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Jafar Majidpour
Raparin University Rania
Electrical & Electronics Engineering

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Application of fuzzy logic in order to plan and create software for converting sound to text Hiwa Hasanzadeh; Jafar Majidpour
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (786.366 KB) | DOI: 10.11591/eei.v9i3.1723

Abstract

The challenge of software in the field of converting sound to text is that some words have different meanings. In these situations, the functions of each one is distinguished by the use of fuzzy logic and division of the working part of the work. In this regard, the words must first be classified according to the specialized discipline. In the second place, the principles of writing should be maintained. In this regard, the points and commands and the alignment of the sentence are all items that should be considered in the software. The next item is the volume and speed of word processing and the lack of restrictions in the number of languages used. But the time problem is very important. In this regard, the use of existing codes and the optimization and remedy of the problems is a suitable method.In this research, some issues including security and maintenance of software proprietary rights, and the speed of converting sound to text and reducing noise and optimal performance of software to convert audio to text based on software method and the principles of fuzzy logic and neural network were investigated. Results showed improvement algorithm Analytical and results outcomes.
Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks Jafar Majidpour; Hiwa Hasanzadeh
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (674.163 KB) | DOI: 10.11591/eei.v9i3.1724

Abstract

Application of deep learning to enhance the accuracy of intrusion detection in modern computer networks were studied in this paper. The identification of attacks in computer networks is divided in to two categories of intrusion detection and anomaly detection in terms of the information used in the learning phase. Intrusion detection uses both routine traffic and attack traffic. Abnormal detection methods attempt to model the normal behavior of the system, and any incident that violates this model is considered to be a suspicious behavior. For example, if the web server, which is usually passive, tries to There are many addresses that are likely to be infected with the worm. The abnormal diagnostic methods are Statistical models, Secure system approach, Review protocol, Check files, Create White list, Neural Networks, Genetic Algorithm, Vector Machines, decision tree. Our results have demonstrated that our approach offers high levels of accuracy, precision and recall together with reduced training time. In our future work, the first avenue of exploration for improvement will be to assess and extend the capability of our model to handle zero-day attacks.
Co-Authors Hiwa Hasanzadeh