Azerbaijan National Academy of Science

The first website of Azerbaijan (1995)

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Department №1
Phone (+994 12) 5398548
Fax (+994 12) 5396121

Chief  Academician, doctor of technical sciences Ali Mammad oglu Abbasov
Total number of employees 8
Basic activity directions  

E-government formation Various Aspects of Internet Study formation;

Acquisition of knowledge from big data sets.

Main scientific achievements 

- Algorithms and software were developed for various corporate information systems (e-document turnover with e-signature, e-archive, task execution control, decision support system, etc.):

- Conceptual basis and architectural principles of e-document management systems were developed, the functional and technological architecture of confidence infrastructure of e-documents security was proposed and recommendations were put forward;

- Conceptual model was developed for the system intellectualization, and the algorithm was developed for the automated classification of text documents in the systems;

- Optimization models were provided for automated summarization of text documents;

- Modified binary algorithms based on differential evolution and "herd" intelligence was developed to solve optimization problems.

- An algorithm was developed for initial processing of domain names registration data, and a method was proposed for the authentication of domain names registration data;

- Non-hierarchical method was proposed to be used for domain names registration data clustering;

- A method for detection of hidden knowledge from domain names registration data and a methodology for the assessment of the development dynamics of high-level national domains were proposed;

- A system was developed for the intelligent monitoring of the national domain names with regard to the interests of the Republic of Azerbaijan.

- Researched on Big data technologies;

- Proposed the conceptual architectural model for integrating large volumes of data;

- Developed model based on an ensemble classification for anomaly detecting in the traffic of a computer network;

- Developed deep learning model based on artificial neural networks CNN and LSTM for sentimental analysis of social network data;

- Developed models based on machine learning for oil field data analysis;

- Developed conceptual model to accelerate the search process in the field of digital heritage using artificial intelligence algorithms.