Google Images Research Articles

Attitude towards Utilization of e-Resources of Medical Images among Health Care Professionals

Aim: Medicine is deeply depending on medical images. Medical professionals used medical images particularly digital medical images for education, research and clinical decisions. The aim of this study is to investigate the utilization of medical image e-resources among the medical professionals of Lahore. Methods: The study is based on qualitative research design and in-depth semi-structured interviews of twenty medical professionals (Professor, Associate, Assistant professor, and Consultants) were conducted. The interviewees were purposely selected from various basic health and clinical health science departments of Shalamar Medical & Dental College and Shalamar Hospital, Lahore. Data were collected by using interviews schedule. Validity of interview guide was ensured by expert review of interview schedule by field experts. Results: A large number of participants were used both formats of resources (print and electronic). Hundred percept of medical professionals were used Google image search for seeks of digital image and this is favorite source. Most of the clinical medical professionals were used up-to-date application, they searched quickly and easily for wards round, clinical decisions, early recovery of the patient, training and research purpose in contrast most of the basic health professionals used the college library, print collections, personal collections and Google images for their teaching, research and education. Similar the large number of the clinical sides they were used details images for their clinical decisions, patient recovery, teaching and research on opposite basic medical professionals were used the general images and a small group were used detailed and specialized images for teaching the students. Conclusion: The research is unique in this regard as there is a dearth of literature on the research area particularly in Pakistani context. Keywords: Medical faculty, health professionals, image search, Pakistan,

The impact of cognitive style on the level of satisfaction and image search behavior in the textual and content search system of Google Images

PurposeThe purpose of this study aims to identify the impact of verbal-visual cognitive styles on the level of satisfaction and behavior in the textual and content search of Google Images.Design/methodology/approach“Riding” cognitive style test and satisfaction questionnaire were used as data collection tools. Also, to collect data related to the image search behavior, the subjects’ transaction files were recorded using Camtasia software and then the files observed and reviewed. The research sample was 90 postgraduate students of Shiraz University.FindingsThe results showed that cognitive styles in interaction with the text-based and content-based search system of “Google Images” affected user’s satisfaction. Text-based image retrieval, in which vocabulary-based information needs were expressed, was more compatible with the verbal cognitive style and resulted in greater satisfaction. In contrast, in content-based image retrieval, where it was possible to express information needs in the form of images, users were more satisfied with the visual cognitive style. Verbal users performed more positively in text-based search and visual users in content-based search.Originality/valueConsidering the research gap, which has identified the performance of visual text-based and content-based systems in terms of satisfaction and cognitive style search behavior, the present study could be considered a small effort to promote science.

THE DIGITAL CLOSET: PLATFORM CENSORSHIP AND LGBTQIA+ INDEPENDENCE ONLINE

This presentation draws on data from my forthcoming book with MIT Press to demonstrate how heteronormative and cisnormative bias pervade Silicon Valley culture, get embedded in benchmark datasets and machine learning algorithms, and get formalized in company policies and labor practices surrounding content moderation. The presentation begins with an examination of workplace culture at Google, gaining insights from Department of Labor investigations, testimonials from previous employees, and informal surveys and discourse analysis conducted by employees during the circulation of James Damore's infamous 'Google memo'. The presentation then moves on to examine bias embedded in benchmark datasets like WordNet and ImageNet, both of which served as the training datasets for Google's Image Recognition algorithms (like GoogLeNet). Lastly, the presentation turns to Facebook's heteronormative and cisnormative content moderation policies and the outsourced labor practices it uses to institute what Facebook has described as 'human algorithms' to review content in accordance with these policies. Throughout the presentation I demonstrate that we can piece together information about proprietary code by looking to leaked documents, public records, press releases, open-source code, and benchmark datasets, all of which, in this instance, instigate a systemic heteronormative and cisnormative bias that is increasingly being embedded in the internet.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

Metric hull as similarity-aware operator for representing unstructured data

• We propose the novel concept of metric hulls. • We define the covered operator using mutual distances only. • We evaluate the proposal on synthetic and real-life data. • Our method achieves online response times. • We show that a few hull objects provide high coverage in comparison to the related work. Similarity searching has become widely utilized in many online services processing unstructured and complex data, e.g., Google Images. Metric spaces are often applied to model and organize such data by their mutual similarity. As top-k queries provide only a local view on data, a data analyst must pose multiple requests to observe the entire dataset. Thus, group-by operators for metric data have been proposed. These operators identify groups by respecting a given similarity constraint and produce a set of objects per group. The analyst can then tediously browse these sets directly, but representative members may provide better insight. In this paper, we focus on concise representations of metric datasets. We propose a novel concept of a metric hull which encompasses a given set by selecting a few objects. Testing an object to be part of the set is then made much faster. We verify this concept on synthetic Euclidean data and real-life image and text datasets and show its effectiveness and scalability. The metric hulls provide much faster and more compact representations when compared with commonly used ball representations.

