RadiomixNet Leveraging Radiomics and Feature Extraction for Enhanced Pneumonia Diagnosis
L. Bhagyalakshmi, N. Thulasi Chitra, Seema Raj, Paresh Tanna, Edem Suresh Babu, Sanjay Kumar Suman
Abstract
Suggestion platforms in today’s world are required to handle massive quantities of user data in an effective manner despite retaining a high level of precision. Through the examination of a variety of usage patterns, this research proposes a user interaction architecture that canters on deep understanding and aims to maximize the preciseness of recommendations. Real-time tracking of evolving tastes of users is accomplished by the algorithm via the use of session-based recurrent neural networks, or R as well as focus techniques. There is also an improvement in its ability to detect links across persons and things thanks to the use of graph-based methods for learning. The suggested approach was tested on several different records, and the results showed that it outperformed 97% of traditional joint filtering models in terms of accuracy, medium reciprocate ranks (MRR), and standardized deferred accumulated gain (NDCG). A twenty percent improvement in real-time prescription effectiveness is achieved by the engine, which greatly outperforms the performance of typical session-based suggestions. The findings demonstrate that combining machine learning with session-based analysis leads in a system of suggestions that is more effective, adaptable, and adaptable.