The use of technology, especially mobile phones, has significantly increased over the past few decades and has become an essential element in our daily lives. Mobile and desktop applications based on Augmented Reality (AR) proved to be a revolutionary step in different areas, especially in the educational sector. AR provides an improved and extended version of reality with the superimposition of a virtual object in the real-world environment. These objects can be interacted with and visualized in many ways to provide full AR experiences. By interacting with augmented virtual objects, students can visualize and completely understand various difficult concepts during their studies rather than traditional learning, allowing collaborative learning and hence improving learnability, motivation, and focus. This survey paper discusses the concept of AR and its types, the need for AR applications in education, analysis of various state-of-the-art AR applications in terms of platform, augmented virtual content, interactions, usability, usefulness, performance, effectiveness, and ease-of-use under a single taxonomy. Although applications of AR in education for learning, teaching, and simulating have proven quite effective in delivering the concepts via interactive study, there are also several limitations in terms of complexity and availability of hardware, platforms, internet connections, portability, computational, and rendering speed. This study offers insights into potential trends and directions that AR technology might take in education, furnishing readers with a glimpse of forthcoming developments. This comprehensive perspective equips educators, researchers, and practitioners with valuable insights into the pedagogical implications, technological aspects, and practical considerations related to AR.

Human skin is the most exposed part of the human body that needs constant protection and care from heat, light, dust, and direct exposure to other harmful radiation, such as UV rays. Skin cancer is one of the dangerous diseases found in humans. Melanoma is a form of skin cancer that begins in the cells (melanocytes) that control the pigment in human skin. Early detection and diagnosis of skin cancer, such as melanoma, is necessary to reduce the death rate due to skin cancer. In this paper, the classification of acral lentiginous melanoma, a type of melanoma with benign nevi, is being carried out. The proposed stacked ensemble method for melanoma classification uses different pre-trained models, such as Xception, Inceptionv3, InceptionResNet-V2, DenseNet121, and DenseNet201, by employing the concept of transfer learning and fine-tuning. The selection of pre-trained CNN architectures for transfer learning is based on models having the highest top-1 and top-5 accuracies on ImageNet. A novel stacked ensemble-based framework is presented to improve the generalizability and increase robustness by fusing fine-tuned pre-trained CNN models for acral lentiginous melanoma classification. The performance of the proposed method is evaluated by experimenting on a Figshare benchmark dataset. The impact of applying different augmentation techniques has also been analyzed through extensive experimentations. The results confirm that the proposed method outperforms state-of-the-art techniques and achieves an accuracy of 97.93%.

Person re-identification (ReID) aims at identifying a person of interest (POI) across multiple non-overlapping cameras. The POI can be either in an image or in a video sequence. Factors such as occlusion, variable viewpoint, misalignment, unrestrained poses, background clutter are the major challenges in developing robust, person ReID models. To address these issues, an attention mechanism that comprises local part/region-aggregated feature representation learning is presented in this paper by incorporating long-range local and global context modeling. The part-aware local attention blocks are aggregated into the widely used modified pre-trained ResNet50 CNN architecture as a backbone employing two attention blocks, i.e., Spatio-Temporal Attention Module (STAM) and Channel Attention Module (CAM). The spatial attention block of STAM can learn contextual dependencies between different human body parts/regions like head, upper body, lower body, and shoes from a single frame. On the other hand, the temporal attention modality can learn temporal contextual dependencies of the same person’s body parts across all video frames. Lastly, the channel-based attention modality, i.e., CAM, can model semantic connections between the channels of feature maps. These STAM and CAM blocks are combined sequentially to form a unified attention network named as Spatio-Temporal Channel Attention Network (STCANet) that will be able to learn both short-range and long-range global feature maps, respectively. Extensive experiments are carried out to study the effectiveness of STCANet on three image-based and two video-based benchmark datasets, i.e., Market-1501, DukeMTMC-ReID, MSMT17, DukeMTC-VideoReID, and MARS. K-reciprocal re-ranking of gallery set is also applied in which the proposed network showed a significant improvement over these datasets in comparison with state of the art. Lastly, to study the generalizability of STCANet on unseen test instances, cross-validation on external cohorts is also applied that showed the robustness of the proposed model that can be easily deployed to the real world for practical applications.

Objective: To assess the knowledge about oral hygiene and practice of oral health maintenance among women of shelter home. Materials and Methods: This was a cross-sectional survey using a self-administered structured questionnaire(5) written in English and validated through a pilot study. Data collection was done from women of shelter home in five visits and total duration of study was 6 months (January-June 2021). All the participants were explained the nature and purpose of the study. Total number of 92 Women responded. Data was analyzed by using SPSS version 20. Results: Majority of the participants had knowledge about oral health(99%) knowledge regarding consequences of poor oral hygiene was (90%) and its effect on general health (93%) .knowledge related to inter dental aids was (6.5%). The second section showed oral health behavior and the attitudes of the participants: majority responded to practicing cleaning their teeth (99%), in which only 58% clean once daily and 54% uses tooth paste/brush. About 87% of the respondents had bleeding gums. Similarly 84% had halitosis and experienced symptoms of gingivitis Only 11% of the participants used mouth washes, visiting a dentist when having a problem was 81%. The third section was related to habits / addiction : majority were addicted to tea/coffee 86%, while frequency of intake 2-5 cup daily. Conclusion: Women of shelter home have satisfactory knowledge about oral health but putting it into practice is not optimistic. There is need to improve their oral health through organized efforts and establishing a good oral health care system within the shelter home.