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%.

Objective: Glioblastoma multiforme (GBM) is the most aggressive and prevalent type of brain tumor, with a median survival time of approximately 15 months despite treatment advancements. Determining the O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter status, specifically its methylation, is crucial for treatment planning as it provides valuable prognostic information and indicates chemosensitivity. However, current methods require invasive tissue sampling and genetic testing, resulting in time-consuming processes. The non-invasive technique of assessing MGMT status in GBM patients may offer valuable insights to neuro-oncologists, aiding in precise treatment and surgical planning. Methods: This research study utilizes two benchmark datasets—BraTS2021 brain tumor segmentation dataset and MGMT promoter status classification dataset—and proposes a pipeline of segmentation-based classification of MGMT promoter status utilizing all modalities of magnetic resonance imaging (MRI) scans by stacking them. The pipeline consists of two phases: in the first phase, a 3D Residual U-Net (3D ResU-Net) architecture is used to segment the brain tumor into sub-regions using a stack of MRI modalities. In the second phase, the segmented tumor voxel obtained from the first phase is input into a 3D ResNet10 model to predict MGMT promoter status. Results: The segmentation phase of the pipeline achieves promising results with average dice scores of 0.81, 0.84, and 0.80 for tumor core (TC), whole tumor (WT), and enhancing tumor (ET) regions, respectively, on the internal validation set. The classification phase obtains a ROC–AUC score of 0.66 on the internal validation set. Conclusion: This pipeline demonstrates the potential of a non-invasive approach to support neuro-oncologists in brain tumor diagnosis and treatment planning. While still at the research stage, it provides insights into tumor sub-regions and MGMT promoter status, highlighting the role of AI-driven methods in assessing molecular data. Future studies and clinical validation are needed to further explore its applicability in real-world clinical settings.

Neuro-degenerative diseases pose a significant health concern for human society, especially among the elderly population. Given their high prevalence and the limited availability of clinical expertise and services, there is an urgent requirement for the integration of artificial intelligence (AI) systems to support healthcare professionals in addressing this issue. This paper presents a comparative analysis of seven deep learning architectures for neuro-degenerative disease classification by using a gait dynamics dataset. The models used in the analysis include LSTM, GRU, InceptionTime, ResNet, FCN, TST, and PatchTST. The models are extensively evaluated for different classification tasks. The findings of the study suggest that deep learning techniques have the potential to diagnose and classify different neurodegenerative diseases effectively. It can be inferred that ResNet exhibits superior performance compared to other models in tasks involving the classification of healthy controls (HC) and class of individuals with all neuro-degenerative diseases (NDDs), and classification of healthy controls (HC) from individuals with Parkinson's disease (PD). TST demonstrates the highest level of performance among all models in tasks involving distinguishing between Amyotrophic Lateral Sclerosis (ALS) and healthy controls (HC), as well as Huntington's disease (HD) and healthy controls (HC).