Application of Computational and Geocomputation Techniques for Geospatial Analysis
Automated Diabetic Retinopathy Screening in Resource-Limited Areas with Attention-Enhanced Deep Learning on Fundus Images
Detection and Classification of Player Formations in Sports Using Neural Networks
Digital Beauty Standards: AI Approaches to Objectively Measure and Score Beauty Quotients
Automated Detection of Tomato Leaf Diseases through Convolutional Neural Networks
Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter
Computational science in general emerged many decades ago enhancing theory and experiment. Specifically, computational geography arose in the 1980s due to the reductionist limitations of initial GIS software constraining deep analyses of rich geographic data. The utilization of computational method for spatial data analysis has become more probable with the emergence of relational databases. The main purpose of this review is to discuss the application of computational technique and geocomputation in the spatial analysis of geographical phenomenon. First, the literature search and data synthesis was conducted. Then, discussion was done on computational method, geocomputation, spatial data representation, storage, and organization, spatial analytics, and GeoAI. Summarily, geocomputation as concept has been used to define the application of computer-intensive approaches to discern knowledge, especially those that apply non-conventional data clustering and analysis methods. Nowadays, computational method and geocomputation can be used to integrate many areas or fields to enable spatial analyses that require computational resources or ontological paradigms that may not be found in the traditional GIS software suites.
Diabetic retinopathy (DR) is a major cause of vision loss, particularly in areas with limited resources where access to specialized care is scarce. This study introduces an automated screening system for DR using attention-enhanced deep learning on retinal fundus images, designed specifically for these regions. The system utilizes a convolutional neural network (CNN) with integrated attention mechanisms to focus on critical features indicative of DR, such as microaneurysms and hemorrhages. This attention- enhanced approach improves detection accuracy and reliability. A varied collection of retinal fundus images was employed for both training and validation purposes, with data augmentation to enhance model robustness. The model was optimized for deployment on low-cost hardware, ensuring feasibility in resource- limited settings. Performance evaluation showed high sensitivity and specificity, and attention maps provided interpretability for healthcare providers. This automated system has the potential to improve early DR detection in underserved areas, facilitating timely intervention and reducing the risk of blindness. By making advanced diagnostic tools accessible, this approach promotes equitable healthcare and helps prevent vision loss globally.
In order to improve tactical analysis and decision-making, this study investigates the use of neural networks for identifying and categorizing player formations in sports. To precisely identify player positions on the field, high-resolution match film is gathered, carefully labeled, and then subjected to sophisticated object detection algorithms. To identify different formations, a convolutional neural network (CNN) is created and trained. It recognizes tactical settings in a variety of scenarios with remarkable accuracy. The outcomes demonstrate how well the approach works to give analysts and coaches insightful information. In order to improve strategic planning and team performance in competitive sports, future work will concentrate on improving the model's performance, growing the dataset, and incorporating real-time analysis capabilities
In recent years, artificial intelligence (AI) has gained traction in the field of aesthetic dentistry, where precision and personalization are essential for enhancing patient satisfaction. AI-driven techniques are now applied to quantify and optimize facial and dental aesthetics, enabling dentists to offer customized beauty care solutions. This study explores the use of AI for calculating aesthetic metrics related to smile design, symmetry, and facial harmony in dentistry, aiming to establish objective standards that support treatment planning and patient outcomes. The integration of artificial intelligence (AI) in dentistry has introduced transformative changes, offering new possibilities for diagnostics, treatment planning, and patient care. AI-driven approaches in dentistry promise to enhance accuracy, streamline processes, and provide personalized care, making them essential for modern dental practices. This study examines AI's role in objective diagnostic support, dental imaging analysis, and automated assessment frameworks that can aid dental practitioners in making evidence-based decisions.
The research aims to use Convolutional Neural Networks (CNNs) to create an automated system for identifying and categorizing tomato leaf diseases in order to increase agricultural productivity and crop management. By addressing the inefficiencies of traditional manual inspection methods, this study aims to provide timely and accurate disease diagnoses, ultimately benefiting farmers. The methodology involves several key steps, including data collection from high- resolution images of tomato leaves, data preprocessing, and the implementation of CNNs for feature extraction and classification. The model achieved impressive accuracy rates in identifying various diseases, demonstrating the effectiveness of deep learning in agricultural applications. Additionally, the results highlight the robustness of the proposed system against variations in image quality and conditions. This research contributes to the ongoing efforts to improve disease management practices in agriculture. Future work will focus on expanding the model's capabilities to include other plant species and integrating real-time monitoring solutions for enhanced field application.
