Predictive modeling of consumer purchase behavior on social media: Integrating theory of planned behavior and machine learning for actionable insights.

In recent times, it has been observed that social media exerts a favorable influence on consumer purchasing behavior. Many organizations are adopting the utilization of social media platforms as a means to promote products and services. Hence, it is crucial for enterprises to understand the consumer buying behavior in order to thrive. This article presents a novel approach that combines the theory of planned behavior (TPB) with machine learning techniques to develop accurate predictive models for consumer purchase behavior. This study examines three distinct factors of the theory of planned behavior (attitude, social norm, and perceived behavioral control) that provide insights into the primary determinants influencing online purchasing behavior. A total of eight machine learning algorithms, namely K-nearest neighbor, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, Support Vector Machine, AdaBoost, and Gradient Boosting, were utilized in order to forecast consumer purchasing behavior. Empirical findings indicate that gradient boosting demonstrates superior performance in predicting customer buying behavior, with an accuracy rate of 0.91 and a macro F1 score of 0.91. This holds true when all factors, namely attitude (ATTD), social norm (SN), and perceived behavioral control (PBC), are included in the analysis. Furthermore, we incorporated Explainable AI (XAI), specifically LIME (Local Interpretable Model-Agnostic Explanations), to elucidate how the best machine learning model (i.e. gradient boosting) makes its prediction. The findings indicate that LIME has demonstrated a high level of confidence in accurately predicting the influence of low and high behavior. The outcome presented in this article has several implications. For instance, this article presents a novel way to combine the theory of planned behavior with machine learning techniques in order to predict consumer purchase behavior. This integration allows for a comprehensive analysis of factors influencing online purchasing decisions. Also, the incorporation of Explainable AI enhances the transparency and interpretability of the model. This feature is valuable for organizations seeking insights into factors driving predictions and the reasons behind certain outcomes. Moreover, these observations have the potential to offer valuable insights for businesses in customizing their marketing strategies to align with these influential factors.

Influence of thermal conductivity on transient mixed convection in a vented cavity with a hollow cylinder and filled with CNT-water nanofluid.

This study is aimed to perform a numerical time-dependent investigation thermal conductivity effect of the annular cylinder within a vented cavity using CNT based-water nanofluid. For demonstrating the effect of thermal conductivity, four distinct hollow cylinder materials such as Ks = 0.5(Plastic tiles), Ks = 0.84(Clay tiles), Ks = 1.1(Concrete tiles), and Ks = 2(Slate tiles) are introduced together with a suitable variation of dimensionless time (0 ≤ τ ≤ 1). The governing equations of the model with associated boundary conditions is solved using finite element based Galerkin's weighted residual method. Different contour plots for thermal and flow field transformation and mean Nusselt number, mean fluid temperature, bulk convective field temperature, temperature gradient, pressure gradient, vortices, and fluid velocity magnitude are presented for qualitative and quantitative thermal performance analysis. With the decrease of solid thermal conductivity, 27.3% thermal transport enhancement is noted from the heated surface of the cylinder. However, a 16.3% increase in the bulk fluid temperature has been recorded with the increase in cylinder conductivity. The numerical outcomes from this investigation propose a better thermo-fluid efficiency compared to the existing methodology which can be suggestive to engineers and researchers for designing heat exchangers, heat pipes, and other thermal systems.

Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework

World economy as well as public health have been facing a devastating effect caused by the disease termed as Coronavirus (COVID-19). A significant step of COVID-19 affected patients treatment is the faster and accurate detection of the disease which is the motivation of this study. In this paper, implementation of a deep transfer learning-based framework using a pre-trained network (ResNet-50) for detecting COVID-19 from the chest X-rays was done. Our dataset consists of 2905 chest X-ray images of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). The implemented neural network demonstrates significant performance in classifying the cases with an overall accuracy of 96%. Most importantly, the model has shown a significantly good performance over the current research-based methods in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Recall = 1.00, F1-score = 1.00 and Specificity = 1.00). Therefore, our proposed approach can be adapted as a reliable method for faster and accurate COVID-19 affected case detection.