Results 11 to 20 of about 99,068 (260)
The Limitations of Deep Learning in Achieving Real Artificial Intelligence
Proceedings, 2022 The achievement of artificial intelligence has been one of the goals in the field of machine learning, and achievements in the field of deep learning have led to the idea that the goal of so-called “intelligence” in artificial intelligence can be ...
Tianqi Wu, Ruiqi Jin
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Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †
Energies, 2018 Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech ...
Gregory D. Merkel +2 more
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Artificial Intelligence in Surgery: Neural Networks and Deep Learning
CoRR, 2020 Deep neural networks power most recent successes of artificial intelligence, spanning from self-driving cars to computer aided diagnosis in radiology and pathology. The high-stake data intensive process of surgery could highly benefit from such computational methods.
Deepak Alapatt +3 more
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USING NEURAL NETWORKS AND DEEP LEARNING ALGORITHMS IN ELECTRICAL IMPEDANCE TOMOGRAPHY
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2017 This paper refers to the cases of the use of Artificial Neural Networks and Convolutional Neural Networks in impedance tomography. Machine Learning methods can be used to teach computers different technical problems.
Grzegorz Kłosowski, Tomasz Rymarczyk
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APL Machine Learning, 2023 The design and development of polymeric materials have been a hot domain for decades. However, traditional experiments and molecular simulations are time-consuming and labor-intensive, which no longer meet the requirements of new materials development ...
Dazi Li +5 more
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Decorrelation-Based Deep Learning for Bias Mitigation
Future Internet, 2022 Although deep learning has proven to be tremendously successful, the main issue is the dependency of its performance on the quality and quantity of training datasets. Since the quality of data can be affected by biases, a novel deep learning method based
Pranita Patil, Kevin Purcell
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Revolutionizing Medical Imaging through Deep Learning Techniques:An Overview. [PDF]
International Journal of Intelligent Computing and Information Sciences, 2023 Medical imaging is a crucial tool for various clinical applications, including examination of medical issues, including early identification, monitoring, diagnosis, and therapy. To analyse medical images using computer vision, it is crucial to comprehend
Salma Elgayar +2 more
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Frontiers in Computational Neuroscience, 2022 IntroductionIn recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance ...
Cai Wingfield +9 more
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Survey of Graph Neural Network [PDF]
Jisuanji gongcheng, 2021 With the continuous development of the computer and Internet technologies,graph neural network has become an important research area in artificial intelligence and big data.Graph neural network can effectively transmit and aggregate information between ...
WANG Jianzong, KONG Lingwei, HUANG Zhangcheng, XIAO Jing
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An Introductory Review of Deep Learning for Prediction Models With Big Data
Frontiers in Artificial Intelligence, 2020 Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in
Frank Emmert-Streib +9 more
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