The impact of mental models on the treatment and research of chronic infections due to biofilms.

Research on biofilms is predominantly made ininvitrocontexts. However, invivoobservation of biofilms in human chronic infections shows distinct differences compared toinvitrobiofilm growth.This could imply the use of an inadequate mental model both in research and healthcare practices. Drawing on knowledge from the cognitive sciences, we hypothesise that the predominance of invitro research on biofilms is skewed towards a mental model promoting wrong inferences for researchers and healthcare professionals (HCPs) in the invivo context. To explore the prevalence of such a mental model, we carried out a qualitative image analysis in which biofilm illustrations from a Google image search were coded for typical invitro or invivo characteristics. Further, to investigate potential misinformed and unhelpful clinical interventions related to biofilms, we conducted a quantitative questionnaire among HCPs. The questions were designed to test whether knowledge about invitro biofilms was used in an invivo context. This questionnaire was analysed through a chi-squared test. Most biofilm illustrations were consistent with the invitro model. A statistical analysis of survey responses revealed that HCPs have adequate knowledge about biofilm but often respond incorrectly when asked to apply their knowledge to invivo contexts. The outcome of this research points to a prevalent and consolidated mental model derived from invitro observations. This model has likely been made dominant by HCPs' frequent exposure to visual depictions in articles and presentations. The prevalence of the invitro model sets up the possibility of erroneous claims when the invitro model is inadequately applied to invivo contexts. This has potential implications for HCPs working in fields involving biofilm, such as wound care treatment.

The Representation of Millenial Hijrah Image in Online Media: Gender and Multimodal Critical Discourse Analysis

This study aims to see how millennial hijrah is represented in images and how men and women are represented in hijrah images. Feminist methodology is used with multimodal and gender as a tool of analysis. This is based on the assumption that a text in the study of critical discourse analysis is not to be value-free and has a specific ideological purpose. Data collection techniques by searching on google images with the keyword 'gambar hijrah'. The results showed that the majority of hijrah in the google image was represented by women. There are indications of the dominance of patriarchal ideology in millennial hijrah and gender inequality in women. In addition to patriarchal ideology, the struggle for the hegemony of signs and gender hegemony in the millennial hijrah cannot be separated from the textual reading of the al-Qur'an and al-Hadith.

Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts

The objective of this research was to evaluate the deep learning neural network in artificial intelligence (AI) technologies to rapidly classify seven different beef cuts (bone in rib eye steak, boneless rib eye steak, chuck steak, flank steak, New York strip, short rib, and tenderloin). Color images of beef samples were acquired from a laboratory-based computer vision system and collected from the Internet (Google Images) platforms. A total of 1,113 beef cut images were used as training, validation, and testing data subsets for this project. The model developed from the deep learning neural network algorithm was able to classify certain beef cuts (flank steak and tenderloin) up to 100% accuracy. Two pretrained convolution neutral network (CNN) models Visual Geometry Group (VGG16) and Inception ResNet V2 were used to train, validate, and test these models in classifying beef cut images. An image augmentation technique was incorporated in the convolution neutral network models for avoiding the overfitting problems, which demonstrated an improvement in the performance of the image classifier model. The VGG16 model outperformed the Inception ResNet V2 model. The VGG16 model coupled with data augmentation technique was able to achieve the highest accuracy of 98.6% on 116 test images, whereas Inception ResNet V2 accomplished a maximum accuracy of 95.7% on the same test images. Based on the performance metrics of both models, deep learning technology evidently showed a promising effort for beef cuts recognition in the meat science industry.

