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Queuing model for SIP server hysteretic overload control with K-state MMPP bursty traffic
Abaev P., Khachko A., Beschastny V.
SIP server in the case of bursty input message flow. Algorithms for computation the key performance
On auction equilibrium models with network applications
Konnov I.
that the basic network flow equilibrium problems are particular cases of this auction market model. Besides, we
Automated Analysis of the Pigment Network in Dermatoscopic Images of Melanocytic Skin Tumors
Nikitaev V.G., Pronichev A.N., Tamrazova O.B., Sergeev V.Y., Sergeev Y.Y., Kozyreva A.V., Polyakov E.V., Druzhinina E.A.
A method for recognition of the pigment network lines in dermatoscopic images of skin tumors
About Probability Characteristics Evaluation in Queuing System with Limited Resources and Random Requirements
Вихрова О.Г.
in the near future. Exponential growth of data traffic in wireless networks accelerates the development of new
Neural network for parametric optimization in the problem of traffic management
Kazaryan D.E., Mihalyev V.A., Sofronova E.A.
A problem of adaptive urban traffic flows control is considered. The traffic flow control
Fast Random Search Algorithm in Neural networks Training
Matskevich, V.
to be critical. The paper proposes a fast algorithm for neural networks training based on random search. It has
ARTIFICIAL NEURAL NETWORK APPROACH TO TRAFFIC FLOW CONTROL
Kazaryan D.E., Mihalyev V.A., Sofronova E.A.
A problem of optimal urban traffic flows control is considered. A mathematical model of control
Sensor Location Problem’s Software Optimization
Pilipchuk, A., Pilipchuk, L., Polyachok, E.
of the algorithm’s for finding the flow arcs for the non observered part on the network. In the pseudocode
Оптимизация проектных решений по организации движения в зоне железнодорожных переездов методами имитационного моделирования
Карасевич, С. Н.
reliability of transport flows simulation with group and dependent traffic characteristics an algorithm
Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow
Bouchnita A., Nony P., Llored J.-P., Volpert V.
neural network (ANN) and we use it to predict patient's response to anticoagulant therapy under flow

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