|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.