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Transfer learning based cascaded deep learning network and mask recognition for COVID-19
The COVID-19 is still spreading today, and it has caused great harm to human beings. The system at the entrance of public places such as shop**...
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Image steganalysis using deep learning models
In the domain of digital steganography, the problem of efficient and accurate steganalysis is of utmost importance. Steganalysis seeks to detect the...
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Deep learning for higher-order nonparametric spatial autoregressive model
Deep learning technology has been successfully applied in more and more fields. In this paper, the application of deep neural networks in...
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Deep Machine Learning in Optimization of Scientific Research Activities
Abstract—This article provides a general overview of machine learning, a subdomain of artificial intelligence. The substance of the deep learning...
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COVID-19 classification based on a deep learning and machine learning fusion technique using chest CT images
Coronavirus disease (COVID-19), impacted by SARS-CoV-2, is one of the greatest challenges of the twenty-first century. COVID-19 broke out in the...
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Deep video representation learning: a survey
This paper provides a review on representation learning for videos . We classify recent spatio-temporal feature learning methods for sequential visual...
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Attractor Inspired Deep Learning for Modelling Chaotic Systems
Predicting and understanding the behavior of dynamic systems have driven advancements in various approaches, including physics-based models and...
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Manifold learning by a deep Gaussian process autoencoder
The paper presents a novel manifold learning algorithm, the deep Gaussian process autoencoder (DPGA), based on deep Gaussian processes. Deep Gaussian...
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Yoga with Deep Learning: Linking Mind and Machine
Health and fitness play a crucial role in every aspect of an individual’s life. In an era where well-being is an absolute target, Yoga is one of the...
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Prediction of fiber Rayleigh scattering responses based on deep learning
Distributed acoustic sensing (DAS) is a fiber sensing technology based on Rayleigh scattering, which transforms optical fiber into a series of...
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Image classification of intracranial tumor using deep residual learning technique
Classifying brain tumours is essential for diagnosing tumour progression and planning effective treatments. Different imaging modalities are used to...
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Stroke detection in the brain using MRI and deep learning models
When it comes to finding solutions to issues, deep learning models are pretty much everywhere. Medical image data is best analysed using models based...
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Software fault prediction using deep learning techniques
Software fault prediction (SFP) techniques identify faults at the early stages of the software development life cycle (SDLC). We find machine...
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DDoS attack traffic classification in SDN using deep learning
Software-defined networking will be a critical component of the networking domain as it transitions from a standard networking design to an...
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Skin cancer detection using ensemble of machine learning and deep learning techniques
Skin cancer is one of the most common forms of cancer, which makes it pertinent to be able to diagnose it accurately. In particular, melanoma is a...
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A survey of deep learning-based 3D shape generation
Deep learning has been successfully used for tasks in the 2D image domain. Research on 3D computer vision and deep geometry learning has also...
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Abstraction, mimesis and the evolution of deep learning
Deep learning developers typically rely on deep learning software frameworks (DLSFs)—simply described as pre-packaged libraries of programming...
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Age transformation based on deep learning: a survey
Age transformation aims to preserve personalized facial information while altering a given face to appear at a target age. This technique finds...
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Deep neural networks watermark via universal deep hiding and metric learning
With the rising costs of model training, it is urgent to safeguard the intellectual property of deep neural networks. To achieve this, researchers...
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Adversarial Deep Learning
Deep learning is not provably secure. Deep neural networks are vulnerable to security attacks from malicious adversaries, which is an ongoing and...