In recent years, the number of unmanned aerial vehicle (UAV) applications has increased. However, navigating them indoors is still tricky because no GPS signals are available, and the obstacles constantly change. This study exploits the collaboration between convolutional neural networks (CNNs) and reinforcement learning (RL) to overcome these challenges, improving the level of independence of UAVs in indoor settings. We present a new technique known as potential-based reward shaping, which directly incorporates expert knowledge into the reinforcement learning framework. This technique efficiently addresses the problem of sparse rewards that often hinder learning in conventional RL configurations. In addition, we have created a reflective reinforcement learning agent that systematically assesses its actions to prioritize those that result in the most substantial enhancements. We have extensively evaluated the performance of our method with the High-Definition Indoor Navigation (HDIN) dataset to show that our method continuously outperformed current non-introspective RL techniques in terms of decision-making speed and navigation accuracy in this evaluation. The results show that this new CNN-RL combination is feasible and suggests a scalable method for complex indoor navigation problems.

Background/Objectives: Brain tumor segmentation is critical for accurate identification and treatment planning. However, delineating intricate tumor areas like Edema, Tumor Core, and Enhancing Tumor remains challenging owing to diversity in dimensions, morphology, and delineation. Methods: This introduces an innovative a novel deep learning model integrating a Pretrained Vision Transformer (ViT) for global context representation, Atrous Spatial Pyramid Pooling (ASPP) in both encoder and merging stages, and ReLU6. Additionally, attention mechanisms and Conditional Random Fields (CRF) were incorporated for refining feature emphasis and segmentation boundaries. Results: The model was trained and tested on the BraTS 2020 dataset and compared with state-of-the-art methods. The suggested model obtained the highest Dice coefficients across all tumor regions, with improvements of up to 4% for Enhancing Tumor and Edema. Grad-CAM heatmaps illustrated the model's proficiency in precisely identifying tumor areas, hence enhancing the interpretability of predictions. Conclusions: The incorporation of multi-scale feature extraction, attention mechanism, and global context representation markedly improved segmentation accuracy and interpretability. The findings underscore the proposed model's capacity to enhance clinical processes and establish a standard for brain tumor segmentation tasks. Future endeavors involve testing on varied datasets and investigating lightweight structures for real-time applications.