higher-order sensory substitution. Acknowledging the inevitable presence of unknown categories in the real world, we propose two methods to generalize obstacle identification across all classes. The first method focuses on detecting a single type of object: the ground, which is universally present and ...
The Prediction capability is embodied in the AI model's ability to forecast potential waste in terms of resources and time and deteriorations in customer satisfaction by preventing damaged packages from proceeding to shipping operations. The YOLO v7 model is utilised here, having demonstrated high ...
Section2summarizes a brief history, famous scholars, and important achievements of DL, RL, and deep reinforcement learning (DRL) in the form of a timeline, and this is also the first time that the development process is fully displayed in the form ...
An accuracy of 95.32% was achieved on the INbreast dataset when the InceptionResNet-V2 classifier was applied. Even though the YOLO detector accurately predicts input images, it might be challenging to find small clusters of micro-calcification objects [30]. As the authors claimed, the micro-...
(2014) describe a simulator named SimBully to illustrate the impact of public belief and attitudes on abuse occurrences by classmates. Ali et al. (2020) use the YOLOv3 network to recognize student behaviours such as calling, napping, or reading a book indoors or outdoors with the goal to ...
(region-based fully convolution network), Fast RCNN, Faster RCNN and Cascade RCNN, and one-stage detectors like YOLO (you look only once), SSD (single shot multibox detector), and RetinaNet, etc. In Ref. [73] YOLOv4416 can process 100 images per second, which is faster than previous...
However, it is possible to extend results from image recognition to other Computer Vision tasks such as Object Detection led by the algorithms YOLO [50], R-CNN [51], fast R-CNN [52], and faster R-CNN [53] or Semantic Segmentation [54] including algorithms such as U-Net [55]. ...
She co-authored California Wildlife: Conservation Challenges prepared at the University of California, Davis, and her work has appeared in the Yolo Crow, Pilgrimage, River Teeth online, and a number of scientific journals. She can be reached at http://andreamummertpuccini.blogspot.com/ Follow ...