Google Images Research Articles

The discursivity of the covers of The country of women, by writer Gioconda Belli on utopia and dystopia

Nicaraguan author Gioconda Belli, born in the city of Managua on December 9, 1948, wrote the novel The Country of Women. In addition to being an activist and militant, she is a writer who brings to her literary productions love for her country and the search for gender equity in public and private spaces. This article provides a brief discursive analysis of the covers of this novel, from the perspective of utopia and dystopia. The problem presented is: what is the discursive nature of the covers of O País das Mulheres about utopia and dystopia in the context of contemporary feminist critical theory in Latin America? The general objective is to reflect on the aspects of utopia and dystopia on the covers of the aforementioned book, observing whether they corroborate feminist critical theory and cultural studies in Latin America, reflecting on the discourses of utopia and dystopia. The methodology used for theoretical and analytical production is of a bibliographic nature, as authors of feminist literary criticism and utopia and/or dystopia were used. For the analysis, covers of the book O País das Mulheres were collected on the public domain website – Google Images – for analysis and discussion purposes on how the structure of patriarchy is deconstructed on these covers. From this approach, it was understood that the social roles of men and women are redefined in a utopian characteristic of equity and in a plan of hope and transformation, widely discussed and claimed by feminist literary criticism, in addition to also bringing dystopian moments necessary for the deconstruction of patriarchy. It is believed that this work is fundamental for thinking about Latin American feminist demands, as it brings up struggles that sister countries also experience in contemporary times, involving gender politics between men and women.

1975–2020年黄土塬区关键带道路网络数据

On the Loess Plateau, the traffic network plays an important role in shaping human activities in the critical zone. It usually exacerbates soil erosion and material migration in the critical zone of the loess, particularly during rainstorms. Known as "the First Loess Tableland in the world", Dongzhi Loess Tableland (35°15'23"-36°04'20"N, 107°26'47"-107°57'51"E) is the largest complete tableland on the Loess Plateau. With the rapid pace of urbanization, Dongzhi Tableland has been experiencing a surge in road construction. The expansion of road networks has an important impact on the landscape ecology and soil erosion. This study is based on multi-source data, including historical documents, field surveys, and Open Street Map (OSM) data. We used ArcGIS software to interpret and vector the road network data on Dongzhi Tableland from 1975 to 2020, in combination with the Google image. The accuracy of this dataset was strictly controlled from the data collection, processing, and analysis to verification. This dataset can provide a strong data basis to carry out research on the study of road network expansion and its impacts on landscape patterns and soil erosion on Dongzhi Tableland.

Analysis of interest of Google users (Web , Google Image, and YouTube search) on oral health information among South Indian states

Objectives: The study aimed to analyze the trends of oral health information searches on the web, Google Images, and YouTube in South Indian states. Material and Methods: The retrospective longitudinal study utilized computational metadata from Google Trends, examining the relative search volume of oral health information from 2018 to 2023. Results: The results revealed a significant demand for aligners, dental implants, teeth sets, root canal treatments, and teeth whitening in these states, highlighting the importance of accessible information and education to facilitate informed decisions about oral health care. Regardless of development levels, oral health-related searches increased, influenced by education levels and Internet availability. Conclusion: Oral health-related searches increased in South Indian states regardless of their development levels, with media preference influenced by education levels and Internet availability. This analysis suggests that investing in health education programs can empower individuals to make informed oral health choices, reducing misconceptions regarding dental treatment.

Online Depiction of Urticaria Is Often Flawed and Does Not Reflect the Spectrum of Clinical Manifestation