Do you see what I see? Images of the COVID-19 pandemic through the lens of Google

During times of crisis, information access is crucial. Given the opaque processes behind modern search engines, it is important to understand the extent to which the “picture” of the Covid-19 pandemic accessed by users differs. We explore variations in what users “see” concerning the pandemic through Google image search, using a two-step approach. First, we crowdsource a search task to users in four regions of Europe, asking them to help us create a photo documentary of Covid-19 by providing image search queries. Analysing the queries, we find five common themes describing information needs. Next, we study three sources of variation – users’ information needs, their geo-locations and query languages – and analyse their influences on the similarity of results. We find that users see the pandemic differently depending on where they live, as evidenced by the 46% similarity across results. When users expressed a given query in different languages, there was no overlap for most of the results. Our analysis suggests that localisation plays a major role in the (dis)similarity of results, and provides evidence of the diverse “picture” of the pandemic seen through Google.

Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)’s main route (Karakorum Highway)

The development of landslide inventory is the first step towards landslide hazard management and planning. The proper inventory will also help to develop landslide susceptibility maps which will result to minimize economic and human losses. This work investigates landslide hazard mapping in the rugged mountain terrain vis-a-vis highly economically significant route between China and Pakistan i.e. Karakuram Highway (KKH). KKH is passing through the Karakorum mountainous region where landslides occur frequently and pose a serious threat to local travelers and tourists as well as to trading caravans. In this work, landslide inventory was developed (302 landslides) along KKH by visual interpretation of Sentinel and google images. Field survey was also carried to validate landslide datasets. The landslide dataset was divided into modelling/training (70%) and testing/validation (30%) datasets to develop and validate landslide susceptibility maps (LSM) using three models Frequency Ratio (FR), weight of evidence, and Analytic Hierarchy Process (AHP). To develop LSMs, landslide controlling factors that include Slope, Aspect, Landcover, Geology, Proximity to Fault, Distance to Road and Stream, and Precipitation are correlated and considered using GIS techniques. LSMs generated using testing datasets are validated by Area Under Curve (AUC) criterion. The results show that weight of evidence, FR and AHP have success rate curves of 61%, 84% and 72%, respectively. In addition, most highly accurate three models are validated for their prediction power using testing landslide datasets. The results for prediction capacity for weight of evidence, FR and AHP are 72%, 64%, and 58%, respectively. At the end, landslide susceptibility index (LSI) maps were classified into susceptibility zones. We also compared variation of weights in each class in AHP and FR model. The overall trend of increase or decrease in weights remains same except in few classes like precipitation class. The validation and prediction results show that FR model is the most reliable and accurate model which can be used for landslide management and planning. Our results will be helpful to minimize landslide hazard losses along KKH, ultimately assisting in successful implementation of CPEC idea between China and Pakistan.

Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)’s main route (Karakorum Highway)

The development of landslide inventory is the first step towards landslide hazard management and planning. The proper inventory will also help to develop landslide susceptibility maps which will result to minimize economic and human losses. This work investigates landslide hazard mapping in the rugged mountain terrain vis-a-vis highly economically significant route between China and Pakistan i.e. Karakuram Highway (KKH). KKH is passing through the Karakorum mountainous region where landslides occur frequently and pose a serious threat to local travelers and tourists as well as to trading caravans. In this work, landslide inventory was developed (302 landslides) along KKH by visual interpretation of Sentinel and google images. Field survey was also carried to validate landslide datasets. The landslide dataset was divided into modelling/training (70%) and testing/validation (30%) datasets to develop and validate landslide susceptibility maps (LSM) using three models Frequency Ratio (FR), weight of evidence, and Analytic Hierarchy Process (AHP). To develop LSMs, landslide controlling factors that include Slope, Aspect, Landcover, Geology, Proximity to Fault, Distance to Road and Stream, and Precipitation are correlated and considered using GIS techniques. LSMs generated using testing datasets are validated by Area Under Curve (AUC) criterion. The results show that weight of evidence, FR and AHP have success rate curves of 61%, 84% and 72%, respectively. In addition, most highly accurate three models are validated for their prediction power using testing landslide datasets. The results for prediction capacity for weight of evidence, FR and AHP are 72%, 64%, and 58%, respectively. At the end, landslide susceptibility index (LSI) maps were classified into susceptibility zones. We also compared variation of weights in each class in AHP and FR model. The overall trend of increase or decrease in weights remains same except in few classes like precipitation class. The validation and prediction results show that FR model is the most reliable and accurate model which can be used for landslide management and planning. Our results will be helpful to minimize landslide hazard losses along KKH, ultimately assisting in successful implementation of CPEC idea between China and Pakistan.

