Phone | (+994 12) 5398548 | |
Fax | (+994 12) 5396121 | |
makrufa@iit.science.az, | ||
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. |
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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. |