Introduction: The internet is a popular source of health information including images of disease manifestations. Online photographs of skin lesions may aid patients in identifying their disease, if these pictures are of good quality and of the disease they claim to show. If not, patients may be at risk of delayed diagnosis, misdiagnosis, and suboptimal treatment. For urticaria, the mismatch rate and quality of online pictures are unknown. The objective of this study was therefore to evaluate the content and quality of online images of urticaria. Methods: The search term “urticaria” was applied to Google Images and Shutterstock. The top 100 photographs from each search engine were retrieved on October 9th, 2022. Illustrations, drawings, and heavily edited photographs were excluded. Each image was evaluated for patient characteristics, characteristics of urticarial lesions, and image quality. Results: Across 194 unique images of urticaria (after removing duplicates), 35 (18.0%) did not depict urticarial lesions, and 38 (19.6%) were ambiguous. Less than two-thirds of images 121 (62.4%) showed bona fide urticarial lesions. Pictures of urticarial lesions under-represented children and did not reflect female preponderance of the disease. Images predominantly depicted urticaria lesions on Caucasian skin (59.8%) and were typical of spontaneous rather than inducible urticaria. Only 3 (1.5%) pictures showed angioedema, a common clinical sign in patients with urticaria. The overall quality of online urticaria pictures was mostly good or very good. Conclusion: Physicians and patients should be aware that one in five online pictures of urticaria does not show urticarial skin lesions, and children, females, non-Caucasian patients, inducible urticaria, and angioedema are under-represented. These findings should prompt efforts to improve the accuracy and representativeness of online urticaria pictures.

A model of intended and ideal prescribing and medication use in symptom control in palliative care: an international scoping review

Abstract Introduction UK health policy documents are committed to improving personalised palliative care for people of all ages, prioritising access to coordinated care and health inequity. The UK Health and Care Act 2022 has stated it is a statutory requirement for Integrated Care Boards to commission palliative and end of life care services. Palliative care focuses on improving the quality of life of patients and families who are facing problems associated with life-limiting illness, and often involves medication for symptom control. Medication has been reported in 19% of NHS serious incident reports involving palliative patients, mostly occurring in patient homes, with half when specialists did not provide care.1 Aim To document a model of existing evidence illustrating intended processes when palliative medications are prescribed for symptom control and used by adults at home, in hospital and hospice settings. Methods We performed a scoping review using JBI methodology.2 Population (P): (i) adults in the last phase of life, (ii) informal carers supporting an adult as described above, (iii) healthcare professionals providing palliative care for symptom control. Concept (C): multistep processes for medication use for symptom control in palliative care. Context (C): care settings where palliative care may be anticipated, planned or happen. A systematic search were undertaken in Medline, CINAHL, Embase, plus Google scholar and Google images. There were no date limits; searches were confined to English language. Published and unpublished literature meeting the PCC framework were included. Screening and extraction of data were performed by two independent reviewers (SAF and SY). Studies were categorised inductively and results collated descriptively. Ethical approval was obtained from the NHS HRA and HCRW (ref: 21/LO/0459). Results 19,753 titles and abstracts were screened; 929 underwent full text review; 308 peer-reviewed articles and grey literature documents were retained to build the intended model. The majority of these papers (212; 69%) provided simple process steps involving expertise/judgement, supply chain/access, administration, prescribing/deprescribing, rationalisation, repurposing. The remaining 96 papers warranted richer thematic analysis. Most studies took place in the UK, Australia or USA in home/community settings. Analysis illuminated detail about ‘hidden work’ (participants’ actions in medication management), ‘hot spots’ (problematic areas e.g. out-of-hours care and the reliance on carers) and ‘cold spots’ (areas with less attention such as whose responsibility for keeping carers informed of changes and what is adequate support for safe medicines use at home). Discussion/Conclusion There is a growing interest in how best to work together across boundaries to enable getting the right medication to the right person at the right time and place. This scoping review documents the existing evidence of intended processes and a model of ideal prescribing and medication use in symptom control in palliative care. This study is limited to adult palliative care services only. A strength of the study is the use of JBI methodology. Using ethnographic methods and the conceptual tools of Activity Theory, we will refine this intended model with a study of what happens in practice3 to build a theoretically-informed, empirically-evidenced model of medication management across palliative care contexts.

