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The current research explores the useful impact of artificial neural networks (ANN) back-propagation along Levenberg-Marquardt Method (ANN-BLMM), for findng the influence of dimensionless numbers on the flow distribution pertaining magnetohydrodynamics (MHD) Jeffery–Hamel fluid amid two plates which are enclined at angles 2α. We have employed a numerical approach, ANN-BLMM to assess various aspects of our data, including testing, training, validation, Mean Square Errors (MSE), performance, and fitting. The methodology being used has been tested and validated through comparison with other results obtain numerically, showing extreme level of accuracy. Moreover, we have confirmed our findings through error histograms and regression tests. We used OHAM for the data set. Furthermore, we have also explored the influence of Reynolds number (R) on both flow and pressure distribution, visually representing our findings through graphical analysis. We have discussed about the nature and variations of velocity profiles within MHD Jefery–Hamel flow (MHDJHF), taking into account various values of Ha and Re in both convergent and divergent channels. It was discovered that a significant stabilizing influence of an increase in the magnetic field intensity was observed for both diverging and converging channel geometries and as the Hartman numbers rise, so does the fluid velocity. The absolute error is reduced to 10–2 to 10–6.

The current investigation formulated an efficient computing technique by incorporating the algorithm of Artificial Neural Network Levenberg Marquardt Method (ANNLMM) to approximate Soret and Dufour effects on Magnetohydrodynamic squeezing flow of Jeffrey fluid (MHDSFJF) in MATLAB environment. Due to its efficiency and flexibility, the applied neural network model can accurately predict the output results. In the ANNLMM, 80% of the data is used for training and 10% is set for both the testing and validation processes. The flow under consideration is mathematically formulated with the help of Navier Stokes Equations. The partial differential equations (PDEs) are converted into ordinary differential equations (ODEs) by similarity variables. The effects of the physical parameters are discussed briefly. When the Hartmann number and Jeffrey fluid parameter increase, the concentration, temperature, and velocity profiles decreases. The temperature profile and heat transfer rate are improved by the effects of joule dissipation, Dufour numbers, and heat generation/absorption. On the other hand, as thermal radiation rises, the temperature profile decreases and the heat transfer rate increases. With an augment in Soret number, the mass transfer rate increases and the concentration profile slows down. Additionally, increasing chemical reactions, the mass transfer rate increases, but in convective chemical reactions, the contradiction is observed.

Due to exponential growth in the daily usage of vehicles, the traffic congestion and roadside accidents are increasing day by day. The communication among vehicles is critical to avoid the emergencies and to address the issue of congestion of vehicles. Internet of vehicles provides the communication channel between the vehicles, but existing solutions require a centralized communication system to distribute the message and to authenticate the source. This centralized infrastructure is subject to disturb the vehicular communication in case of server breakdown or due to any natural disaster where hardware stops working. Also, the centralized system proves to be costly as the communication of each vehicle necessitates access to the central server resulting in the more resource requirement. A secure distributed system is required to avoid the emergencies and reduce the traffic rate. To address the issues, we proposed a secure distributed message-passing framework that does not require a centralized server, and it rates the credibility of message source using blockchain technology. The messages in the proposed system are forwarded through dedicated short-range communication protocol. To validate the proposed system, we have performed different simulations using SUMO, OMNET, and VEINS. The results demonstrated an increase in the average speed of vehicles that showed a reduced congestion rate. Moreover, our system identified the malicious vehicles with 77.1% accuracy.