Website Image Research Articles

АНАЛІЗ ІСНУЮЧИХ МЕТОДІВ РОЗПІЗНАВАННЯ ОБ'ЄКТІВ НА ПРИКЛАДІ ЗОБРАЖЕННЯХ ВЕБСТОРІНОК

Object recognition on web pages is the process of automatic detection and classification of objects on website images. The work analyzes the methods of recognition of objects on digital images for segmentation of websites for further analysis. Based on the analysis of the methods of recognition of objects on the images of web pages, it is concluded that deep learning methods can be very effective for recognizing objects with a lot of data, but they may require significant computing resources and data for training. Geometric functions can be effective for recognizing objects with standardized shapes and sizes, but they can be less effective for recognizing objects with high variability in forms and sizes. The choice of object recognition method on the images of websites depends on various factors, such as the size of the data set, the characteristics of the identified objects, computing resources available for use, and many other factors.

Analysis of The Influence of Trust In Digital Transactions and Website Reputation on Customers Purchase Intention Through Footwear Website

This study examines how attitudes regarding online buying and the consequences for purchase intention are influenced by perceived website repute, relative advantage, perceived website image, and trust. Researchers employed a survey method in this study to conduct descriptive research. A questionnaire was used in this survey approach. E-commerce consumers who reside in Jabodetabek make up the study's population. Using judgmental sampling, the researcher employed a non-probability sampling technique. In this study, the single-cross-sectional approach was employed for data collecting. The structural equation model method will be used to analyze the data. Amos is the program that's being used. According to the data processing results, it was discovered that, in the first place, relative profits, trust, and website image are all significantly positively impacted by perceived website reputation. Second, trust, the perception of websites, and attitudes toward online commerce are considerably improved by relative advantage. Third, perceptions of websites have a detrimental effect on attitudes toward internet buying. Fourth, sentiments toward internet purchasing are positively impacted by trust, though not much. Opinions regarding internet purchasing significantly positively influence fifth intentions to purchase. This demonstrates that consumers' intentions to buy through a website positively correlate with their attitude about online buying.

Deep Learning-Based Localization and Orientation Estimation of Pedicle Screws in Spinal Fusion Surgery.

This study investigated the application of a deep learning-based object detection model for accurate localization and orientation estimation of spinal fixation surgical instruments during surgery. We employed the You Only Look Once (YOLO) object detection framework with oriented bounding boxes (OBBs) to address the challenge of non-axis-aligned instruments in surgical scenes. The initial dataset of 100 images was created using brochure and website images from 11 manufacturers of commercially available pedicle screws used in spinal fusion surgeries, and data augmentation was used to expand 300 images. The model was trained, validated, and tested using 70%, 20%, and 10% of the images of lumbar pedicle screws, with the training process running for 100 epochs. The model testing results showed that it could detect the locations of the pedicle screws in the surgical scene as well as their direction angles through the OBBs. The F1 score of the model was 0.86 (precision: 1.00, recall: 0.80) at each confidence level and mAP50. The high precision suggests that the model effectively identifies true positive instrument detections, although the recall indicates a slight limitation in capturing all instruments present. This approach offers advantages over traditional object detection in bounding boxes for tasks where object orientation is crucial, and our findings suggest the potential of YOLOv8 OBB models in real-world surgical applications such as instrument tracking and surgical navigation. Future work will explore incorporating additional data and the potential of hyperparameter optimization to improve overall model performance.

Harmful female footwear: A public health perspective

Footwear fashion is an instance of a socially formed attitude affecting somatic population health. High-heeled, particularly pointy-toed shoes are posed to structurally distort and overload feet leading to musculoskeletal sequelae. Here we compiled multilanguage website images presenting female footwear produced by the top manufacturers to assess the advertising effects on the prevailing height of heels worn by women. The method was based on the analysis of websites using the command “woman shoes” in scores of languages of the Internet Google browser. We then compared the results of the internet search with those of a live street surveillance of the footwear worn by 100 adult women in the downtown Warsaw metropolis in Poland. We found that stiletto heels with pointed shoe tips significantly predominated in images representing the countries belonging to the Western cultural sphere compared to less affluent world areas where low or flat heels prevailed. However, we noted a gradual departure from the fashion of high heels over the last decade, confirmed by live street surveillance, liable to reflect changes in the website presentations of top shoe manufacturers consistent with increasing awareness of potential harm by high heels. Yet the female aptitude for wearing more physiologic shoe models appears to exceed that resulting from marketing campaigns. Doing away with high-heeled pointy-toed shoes requires intensification of pro-health preventive measures in the field of public health.

