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As a distinctive form of communist heritage tourism, the ideology and government-led form of red tourism warrants an in-depth examination of how tourists consume and perceive it. This study aims to reveal tourists’ perception of red tourism through the lens of destination image.

This study collected 9,819 user-generated photographs within four types of red tourism destinations (RTDs) and used a computer visual and semiotic analysis approach to conduct photograph-based cognitive and affective attributes extraction. Network analysis further visualized the co-relations between cognitive images and affective images. ANOVA analysis compared the differences of the four types of destination images.

Ten dimensions of cognitive image and eight categories of affective image of red tourism were identified. It found that monuments, statues, memorial symbols were the distinctive cognitive features, and admiration was the most dominant emotion. Heterogeneity of destination images was identified among the four types of RTDs.

To the best of the authors’ knowledge, the study is one of the first to explore tourists’ consumption of red tourism through the lens of destination image, which reveals the inconsistencies between the officially projected images and tourists’ perceived images of red tourism. Using Plutchik’s model, it validates a series of positive and negative emotions contributing to the affective images of red tourism, which expands the findings of emotions within the extant red tourism research. Through combined applications of computer visual and semiotic analysis, ANOVA, network analysis and model visualization, the study provides an important methodological triangulation for photograph-based destination image studies.

红色旅游作为共产主义旅游的独特形式, 游客如何感知这种国家意识形态植入与政府主导型旅游值得深入研究。本研究旨在从目的地意象视角揭示游客红色旅游感知。

本研究收集四种类型的红色旅游地9819张用户生成照片, 利用计算机视觉-情感析法对照片进行认知和情感元素提取。复杂网络分析揭示了认知意象与情感意象之间的关联。方差分析比较了四种红色旅游地意象的差异。

本研究确定了红色旅游认知意象的十个维度和情感意象的八个类别。研究发现, 纪念碑、雕像、纪念符号是其独特的认知意象元素, 钦佩是其最主要的情感，四种类型红色旅游地意象存在差异性。

本文是同类研究中首次从目的地意象视角探索游客对红色旅游地感知, 揭示了红色旅游官方投射意象与游客感知意象之间的差异。利用Plutchik情感之轮模型, 验证了一系列积极和消极情绪构成红色旅游地情感意象, 拓展了红色旅游的情感发现。综合运用计算机视觉-情感分析、方差分析、网络分析和模型可视化等方法, 为基于照片的旅游目的地意象研究提供了一个重要方法。

Como forma distintiva del turismo del patrimonio comunista, la ideología y la forma gubernamental del turismo rojo justifican un examen en profundidad de cómo lo consumen y perciben los turistas. Este estudio pretende revelar la percepción que tienen los turistas del turismo rojo desde la perspectiva de la imagen del destino.

Este estudio recopiló 9.819 fotografías generadas por los usuarios dentro de cuatro tipos de destinos de turismo rojo, y utilizó un enfoque de análisis visual y semiótico por ordenador para llevar a cabo la extracción de atributos cognitivos y afectivos basados en fotografías. El análisis de redes visualizó además las correlaciones entre las imágenes cognitivas y las imágenes afectivas. El análisis ANOVA comparó las diferencias de los cuatro tipos de imágenes de destino.

Se identificaron diez dimensiones de imagen cognitiva y ocho categorías de imagen afectiva del turismo rojo. Se descubrió que los monumentos, las estatuas y los símbolos conmemorativos eran los rasgos cognitivos distintivos, y la admiración la emoción más dominante. Se identificó una heterogeneidad de imágenes de destino entre los cuatro tipos de destinos de turismo rojo.

