This study aims to create a machine learning-based method for categorizing ocular impairment. Congenital, refractive error, age, diabetes, and unknown are the five primary causes that specialists consider. The suggested technique automatically classifies patients into one of the five groups based on their unique features by evaluating the ODIR dataset of patient records, which includes numerous demographic and clinical information, and utilizing machine learning algorithms. Most previous studies in this area have focused on classifying illnesses; hence, this study's main contribution is its innovative focus on categorizing the causes of eye disorders. To the best of our knowledge, no ocular dataset has a label that specifies the cause of eye disease. The classes of eye disease have been added by Ophthalmologists. Better patient outcomes and more effective use of healthcare resources can be achieved by increasing the precision of physicians' diagnoses and streamlining their decision-making. Compared to the other classification methods, the Quadratic SVM model has the highest accuracy of 71.3%.

Background: Introducing drone technology has considerably improved data collecting and logistics. Despite these advances, the difficulty of effectively operating drone swarms in dynamic and diverse situations still needs to be addressed. Inspired by the collective behavior of social insects, Swarm Intelligence (SI) provides potential solutions for improving drone network performance, decision-making, and resilience. Objective: The purpose of the article is to investigate the use of Swarm Intelligence principles in drone networks, focusing on their potential to transform future drone-based data systems through increased communication, cooperation, and coordinated decision-making capabilities. Methods: The article uses interdisciplinary computer science, robotics, and behavioral ecology methodologies to conduct extensive tests on drone swarms. Using a computational model, the study compares SI-based drone networks against standard drone management frameworks to assess their efficiency, dependability, and flexibility in various operational settings. Results: Findings show that SI-enhanced drone networks are more flexible, fault-tolerant, and operationally efficient across various activities and environmental circumstances. SI-based solutions outperform traditional methods in data relay, resource utilization, and adaptive maneuverings, especially in circumstances that need decentralized control and autonomous coordination. Conclusion: Integrating Swarm Intelligence into drone networks significantly enhances functionality, making them more adaptive, resilient, and efficient. This achievement provides the path for creating extremely scalable and networked computer systems and highlights the importance of biologically inspired algorithms in the refinement of autonomous systems. The ramifications of this research go beyond drone technology, providing insights into the broader use of SI in complex, dynamic systems.