What Makes Consumers Loyal to a Particular Online Travel Website? Case of booking.com

It is critical for an online travel website to not only attract new customers but only enhance existing customer loyalty to improve the website's image and generate revenue. This research aims to find out the factors that affect loyalty among consumers of online travel websites. The variables in this study were selected after profound research on the literature review and review of the customers' feedback who were using the Booking.com website. Models of antecedents were tested such as price, convenience, experience, and interaction in predicting consumer satisfaction and the impact on brand loyalty among online travel website customers. Data was collected from 420 users of an online travel website (Booking.com) and analysed through the Statistical Package for the Social Sciences (SPSS). The findings show that all constructs influence consumer satisfaction toward online travel websites and consequently influence customer brand loyalty. The research results will create awareness for entrepreneurs, organisations, researchers, and the public to improve marketing strategies and enhance customer retention and brand loyalty. Websites need to consistently deliver high-quality packages and services to stay on top of trends and build trust with customers by providing good service. Live customer service is one of the most significant implications since interaction with the website significantly affects customer satisfaction. As such, websites need to be available in real-time for the customers to enhance brand loyalty. The research outcomes can be beneficial to enhance the reputation of the website and generate more revenue.

Purchase Determinants Of Customer Loyalty: The Mediating Effect Of E-Trust In E-Commerce

This study aims to find out and explain how the effect of e-service quality and website image on e-customer loyalty when online shopping is mediated by e-customer trust in e-commerce, both directly and indirectly. this research is quantitative. The sampling method used was purposive sampling with a research instrument in the form of a questionnaire which was distributed to 280 respondents who had used the e-commerce service. The results showed that the e-service quality and website image quality had a direct effect on e-customer trust and e-customer loyalty. E-trust has an important role in mediating the relationship between e-service quality and recovery service quality on e-customer loyalty. Therefore, an e-commerce must be able to make customers trusted by providing good e-service quality and good image in the event of a service failure by the company, learning from these findings we must maintain customer trust in terms of customer service so that these customers remain loyal and do not switch to competitors.

Content Analysis of Websites of Didactic Programs in Dietetics for Evidence of Diversity, Equity, and Inclusion

ObjectiveTo examine websites of didactic programs in dietetics (DPD) for diversity, equity, and inclusion (DEI)-related language and to assess perceived gender and ethnic/racial representation in images. MethodsWe conducted a content analysis on the first 6 website images and 3-elements of DPD (n = 212) websites (mission statement, goals and objectives, and the immediate landing page). Descriptive statistics and a website DEI score were calculated. ResultsEquity and inclusion-related terms appeared on < 5% of websites. Diversity and culture-related terms appeared on about one-third of websites. On average, researchers perceived 28.45% of people in website images as non-White, and 20.11% as non-female. Most websites received low DEI scores. Conclusions and ImplicationsThe DPD websites had a limited representation of DEI-related terms and image representation of individuals perceived to be non-female and non-White. Leaders of DPDs may consider displaying and describing the program's commitment to DEI on the website for potential students.

A Hybrid Multimodal Data Fusion-Based Method for Identifying Gambling Websites

With the development of network technology, the number of gambling websites has grown dramatically, causing a threat to social stability. There are many machine learning-based methods are proposed to identify gambling websites by analyzing the URL, the text, and the images of the websites. Nevertheless, most of the existing methods ignore one important piece of information, i.e., the text within the website images. Only the visual features of images are extracted for detection, while the semantic features of texts on the images are ignored. However, these texts have key information clearly pointing to gambling websites, which can help us identify such websites more accurately. Therefore, how to fuse image and text multimodal data is a challenge that should be met.Motivated by this, in this paper, we propose a hybrid multimodal data fusion-based method for identifying gambling websites by extracting and fusing visual and semantic features of the website screenshots. First, we fine tune the pretrained ResNet34 model to train an image classifier and to extract visual features of webpage screenshots. Second, we extract textual content from webpage screenshots through the optical character recognition (OCR) technique. We use pretrained Word2Vec word vectors as the initial embedding layer and use Bi-LSTM to train a text classifier and extract semantic features of textual content on the screenshots. Third, we use self-attention to fuse the visual and semantic features and train a multimodal classifier. The prediction results of image, text, and multimodal classifiers are fused by the late fusion method to obtain the final prediction result. To demonstrate the effectiveness of the proposed method, we conduct experiments on the webpage screenshot dataset we collected. The experimental results indicate that OCR text on the webpage screenshots has strong semantic features and the proposed hybrid multimodal data fusion based method can effectively improve the performance in identifying gambling websites, with accuracy, precision, recall, and F1-score all over 99%.

