This study investigates the potential of a simple artificial neural network for the prediction of COVID-19 New Confirmed Cases in Algeria (CNCC). Four different ANN models were built (GRNN, RBFNN, ELM, and MLP). The performance of the predictive models is evaluated based on four numerical parameters, namely root mean squared error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and Pearson correlation coefficient (R). Taylor diagram was also used to examine the similarities and differences between the observed and predicted values obtained from the proposed models. The results showed the potential of the multi-layer perceptron neural network (MLPNN) which exhibited a high level of accuracy in comparison to the other models.

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Predictive modeling of COVID-19 New Confirmed Cases in Algeria using Artificial Neural Network

We analyse a cell of Cognitive Radio Network (<inline-formula> <tex-math notation="LaTeX">$CRN$ </tex-math></inline-formula>) as the multiline queueing system supplying service to two Markovian arrival flows of users. Primary (or licensed) users called as High Priority Users (<inline-formula> <tex-math notation="LaTeX">$HPU\text{s}$ </tex-math></inline-formula>) have a preemptive priority over the secondary (cognitive) users called as Low Priority Users (<inline-formula> <tex-math notation="LaTeX">$LPU\text{s}$ </tex-math></inline-formula>). The <inline-formula> <tex-math notation="LaTeX">$HPU\text{s}$ </tex-math></inline-formula> are dropped upon the arrival only if all servers are occupied by <inline-formula> <tex-math notation="LaTeX">$HPU\text{s}$ </tex-math></inline-formula>. If at the arrival epoch all servers are busy but some of them provide service to <inline-formula> <tex-math notation="LaTeX">$LPU\text{s}$ </tex-math></inline-formula>, service of one <inline-formula> <tex-math notation="LaTeX">$LPU$ </tex-math></inline-formula> is immediately interrupted and service of the <inline-formula> <tex-math notation="LaTeX">$HPU$ </tex-math></inline-formula> begins in the released server. A <inline-formula> <tex-math notation="LaTeX">$LPU$ </tex-math></inline-formula> is accepted only if the number of busy servers at arrival epoch is less than the defined in advance threshold <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>. Otherwise, the <inline-formula> <tex-math notation="LaTeX">$LPU$ </tex-math></inline-formula> is permanently lost or becomes a retrial user. A retrial user repeats attempts to receive service later after random time intervals. The <inline-formula> <tex-math notation="LaTeX">$LPU$ </tex-math></inline-formula> whose service is interrupted is either lost or transferred to a virtual place called as orbit. The users placed in the orbit may be impatient and can renege the system. The service time follows an exponential probability distribution with the rate determined by the user’s type. After loss of a <inline-formula> <tex-math notation="LaTeX">$HPU$ </tex-math></inline-formula>, admission of <inline-formula> <tex-math notation="LaTeX">$LPU\text{s}$ </tex-math></inline-formula> is blocked. <inline-formula> <tex-math notation="LaTeX">$LPU\text{s}$ </tex-math></inline-formula> are informed that their access is temporarily suspended and do not generate new requests until blocking expires. The purpose of the research is the optimization of threshold <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> and admission blocking period duration. Behavior of the system is described by a multidimensional continuous-time Markov chain. Its generator, ergodicity condition and invariant distribution are derived. Expressions for performance indicators are given. Numerical results demonstrating usefulness of blocking and significance of account of correlation in arrivals are presented. E.g., in the presented example of cost criterion optimization blocking gives 18 percent profit comparing to the system without blocking.

Admission Control in Priority Queueing System With Servers Reservation and Temporal Blocking Admission of Low Priority Users

We analyse a cell of Cognitive Radio Network ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CRN$ </tex-math></inline-formula> ) as the multiline queueing system supplying service to two Markovian arrival flows of users. Primary (or licensed) users called as High Priority Users ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$HPU\text{s}$ </tex-math></inline-formula> ) have a preemptive priority over the secondary (cognitive) users called as Low Priority Users ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LPU\text{s}$ </tex-math></inline-formula> ). The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$HPU\text{s}$ </tex-math></inline-formula> are dropped upon the arrival only if all servers are occupied by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$HPU\text{s}$ </tex-math></inline-formula> . If at the arrival epoch all servers are busy but some of them provide service to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LPU\text{s}$ </tex-math></inline-formula> , service of one <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LPU$ </tex-math></inline-formula> is immediately interrupted and service of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$HPU$ </tex-math></inline-formula> begins in the released server. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LPU$ </tex-math></inline-formula> is accepted only if the number of busy servers at arrival epoch is less than the defined in advance threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> . Otherwise, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LPU$ </tex-math></inline-formula> is permanently lost or becomes a retrial user. A retrial user repeats attempts to receive service later after random time intervals. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LPU$ </tex-math></inline-formula> whose service is interrupted is either lost or transferred to a virtual place called as orbit. The users placed in the orbit may be impatient and can renege the system. The service time follows an exponential probability distribution with the rate determined by the user’s type. After loss of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$HPU$ </tex-math></inline-formula> , admission of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LPU\text{s}$ </tex-math></inline-formula> is blocked. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LPU\text{s}$ </tex-math></inline-formula> are informed that their access is temporarily suspended and do not generate new requests until blocking expires. The purpose of the research is the optimization of threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> and admission blocking period duration. Behavior of the system is described by a multidimensional continuous-time Markov chain. Its generator, ergodicity condition and invariant distribution are derived. Expressions for performance indicators are given. Numerical results demonstrating usefulness of blocking and significance of account of correlation in arrivals are presented. E.g., in the presented example of cost criterion optimization blocking gives 18 percent profit comparing to the system without blocking. 

/pdf/admission-control-in-priority-queueing-system-with-servers-3f92gu4q.pdf

Energy harvesting cognitive radio networks with strategic users: A two-class queueing model with retrials

Based on the principle of Radial Basis Function (RBF) Neural Network, a learning method is presented for the identification of a complex system model. The RBF algorithm is employed on the learning and identifying process of the nonlinear model. The simulation results show that the presented method has good effect on speeding up the learning and approaching process of the nonlinear complex model, and has an excellent performance on learning convergence.

Kaili Li

Papers

Energy harvesting cognitive radio networks with strategic users: A two-class queueing model with retrials

Complex Model Identification Based on RBF Neural Network