Automated Recognition Systems: Theoretical and Practical Implementation of Active Learning for Extracting Knowledge in Image-based Transfer Learning of Living Organisms

In our research, we propose a model that leverages transfer learning and active learning techniques to accumulate knowledge and effectively solve complex problems in the field of artificial intelligence. This model operates within a parallel learning paradigm, aiming to mimic the continuous learning and improvement observed in human beings. To facilitate knowledge accumulation, we introduce a convolutional deep classification auto encoder that extracts spatially localized features from images. This enhances the model's ability to extract relevant information. Additionally, we propose a learning classification system based on a code fragment, enabling effective representation and transfer of knowledge across different domains. Our research culminates in a theoretical and practical prototype for active learning-based knowledge extraction in various living organisms, including humans, plants, and animals. This knowledge extraction is achieved through image-based learning transfer, focusing on advancing activity recognition in image processing. Experimental results confirm that our method outperforms both baseline approaches and state-of-the-art convolutional neural network methods, underscoring its effectiveness and potential.