What are the challenges of developing autonomous vehicles and intelligent transportation infrastructure?

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Nov 30, 2018 ... We have explored the potential of microscopy images of fluorescently labeled nuclei for the prediction of toxicity based on nucleus pattern recognition. May 4, 2021 ... This work focuses on the use of different course of action to train the deep neural network models. A useful way of thinking about model ... Jun 27, 2024 ... ... machine learning by regression-based CNN. As a result, the lowest nonlinearity of 0.3% with precision as high as 98.7 is given in our PSD as ... Explore CNNs in self-driving cars, including reinforcement learning and Partially Observable Markov Decision Processes. We have proposed a deep learning method called BCM-SegNet, an optimized Mask R-CNN-based algorithm for segmenting BSE images of bronze casting moulds and cores. Explore and run machine learning code with Kaggle Notebooks | Using data from Digit Recognizer. With the development of deep learning, object detection has achieved very promising results. There are several algorithms and models, such as R-CNN, Faster R- ... Apr 12, 2025 ... CNN in deep learning excels at image classification, which involves sorting images into predefined categories. They can effectively identify ... 3 From CNN to Faster R-CNN. Deep learning (DL) is a subfield of machine learning which attempts to acquire high-level abstractions in data by utilising ... A convolutional neural network (CNN/ConvNet) is a class of deep neural networks, most commonly applied to analyze visual imagery.
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