Comparison of deep learning models to extract silt storage dams from remote sensing images

Determining the locations and shapes of silt storage dams (SSDs) is necessary before planning and constructing new ones or maintaining old ones. Google images with a spatial resolution of 0.54 m were cropped, labelled and enhanced to establish two schemes of remote sensing images that contain SSDs with different input and batch sizes. Five deep learning models (FCN (fully connected convolutional neural network, SegNet (deep convolutional encoder–decoder architecture for image segmentation), U-Net (convolutional networks for biomedical image segmentation), PSPNet (pyramid scene parsing network) and DeepLab-V3+) were constructed to extract SSDs from the images based on the two schemes. The loss curves, accuracies and extraction results derived from the five models were compared to identify the optimal model for SSD extraction. The results show that the overall accuracies, F1 scores and mean intersections over unions obtained from DeepLab-V3+ were, respectively, 95.29%, 70.33% and 74.13% for scheme 1 (S1) and 96.29%, 73.34% and 76.99% for scheme 2 (S2), which were better than the values for other models. PSPNet had the shortest training times (128 s/step for S1 and 348 s/step for S2). An input size of 480 × 480 pixels, a batch size of 4 and 2304 images enhanced the extraction accuracy and prevented overfitting. The results provide a reference for the planning, construction and maintenance of water and soil conservation measures.

Smiling women pitching down: auditing representational and presentational gender biases in image-generative AI

Abstract Generative Artificial Intelligence (AI) models like DALL·E 2 can interpret prompts and generate high-quality images that exhibit human creativity. Though public enthusiasm is booming, systematic auditing of potential gender biases in AI-generated images remains scarce. We addressed this gap by examining the prevalence of two occupational gender biases (representational and presentational biases) in 15,300 DALL·E 2 images spanning 153 occupations. We assessed potential bias amplification by benchmarking against the 2021 U.S. census data and Google Images. Our findings reveal that DALL·E 2 underrepresents women in male-dominated fields while overrepresenting them in female-dominated occupations. Additionally, DALL·E 2 images tend to depict more women than men with smiles and downward-pitching heads, particularly in female-dominated (versus male-dominated) occupations. Our algorithm auditing study demonstrates more pronounced representational and presentational biases in DALL·E 2 compared to Google Images and calls for feminist interventions to curtail the potential impacts of such biased AI-generated images on the media ecology.

Refugee education: a critical visual analysis

ABSTRACT Refugee education has become an issue of national and international concern as the numbers of refugees rise globally. In a world where global issues such as forced migration are communicated and consumed through the visual mode, understanding how discursive agendas are constituted visually is important. This paper explores the representations of Refugee Education, toggled with Refugees and Education with a focus on images available from a Google Images search. The analysis is framed by critical visual literacy, with the assumption that visual images are embedded in wider sociocultural practices and ideologies. Images of Refugee Education depict impoverished, teacher-centred classrooms, restricted knowledge and a docile population. Toggling with images of Refugees emphasises the racial otherness of refugee students, their numbers, and schooling as the means to contain the discursively constructed representations of refugees-as-threat. Toggling with Education emphasises Refugee Education as merely an issue of access, rather than quality, inclusive or lifelong learning as envisaged in Sustainable Development Goal 4. We conclude that images of refugee education undermine the quest for a compassionate inclusive education for refugees of all ages and that caution and critique are needed in the consumption of images.

Design And Development Of Karo Traditional Music Instrument Recognition Application Based On Digital Image Using Convolutional Neural Network Method

In this modern era, multimedia technology has role important in various field like education, health, and publications. Thesis This focused on development application introduction tool music traditional Karo tribe based digital image use Convolutional Neural Network (CNN) method. Karo music has characteristic typical unique and important for culture of North Sumatra. Study This use method Squeezenet and MobileNetV2 for compare accuracy in introduction object tool music Karo tribe. The Convolutional Neural Network (CNN) method is used in the introduction process. Steps covers convolution, function Activation ReLU, pooling, layers connected full, and the output layer with function Activation Soft max. Image data tool music Karo tribe gathered from Google Images. System This own steps like preprocessing image, model training with CNN method, and analysis results training. Research results This give comparison accuracy between method Squeezenet and MobileNetV2 in recognize tool music Karo tribe. Supporting data used in analysis, especially image tool music obtained Karo tribe from Google Images, with structured design and implementation of CNN, applications This succeed recognize type tool music with level good accuracy.

Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

Against the backdrop of rising road traffic accident rates, measures to prevent road traffic accidents have always been a pressing issue in Taiwan. Road traffic accidents are mostly caused by speeding and roadway obstacles, especially in the form of rockfalls, potholes, and car crashes (involving damaged cars and overturned cars). To address this, it was necessary to design a real-time detection system that could detect speed limit signs, rockfalls, potholes, and car crashes, which would alert drivers to make timely decisions in the event of an emergency, thereby preventing secondary car crashes. This system would also be useful for alerting the relevant authorities, enabling a rapid response to the situation. In this study, a hierarchical deep-learning-based object detection model is proposed based on You Only Look Once v7 (YOLOv7) and mask region-based convolutional neural network (Mask R-CNN) algorithms. In the first level, YOLOv7 identifies speed limit signs and rockfalls, potholes, and car crashes. In the second level, Mask R-CNN subdivides the speed limit signs into nine categories (30, 40, 50, 60, 70, 80, 90, 100, and 110 km/h). The images used in this study consisted of screen captures of dashcam footage as well as images obtained from the Tsinghua-Tencent 100K dataset, Google Street View, and Google Images searches. During model training, we employed Gaussian noise and image rotation to simulate poor weather conditions as well as obscured, slanted, or twisted objects. Canny edge detection was used to enhance the contours of the detected objects and accentuate their features. The combined use of these image-processing techniques effectively increased the quantity and variety of images in the training set. During model testing, we evaluated the model’s performance based on its mean average precision (mAP). The experimental results showed that the mAP of our proposed model was 8.6 percentage points higher than that of the YOLOv7 model—a significant improvement in the overall accuracy of the model. In addition, we tested the model using videos showing different scenarios that had not been used in the training process, finding the model to have a rapid response time and a lower overall mean error rate. To summarize, the proposed model is a good candidate for road safety detection.

Body map image coding to support wearable design for skin-to-skin contact

People place tools on their bodies during daily activities that impact their health and safety. Mapping the locations of this equipment and evaluating the appropriateness of these locations can aid people to achieve their goals. This paper considers the location of equipment, life support systems such as ventilation tubing and intravenous lines, on the human body during kangaroo care in hospitals. Kangaroo care (KC) is skin-to-skin contact between infants and adult caregivers, which dramatically improves health outcomes for both. This research provides information useful for design to support safe KC in the Neonatal Intensive Care Unit (NICU). Assessment of 100 images from a Google Image search conducted on May 19, 2021, using the search text ‘NICU kangaroo care tubes’ was undertaken. An expert NICU nurse verified that tube location followed best practices. This method documents human-body-tool interaction for improved safety and functioning across contexts.

Diversity in online resources for breast reconstruction: What do patients see?

Online resources are commonly used by patients to obtain information on breast reconstruction. Despite the key role of these resources in patient decision-making, their visual content has not yet been evaluated. This study sought to 1) characterize the presence and content of visual aids in online patient education breast reconstruction resources and 2) determine if the women represented in these visual aids reflect the breast reconstruction patient population in the United States. The top 10 Google websites and the first 400 Google Images containing photographs/graphics depicting human skin for the search phrase "breast reconstruction" were analyzed. Images were categorized by content as "Before/After," "Surgical/Anatomical," "Step-by-Step," or "Breast-Centric Stock Images." Image subjects were classified by skin tone into "White" or "Non-White" using the Fitzpatrick scale and by body type into "Lean" or "Full-Figured." In total, 471 images were analyzed. These were predominantly "Before/After" images (43.9%), followed by "Breast-Centric Stock Images" (27.4%), "Surgical/Anatomical" (24.2%), and "Step-by-Step" (4.5%). The majority of all images depicted "White" skin types (90.7%) and "Lean" body types (73.0%). "Before/After" images were more likely to show "Full-Figured" women than the other content categories (p<0.0001) and had the highest percentage of "Non-White" skin types (35.3%). Our findings demonstrate that breast reconstruction online resources are not reflective of the patient population seeking reconstruction. Improving the diversity of online image resources can both better represent our diverse patient population as well as better align patient expectations with postoperative outcomes, likely improving patient satisfaction.

