The rise of online platforms has led to a growing trend of people expressing their thoughts and emotions in their native languages. Movies have been a predominant topic of discussion on online platforms where people reflect on various aspects of movies. Aspect-based Sentiment Analysis (ABSA), a computational technique, assists in examining the sentiments hidden in these discussions. Two challenges arise when attempting to use ABSA to identify sentiments in movie reviews written in the Indian regional language Tamil; the former being the unavailability of potential Tamil movie review datasets and the latter being the difficulty that arises due to the agglutinative nature of Tamil Language. This work addresses the first challenge by curating an annotated movie review dataset in Tamil, MADTRAS (Dataset for Aspect-based Sentiment Analysis of Movie Reviews in Tamil). The quality of the dataset is ensured through content and annotation evaluation. To prove the efficiency of the dataset, the multilingual BERT (mBERT) was used, and the performance was compared with other Deep Learning(DL) models. 2025 The Authors
Yellow Leaf Disease (YLD), or chlorosis, reduces crop health and productivity, affecting plants like citrus, wheat, and bananas. This study reviews the causes of YLD, including bacterial, viral, and fungal infections, along with poor nutrition and environmental stress. It highlights the importance of early detection through novel methods like molecular diagnostics and remote sensing. The review also stresses the need to understand the interaction between disease, nutrition, and environment for effective management. Breeding YLD-resistant crops is proposed as a potential solution. This work serves as a foundation for future research to mitigate YLD's impact on agriculture. Grenze Scientific Society, 2025.
Plant health plays a critical role in ensuring global food security and sustaining agricultural productivity, as it directly influences crop yields and economic stability. Reducing losses and enhancing farm management techniques depend on early plant disease detection. This research suggests a new hybrid framework that combines deep learning (DL) and machine learning (ML) to improve disease detection's precision and effectiveness. The ML component effectively processes structured data, providing clear and reliable recommendations, while the DL model focuses on extracting detailed features from high resolution plant images through advanced image processing. By combining these complementary techniques, the framework achieves high precision, scalability, and real-time disease monitoring capabilities. This innovation supports farmers and agricultural experts in making timely, informed decisions, reducing crop losses and advancing sustainable farming practices. Ultimately, better precision agriculture is made possible by the integration of these cutting edge technology, which supports sustainable agricultural development and global food security. Grenze Scientific Society, 2025.
