Search Results for author:
Found 7 papers, 1 papers with code
A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Perspectives
Aiming at the traditional grasping method for manipulators based on 2D camera, when faced with the scene of gathering or covering, it can hardly perform well in unstructured scenes that appear as gathering and covering, for the reason that can't recognize objects accurately in cluster scenes from a single perspective and the manipulators can't make the environment better for grasping.
Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercises
The classification problem was to predict the known level of the in-exercise loads (in three categories by calories) by the heart rate activity features measured during the short period of time (1 minute only) after training, i. e by features of the post-exercise load.
Capsule Deep Neural Network for Recognition of Historical Graffiti Handwriting
The confusion matrixes were much better for capsule network than for CNN model and gave the much lower type I (false positive) and type II (false negative) values in all three regimes of data augmentation.
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti
A new image dataset of these carved Glagolitic and Cyrillic letters (CGCL) was assembled and pre-processed for recognition and prediction by machine learning methods.
Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation
Lossless data augmentation of the segmented dataset leads to the lowest validation loss (without overfitting) and nearly the same accuracy (within the limits of standard deviation) in comparison to the original and other pre-processed datasets after lossy data augmentation.
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer
Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outliers, is estimated for analysis of chest X-ray (CXR) 2D images by deep learning approach to help radiologists identify marks of lung cancer in CXR.
Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer
The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs).