The conventions and politics of migration data visualizations

What visual features characterize online migration data visualizations, and what do they suggest for the politics of representing migration and informing public attitudes? Audiences increasingly encounter quantitative information through visualization, especially in digital environments. Yet visualizations have political dimensions that manifest themselves through “conventions,” or shared symbols and practices conveying meaning. Using content analysis, I identify patterns of representation in a sample of 277 migration data visualizations scraped from Google Images. I find evidence of several conventions including appeals to objectivity and traceability as well as perspectives and units of analysis centered on states—particularly higher income migrant destinations. Then, by locating my analysis within the growing field of digital migration studies, I argue these conventions potentially shape public attitudes and understandings about migrants, and contribute to broader migration politics involving categorization and quantification that have relevance both on- and off-line.

Evaluation of Spatial Matching between Urban Green Space and Population: Dynamics Analysis of Winter Population Data in Xi’an

From the perspective of city dynamics, the spatial matching relationships between urban green space (UGS) and population are of great significance to the construction of an eco-livable city. In this study, Xi’an was taken as an example based on the big data of Google images and a Baidu heat map. The spatial matching relationships between UGS and the population were explored by using the coupled coordination model through the service capability index and population density index. The results show that there is a higher coupling degree, a lower coordination degree, and a larger spatiotemporal heterogeneity in coordination type between UGS and population in Xi’an. The reason for this is that many factors including urban spatial structure, expansion mode, functional differentiation, distribution pattern of large-scale ruins and relic sites, and urban planning management and control each have significant impacts on the spatial matching between UGS and population. Therefore, for the dense urban habitat, it is necessary not only to consider the spatial differences of job-housing and urban location conditions but also to actively protect the existing UGS, improve its accessibility, and enhance its quality. The dynamic evaluation of spatial matching between UGS and population is based on big data, which can maximize the utility of UGS resources in the dense urban habitat and provide theoretical guidance for spatial optimization, planning, and design.

Vocabulary Learning Based on Learner-Generated Pictorial Annotations: Using Big Data as Learning Resources

This research discusses the potential of using big data for vocabulary learning from the perspective of learner-generated pictorial annotations. Pictorial annotations lead to effective vocabulary learning, the creation of which is however challenging and time-consuming. As user-generated annotations promote active learning, and in the big data era, data sources in social media platforms are not only huge but also user-generated, the proposal of using social media data to establish a natural and semantic connection between pictorial annotations and words seems feasible. This research investigated learners’ perceptions of creating pictorial annotations using Google images and social media images, learners’ evaluation of the learner-generated pictorial annotations, and the effectiveness of Google pictorial annotations and social media pictorial annotations in promoting vocabulary learning. A total of 153 undergraduates participated in the research, some of whom created pictorial annotations using Google and social media data, some evaluated the annotations, and some learned the target words with the annotations. The results indicated positive attitudes towards using Google and social media data sets as resources for language enhancement, as well as significant effectiveness of learner-generated Google pictorial annotations and social media pictorial annotations in promoting both initial learning and retention of target words. Specifically, we found that (i) Google images were more appropriate and reliable for pictorial annotations creation, and therefore they achieved better outcomes when learning with the annotations created with Google images than images from social media, and (ii) the participants who created word lists that integrate pictorial annotations were likely to engage in active learning when they selected and organized the verbal and visual information of target words by themselves and actively integrated such information with their prior knowledge.

Differentiation Between Malignant and Benign Endoscopic Images of Gastric Ulcers Using Deep Learning.

Background and AimEndoscopic differentiation between malignant and benign gastric ulcers (GU) affects further evaluation and prognosis. The aim of our study was to evaluate a deep learning algorithm for discrimination between benign and malignant GU in a database of endoscopic ulcer images.MethodsWe retrospectively collected consecutive upper gastrointestinal endoscopy images of GU performed between 2011 and 2019 at the Sheba Medical Center. All ulcers had a corresponding histopathology result of either benign peptic ulcer or gastric adenocarcinoma. A convolutional neural network (CNN) was trained to classify the images into either benign or malignant. Endoscopies from 2011 to 2017 were used for training (2011–2015) and validation (2016–2017). Hyper-parameters, image augmentation and pre-training on Google images obtained images were evaluated on the validation data. Held-out data from 2018 to 2019 was used for testing the final model.ResultsOverall, the Sheba dataset included 1978 GU images; 1894 images from benign GU and 84 images of malignant ulcers. The final CNN model showed an AUC 0.91 (95% CI 0.85–0.96) for detecting malignant ulcers. For cut-off probability 0.5, the network showed a sensitivity of 92% and specificity of 75% for malignant ulcers.ConclusionOur study displays the applicability of a CNN model for automated evaluation of gastric ulcers images for malignant potential. Following further research, the algorithm may improve accuracy of differentiating benign from malignant ulcers during endoscopies and assist in patients’ stratification, allowing accelerated patients management and individualized approach towards surveillance endoscopy.

