Utilization of Augmented Reality for Human Organ Analysis

This research paper investigates the utilization of augmented reality (AR) technology for human organ analysis in medical education. The study aims to develop and evaluate an AR application that provides an immersive and interactive learning experience for medical students. The research follows a quantitative methodology, to develop and test the effectiveness of the AR application in improving learning outcomes. The research examines the impact of the AR application on student engagement, retention of information, and performance on assessments. The results show that the AR application has a significant positive impact on learning outcomes. The use of AR technology improves student engagement, retention of information, and performance on assessments. The application's design and functionality were found to be intuitive and user-friendly, making it accessible for both students and educators. The research highlights the potential of AR technology in medical education and provides insights into its effectiveness in improving learning outcomes. The findings suggest that AR technology can be a valuable tool in medical education, enhancing the way students learn about human anatomy. This research can contribute to the existing literature on the use of AR technology in education, paving the way for future research and innovation in the field. Ultimately, the study shows that the integration of AR technology in medical education can significantly enhance the learning experience for students, providing them with an immersive and interactive approach to learning about human anatomy

An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification

In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset’s mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA’s performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%