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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.

This study aims to investigate the effects of sick-, well- and healed-baby appeals used in fashion products on purchase intentions through anticipated emotions. Consumers' perceived saliency of the environmental issues in the fashion industry was examined as an influencing factor that further explains the persuasion of the advertising appeals.

Two sets of experimental studies were conducted with 201 participants in Study 1 and 186 participants in Study 2.

The results demonstrated that well- and healed-baby appeals increased purchase intentions fully mediated by anticipated positive emotions. In particular, the mediation effect was conditionally significant when individuals' saliency of environmental issues was low. The three types of advertising appeals did not differ in consumers with high saliency for environmental issues. A sick-baby appeal did not induce purchase intentions through anticipated negative emotions. The mediation effect of anticipated negative emotions did not work with any appeal type.

Retail marketers can use the findings to create commercial messages to persuade their fashion consumers. If the brand has consumers with low saliency, either educating consumers about the importance of environmental issues in the fashion industry or using a well- or healed-baby approach in their advertising can increase purchase intentions due to the increased anticipated positive emotions. Increasing the threat level of environmental problems by using a sick-baby appeal would not work, as consumers' anticipated negative emotions (e.g. feeling of guilt from not buying green products) would not convince them to purchase the green product.

Attention is one of the most important factors to affect the academic performance of students. Effectively analyzing students' attention in class can promote teachers' precise teaching and students' personalized learning. To intelligently analyze the students' attention in classroom from the first-person perspective, this paper proposes a fusion model based on gaze tracking and object detection. In particular, the proposed attention analysis model does not depend on any smart equipment.

Given a first-person view video of students' learning, the authors first estimate the gazing point by using the deep space–time neural network. Second, single shot multi-box detector and fast segmentation convolutional neural network are comparatively adopted to accurately detect the objects in the video. Third, they predict the gazing objects by combining the results of gazing point estimation and object detection. Finally, the personalized attention of students is analyzed based on the predicted gazing objects and the measurable eye movement criteria.

A large number of experiments are carried out on a public database and a new dataset that is built in a real classroom. The experimental results show that the proposed model not only can accurately track the students' gazing trajectory and effectively analyze the fluctuation of attention of the individual student and all students but also provide a valuable reference to evaluate the process of learning of students.

The contributions of this paper can be summarized as follows. The analysis of students' attention plays an important role in improving teaching quality and student achievement. However, there is little research on how to automatically and intelligently analyze students' attention. To alleviate this problem, this paper focuses on analyzing students' attention by gaze tracking and object detection in classroom teaching, which is significant for practical application in the field of education. The authors proposed an effectively intelligent fusion model based on the deep neural network, which mainly includes the gazing point module and the object detection module, to analyze students' attention in classroom teaching instead of relying on any smart wearable device. They introduce the attention mechanism into the gazing point module to improve the performance of gazing point detection and perform some comparison experiments on the public dataset to prove that the gazing point module can achieve better performance. They associate the eye movement criteria with visual gaze to get quantifiable objective data for students' attention analysis, which can provide a valuable basis to evaluate the learning process of students, provide useful learning information of students for both parents and teachers and support the development of individualized teaching. They built a new database that contains the first-person view videos of 11 subjects in a real classroom and employ it to evaluate the effectiveness and feasibility of the proposed model.

The purpose of this paper is to present a deep ensemble neural network model for the detection of forest fires in aerial vehicle videos.

Presented deep ensemble models include four convolutional neural networks (CNNs): a faster region-based CNN (Faster R-CNN), a simple one-stage object detector (RetinaNet) and two different versions of the you only look once (Yolo) models. The presented method generates its output by fusing the outputs of these different deep learning (DL) models.

The presented fusing approach significantly improves the detection accuracy of fire incidents in the input data.

The computational complexity of the proposed method which is based on combining four different DL models is relatively higher than that of using each of these models individually. On the other hand, however, the performance of the proposed approach is considerably higher than that of any of the four DL models.

The simulation results show that using an ensemble model is quite useful for the precise detection of forest fires in real time through aerial vehicle videos or images.

By this method, forest fires can be detected more efficiently and precisely. Because forests are crucial breathing resources of the earth and a shelter for many living creatures, the social impact of the method can be considered to be very high.

This study fuses the outputs of different DL models into an ensemble model. Hence, the ensemble model provides more potent and beneficial results than any of the single models.

The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification.

In this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent.

The proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors.

It can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers.

It can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer.

In this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types.

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.

