Heritage allows us to learn about the different monuments of importance and the inherited traditions from our ancestors. However, many times the monuments get partly ruined due to natural wear and tear, sometimes due to attacks by invaders. To preserve the cultural heritage virtually, many researchers have used augmented, virtual and mixed reality for bringing ancient environments to live in those heritage sites. This study aims to identify publications related to virtual, augmented and mixed reality in cultural heritage and to present the bibliometric analysis of these studies. The research articles on virtual, augmented, and mixed reality in cultural heritage are retrieved using the Scopus database. The analysis is performed using VOSviewer and various parameters such as bibliographic coupling of the countries, publications, journals, authors, and cooccurrences of the author keywords are performed. From the analysis done in this study, it is discovered that the augmented, virtual and mixed reality research in the domain of cultural heritage is mostly concentrated in Italy and surrounding European countries. However, it is also found that the research in this domain is lagging in many countries even if those countries are the homes of various heritage sites. This study provides an extensive analysis of the recent literature related to augmented, virtual and mixed reality research in cultural heritage. This information science based analysis will help researchers to identify the prominent journals in this domain, recognize stalwarts in this field and follow their works, find out path-breaking publications to refer to, and predict the direction of future studies.
With the technological progress of human beings, more and more animal and bird species are being endangered and sometimes even going to the verge of extinction. However, the existence of birds is highly beneficial for human civilization as birds help in pollination, destroying harmful insects for crops, etc. To ensure the healthy co-existence of all species along with human beings, almost all advanced countries have taken up some conservation measures for endangered species. To ensure conservation, the first step is to identify the species of birds found in different locations. Deep learning-based techniques are best suited for the automated identification of bird species from the captured images. In this paper, a Convolutional Neural Network based bird image identification methodology has been proposed. Four different transfer learning-based architectures, namely Resnet152V2, Inception V3, Densenet201, and MobileNetV2 have been used for bird image classification and identification. The models have been trained using 58388 images belonging to 400 species of birds, and the models have been tested using 2000 images belonging to 400 species of birds. Out of these four models, Resnet152V2 and DenseNet201 performed comparatively well. The accuracy of Resnet152V2 was highest at 95.45%, but it faced a large loss of 0.8835. But based on the results, even though DenseNet201 had an accuracy of 95.05%, it faced less loss i.e., of 0.6854. The results show that the DenseNet201 model can further be used for real-life bird image classification.
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