Detrac: Transfer learning of class decomposed medical images in convolutional neural networks
A Abbas, MM Abdelsamea, MM Gaber - IEEE Access, 2020 - ieeexplore.ieee.org
Due to the high availability of large-scale annotated image datasets, paramount progress
has been made in deep convolutional neural networks (CNNs) for image classification tasks …
has been made in deep convolutional neural networks (CNNs) for image classification tasks …
Prediction of highway tunnel pavement performance based on digital twin and multiple time series stacking
G Yu, S Zhang, M Hu, YK Wang - Advances in Civil Engineering, 2020 - Wiley Online Library
The existing pavement performance prediction methods are limited to single‐factor
predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It …
predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It …
A deep reinforcement learning approach to the optimization of data center task scheduling
H Che, Z Bai, R Zuo, H Li - Complexity, 2020 - Wiley Online Library
With more businesses are running online, the scale of data centers is increasing
dramatically. The task‐scheduling operation with traditional heuristic algorithms is facing the …
dramatically. The task‐scheduling operation with traditional heuristic algorithms is facing the …
Monitoring file integrity using blockchain and smart contracts
A Pinheiro, ED Canedo, RT De Sousa… - IEEE …, 2020 - ieeexplore.ieee.org
The adoption of cloud computing solutions is an established reality in government agencies
and in small, medium, and large companies due to procurement easiness and the variety of …
and in small, medium, and large companies due to procurement easiness and the variety of …
[HTML][HTML] Link quality prediction in wireless community networks using deep recurrent neural networks
Wireless community networks (WCNs) are large, heterogeneous, dynamic, and
decentralized networks. Such complex characteristics raise different challenges, such as the …
decentralized networks. Such complex characteristics raise different challenges, such as the …
Reliability-aware and deadline-constrained mobile service composition over opportunistic networks
An opportunistic link between two mobile devices or nodes can be constructed when they
are within each other's communication range. Typically, cyber–physical environments …
are within each other's communication range. Typically, cyber–physical environments …
Diagnosis of chronic obstructive pulmonary disease based on transfer learning
Q Wang, H Wang, L Wang, F Yu - Ieee Access, 2020 - ieeexplore.ieee.org
Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease that
seriously endangers human health and has high incidence and mortality worldwide …
seriously endangers human health and has high incidence and mortality worldwide …
Regression models with graph-regularization learning algorithms for accurate fault location in smart grids
This article focuses on the design of a hierarchical framework for locating faults in smart
grids by resorting to only modal components of three-phase voltage measurements. The …
grids by resorting to only modal components of three-phase voltage measurements. The …
An improved attention-based LSTM for multi-step dissolved oxygen prediction in water environment
The prediction of accurate water quality has great significance to the sustainable
management of water resources and pollution prevention. Due to the complexity of water …
management of water resources and pollution prevention. Due to the complexity of water …
Data-driven surplus material prediction in steel coil production
Z Zhao, X Yong, S Liu, M Zhou - 2020 29th Wireless and Optical …, 2020 - ieeexplore.ieee.org
A steel enterprise is currently trying to avoid the presence of surplus materials since they can
greatly increase its operational cost. The complicated production process of steel products …
greatly increase its operational cost. The complicated production process of steel products …