Research paper on application of neural network

From: Ryan M.
Added: 26.04.2021
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The Machine Making sense of AI. Join Transform this July Register fo r the AI event of the year. But safety and security remain two major concerns in robotics. And the current methods used to address these two issues can produce conflicting results, researchers at the Institute of Science and Technology Austria, the Massachusetts Institute of Technology, and Technische Universitat Wien, Austria have found.
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Convolutional neural networks: an overview and application in radiology

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Adversarial training reduces safety of neural networks in robots: Research | VentureBeat

An artificial neural network ANN typically refers to a computational system inspired by the processing method, structure, and learning ability of a biological brain. It acts like a real neural network because it simulates how biological neurons act in the human brain. By learning to recognize patterns from data, an artificial neural network can anticipate and solve dynamic and complex human problems in real-time. An artificial neural network is trained in a supervised or unsupervised manner. In supervised learning, the network is trained by providing input and output data samples to get the ANN to provide a desired output from a given input.
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Neural Networks

Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. Submit Your Paper. Supports Open Access.
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In this research paper, Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks, recently published in established transportation science journal Transportation Research: Part B , SMART researchers explain their developed TB-ResNet framework and demonstrate the strength of combining the DCMs and DNNs methods, proving that they are highly complementary. As machine learning is increasingly used in the field of transportation, the two disparate research concepts, DCMs and DNNs, have long been viewed as conflicting methods of research. By synergising these two important research paradigms, TB-ResNet takes advantage of DCMs' simplicity and DNNs' expressive power to generate richer findings and more accurate predictions for individual decision-making analysis, which is important for improved travel behaviour research.
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