1. Contributed to the "Diabetic Retinopathy Detection using Machine Learning" project, emphasizing the utilization of retinal images from diabetic patients for early detection. Conducted image preprocessing to enhance quality and extract essential patterns and textures for accurate analysis.
Employed Convolutional Neural Networks (CNNs) to effectively categorize retinal images based on extracted features, ensuring rigorous validation to ascertain model accuracy before deployment.
2. Currently involved in the "Spider Monkey and African Vulture Optimization: A Hybrid Model of Sentiment Classification" project, pioneering an innovative approach to sentiment analysis.
Leveraging Spider Monkey Optimization (SMO) and African Vulture Optimization (AVO) algorithms to develop a unique hybrid model for precise sentiment classification in textual data.
Collaborating with a Professor leading the research paper on this topic, aiming to refine sentiment analysis techniques and gain deeper insights into emotional expression within textual content.