Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism

Enhancing plant disease detection from leaf imagery remains a persistent challenge due to scarce labeled data and complex contextual factors. We introduce a transformative two-stage methodology, Mid Point Normalization (MPN) for intelligent image preprocessing, coupled with sophisticated attention mechanisms that dynamically recalibrate feature representations. Our classification pipeline, merging MPN with Squeeze-and-Excitation (SE) blocks, achieves remarkable 93% accuracy while maintaining exceptional class-wise balance. The perfect F1 score attained for our target class exemplifies attention's power in adaptive feature refinement. For segmentation tasks, we seamlessly integrate identical attention blocks within U-Net architecture using MPN-enhanced inputs, delivering compelling performance gains with 72.44% Dice score and 58.54% IoU, substantially outperforming baseline implementations. Beyond superior accuracy metrics, our approach yields computationally efficient, lightweight architectures perfectly suited for real-world computer vision applications.

Unlocking the Potential of Multiple BERT Models for Bangla Question Answering in NCTB Textbooks

Evaluating text comprehension in educational settings is critical for understanding student performance and improving curricular effectiveness. This study investigates the capability of state-of-the-art language models-RoBERTa Base, Bangla-BERT, and BERT Base-in automatically assessing Bangla passage-based question-answering from the National Curriculum and Textbook Board (NCTB) textbooks for classes 6-10. A dataset of approximately 3,000 Bangla passage-based question-answering instances was compiled, and the models were evaluated using F1 Score and Exact Match (EM) metrics across various hyperparameter configurations. Our findings revealed that Bangla-BERT consistently outperformed the other models, achieving the highest F1 (0.75) and EM (0.53) scores, particularly with smaller batch sizes, the inclusion of stop words, and a moderate learning rate. In contrast, RoBERTa Base demonstrated the weakest performance, with the lowest F1 (0.19) and EM (0.27) scores under certain configurations. The results underscore the importance of fine-tuning hyperparameters for optimizing model performance and highlight the potential of machine learning models in evaluating text comprehension in educational contexts. However, limitations such as dataset size, spelling inconsistencies, and computational constraints emphasize the need for further research to enhance the robustness and applicability of these models. This study lays the groundwork for the future development of automated evaluation systems in educational institutions, providing critical insights into model performance in the context of Bangla text comprehension.

Advancements and Challenges in Bangla Question Answering Models: A Comprehensive Review

The domain of Natural Language Processing (NLP) has experienced notable progress in the evolution of Bangla Question Answering (QA) systems. This paper presents a comprehensive review of seven research articles that contribute to the progress in this domain. These research studies explore different aspects of creating question-answering systems for the Bangla language. They cover areas like collecting data, preparing it for analysis, designing models, conducting experiments, and interpreting results. The papers introduce innovative methods like using LSTM-based models with attention mechanisms, context-based QA systems, and deep learning techniques based on prior knowledge. However, despite the progress made, several challenges remain, including the lack of well-annotated data, the absence of high-quality reading comprehension datasets, and difficulties in understanding the meaning of words in context. Bangla QA models' precision and applicability are constrained by these challenges. This review emphasizes the significance of these research contributions by highlighting the developments achieved in creating Bangla QA systems as well as the ongoing effort required to get past roadblocks and improve the performance of these systems for actual language comprehension tasks.