
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output
5
Articles
2
Citation Count
3
Supervised Theses
0
5 results
Scholarly Output Search Results
Now showing 1 - 5 of 5
Conference Object Citation Count: 1Algorithm for the Selection of Informative Symptoms in the Classification of Medical Data(World Scientific Publ Co Pte Ltd, 2020) Nishanov, A. Kh; Ruzibaev, O. B.; Chedjou, J. C.; Kyamakya, K.; Abhiram, Kolli; De Silva, Perumadura; Khasanova, M. A.; Department of System and Applied ProgrammingIn this paper the issues like preprocessing of medical data, reclassification of the training sets and determining the importance of classes, formation of reference tables, selection of an informative features set that differentiate between class objects, formed by medical professionals are discussed and solved. Mainly in the most studied references [5-8, 11-13] the Fisher's criterion is used to obtain solutions to problems/tasks. Also for solving problems, the algorithms for an estimate calculation as well as the related software programs are used. For all cases, algorithms and software programs are suggested. The study consists of two important steps. The first step is to build a reference table, based on the importance of the features and objects as well as their contribution to the classes [1-4, 9, 10]; the second step is concerned with the choice of the most useful characteristic features set to be investigated. This corresponds to solving the issue of selection of set of informative features from a given table, their visualization, and the determination of the contribution of the features set to the formation of classes [1-13].Conference Object Citation Count: 1Modification of Decision Rules "Ball Apolonia" the Problem of Classification(Ieee, 2016) Nishanov, A. X.; Ruzibaev, O. B.; Tran, Nguyen H.; Department of System and Applied ProgrammingThis article is devoted to the study of one of the main tasks of data mining, pattern recognition problem. The problem of classification by the modified decision rules "ball Apollonia". Also, in this paper, an algorithm for solving the modified rule "ball Apollonia", based on the algorithm developed by the software in the language C#. The result is applied to the recognition problem for several well-known set of data.Article Citation Count: 0Clustering Algorithm Based on Object Similarity(al-Farabi Kazakh State National University, 2024) Nishanov, A.Kh.; Akbarova, M.Kh.; Tursunov, A.T.; Ollamberganov, F.F.; Rashidova, D.E.; Department of System and Applied ProgrammingThe article examines the issue of drug clustering. Initially, k classes are arbitrarily formed and the resulting training sample is pre-processed, then the similarities between the objects of each class are evaluated based on the proximity function and the criterion for evaluating the contribution of objects to the formation of their own class. Usually, it is in percentage and is the degree of mutual similarity of objects of each class. In the next steps of the algorithm, first, one object is taken from the first class, and by adding it to all k classes, the contribution of this object to this class is measured. The object will be left in the class which has the most contribution. This process is repeated several times in a row for all objects of the class. The process is stopped when the location of objects does not change and the degree of similarity exceeds the required percentage. As a result, the required clusters are formed. © 2024 Al-Farabi Kazakh National University.Conference Object Citation Count: 0Classification Algorithms With a Complex Structure in Pattern Recognition(IEEE Computer Society, 2024) Nishanov, A.Kh.; Allamov, O.; Rakhmanov, A.T.; Yusupova, J.; Ruzibaev, O.B.; Department of System and Applied ProgrammingThe article considers in the intellectual processing of information, moving from a large unit of measurement to a smaller, more important unit of measurement, selecting sets of informative symbols and classifying symbols, developing modern recognition systems that help specialists in the field, as well as evaluating the states of objects to be classified, developing a classification algorithm for multi-class cases of educational samples. The development of a multi-class algorithm for symbol classification serves to develop the theory of symbol recognition. It deals with the construction of a decision rule construction algorithm. Its main purpose is to determine which of the given classes the recognized object belongs to. The complexity or simplicity of the proposed algorithm depends on the number of classes participating in the study sample. If the number of classes is two, then the formulation of decision rules is simple. That is why the classification algorithms proposed by most authors are solved for cases where the number of classes is three or more, the decisive rule in the matter of classification of objects is solved by making two classes. The construction of a multi-class decision rule for classification, which takes into account all classes at the same time, is a complex and urgent issue. In the article, in the classification of objects, a multi-level complex structured decision rule was developed based on special inequalities, and two-, three- and multi-class classification algorithms were developed. © 2024 IEEE.Article Citation Count: 1Analysis of Methodology of Rating Evaluation of Digital Economy and E-Government Development in Uzbekistan(Anadolu Univ, 2022) Nishanov, Akhram Khasanovich; Norboyo'g'li, Saidrasulov Sherzod; Satimbaevich, Babadjanov Elmurad; Department of System and Applied ProgrammingThis article discusses the new rating assessment methodology approved by the Cabinet of Ministers of the Republic of Uzbekistan on June 15, 2021 "On measures to further improve the rating system of the digital economy and e-government" No. 373. initially the indicators are examined. In accordance with the Strategy "Digital Uzbekistan - 2030" in order to assess the state of digital transformation in the regions, a methodology for rating the level of digital development of regions was developed, which made it possible to diagnose the state of digitalization in the regions. In the rating assessment methodology, the indicators used by the United Nations (UN) to assess the development of information technology and the introduction of e-government in countries around the world have been adopted.