Outdoors Evacuation Routes Algorithm Using Cellular Automata and Graph Theory for Uphills and Downhills

This paper proposes an innovative algorithm for the determination of the optimal outdoor evacuation routes in hills. The algorithm uses web services to obtain the Google Image Tile (a portion of the area to evaluate) and each area’s geographic information. Then, the routes are calculated using cellular automata in 3D and graph theory with geographic information obtained from Google Maps (latitude, longitude, and elevation). The calculation of evacuation routes per study area is performed in an adequate time, revealing the potential of the proposed approach for different applications in emergency scenarios such as wildfires, smart transport, and climbing.

A Deep Learning and Transfer Learning Approach for Vehicle Damage Detection

According to the U.S. Department of Transportation, there is an average of six million motor vehicle crashes every year in the United States. For insurance companies, it is very time-consuming and expensive to process claims for detecting and classifying vehicle damages; thus, deep learning techniques have been used to automate this process to reduce the time and the cost. In this paper, Mask R-CNN is used for image segmentation to identify and crop vehicles from images. Then a convolutional neural network (CNN) model is built to classify whether or not the vehicles have damages. In addition, transfer learning is utilized in both image segmentation and classification phases to help build the models for vehicle detection and damage detection, using the pre-trained weights from Microsoft COCO dataset and ImageNet, respectively. 864 images of damaged vehicles collected from public websites, such as Google Images, are used in this research. The experiment on the detection of bumper damages has achieved 87.5% accuracy.

Teacher Competence in Making Learning Media of ICT-Based PowerPoint-iSpring with Emphasis on Three Levels of Representation

The aim of the Community Partnership Program (PKM) service activity is to increase teacher competency in making learning media of ICT-based PowerPoint-iSpring with emphasis on three levels of representation. The method of PKM was lectures, demonstrations and workshops. The lecture and demonstration methods were used to deliver the topics i.e ICT and internet terminology, three levels representation and 21st century learning, making learning media of PowerPoint-iSpring, using Camtasia software. The practice includes quick tricks of editing images from ebooks, google image, video learning and transferring them to PowerPoint-iSpring learning media.Workshops were to make PowerPoint-iSpring media accompanied by videos of learning, animation of chemical, physical and biological concepts. This activity can improve the knowledge and skills of chemistry teachers using standard chemistry, physics and biology e-books, videos of learning and animation from internet. This community service activities can improve the knowledge and skills of chemistry teachers in integrating a variety of learning resources that can be used to create ICT-based PowerPoint-iSpring with emphasizing on three levels of representation

Representation of skin color in dermatology-related Google image searches

To the Editor: Although the United States is becoming more racially and ethnically diverse, skin of color continues to be underrepresented in dermatology education materials.1 While it has been documented that imagery in dermatology textbooks lacks diversity in skin tones, online resources have not been well studied.2 Although textbooks were once the gold standard for gathering medical information, physicians and patients rely more heavily on the internet. Studies revealed that 82.4% of dermatology patients use the internet or social media to acquire medical information, with Google being the most accessed source.

Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

This study presents a fuzzy approach, for detection of transitioned building footprints in urban area using medium resolution datasets. Multi-temporal remote sensing data sets from Landsat-8 Operational Land Imager and Sentinel-2A were used for generation of temporal indices database. The database was generated using class-based sensor independent-normalized difference vegetation index approach, with an aim to reduce spectral dimensionality of each image and maintain temporal dimensionality. The temporal indices database was subsequently used as input in Modified Possibilistic c-means classifier for transitioned building footprints extraction. The identified transitioned building locations were validated using ground samples as well as from Google images at four different test sites. For accuracy assessment, F-measure was calculated and its value was 0.75 or higher for all training and testing sites. Thus, using proposed fuzzy approach, transitioned building footprints were accurately identified compared to traditional techniques.