The present study aims to design stochastic intelligent computational heuristics for the numerical treatment of a nonlinear SITR system representing the dynamics of novel coronavirus disease 2019 (COVID-19). The mathematical SITR system using fractal parameters for COVID-19 dynamics is divided into four classes; that is, susceptible (S), infected (I), treatment (T), and recovered (R). The comprehensive details of each class along with the explanation of every parameter are provided, and the dynamics of novel COVID-19 are represented by calculating the solution of the mathematical SITR system using feed-forward artificial neural networks (FF-ANNs) trained with global search genetic algorithms (GAs) and speedy fine tuning by sequential quadratic programming (SQP)—that is, an FF-ANN-GASQP scheme. In the proposed FF-ANN-GASQP method, the objective function is formulated in the mean squared error sense using the approximate differential mapping of FF-ANNs for the SITR model, and learning of the networks is proficiently conducted with the integrated capabilities of GA and SQP. The correctness, stability, and potential of the proposed FF-ANN-GASQP scheme for the four different cases are established through comparative assessment study from the results of numerical computing with Adams solver for single as well as multiple autonomous trials. The results of statistical evaluations further authenticate the convergence and prospective accuracy of the FF-ANN-GASQP method.

https://www.mdpi.com/2073-8994/12/10/1628/pdf

A Stochastic Intelligent Computing with Neuro-Evolution Heuristics for Nonlinear SITR System of Novel COVID-19 Dynamics

The particle swarm optimization (PSO) is a population-based stochastic optimization technique by the social behavior of bird flocking and fish schooling. The PSO has a high convergence rate. It is prone to losing diversity along the iterative optimization process and may get trapped into a poor local optimum. Overcoming these defects is still a significant problem in PSO applications. In contrast, the backtracking search optimization algorithm (BSA) has a robust global exploration ability, whereas, it has a low local exploitation ability and converges slowly. This paper proposed an improved PSO with BSA called PSOBSA to resolve the original PSO algorithm’s problems that BSA’s mutation and crossover operators were modified through the neighborhood to increase the convergence rate. In addition to that, a new mutation operator was introduced to improve the convergence accuracy and evade the local optimum. Several benchmark problems are used to test the performance and efficiency of the proposed PSOBSA. The experimental results show that PSOBSA outperforms other well-known metaheuristic algorithms and several state-of-the-art PSO variants in terms of global exploration ability and accuracy, and rate of convergence on almost all of the benchmark problems.

An improved particle swarm optimization with backtracking search optimization algorithm for solving continuous optimization problems

The stochastic block model is a powerful tool for inferring community structure from network topology. However, the simple block model considers community structure as the only underlying attribute for forming the relational interactions among the nodes, this makes it prefer a Poisson degree distribution within each community, while most real-world networks have a heavy-tailed degree distribution. This is essentially because the simple assumption under the traditional block model is not consistent with some real-world circumstances where factors other than the community memberships such as overall popularity also heavily affect the pattern of the relational interactions. The degree-corrected block model can accommodate arbitrary degree distributions within communities by taking nodes' popularity or degree into account. But since it takes the vertex degrees as parameters rather than generating them, it cannot use them to help it classify the vertices, and its natural generalization to directed graphs cannot even use the orientations of the edges. We developed several variants of the block model with the best of both worlds: they can use vertex degrees and edge orientations in the classification process, while tolerating heavy-tailed degree distributions within communities. We show that for some networks, including synthetic networks and networks of word adjacencies in English text, these new block models achieve a higher accuracy than either standard or degree-corrected block models. Another part of my work is to develop even more generalized block models, which incorporates other attributes of the nodes. Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, patents and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on such data sets, and make predictions and recommendations, it is useful to have models that are able to capture the processes which generate the text at each node as well as the links between them. Our work combines classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community. The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm. We test our model on three data sets, performing unsupervised topic classification and link prediction. For both tasks, our model outperforms several existing state-of-the-art methods, achieving higher accuracy with significantly less computation, analyzing a data set with 1.3 million words and 44 thousand links in a few minutes.

/pdf/community-detection-in-complex-networks-1j75o2s2yv.pdf

Community detection in complex networks

Design of multi innovation fractional LMS algorithm for parameter estimation of input nonlinear control autoregressive systems

An innovative fractional order LMS algorithm for power signal parameter estimation

The use of fractional calculus based novel adaptive algorithms to solve various applied physics and engineering problems is an emerging area of research. In the present study, the parameter estimation problem in adaptive beamforming is explored through the fractional least mean square (FrLMS) adaptive algorithm. The FrLMS algorithm uses the concept of the fractional order gradient in addition to the standard integer order gradient calculation in the recursive parameter update mechanism of optimization. The unknown parameters of adaptive beamforming networks are effectively estimated using FrLMS for various scenarios based on the number of antenna elements in a uniform linear array, the number of interference signals, the signal to noise ratios (SNRs) as well as fractional orders. A comparative study of the proposed FrLMS with standard LMS for different scenarios of adaptive beamforming shows the quality of the design scheme in terms of accuracy, convergence, robustness and stability.

A new computing paradigm for the optimization of parameters in adaptive beamforming using fractional processing

In this study, a novel theoretical model for three-dimensional (3D) laminar magnetohydrodynamic (MHD) flow of a non-Newtonian second-grade fluid along a plate of semi-infinite length is developed based on slightly sinusoidal transverse suction velocity. The suction velocity involves a steady distribution with a low superimposed perpendicularly varying dispersion. The strength of the uniform magnetic field is incorporated in the normal direction to the wall. The variable suction transforms the fluidic problem into a 3D flow problem because of variable suction velocity in the normal direction to the plane wall. The proposed mathematical modeling and its dynamical analysis are prescribed for the boundary layer flow keeping the magnetic effects without taking into consideration the induced magnetic field. An analytical perturbation technique is employed for the series solutions of the system of ordinary differential equations of velocity profile and pressure. Graphical illustrations are used to analyze the behavior of different proficient parameters of interest. The magnetic parameter is responsible for accelerating the main flow velocity, while controlling the cross flow velocities.

/pdf/a-novel-design-of-three-dimensional-mhd-flow-of-second-grade-5emaq8qc65.pdf

A Novel Design of Three-Dimensional MHD Flow of Second-Grade Fluid past a Porous Plate

Design of backtracking search optimization paradigm for joint amplitude-angle measurement of sources lying in fraunhofer zone

/pdf/intelligent-backpropagated-neural-networks-for-numerical-urv76m9jqx.pdf

Muhammad Asif Zahoor Raja

Papers

A new computing paradigm for the optimization of parameters in adaptive beamforming using fractional processing

A Novel Design of Three-Dimensional MHD Flow of Second-Grade Fluid past a Porous Plate

Design of backtracking search optimization paradigm for joint amplitude-angle measurement of sources lying in fraunhofer zone

Intelligent Backpropagated Neural Networks for Numerical Computations for MHD Squeezing Fluid Suspended by Nanoparticles between Two Parallel Plates