Determination of input parameters of the neural network model, intended for phoneme recognition of a voice signal in the systems of distance learning

The article is devoted to the problem of voicesignals recognition means introduction in the system of distancelearning. The results of the conducted research determine theprospects of neural network means of phoneme recognition.It is also shown that the main diculties of creation of theneural network model, intended for recognition of phonemesin the system of distance learning, are connected with theuncertain duration of a phoneme-like element. Due to thisreason for recognition of phonemes, it is impossible to usethe most eective type of neural network model on the basisof a multilayered perceptron, at which the number of inputparameters is a xed value. To mitigate this shortcoming, theprocedure, allowing to transform the non-stationary digitizedvoice signal to the xed quantity of mel-cepstral coecients,which are the basis for calculation of input parameters ofthe neural network model, is developed. In contrast to theknown ones, the possibility of linear scaling of phoneme-like elements is available in the procedure. The number ofcomputer experiments conrmed expediency of the fact thatthe use of the oered coding procedure of input parametersprovides the acceptable accuracy of neural network recognitionof phonemes under near-natural conditions of the distancelearning system. Moreover, the prospects of further research inthe eld of development of neural network means of phonemerecognition of a voice signal in the system of distance learningis connected with an increase in admissible noise level. Besides,the adaptation of the oered procedure to various naturallanguages, as well as to other applied tasks, for instance, aproblem of biometric authentication in the banking sector, isalso of great interest

Development of decision support system based on feature matrix for cyber threat assessment

The article herein presents the method and algorithmsfor forming the feature space for the base of intellectualizedsystem knowledge for the support system in the cyber threatsand anomalies tasks. The system being elaborated might be usedboth autonomously by cyber threat services analysts and jointlywith information protection complex systems. It is shown, that advisedalgorithms allow supplementing dynamically the knowledgebase upon appearing the new threats, which permits to cut thetime of their recognition and analysis, in particular, for cases ofhard-to-explain features and reduce the false responses in threatrecognizing systems, anomalies and attacks at informatizationobjects. It is stated herein, that collectively with the outcomes ofprevious authors investigations, the offered algorithms of formingthe feature space for identifying cyber threats within decisionsmaking support system are more effective. It is reached at theexpense of the fact, that, comparing to existing decisions, thedescribed decisions in the article, allow separate considering thetask of threat recognition in the frame of the known classes, andif necessary supplementing feature space for the new threat types.It is demonstrated, that new threats features often initially arenot identified within the frame of existing base of threat classesknowledge in the decision support system. As well the methodsand advised algorithms allow fulfilling the time-efficient cyberthreats classification for a definite informatization object

DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging). The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites

Development of decision support system based on feature matrix for cyber threat assessment

The article herein presents the method and algorithms for forming the feature space for the base of intellectualized system knowledge for the support system in the cyber threats and anomalies tasks. The system being elaborated might be used both autonomously by cyber threat services analysts and jointly with information protection complex systems. It is shown, that advised algorithms allow supplementing dynamically the knowledge base upon appearing the new threats, which permits to cut the time of their recognition and analysis, in particular, for cases of hard-to-explain features and reduce the false responses in threat recognizing systems, anomalies and attacks at informatization objects. It is stated herein, that collectively with the outcomes of previous authors investigations, the offered algorithms of forming the feature space for identifying cyber threats within decisions making support system are more effective. It is reached at the expense of the fact, that, comparing to existing decisions, the described decisions in the article, allow separate considering the task of threat recognition in the frame of the known classes, and if necessary supplementing feature space for the new threat types. It is demonstrated, that new threats features often initially are not identified within the frame of existing base of threat classes knowledge in the decision support system. As well the methods and advised algorithms allow fulfilling the time-efficient cyber threats classification for a definite informatization object