44445 Representation of skin tones in Google image results for psoriasis

44445 Representation of skin tones in Google image results for psoriasis

Employing artificial intelligence to verify and detect fake news: A case study of Al Ekhbariya news channel (Arabic)

The primary objective of this study is to identify the use of artificial intelligence (AI) as a mechanism for verification and detection of fake news in the Saudi Al Ekhbariya channel. The study also aims to know the vision of senior channel administration in terms of employing artificial intelligence as a mechanism for verification, auditing, and the detection of fake news, and to reveal the AI technology that the channel employs. The study was based on the case study methodology and researchers used in-depth interview and scientific observation tools, as well as intentional sampling in collecting information for the study. In-depth interviews were conducted with officials and the AI newsroom team of the news channel. The research found that the channel's first source of internal news is its correspondents, while the reliable sources for external news were the Ministry of Foreign Affairs, the Saudi Press Agency, and international news agencies. The respondents also mentioned that the news channel relied on a number of mechanisms for technical verification, the most prominent of which is the (INews) program. The smart newsroom of Al Ekhbariya channel uses a number of photo and video verification tools, foremost of which is Google image.

Land use land cover change and its implication on water fowl diversity: A case study of Taudaha Lake, Kathmandu, Nepal

The wetlands of Nepal are well known for unusually rich biodiversity. Taudaha is famous for winter migrant water fowls as far as from Siberian. The natural form of Taudaha has been neglected and it is surrounded by concrete homes instead of trees and paddy field. Land use change is often regarded as one of the main reasons for habitat degradation and subsequent decrease in migratory birds’ population. This article aims to quantify the land use change scenario in and around the lake area using GIS tool. Google images from February 2004 and April 2022 were obtained from “Google Earth Pro” software and used to prepare the land use map by using “QGIS 3.12” software. The cultivated land covered about 73.42% of the total study area followed by settlement (8.61%), vegetation (8.02%) and the lake (9.95%) itself in 2004. But 18 years later, in the year 2022, Taudaha is gradually surrounded by urban areas. The cultivated land has been changed to be settlement areas. The cultivated land has decreased for construction of houses, hotels and resorts. Human activities in hotels and resorts near the lake area have caused distraction to migratory birds visiting the lake. This is causing degradation of habitat of the migratory bird species. Literatures showed the declining number of migratory bird species visiting the lake areas during winter.

Patient photographs on Google Images: a commentary on informed consent, copyright, and privacy laws

ABSTRACT Patient photographs published in medical journals have been found on Google Images in two studies, raising concerns about informed consent and patient privacy. It is unlikely that patient informed consent includes consideration of these circumstances, as there are no uniform guidelines for obtaining consent for the publishing of clinical photographs across online medical journals. Health data privacy legislation in the EU, US, and Canada is only concerned with identifiable patient photographs, and their guidelines for deidentification are inconsistent. Patients have limited legal recourse in such cases, as the liability of search engines, publishers, and clinicians is unclear under law. Better informed consent and publication practices, as well as ethical dissemination of sensitive information online by tech companies, are necessary to address these issues.

Bird predation on Roseau cane scale as revealed by a web image search and querying a citizen monitoring database

AbstractAn invasion of Roseau cane scale, Nipponaclerda biwakoensis, native to East Asia, is associated with recent widespread dieback of common reed stands in the Mississippi River Delta, Louisiana. A previous study suggested that, in its native range, the combined mortality from Common Reed Bunting Emberiza schoeniclus and parasitoid wasps may control the abundance of Roseau cane scale. However, there is limited knowledge about which other bird species may prey on Roseau cane scale or the feeding patterns of these birds. Here we used web search and citizen science monitoring data to tabulate bird predation events on Roseau cane scales across sites in Japan and determine which bird species may be the most important predators and when the observed feeding typically occurs. We scanned web entries using both Google image searches and the social networking services including Twitter, Instagram, and Facebook. Additionally, we accessed long‐term field observation records obtained by volunteer monitoring conducted by the members of the Kanagawa Branch of the Wild Bird Society of Japan. By using these citizen science approaches, we detected 16 bird species, mainly Common Reed Bunting but also other migratory and resident species, preying on the Roseau cane scale in reed beds throughout central Japan and most detection occurred during the winter. This study demonstrates that biological information obtained from publicly available data from citizen observers can be useful for exploring ecological patterns of various species including potential predators against invasive species.

