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Towards Proving the Adversarial Robustness of Deep Neural Networks [PDF]
Electronic Proceedings in Theoretical Computer Science, 2017 Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations. Manually crafting software controllers for these vehicles is difficult, but there has been some success in using deep neural networks generated
Guy Katz +4 more
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DEEP NEURAL NETWORKS APPLICATIONS IN THE STUDY OF A GEOLOGICAL INDICATOR [PDF]
Sustainable Extraction and Processing of Raw Materials Journal, 2021 . The differences between shallow neural networks and deep neural networks are considered. Data from operational exploration of an open pit mine are used to train different types of deep neural networks to predict a useful indicator.
Kremena Arsova–Borisova +1 more
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Implementation of deep learning in drug design
MedComm – Future Medicine, 2022 The field of deep learning has witnessed dramatic and rapid progress in the past several years, largely driven by the availability of massive datasets and increased computational power.
Bo Yang, Kan Li, Xiuqin Zhong, Jun Zou
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Deep Neural Networks and An Application in Health Sciences
Van Tıp Dergisi, 2021 INTRODUCTION: Because there is more than one hidden layer between the input and output layers in the neural network algorithm, it is called "Deep Neural Networks". In the study, the Deep Neural Networks algorithm; different input (number of layers, epoch,
Sadi Elasan
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Digital holographic microscopy applied to 3D computer microvision by using deep neural networks [PDF]
EPJ Web of Conferences, 2023 Deep neural networks are increasingly applied in many branches of applied science such as computer vision and image processing by increasing performances of instruments.
Brito Carcaño Jesús E. +6 more
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IEEE Access, 2022 Due to the popularity of 5G connectivity and The Internet of Things sensors, deep learning algorithms are being extended to edge devices. Compared with AI(Artificial Intelligence) cloud platforms, the deployment of deep neural networks on edge devices ...
Lin He, Lijun Peng, Lile He
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Deep learning for protein secondary structure prediction: Pre and post-AlphaFold
Computational and Structural Biotechnology Journal, 2022 This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure
Dewi Pramudi Ismi +2 more
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Distributed training method for deep neural networks
Dianzi Jishu Yingyong, 2023 ： Deep neural networks have achieved great success in classification and prediction of high-dimensional data. Training deep neural networks is a data-intensive task, which needs to collect large-scale data from multiple data sources.
Yuan Ye, Tian Yuan, Jiang Qibing
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IEEE Access, 2020 By the development of social media, sentiment analysis has changed to one of the most remarkable research topics in the field of natural language processing which tries to dig information from textual data containing users' opinions or attitudes toward a
Hossein Sadr +2 more
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Estimation and application of matrix eigenvalues based on deep neural network
Journal of Intelligent Systems, 2022 In today’s era of rapid development in science and technology, the development of digital technology has increasingly higher requirements for data processing functions.
Hu Zhiying
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