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Case Study: Deep Convolutional Networks in Healthcare
(Springer Verlag, 2020)
Technological improvements lead big data producing, processing and storing systems. These systems must contain extraordinary capabilities to overcome complexity of the big data. Therefore, the methodologies utilized for ...
Deep neural network approach to estimation of power production for an organic Rankine cycle system
(Springer, 2020)
In this study, the possibility of using Stepwise multilinear regression and deep learning models to estimate the behaviour of the organic Rankine cycle (ORC) has been investigated. It was found that a number of parameters ...
Comparison of different techniques for estimation of incoming longwave radiation
(Springer, 2020)
Global warming and climate change have left developing countries fragile in terms of agricultural production, and this vulnerability is expected to increase in the near future. The surface energy budget approach is a ...
Prediction of Leakage from an Axial Piston Pump Slipper with Circular Dimples Using Deep Neural Networks
(Springer, 2020)
Oil leakage between the slipper and swash plate of an axial piston pump has a significant effect on the efficiency of the pump. Therefore, it is extremely important that any leakage can be predicted. This study investigates ...
Deep Convolutional Generalized Classifier Neural Network
(Springer, 2020)
Up to date technological implementations of deep convolutional neural networks are at the forefront of many issues, such as autonomous device control, effective image and pattern recognition solutions. Deep neural networks ...
Transfer learning to detect neonatal seizure from electroencephalography signals
(Springer, 2021)
This paper offers a solution to the problem of detecting neonatal seizures via a transfer learning technique that judiciously reconstructs pre-trained deep convolution neural networks (p-DCNN), including alexnet, resnet18, ...
Plant leaf disease classification using EfficientNet deep learning model
(Elsevier, 2021)
Most plant diseases show visible symptoms, and the technique which is accepted today is that an experienced plant pathologist diagnoses the disease through optical observation of infected plant leaves. The fact that the ...
SecureDeepNet-IoT: A deep learning application for invasion detection in industrial Internet of Things sensing systems
(Wiley, 2021)
Deep learning (DL) is a special field of artificial intelligence that has increased its use in various fields and has proved its effectiveness in classification. The feasibility of using many hidden layers and many neurons ...
A deep learning architecture for identification of breast cancer on mammography by learning various representations of cancerous mass
(Springer, 2021)
Deep Learning (DL) is a high capable machine learning algorithm with the detailed analysis abilities on images. Although DL models achieve very high classification performances, the applications are trending on using and ...
Deep Learning-based Mammogram Classification for Breast Cancer
(International Journal of Intelligent Systems and Applications in Engineering, 2020)
Deep Learning (DL) is a rising field of researches in last decade by exposing a hybrid analysis procedure including advanced level image processing and many efficient supervised classifiers. Robustness of the DL algorithms ...