Plant Recognition and its Care using TfLite, Teachable Machine

Abstract: Plant identification is a significant undertaking however its care is considerably more significant. The identification of plants has a lot of benefits for wide range of people ranging from forestry services, pharmaceutical labs, government and common public. The research paper aims on developing a machine learning technology combined with mobile app development which is able to recognize plants and suggest good care for them like how much water, sunlight, fertilizer, etc is optimum for the plant. Plant identification with its care, based on leaf, is becoming an interesting task. Each plant leaf carries unique identity, which matches to no other plant, information that can be used in the identification and subsequently, determining measures for its care. A large number of features were taken from each leaf such as its length, area of hull, perimeter and color. All the work of research in this filed has further aggravated an interest in the development of automated systems for the recognition of different plant species. A full autonomous method of recognizing plants using Artificial Intelligence tools like Teachable Machine and TfLite. We gathered dataset from google images and several images with varying textures and lighting conditions clicked from my smartphone camera from the Kangra forest range. There are several applications that help in recognizing the plant species using features from their leaves but we are also adding an additional feature of its care.

Forest Age Mapping Using Landsat Time-Series Stacks Data Based on Forest Disturbance and Empirical Relationships between Age and Height

Forest age is a critical parameter for the status and potential of carbon sequestration in forest ecosystems and reflects major forest disturbance information. However, reliable forest age data with high spatial resolution are lacking to date. In this study, we proposed a forest age mapping method with a 30 m resolution that considers forest disturbance. Here, we used the Landsat time-series stacks (LTSS) data from 1986 to 2021 and implemented the LandTrendr algorithm on the Google Earth Engine (GEE) platform to detect the age of disturbed forests. The age of non-disturbed forests was extracted based on forest canopy height data and the empirical relationship between age and height. High-resolution Google images combined with the forest management archive data of forestry departments and national forest inventory (NFI) data were used for the validation of disturbed and non-disturbed forest age, respectively. The results showed that the LandTrendr algorithm detected disturbance years with producer and user accuracies of approximately 94% and 95%, respectively; and the age of non-disturbed forests obtained using the empirical age–height relationship showed an R2 of 0.8875 and a root mean squared error (RMSE) value of 5.776 with NFI-based results. This confirms the reliability of the proposed 30 m resolution forest age mapping method considering forest disturbance. Overall, the method can be used to produce spatially explicit forest age data with high resolution, which can contribute to the sustainable use of forest resources and enhance the understanding of carbon budget studies in forest ecosystems.

Does the Rational Function Model’s Accuracy for GF1 and GF6 WFV Images Satisfy Practical Requirements?

The Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites have acquired many GF-1 and GF-6 wide-field-view (WFV) images. These images have been made available for free use globally. The GF-1 WFV (GF-1) and GF-6 WFV (GF-6) images have rational polynomial coefficients (RPCs). In practical applications, RPC corrections of GF-1 and GF-6 images need to be completed using the rational function model (RFM). However, can the accuracy of the rational function model satisfy practical application requirements? To address this issue, a geometric accuracy method was proposed in this paper to evaluate the accuracy of the RFM of GF-1 and GF-6 images. First, RPC corrections were completed using the RFM and refined RFM, respectively. The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM was refined via affine transformation based on control points (CPs), which resulted in a refined RFM. Then, an automatic matching method was proposed to complete the automatic matching of GF-1/GF-6 images and reference images, which enabled us to obtain many uniformly distributed CPs. Finally, these CPs were used to evaluate the geometric accuracy of the RFM and refined RFM. The 14th-layer Google images of the corresponding area were used as reference images. In the experiments, the advantages and disadvantages of BRIEF, SIFT, and the proposed method were first compared. Then, the values of the root mean square error (RSME) of 10,561 Chinese, French, and Brazilian GF-1 and GF-6 images were calculated and statistically analyzed, and the local geometric distortions of the GF-1 and GF-6 images were evaluated; these were used to evaluate the accuracy of the RFM. Last, the accuracy of the refined RFM was evaluated using the eight GF-1 and GF-6 images. The experimental results indicate that the accuracy of the RFM for most GF-1 and GF-6 images cannot meet the actual use requirement of being better than 1.0 pixel, the accuracy of the refined RFM for GF-1 images cannot meet practical requirement of being better than 1.0 pixel, and the accuracy of the refined RFM for most GF-6 images meets the practical requirement of being better than 1.0 pixel. However, the RMSE values that meet the requirements are between 0.9 and 1.0, and the geometric accuracy can be further improved.