The impact of convection and radiation on the thermal distribution of the wavy porous fin is examined in the present study. A hybrid model that combines the differential evolution (DE) algorithm with an artificial neural network (ANN) is proposed for predicting the heat transfer of the wavy porous fin. The equation representing the thermal variation in the wavy porous fin is reduced to its dimensionless arrangement and is numerically solved using Rung, e-Kutta-Fehlberg’s fourth-fifth order method (RKF-45). The study demonstrates the effectiveness of this hybrid model, and the results indicate that the proposed approach outperforms the ANN model with parameters obtained through grid search (GS), showcasing the superiority of the hybrid DE-ANN model in terms of accuracy and performance. This research highlights the potential of utilizing DE with ANN for improved predictive modeling in the heat transfer sector. The originality of this study is that it addresses the heat transfer problem by optimizing the selection of parameters for the ANN model using the DE algorithm.

The growing demand for fiber-reinforced polymer (FRP) in industrial applications has prompted the exploration of natural fiber-based composites as a viable alternative to synthetic fibers. Using jute–rattan fiber-reinforced composite offers the potential for environmentally sustainable waste material decomposition and cost reduction compared to conventional fiber materials. This article focuses on the impact of different machining constraints on surface roughness and delamination during the drilling process of the jute–rattan FRP composite. Inspired by this unexplored research area; this article emphasizes the influence of various machining constraints on surface roughness and delamination in drilling jute–rattan FRP composite. Response surface methodology designs the experiment using drill bit material; spindle speed; and feed rate as input variables to measure surface roughness and delamination factors. The technique of order of preference by similarity to the ideal solution method is used to optimize the machining parameters; and for predicting surface roughness and delamination; two machine learning-based models named random forest (RF) and support vector machine (SVM) are utilized. To evaluate the accuracy of the predicted values; the correlation coefficient (R2); mean absolute percentage error; and mean squared error were used. RF performed better in comparison with SVM; with a higher value of R2 for both testing and training datasets; which is 0.997; 0.981; and 0.985 for surface roughness; entry delamination; and exit delamination; respectively. Hence; this study presents an innovative methodology for predicting surface roughness and delamination through machine learning techniques.

Internet of Things (IoT) is one of the efflorescing technologies of recent years with splendid real-time applications in the fields of healthcare, agriculture, transportation, industry, and environmental monitoring. In addition to the dominant applications and services of IoT, many challenges exist. As there is a lack of standardization for IoT technologies, the architecture emerged as the foremost challenge. The salient issues in designing an IoT architecture encompass connectivity, data handling, heterogeneity, privacy, scalability, and security. The standard IoT architectures are the ETSI IoT Standard, the ITU-T IoT Reference Model, IoT-A Reference Model, Intel’s IoT Architecture, the Three-Layer Architecture, Middle-Based Architecture, Service-Oriented Architecture, Five-Layer Architecture, and IWF Architecture. In this paper, we have reviewed these architectures and concluded that IWF Architecture is most suitable for the effortless development of IoT applications because of its immediacy and depth of insight in dealing with IoT data. We carried out this review concerning smart healthcare as it is among the major industries that have been leaders and forerunners in IoT technologies. Motivated by this, we designed the novel Smart Healthcare Reference Architecture (SHRA) based on IWF Architecture. Finally, present the significance of smart healthcare during the COVID-19 pandemic. We have synthesized our findings in a systematic way for addressing the research questions on IoT challenges. To the best of our knowledge, our paper is the first to provide an exhaustive investigation on IoT architectural challenges with a use case in a smart healthcare system.

This study emphasizes the aspects of heat transfer and transient thermal distribution through a rectangular fin profile when a stretching or shrinking mechanism is mounted on the surface of the fin. Furthermore, the effects of radiation, internal heat generation, and convection are all considered when developing the corresponding fin problem. The simulated time-dependent heat transfer equation is a partial differential equation that can be represented by dimensionless arrangement using appropriate nondimensional terms. The nonlinear dimensionless problem concerning the stretching/shrinking of a fin is numerically solved using the finite difference method (FDM), and the Levenberg–Marquardt method of backpropagation artificial neural network (LMM-BANN) has been used in this investigation. By varying the stretching/shrinking parameter, a set of data for the presented artificial neural network (ANN) is produced to discuss stretching and shrinking scenarios. The testing, training, and validation procedure of LMM-BANN, as well as correlation for verification of the validity of the proposed approach, establish the approximate solution to stretching/shrinking scenarios. The suggested model LMM-BANN is then validated using regression interpretation, mean square error, and histogram explorations. The ANN results and the procured numerical values agree well with the current numerical results.

Walking (gait) irregularities and abnormalities are predictors and symptoms of disorder and disability. In the past, elaborate video (camera-based) systems, pressure mats, or a mix of the two has been used in clinical settings to monitor and evaluate gait. This article presents an artificial intelligence-based comprehensive investigation of ground reaction force (GRF) pattern to classify the healthy control and gait disorders using the large-scale ground reaction force. The used dataset comprised GRF measurements from different patients. The article includes machine learning- and deep learning-based models to classify healthy and gait disorder patients using ground reaction force. A deep learning-based architecture GaitRec-Net is proposed for this classification. The classification results were evaluated using various metrics, and each experiment was analysed using a fivefold cross-validation approach. Compared to machine learning classifiers, the proposed deep learning model is found better for feature extraction resulting in high accuracy of classification. As a result, the proposed framework presents a promising step in the direction of automatic categorization of abnormal gait pattern.

In the present article, we investigate the free convective flow of a ternary hybrid nanofluid in a two-phase inclined channel saturated with a porous medium. The flow has been propelled using the pressure gradient, thermal radiation, and buoyancy force. The flow model’s governing equations are resolved using the regular perturbation approach. The governing equations are solved with the help of the regular perturbation method. Polyethylene glycol and water (at a ratio of 50%:50%) fill up Region I, while a ternary hybrid nanofluid based on zirconium dioxide, magnesium oxide, and carbon nanotubes occupies Region II. The ternary hybrid nanofluids are defined with a mixture model in which three different shapes of nanoparticles, namely spherical, platelet, and cylindrical, are incorporated. The consequences of the most significant variables have been examined using both visual and tabular data. The main finding of this work is that utilising a ternary hybrid nanofluid at the plate y = 1 increases the rate of heat transfers by 753%, demonstrating the potential thermal efficiency. The overall heat and volume flow rates are amplified by buoyant forces and viscous dissipations and dampened by the thermal radiation parameter. The optimum enhancement of temperature is achieved by the influence of buoyancy forces. A ternary nanofluid region experiences the maximum temperature increase compared to a clear fluid region. To ensure the study’s efficiency, we validated it with prior studies.