Aquila coyote-tuned deep convolutional neural network for the classification of bare skinned images in websites

Aquila coyote-tuned deep convolutional neural network for the classification of bare skinned images in websites

From one to five stars: An exploratory study of how consumer reviews and digital brand identity shape maternal pediatrician selection

Digital media initiated a paradigm shift of the doctor-patient relationship in which prospective patients “shop” for physicians as they do other goods and services: using search engines’ results, aggregated review websites, and star ratings as cues. These are processed centrally or peripherally in accordance with the Elaboration Likelihood Model (ELM). Electronic word of mouth (EWOM) in conjunction with a pediatrician’s digital brand management strategy impacts practice revenue through patient recruitment and retention. This exploratory study found that a pediatrician’s placement on a review website and overall digital brand image leads to increased or decreased trust in expertise, competence, and likability.

Online Nursery Product Management System

Our web application provide vegetable seeds and flower plants to the users its only show the list of vegetable seeds and flower plant available in the website. User can view and buy the product from the website user can update his/her profile in website. In the website images of product is view on the screen. Admin can add product, view details to product, view details to users, send mail to user and delete users. Admin is allowed to edit and delete the customer information.

Deep Learning Frameworks for Cervical Mobilization Based on Website Images

Background: Deep learning related research works on website medical images have been actively conducted in the field of health care, however, articles related to the musculoskeletal system have been introduced insufficiently, deep learning-based studies on classifying orthopedic manual therapy images would also just be entered. Objectives: To create a deep learning model that categorizes cervical mobilization images and establish a web application to find out its clinical utility. Design: Research and development. Methods: Three types of cervical mobilization images (central posteroanterior (CPA) mobilization, unilateral posteroanterior (UPA) mobilization, and anteroposterior (AP) mobilization) were obtained using functions of ‘Download All Images’ and a web crawler. Unnecessary images were filtered from 'Auslogics Duplicate File Finder' to obtain the final 144 data (CPA=62, UPA=46, AP=36). Training classified into 3 classes was conducted in Teachable Machine. The next procedures, the trained model source was uploaded to the web application cloud integrated development environment (https://ide.goorm.io/) and the frame was built. The trained model was tested in three environments: Teachable Machine File Upload (TMFU), Teachable Machine Webcam (TMW), and Web Service webcam (WSW). Results: In three environments (TMFU, TMW, WSW), the accuracy of CPA mobilization images was 81-96%. The accuracy of the UPA mobilization image was 43~94%, and the accuracy deviation was greater than that of CPA. The accuracy of the AP mobilization image was 65-75%, and the deviation was not large compared to the other groups. In the three environments, the average accuracy of CPA was 92%, and the accuracy of UPA and AP was similar up to 70%. Conclusion: This study suggests that training of images of orthopedic manual therapy using machine learning open software is possible, and that web applications made using this training model can be used clinically.

Is a picture always worth 1000 words? Website images of classrooms and perceptions of the institution

When classroom facilities are out of date, students complain (Habaci et al., in Procedia Soc Behav Sci 64:58-64 2012, https://doi.org/10.1016/j.sbspro.2012.11.008 ). But, before students arrive on campus, what do they know about their classrooms? Media has changed how people acquire information; websites are second only to college tours in influencing prospective students (Langmead in How college websites influence students’ web experience. eCampusNews. Today’s Innovation in Education (2013). https://www.ecampusnews.com/2013/03/21/how-college-websites-influence-students-web-experience/?all ); furthermore, photographs impact website design (Kolowich in 14 of the best college websites (And why they’re so awesome) (2019), Hubspot. https://blog.hubspot.com/marketing/best-college-websites ). Can photographed classrooms influence judgments of the institution itself? In this 2 × 2 between-subjects experiment, participants read a scenario about a college too far away to visit and viewed a website picture of a seminar room (unrenovated or renovated) before responding to measures of classroom satisfaction and college academic life more broadly (e.g., student retention). Institutional rank (mentioned or not), was the 2nd variable. Participants were 300 AmazonMTurk workers, 237 of whom passed manipulation checks and were retained in analyses. The classroom furnishings comprised a scale (10 items, Cronbach’s alpha = 0.879), as did questions dealing with perceptions of the institution (9 items, Cronbach’s alpha = 0.897). Analyses revealed a significant main effect for renovation status, for both the furnishings scale (p < 0.001) and perceptions of the institution (p < 0.001), but not for rank (p = 0.052), with an institution being ranked in the top 50 being viewed more positively. For renovation status, the newer classroom was viewed more positively for both scales. Classroom status also significantly influenced estimates of first-year student retention (renovation status, p < 0.001; rank, p = 0.027), with higher estimates of retention for the renovated classroom and when rank was mentioned.