This paper aims to explore how the establishment modes used by emerging economy multinational corporations (EE-MNCs) influence their subsequent experiences of liability of origin (LOO) in developed economies based on the causal-model theory of categorization.

Taking Chinese listed firms' direct investments in developed economies as the sample, this paper utilizes Heckman (1979)'s self-selection model to examine the effect of establishment modes. Besides, when checking the robustness, subsample analyses and 2SLS regressions are used to rule out the alternative explanation associated with LOO mitigation.

EE-MNCs that enter a developed economy by greenfield investment experience heightened LOO while entries using M&A are associated with the mitigated liability. When EEMNCs enter a more institutionally distant developed country, the establishment modes will be more determinant of their subsequent experiences of this liability. Moreover, the effect of establishment modes can recede when EE-MNCs have established their presence in a developed country for a longer time.

This paper utilizes the causal-model theory of categorization to articulate the underlying mechanisms through which the country-of-origin cue is weakened by the cue transmitted by M&A. It further considers the context-saliency of the cue of M&A and clarifies boundary conditions for the effectiveness of this establishment mode to mitigate LOO.

Although photographs in sustainability reports are useful in conveying complex messages, they may also be used to manipulate the presentation of disclosures to exploit the limited cognitive processing capacity of humans. Therefore, this paper aims to examine the features of photographs aimed at capturing individuals’ attention through visual structures and evoking specific emotions through carefully chosen content. Furthermore, it examines whether such framing practice is explained by incentives for legitimizing behaviours and influencing reputation.

The authors conduct a content analysis of photographs in 154 sustainability reports published by US companies. The authors captured the nature of photographs, the context in which they are being used, their themes and emotional content and layout and interaction features to understand how photographs are used for attribute framing to influence information processing. Furthermore, the authors statistically examine the framing practice between companies with different characteristics to identify any patterns for the impression management use of photographs in sustainability reports.

Photographs are often large with a horizontal orientation to capture attention and show content viewed at eye level and in either medium or close-up shots to engage viewers. Furthermore, photographs are emotionally loaded with different themes such as depictions of people, technology and nature. These themes are used to predominately evoke positive emotions of awe, nurturance, pride, amusement and attachment. This practice is often used by companies in environmentally sensitive areas that have close consumer relationships or are covered controversially in the media.

The authors reveal reporting practices and identify photographic features that attract attention and convey emotions that go beyond aesthetic qualities. This is important because emotions conveyed through photographs can be potentially misleading and influence judgements subconsciously.

Explainable artificial intelligence (XAI) has importance in several industrial applications. The study aims to provide a comparison of two important methods used for explainable AI algorithms.

In this study multiple criteria has been used to compare between explainable Ranked Area Integrals (xRAI) and integrated gradient (IG) methods for the explainability of AI algorithms, based on a multimethod phase-wise analysis research design.

The theoretical part includes the comparison of frameworks of two methods. In contrast, the methods have been compared across five dimensions like functional, operational, usability, safety and validation, from a practical point of view.

A comparison has been made by combining criteria from theoretical and practical points of view, which demonstrates tradeoffs in terms of choices for the user.

Our results show that the xRAI method performs better from a theoretical point of view. However, the IG method shows a good result with both model accuracy and prediction quality.

Much of what we know about work from home (WFH) is based on data collected in routine times, where WFH is applied on a partial and voluntary basis. This study leverages the conditions of mandatory WFH imposed by COVID-19 lockdowns to shed new light on factors that relate to well-being and performance among employees who WFH. Specifically, the authors explore how boundary control and push–pull factors (constraints and benefits that employees associate with WFH) interact to shape employees' exhaustion and goal setting/prioritization.

Surveys were administered in Israel and in the USA to 577 employees in “teleworkable” roles who were mandated to WFH shortly after the COVID-19 outbreak (March–April 2020).

(1) Boundary control is negatively related to exhaustion and positively related to goal setting/prioritization. (2) These associations are weakened by perceptions of high WFH constraints (push factors). (3) WFH benefits (pull factors) attenuate the moderating effect of WFH constraints.

Organizations may benefit from identifying and boosting the saliency of WFH benefits, while considering and remedying WFH constraints.

The authors contribute theoretically by integrating push–pull factors into the discussion about WFH and boundary management. We also make a contextual contribution by drilling down into the specificities of nonvoluntary WFH. The expected upward trends in nonvoluntary WFH rates underscore the need to understand factors that improve outcomes among individuals who lack agency in the decision to WFH.