A deep network solution for intelligent fault detection in analog circuit
Sign Up to like & getrecommendations! Published in 2020 at "Analog Integrated Circuits and Signal Processing"
DOI: 10.1007/s10470-020-01732-8
Abstract: Automatic fault diagnosis in analog electronic circuits is one of the interesting and important cases for researchers of this field that has gained substantial improvements in recent decades. Fault detection issue could be transferred into… read more here.
Keywords: fault detection; deep network; fault; analog ... See more keywords
Caries Segmentation on Tooth X-ray Images with a Deep Network.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of dentistry"
DOI: 10.1016/j.jdent.2022.104076
Abstract: OBJECTIVES Deep learning has been a promising technology in many biomedical applications. In this study, a deep network was proposed aiming for caries segmentation on the clinically collected tooth X-ray images. METHODS The proposed network… read more here.
Keywords: tooth ray; ray images; caries segmentation; deep network ... See more keywords
Active one-shot learning by a deep Q-network strategy
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.11.017
Abstract: Abstract One-shot learning has recently attracted growing attention to produce models which can classify significant events from a few or even no labeled examples. In this paper, we introduce a deep Q-network strategy into one-shot… read more here.
Keywords: deep network; network strategy; one shot; shot learning ... See more keywords
Deep network compression based on partial least squares
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.03.108
Abstract: Abstract Modern visual pattern recognition methods are based on convolutional networks since they are able to learn complex patterns directly from the data. However, convolutional networks are computationally expensive in terms of floating point operations… read more here.
Keywords: convolutional networks; network; least squares; accuracy ... See more keywords
Category-consistent deep network learning for accurate vehicle logo recognition
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.08.030
Abstract: Abstract Vehicle logo recognition (VLR) is essential in intelligent transportation systems. Although many VLR algorithms have been proposed, efficient and accurate VLR remains challenging in machine vision. Many VLR algorithms explicitly detect the coarse region… read more here.
Keywords: category consistent; deep network; vehicle logo;
In vivo deep network tracing reveals phosphofructokinase-mediated coordination of biosynthetic pathway activity in the myocardium.
Sign Up to like & getrecommendations! Published in 2021 at "Journal of molecular and cellular cardiology"
DOI: 10.1016/j.yjmcc.2021.08.013
Abstract: Glucose metabolism comprises numerous amphibolic metabolites that provide precursors for not only the synthesis of cellular building blocks but also for ATP production. In this study, we tested how phosphofructokinase-1 (PFK1) activity controls the fate… read more here.
Keywords: deep network; biosynthetic pathway; activity; pathway activity ... See more keywords
Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization
Sign Up to like & getrecommendations! Published in 2019 at "Scientific Reports"
DOI: 10.1038/s41598-019-42557-4
Abstract: Automated diagnosis of tuberculosis (TB) from chest X-Rays (CXR) has been tackled with either hand-crafted algorithms or machine learning approaches such as support vector machines (SVMs) and convolutional neural networks (CNNs). Most deep neural network… read more here.
Keywords: network; efficient deep; visualization; tuberculosis ... See more keywords
Exploratory Analysis for Big Social Data Using Deep Network
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2898238
Abstract: Exploratory analysis is an important way to gain understanding and find unknown relationships from various data sources, especially in the era of big data. Traditional paradigms of social science data analysis follow the steps of… read more here.
Keywords: analysis; big social; deep network; using deep ... See more keywords
Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2902846
Abstract: Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple conflicting objectives, are yet to be addressed properly. The… read more here.
Keywords: objective workflow; deep network; workflow scheduling; multi ... See more keywords
DDoS Traffic Control Using Transfer Learning DQN With Structure Information
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2923993
Abstract: A DDoS attack is one of the most serious threats to the current Internet. The Router throttling is a popular method to response against DDoS attacks. Currently, coordinated team learning (CTL) has adopted tile coding… read more here.
Keywords: ddos; transfer learning; transfer; deep network ... See more keywords
Joint Deep Network With Auxiliary Semantic Learning for Popular Recommendation
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2976498
Abstract: There is a cold-start problem in the recommendation system field, which is how to profile new users and new items. The popular recommendation algorithm is an important solution to the cold-start problem. In this paper,… read more here.
Keywords: popular recommendation; joint deep; auxiliary semantic; deep network ... See more keywords