Predicting Traffic Flow with Federated Learning and Graph Neural with Asynchronous Computations Network

Real-time traffic flow prediction holds significant importance within the domain of Intelligent Transportation Systems (ITS). The task of achieving a balance between prediction precision and computational efficiency presents a significant challenge. In this article, we present a novel deep-learning method called Federated Learning and Asynchronous Graph Convolutional Network (FLAGCN). Our framework incorporates the principles of asynchronous graph convolutional networks with federated learning to enhance the accuracy and efficiency of real-time traffic flow prediction. The FLAGCN model employs a spatial-temporal graph convolution technique to asynchronously address spatio-temporal dependencies within traffic data effectively. To efficiently handle the computational requirements associated with this deep learning model, this study used a graph federated learning technique known as GraphFL. This approach is designed to facilitate the training process. The experimental results obtained from conducting tests on two distinct traffic datasets demonstrate that the utilization of FLAGCN leads to the optimization of both training and inference durations while maintaining a high level of prediction accuracy. FLAGCN outperforms existing models with significant improvements by achieving up to approximately 6.85% reduction in RMSE, 20.45% reduction in MAPE, compared to the best-performing existing models.Comment: I request to withdraw my paper from arXiv due to significant updates and improvements identified post-submission. These enhancements will substantially elevate the work's quality and impact. I plan to resubmit the revised paper upon completion of these updates. Thank you for accommodating this reques

Investigation of entropy generation in power law fluid-filled bulging enclosures: effects of flow and heat transfer

This paper presents a complete investigation into the phenomenon of dual convection flow within a bulging enclosure along with non-Newtonian power-law fluids and employs the Galerkin finite element method for simulation. The main objective is to study the heat and mass transfer and entropy generation within the enclosure. Various factors, such as the Rayleigh number, power law coefficient, Lewis number, and effects of magnetic inclination are evaluated for their influence on flow dynamics and heat distribution. The study also investigates the relationship between these parameters and entropy generation, providing insights into the irreversible processes within the system. A comparative analysis of averaged Nusselt and Sherwood coefficients reveals distinct thermal and fluidic behaviours across varying power-law indices, emphasizing differences between shear thickening, shear thinning, and Newtonian fluid behaviours. The findings provide valuable insights into non-Newtonian power-law fluids in complex flows, enhancing our understanding of flow and heat transfer in bulging enclosures

Numerical Investigation of Double-Diffusive Convection in an Irregular Porous Cavity Subjected to Inclined Magnetic Field Using Finite Element Method

Purpose—This study aims to perform an in-depth analysis of double-diffusive natural convection (DDNC) in an irregularly shaped porous cavity. We investigate the convective heat transfer process induced by the lower wall treated as a heat source while the side walls of the enclosure are maintained at a lower temperature and concentration, and the remaining wall is adiabatic. Various factors, such as the Rayleigh number, Darcy effects, Hartmann number, Lewis number and effects of magnetic inclination are evaluated for their influence on flow dynamics and heat distribution. Design/methodology/approach—After validating the results, the FEM (finite element method) is used to simulate the flow pattern, temperature variations, and concentration by solving the nonlinear partial differential equations with the modified Rayleigh number (104 ≤ Ra ≤ 107), Darcy number (10−4 ≤ Da ≤ 10−1), Lewis number (0.1≤Le≤10), and Hartmann number 0≤Ha≤40 as the dimensionless operating parameters. Findings—The finding shows that the patterns of convection and the shape of the isotherms within porous enclosures are notably affected by the angle of the applied magnetic field. This study enhances our understanding of how double-diffusive natural convection (DDNC) operates in these enclosures, which helps improve heating and cooling technologies in various engineering fields. Research limitations/implications—Numerical and experimental extensions of the present study make it possible to investigate differences in thermal performance as a result of various curvatures, orientations, boundary conditions, and the use of three-dimensional analysis and other working fluids. Practical implications—The geometry configurations used in this study have wide-ranging applications in engineering fields, such as in heat exchangers, crystallization, microelectronics, energy storage, mixing, food processing, and biomedical systems. Originality/value—This study shows how an inclined magnetic field affects double-diffusive natural convection (DDNC) within a porous system featuring an irregularly shaped cavity, considering various multiphysical conditions

DeepLabV3, IBCO-based ALCResNet: A fully automated classification, and grading system for brain tumor

Brain tumors, which are uncontrolled growths of brain cells, pose a threat to people worldwide. However, accurately classifying brain tumors through computerized methods has been difficult due to differences in size, shape, and location of the tumors and limitations in the medical field. Improved precision is critical in detecting brain tumors, as small errors in human judgments can result in increased mortality rates. This paper proposes a new method for improving early detection and decision-making in brain tumor severity using learning methodologies. Clinical datasets are used to obtain benchmark images of brain tumors, which undergo pre-processing, data augmentation with a Generative Adversarial Network, and classification with an Adaptive Layer Cascaded ResNet (ALCResNet) optimized with Improved Border Collie Optimization (IBCO). The abnormal images are then segmented using the DeepLabV3 model and fed into the ALCResNet for final classification into Meningioma, Glioma, or Pituitary. The IBCO algorithm-based ALCResNet model outperforms other heuristic classifiers for brain tumor classification and severity estimation, with improvements ranging from 1.3% to 4.4% over COA-ALCResNet, DHOA-ALCResNet, MVO-ALCResNet, and BCO-ALCResNet. The IBCO algorithm-based ALCResNet model also achieves higher accuracy than non-heuristic classifiers such as CNN, DNN, SVM, and ResNet, with improvements ranging from 2.4% to 3.6% for brain tumor classification and 0.9% to 3.8% for severity estimation. The proposed method offers an automated classification and grading system for brain tumors and improves the accuracy of brain tumor classification and severity estimation, promoting more precise decision-making regarding diagnosis and treatment