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This research sought to determine the impact of the management of electronic customer relationships through applying 5IS model on the mental image of Umniah Mobile Network Operator Company’s customers in Amman City. To fulfill the goals of the study, the researcher adopted the descriptive, analytic method. He developed an instrument to collect the data through a questionnaire, which was distributed through the simple, random sampling method over 700 customers of Umniah Company, in the City of Amman, out of which 400 analyzable questionnaires were retrieved. The researcher further employed the convenient statistical methods applying SPSS 22 Program for data analysis. The study concluded many results such as there is statistically significant impact at the significance level (α < 0.05) for the administration of consumer relationships through the use of 5IS model on the mental image of the provided services of Umniah Mobile Network Operator Company in Amman city. In this concern, the integration component is the most influential in the mental image with the customers of Umniah Company. Therefore, the study recommended the need Umniah Telecom Company has to search for the best means to positively influence shaping the mental image of its products, especially the means through which it can provide more information about its services.

This research delves into the dynamic realm of app design by examining the impact of app icon familiarity and authority on image fit, influencing users' app usage intention. Focusing on the distinctive circumstances of Chinese and Korean customers, the study aims to provide insightful information about how application user behavior changes.

Utilizing structural equation modeling, the study employs data from 293 Korean and Chinese consumers. The research design incorporates a thoughtful approach, including parallel translation methods, focus group interviews, and pre-experimental testing to ensure survey accuracy and validity. The study strategically selects stimuli from the Apple App Store rankings, emphasizing icon features and type considerations.

The results provide important new information about the connections between usage intention, image fit, authority, and familiarity with app icons. Notably, app icon familiarity and authority positively influence image fit. Furthermore, app icon image fit emerges as a positive predictor of usage intention, mediating the complex interplay between familiarity, authority, and intention. The study also identifies moderating effects, shedding light on the nuanced role of app icon features and types.

Originating from a comprehensive exploration of icons, this study significantly contributes to the field by exploring icon differences and uncovering the intricate mechanisms guiding users' decisions. The findings offer valuable insights for app designers, marketers, and researchers seeking a deeper understanding of user behavior in diverse cultural contexts, thereby enhancing the theoretical and practical foundations in app usability and consumer behavior.

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.

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.

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.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction 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.

Using computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.

This paper proposes a binocular image classification model to solve ferrographic image classification problems.

This paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/

Fixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.

Through an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.

The comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).

In this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

This paper aims to identify different combinations of causal conditions (celebrity attributes) that explain our outcome: destination image. More specifically, three main research questions guide our work: (1) Which attributes should an outstanding sportsperson have to enhance the image of his/her country as a destination image? (2) Are these the same for different product categories? (3) Do tourists and residents differ?

To this end, the fuzzy-set Qualitative Comparative Analysis (fsQCA) was used with a sample of 187 participants (105 tourists and 82 residents).

Results show that some attributes of a sports celebrity are more critical than others in enhancing destination image. Those attributes of sports celebrities appearing in the intermediate and parsimonious analysis should be prioritized. This is the case of trustworthiness. Second, experience is a peripheral requirement (only appeared in the intermediate analysis). Third, attractiveness is unnecessary and an even and undesired attribute in many solutions. Fourth, when comparing tourists and residents, both groups value the role of football players, while residents also appreciate the role of marathon runners. Tennis players are the less relevant sports celebrities to build Spain’s destination image.

First, a new statistical analysis in the marketing discipline, QCA, has been used. The use of qualitative approaches to investigate destination images has been scarce. Second, the study of the role of sports celebrity endorsement on brand–place attachment has yet to be investigated. Third, studies about the role of residents in the image of a tourism destination/city are scarce. Tourists and residents must be investigated because they can benefit from sports celebrities' activities.