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This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.

This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.

From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.

This paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.

The purpose of this paper is to profile wayfinders into homogeneous sub-groups according to their wayfinding ability, and to investigate the differences between the clusters identified and their evaluations of satisfaction.

This study uses survey data collected in a hospital in the Northern part of Italy. The survey questionnaire assessed the participants’ self-estimation of wayfinding ability in terms of wayfinding competence, wayfinding strategy and wayfinding anxiety, as well as the wayfinder’s satisfaction.

The findings propose that three factors, namely, individual orientation skills, confidence in servicescape elements and anxiety control, contribute to defining wayfinding ability. Based on these factors, cluster analysis reveals three profiles of wayfinders, as follows: the Easy Goings, the Do-it-yourselves and the Insecures. Group differentiation comes from wayfinding ability and customer satisfaction levels.

The results of this study advance the segmentation literature by analyzing different types of wayfinding ability that can lead to different satisfaction levels.

These findings will help service managers improve servicescape design and help them formulate effective targeting strategies.

While previous research outlined the importance of some factors such as gender differences, familiarity with the service environment and cognitive approaches, this study recommends the examination of the profile of visitors to the service setting to allow them to find their way more effectively.

This research aims to shed greater light on millennials’ green behavior by examining four psychographic variables (selfless altruism, frugality, risk aversion, and time orientation) that may be relevant to millennials’ motives to engage in environmental activities.

Data were collected from a sample of younger millennials (n = 276; age = 18 to 30) using a self-administered questionnaire. The data were then analyzed using structural equation modeling (SEM) technique.

Overall, the results of the study reveal that rational and self-oriented rather than emotional and others-oriented motives lead millennials to act pro-environmentally.

The findings of this study have implications for environmental advocates, policymakers and green marketers. For instance, the findings suggest that environmental regulators and lawmakers should continue their efforts to provide economic incentives to encourage pro-environmental purchases among millennials. Additionally, marketers of green products may pursue self-directed targeting strategies in promoting green products among millennials.

Millennials grasp the environmental consequences of their actions and have the education, motivation and social awareness to participate in the green movement. However, they have not truly begun to fully integrate their beliefs and actions. The present study is an initial attempt to address this issue by investigating various psychological factors that are relevant to the millennials’ core behavioral motives.

The purpose of this paper is to examine whether any differences exist between high- and low/middle-income Generation Y luxury consumers in terms of their service quality perceptions on luxury fashion brands’ own official e-commerce sites.

This study focused on actual luxury consumers who purchased luxury fashion items from luxury fashion brands’ e-commerce sites. An online survey asked participants to evaluate their perceptions of e-service attributes available on luxury fashion brands’ own official e-commerce sites based on their experience with the site. A total of 123 usable respondents obtained.

Of the nine e-service quality dimensions identified, efficiency and web appearance were significant dimensions affecting high-income Generation Y luxury fashion consumers’ overall e-satisfaction. For low/middle-income Generation Y luxury fashion consumers, order/delivery management, personalization and trust were crucial factors that affected overall e-satisfaction.

Despite the growth of luxury e-commerce sales and the increasing interest in luxury consumption by consumers from a variety of demographic groups, little research has focused on how luxury consumers perceive luxury brands’ own official e-commerce site and how luxury fashion brands develop their own e-commerce sites to meet demographically dissimilar customers’ necessities. The findings of the study provide valuable practical implications to luxury fashion brands by proving that luxury consumers are unalike and that their perceptions on e-service quality are dissimilar based on different income levels.

This paper aims to explore the numerical study of the steady two-dimensional MHD hybrid Cu-Fe₃O₄/EG nanofluid flows over an inclined porous plate with an inclined magnetic effect. Iron oxide (Fe₃O₄) and copper (Cu) are hybrid nanoparticles, with ethylene glycol as the base fluid. The effects of several physical characteristics, such as the inclination angle, magnetic parameter, thermal radiation, viscous propagation, heat absorption and convective heat transfer, are revealed by this exploration.

Temperature and velocity descriptions, along with the skin friction coefficient and Nusselt number, are studied to see how they change depending on the parameters. Using compatible similarity transformations, the controlling equations, including those describing the momentum and energy descriptions, are turned into a set of non-linear ordinary differential equations. The streamlined mathematical model is then solved numerically by using the shooting approach and the Runge–Kutta method up to the fourth order. The numerical findings of skin friction and Nusselt number are compared and discussed with prior published data by Nur Syahirah Wahid.

The graphical representation of the velocity and temperature profiles within the frontier is exhibited and discussed. The various output values related to skin friction and the Nusselt number are shown in the table.

The new results are compared to past research and discovered to agree significantly with those authors’ published works.