The present research explores linear as well as nonlinear radiation patterns based on the MHD non-Newtonian (Maxwell) nanofluid flow having Arrhenius activation energy. This study's core focus is MHD properties in non-Newtonian fluid dynamics and boundary layer phenomena analysis. It initiates with time-dependent equations, employing boundary layer approximations. Extensive numerical computations, executed with custom Compact Visual Fortran code and the EFD method, provide profound insights into non-Newtonian fluid behavior, revealing intricate force interactions and fluid patterns. To check the stability of the solution, a convergence and stability analysis is performed. With the values of Delta Y = 0.25, Delta tau = 0.0005, and Delta X = 0.20; it is found that the model convergence occurs to the Lewis number, Le > 0.016 as well as the Prandtl number, Pr > 0.08. In this context, investigating non-dimensional results that depend on multiple physical factors. Explanation and visual representations of the effects of different physical characteristics and their resultant temperatures, concentrations, and velocity profiles are provided. As a result of the illustrations, the skin friction coefficient and Sherwood number, which are calculated, as well as Nusselt values, have all come up in discussion. Additionally, detailed representations of isothermal lines and streamlines are implemented, and it is pointed out that the development of these features occurs at the same time as Brownian motion. Furthermore, the temperature field for Maxwell fluid is modified due to the impression of chemical reaction as well as the Dufour number (Kr and Du). Our research demonstrates the superior performance of non-Newtonian solutions, notably in cases involving activation energy and nonlinear radiation. This paradigm shift carries significant implications. In another context, the interplay between Maxwell fluid and nonlinear radiation is notably affected by activation energy, offering promising applications in fields like medicine and industry, particularly in groundbreaking cancer treatment approaches.

COVID-19 has devastated every aspect of life worldwide, where slum dwellers are particularly vulnerable due to persistent inequities. This study aims to ex

The Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem, commonly studied in computer science and operations research. Due to its complexity and broad applicability, various algorithms have been designed and developed from the viewpoint of intelligent search. In this paper, we propose a two-stage method based on the clustering concept integrated with an intelligent search technique. In the first stage, a set of clustering techniques - fuzzy c-means (FCM), k-means (KM), and k-mediods (KMD) - are employed independently to generate feasible routes for the TSP. These routes are then optimized in the second stage using an improved Genetic Algorithm (IGA). Actually, we enhance the traditional Genetic Algorithm (GA) through an advanced selection strategy, a new position-based heuristic crossover, and a supervised mutation mechanism (FIB). This IGA is implemented to the feasible routes generated in the clustering stage to search the optimized route. The overall solution approach results in three distinct pathways: FCM+IGA, KM+IGA, and KMD+IGA. Simulation results with 47 benchmark TSP datasets demonstrate that the proposed FCM+IGA performs better than both KM+IGA and KMD+IGA. Moreover, FCM+IGA outperforms other clustering-based algorithms and several state-of-the-art methods in terms of solution quality.

Human iris classification remains an active research area in the fields of biometrics as well as computer vision. In iris biometrics, most of the visible or near-infrared (NIR) eye images suffer from multiple noise sources, and the dispersive spectrum changes hugely. These changes occur due to spattering, albedo, and spectrum absorbance selectively. However, accurate iris classification for distance images is still a challenging task. To solve it effectively, we propose a machine learning (ML)-based iris classification employing a dense feature extraction method with various distance metrics. More specifically, this learning model focuses on the Histogram of Oriented Gradients (HOG) descriptor and K-Nearest Neighbour (K-NN) classifier with various distance metrics. The HOG descriptor has some advantages for this proposed distant-based iris classification, for example, insensitive to multiple lighting and noises, shift invariance, capacity to tolerate iris variations within the classes, etc. Additionally, this study investigates the most reliable distance metric that is less affected by different levels of noise. A publicly accessible CASIA-V4 distance image database is conducted for the experimental evaluation. To evaluate the performance of the classification models, we consider different measures such as recall, precision, F1-scores, and accuracy. The reported results are tabulated as well as optimized through Receiver Operating Characteristic (ROC) curves. The experimental results demonstrate that the Canberra distance metric with low dimensional HOG features provides better recognition accuracy (90.55%) compared to other distance metrics.