An empirical study of the impact of hyperparameter tuning and model optimization on the performance properties of deep neural networks
Deep neural network (DNN) models typically have many hyperparameters that can be
configured to achieve optimal performance on a particular dataset. Practitioners usually tune …
configured to achieve optimal performance on a particular dataset. Practitioners usually tune …
A hybrid CNN-LSTM model for aircraft 4D trajectory prediction
L Ma, S Tian - IEEE access, 2020 - ieeexplore.ieee.org
The 4D trajectory is a multi-dimensional time series with plentiful spatial-temporal features
and has a high degree of complexity and uncertainty. Aiming at these features of aircraft …
and has a high degree of complexity and uncertainty. Aiming at these features of aircraft …
Neural architecture search for 1D CNNs—different approaches tests and measurements
In the field of sensors, in areas such as industrial, clinical, or environment, it is common to
find one dimensional (1D) formatted data (eg, electrocardiogram, temperature, power …
find one dimensional (1D) formatted data (eg, electrocardiogram, temperature, power …
Application of image processing and transfer learning for the detection of rust disease
Plant diseases introduce significant yield and quality losses to the food production industry,
worldwide. Early identification of an epidemic could lead to more effective management of …
worldwide. Early identification of an epidemic could lead to more effective management of …
Road surface classification with images captured from low-cost camera-road traversing knowledge (rtk) dataset
T Rateke, KA Justen… - Revista de Informática …, 2019 - seer.ufrgs.br
The type of road pavement directly influences the way vehicles are driven. It's common to
find papers that deal with path detection but don't take into account major changes in road …
find papers that deal with path detection but don't take into account major changes in road …
The short-term prediction of length of day using 1D convolutional neural networks (1D CNN)
Accurate Earth orientation parameter (EOP) predictions are needed for many applications,
eg, for the tracking and navigation of interplanetary spacecraft missions. One of the most …
eg, for the tracking and navigation of interplanetary spacecraft missions. One of the most …
[HTML][HTML] 4D flight trajectory prediction using a hybrid Deep Learning prediction method based on ADS-B technology: A case study of Hartsfield–Jackson Atlanta …
H Shafienya, AC Regan - Transportation Research Part C: Emerging …, 2022 - Elsevier
At the core of any flight schedule is the four dimensional (4D) trajectories which are
comprised of three spatial dimensions with time added as the fourth dimension. Each …
comprised of three spatial dimensions with time added as the fourth dimension. Each …
Automatic stones classification through a CNN-based approach
This paper presents an automatic recognition system for classifying stones belonging to
different Calabrian quarries (Southern Italy). The tool for stone recognition has been …
different Calabrian quarries (Southern Italy). The tool for stone recognition has been …
Applying Multi-CNNs model for detecting abnormal problem on chest x-ray images
Image diagnosis is the significant problem in medicine. Nowadays, with modern facilities
that allow doctors to diagnose early and accurately disease, limiting unnecessary treatment …
that allow doctors to diagnose early and accurately disease, limiting unnecessary treatment …
[PDF][PDF] Bare skin image classification using convolution neural netowrk
Image classification is critical and significant research problems in computer vision
applications such as facial expression classification, satellite image classification, and plant …
applications such as facial expression classification, satellite image classification, and plant …