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Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.

Fashion merchandising hold a paramount position within the realm of retail marketing. Currently, the purpose of this article is that the assessment of display effectiveness predominantly relies on the subjective judgment of merchandisers due to the absence of an effective evaluation method. Although eye-tracking devices have found extensive used in tracking the gaze trajectory of subject, they exhibit limitations in terms of stability when applied to the evaluation of various scenes. This underscores the need for a dependable, user-friendly and objective assessment method.

To develop a cost-effective and convenient evaluation method, the authors introduced an image processing framework for the assessment of variations in the impact of store furnishings. An optimized visual saliency methodology that leverages a multiscale pyramid model, incorporating color, brightness and orientation features, to construct a visual saliency heatmap. Additionally, the authors have established two pivotal evaluation indices aimed at quantifying attention coverage and dispersion. Specifically, bottom features are extract from 9 distinct scale images which are down sampled from merchandising photographs. Subsequently, these extracted features are amalgamated to form a heatmap, serving as the focal point of the evaluation process. The authors have proposed evaluation indices dedicated to measuring visual focus and dispersion, facilitating a precise quantification of attention distribution within the observed scenes.

In comparison to conventional saliency algorithm, the optimization method yields more intuitive feedback regarding scene contrast. Moreover, the optimized approach results in a more concentrated focus within the central region of the visual field, a pattern in alignment with physiological research findings. The results affirm that the two defined indicators prove highly effective in discerning variations in visual attention across diverse brand store displays.

The study introduces an intelligent and cost-effective objective evaluate method founded upon visual saliency. This pioneering approach not only effectively discerns the efficacy of merchandising efforts but also holds the potential for extension to the assessment of fashion advertisements, home design and website aesthetics.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

The purpose of this manuscript is to detect heart fault using Electrocardiogram. Mutually low and high frequency noises such as electromyography (EMG) and power line interference (PLI) degrades the performance of ECG signals.

The ECG record depicts the procedural electrical movement of the heart, which is non-invasive foot age obtained by placing surface electrodes on designated locations of the patient’s skin. The main concept of this manuscript is to present a novel filtering method to cancel the unwanted noises in ECG signal. Here, intrinsic time scale decomposition (ITD) is introduced to suppress the effect of PLI from ECG signals.

In the existing ITD, the gain control parameter is a constant value; however, in this paper it is an adaptive feature that varies according to certain constraints. Simulation outcomes show that the proposed method effectively reduces the effect of PLI and quantitatively express the effectiveness with different evaluation metrics.

The results found by the proposed method are compared with Fourier decomposition technique and eigen value decomposition methods (EDM) to validate the effectiveness of the proposed method.

The purpose of this study is to analyze the effect of wine tourism on the economic, social and environmental performance, i.e. the sustainable performance, of Spanish wineries. In addition, age, size and membership in the protected designation of origin are introduced as control variables to increase the precision of the cause-effect relationships analyzed.

A conceptual model is proposed, which is tested by means of structural equation modeling based on data from a survey of 202 Spanish wineries.

The results indicate the existence of a positive and significant link between wine tourism activities and the three performance typologies analyzed in the Spanish wine context.

The study contributes to the academic literature on wine tourism in a remarkable way, as, to the best of the authors’ knowledge, there is no previous literature that has addressed the effect of wine tourism on the sustainable performance of Spanish wineries, making the study useful for both academics and wine professionals who are considering the implementation or development of this typology of tourism in their facilities.

This research investigates the factors influencing consumers' intention to purchase e-deals from group buying websites, focussing on e-deal proneness, price consciousness and anticipatory regret.

Three studies (n = 539) were conducted using data collected from an online consumer panel and tested via structural equation modelling and PROCESS macro in SPSS.

The findings suggest that subjective norms, perceived behavioural control and attitudes positively influence consumers' e-deal purchase intention. Additionally, price consciousness amplifies the relationship between consumers' e-deal proneness and purchase intention, and price-conscious respondents are more likely to have the intention to buy e-deals when faced with some form of anticipatory regret.

Based on the research findings, practitioners are advised to prioritise social norms and entertainment value when promoting the attractiveness of e-deals, using strategies such as social media and influencer marketing. Brands should also emphasise the value of e-deals by showcasing comparative price savings and discounts to motivate consumers to buy.

This paper addresses an interesting and practical issue related to the effects of group buying websites, focussing on e-deal proneness, price consciousness and anticipatory regret.

This research focuses on analysing the effect of wine tourism on green product and process innovations developed by Spanish wineries. In addition, age, size and membership in a protected designation of origin (PDO) are introduced as control variables to increase the precision of the cause–effect relationship analysed.

The study proposes a conceptual model based on previous studies, which is tested using structural equations (partial least squares structural equation modelling [PLS-SEM]) with data collected from 202 Spanish wineries.

The research results show that wine tourism activity has a positive and significant influence on green product and process innovation.

The research contributes to the academic literature in several ways. First, the study advances knowledge and understanding of the benefits generated by wine tourism. Second, the research contributes to the literature that analyses the wine tourism–sustainability link, since it is predicted that this type of tourism can increase the capacity for green innovation. Third, to the best of the authors’ knowledge, there is no previous research that has analysed wine tourism as a catalytic variable for green innovation. Fourth, the proposed theoretical model has not been previously addressed in the academic literature, so the study represents an important advance in scientific knowledge.

The research focuses on analysing the effect of wine tourism (WT) on the green performance (GP) of Spanish wineries, as well as the mediating role of green intellectual capital (GIC) and the moderating effect of circular economy practices (CEPs) developed by wineries in this main relationship. In addition, age, size and protected designation of origin (PDO) membership are introduced as control variables to increase the precision of the cause–effect relationships analysed.

A conceptual model is proposed through the literature review carried out and then verified through structural equation modelling (PLS-SEM) based on data obtained from a survey of 202 Spanish wineries between September 2021 and January 2022.

The results of the study show that WT activity has a positive and significant effect on the GP of wineries, also demonstrating the mediating effect of GIC and the moderating role of CEPs in this relationship.

The study contributes to the academic literature in several ways. First, to the best of our knowledge, no previous study has addressed the impact of WT on the set of wineries' ecological intangibles. Second, to the authors’ knowledge, no previous study has analysed the mediating effect of GIC on the WT-GP relationship. Third, there is no previous attempt to deal with the moderating role of CEPs in the main relationship under analysis. Fourth, the proposed theoretical model has not been previously addressed in the academic literature.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.