Gendered virtual environments of STEM fields: a cultural-ecological analysis of predominantly white and historically black institutions

Although many studies have examined gender and racial discrepancies in STEM participation, few have considered variation in the gendered construction of STEM across racial spaces. We applied a cultural psychological perspective to investigate whether variation in conceptions of gender identity across African American and European American settings resonates with variation in gendered constructions of science, technology, engineering, and mathematics (STEM) domains across predominantly white institutions (PWIs) and historically black colleges and universities (HBCUs). We further examined implications of engagement with virtual STEM departments across these school categories. In Study 1, independent coders rated website images as less masculine, and evaluated website climates more favorably, for physics and biology departments at HBCUs relative to PWIs. In Studies 2 and 3, we used these website images as stimuli in within- and between-subjects experiments. Participants gave more positive ratings for sense of belonging and perceived climate in response to images from physics departments at HBCUs than at PWIs; women rated physics departments at PWIs as higher in masculinity than those at HBCUs. We replicated these patterns in Study 3, and also found that lower sense of belonging due to exposure to images from PWI physics departments predicted less interest in pursuing STEM fields among women only. We discuss implications of our findings for understanding and addressing gender discrepancies in STEM participation.

A Deep Learning Technique for Web Phishing Detection Combined URL Features and Visual Similarity

The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.

Automated Visual Content Analysis (AVCA) in Communication Research: A Protocol for Large Scale Image Classification with Pre-Trained Computer Vision Models

ABSTRACT The increasing volume of images published online in a wide variety of contexts requires communication researchers to address this reality by analyzing visual content at a large scale. Ongoing advances in computer vision to automatically detect objects, concepts, and features in images provide a promising opportunity for communication research. We propose a research protocol for Automated Visual Content Analysis (AVCA) to enable large-scale content analysis of images. It offers inductive and deductive ways to use commercial pre-trained models for theory building in communication science. Using the example of corporations’ website images on sustainability, we show in a step-by-step fashion how to classify a large sample (N = 21,876) of images with unsupervised and supervised machine learning, as well as custom models. The possibilities and pitfalls of these approaches are discussed, ethical issues are addressed, and application examples for future communication research are detailed.

Upright and Honorable: People Use Space to Understand Honor, Affecting Choice and Perception.

Honor is abstract. We predict that people make sense of honor metaphorically as an up-right position in space and that endorsing honor values makes this metaphor more accessible. Supporting our prediction, people in China (Study 1) and the United States (Studies 1-4) associate honor with up and right and dishonor with down and left, controlling for the association of positive with up-right (Studies 3, 4). We document downstream consequences for choice and perception of this metaphoric representation. Regarding choice, Americans who endorse honor values and voted for then-candidate Trump prefer photographs in which President Trump is positioned in the up-right quadrant (Study 5). Images from conservative news websites position the President's face in the up-right quadrant more than nonconservative ones (Study 6). Regarding perception, Americans who rate President Trump as honorable are more likely to perceive him as facing up and to the right in news website images (Study 7).

Representações do aprender línguas em narrativas visuais: o que mostram as imagens de sites de escolas de inglês

This article is about the discourse on learning languages construed and transmitted by images in websites of private schools specialized in foreign languages teaching, evidencing the representations construed by these establishments to society. The argument favors the consideration of images in language studies, and the theoretical basis adopted includes the socio-semiotic approach to language, the Grammar of Visual Design, and the concept of social representation. The corpus analysis evidences the representation of the learning in optimal conditions of comfort, homogeneity, with the presence of resources and diverse instruments, in which the student is an agent of processes related to linguistic reception. It is concluded that these visual narratives contribute to the idea widely spread in society that the ideal language learning happens in private specialized schools and is reserved for few people.

A Rule-Based Method for Table Detection in Website Images

A Rule-Based Method for Table Detection in Website Images

Web Content Analysis of University Forestry and Related Natural Resources Landing Webpages in the United States in Relation to Student and Faculty Diversity

AbstractUnder-representation of minorities and women in natural resources (NR)-related fields is common, both as students and as professionals. The perception of a predominantly white, male student body and workforce is a potential barrier to recruiting women and minorities to Forestry and related-NR fields. The images of “forestry” and “natural resources-related” fields were examined as they would be from a potential student’s perspective looking at university and college websites with these degree program majors at the 80 National Association of University Forest Resource Programs member institutions during in 2016. Results suggest the percentage of women and minorities enrolled explains little variation in a program’s website image content, whereas there is significant image under-representation of minorities in all NR fields and of women on forestry-major webpages. In addition, women and minorities are more likely to be portrayed in a passive image, such as posing on campus, rather than in an active image, such as measuring a tree. Potential students are also less likely to see diverse faculty online. A lack of diversity image inclusion in Forestry and related NR will not help students see themselves in these fields or enhance society’s view of these critical fields for future employment and economic growth.