El estudio es uno de los primeros en explorar el consumo de turismo rojo por parte de los turistas a través de la lente de la imagen del destino, lo que revela las incoherencias entre las imágenes proyectadas oficialmente y las imágenes percibidas por los turistas del turismo rojo. Utilizando el modelo de Plutchik, valida una serie de emociones positivas y negativas que contribuyen a las imágenes afectivas del turismo rojo, lo que amplía los hallazgos sobre las emociones dentro de la investigación existente sobre el turismo rojo. Mediante aplicaciones combinadas de análisis visual y semiótico por ordenador, ANOVA, análisis de redes y visualización de modelos, el estudio proporciona una importante triangulación metodológica para los estudios de la imagen del destino basados en fotografías.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

The study seeks to contribute to a deeper understanding of the relationship between remediations and participation in new media. By lending some transparency, the analysis hopes to contribute toward generating a critical optics aware of the potentials and pitfalls of emergent media.

The methodology is visual semiotic analysis. The author make no claim for one, true interpretation or critical judgment about the images.

In demonstrating some shortfalls of Instagram affordances, the analysis shows how social media sites can develop tools that encourage users to engage in civic consciousness and respectful political debate. The study makes clear that new media tools can hamper or aid participatory logics.

To author’s knowledge, no other study that has analyzed remediated images related to the controversial confirmation of Brett Kavanaugh to the U.S. Supreme Court. It is also important to place these images in the contexts of “iconicity” in emergent media (a concept increasingly being eroded in new media environment).

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments.

The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information.

The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets.

Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.

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.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

In pursuit of affordable and nutrient-rich food alternatives, the symbiotic culture of bacteria and yeast (SCOBY) emerged as a selected food ink for 3D printing. The purpose of this paper is to harness SCOBY’s potential to create cost-effective and nourishing food options using the innovative technique of 3D printing.

This work presents a comparative analysis of the printability of SCOBY with blends of wheat flour, with a focus on the optimization of process variables such as printing composition, nozzle height, nozzle diameter, printing speed, extrusion motor speed and extrusion rate. Extensive research was carried out to explore the diverse physical, mechanical and rheological properties of food ink.

Among the ratios tested, SCOBY, with SCOBY:wheat flour ratio at 1:0.33 exhibited the highest precision and layer definition when 3D printed at 50 and 60 mm/s printing speeds, 180 rpm motor speed and 0.8 mm nozzle with a 0.005 cm³/s extrusion rate, with minimum alteration in colour.

Food layered manufacturing (FLM) is a novel concept that uses a specialized printer to fabricate edible objects by layering edible materials, such as chocolate, confectionaries and pureed fruits and vegetables. FLM is a disruptive technology that enables the creation of personalized and texture-tailored foods, incorporating desired nutritional values and food quality, using a variety of ingredients and additions. This research highlights the potential of SCOBY as a viable material for 3D food printing applications.

This study aims to examine how veganism is “seen” by young adult non-vegan consumers and how prevailing attitudes reinforce or challenge stigmas around veganism.

Photovoice methodology was used to explore young non-vegan consumers’ attitudes and beliefs towards veganism. Data was collected from students studying advertising at a major university in Australia, who produced images and narratives reflective of their own attitudes towards veganism. Polytextual thematic analysis of the resulting visual data was then undertaken to reveal the dominant themes underpinning participants’ attitudes. Participant narratives were then reviewed to confirm whether the ascribed meaning aligned with participants’ intended meaning.

Participant images were reflective of first, how they saw their world and their place within it, which showed the interplay and interconnectedness between humans, animals and nature, and second, how they saw vegans within this world, with both positive and negative attitudes expressed. Interestingly, vegans were simultaneously admired and condemned. By situating these attitudes along a spectrum of moral evaluation, bounded by stigmatisation and moral legitimacy, participants saw vegans as being either Radicals, Pretenders, Virtuous or Pragmatists. For veganism to become more widely accepted by non-vegans, there is an important role to be played by each vegan type.

This study offers a more nuanced understanding of how and why dissociative groups, such as vegans, become stigmatised, which has implications for messaging and marketing practices around veganism and associated products/services. Future research could use a similar methodology to understand why other minority groups in society are stereotyped and stigmatised, which has broader social implications.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.