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ACM Transactions on Architecture and Code Optimization, 2021 Today’s GPU graph processing frameworks face scalability and efficiency issues as the graph size exceeds GPU-dedicated memory limit. Although recent GPUs can over-subscribe memory with Unified Memory (UM), they incur significant overhead when handling graph-structured data.
Pengyu Wang +5 more
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A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU [PDF]
Journal of Artificial Intelligence, 2023 Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets.
Farhad Shiri +3 more
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Real-time use of the iPad by third-year medical students for clinical decision support and learning: a mixed methods study [PDF]
Journal of Community Hospital Internal Medicine Perspectives, 2014 Purpose: Despite widespread use of mobile technology in medical education, medical students’ use of mobile technology for clinical decision support and learning is not well understood.
Michelle A. Nuss +4 more
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Advances in Distributed Computing and Artificial Intelligence Journal, 2021 Domain Name System (DNS) is a protocol for converting numeric IP addresses of websites into a human-readable form. With the development of technology, to transfer information, a method like DNS tunneling is used which includes data encryption into DNS ...
Dr. Gopal Sakarkar +6 more
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Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms
Energies, 2023 Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating ...
Mobarak Abumohsen, A. Y. Owda, M. Owda
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Data Science in Finance and Economics, 2023 This study is a review of literature on machine learning to examine the potential of deep learning (DL) techniques in improving the accuracy of option pricing models versus the Black-Scholes model and capturingcomplex features in financial data ...
Habib Zouaoui, Meryem-Nadjat Naas
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Gate-variants of Gated Recurrent Unit (GRU) neural networks [PDF]
Midwest Symposium on Circuits and Systems, 2017 The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNNs) by retaining the structure and systematically reducing parameters in the update and reset gates.
Rahul Dey, F. Salem
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Enhancing Privacy-Preserving Intrusion Detection in Blockchain-Based Networks with Deep Learning
Data Science Journal, 2023 Data transfer in sensitive industries such as healthcare presents significant challenges due to privacy issues, which makes it difficult to collaborate and use machine learning effectively. These issues are explored in this study by looking at how hybrid
Junzhou Li, Qianhui Sun, Feixian Sun
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Simulated annealing algorithm optimized GRU neural network for urban rainfall-inundation prediction
Journal of Hydroinformatics, 2023 In the context of global climate change and the continuous development of urban areas, rainfall-inundation modeling is a common approach that provides critical support for the protection and early warning of urban waterlogging protection.
Ying Yan +3 more
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Proceedings of the 2018 International Conference on Supercomputing, 2018 Graphics processing units (GPUs) have been widely adopted by major cloud vendors for better performance and energy efficiency. Recent research has observed a considerable degree of redundancy in managing computation and data in many datacenters, particularly for several important categories of GPU-accelerated applications such as log mining and machine
Husheng Zhou, Soroush Bateni, Cong Liu
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