Low-productivity agricultural belts in India face the compounding realities of land fragmentation, erratic weather, and heterogeneous soils, which together limit harvest outcomes. Precision agriculture and big data analytics together offer an innovative solution that turns large volumes of heterogeneous data into actionable agronomic intelligence. This study proposes a big-data-driven crop yield prediction (CYP) framework tailored to low-productivity districts in India. The architecture combines soil and climatic analytics with data warehousing, machine learning (four base regressors and voting-based hybrid ensembles), and a preprocessing pipeline designed for compatibility with distributed... More >

Around the world, brain tumors are a major cause of human mortality. Accurate brain tumor detection is essential for effective treatment and improved patient outcomes. This study introduces the progressive transfer learning method, using VGG16 and MobileNet for the brain tumor identification and classification task. The outcome demonstrated the importance of the proposed models. The final accuracy of VGG16 and MobileNet on the test data was 98% and 87%, respectively, highlighting the superiority of VGG16 over the MobileNet framework. In addition, future work will explore advanced fine-tuning strategies, regularization techniques, and other methods to further improve model performance for hel... More >