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Found 10 papers, 3 papers with code
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.
Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases
The significant speedup was obtained even for extremely low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training stage (without taking into account the overheads) and speedup up to 2x for prediction stage (with and without taking into account overheads).
Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions
The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa.
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.
Parallel Statistical and Machine Learning Methods for Estimation of Physical Load
Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue .
Performance Evaluation of Deep Learning Networks for Semantic Segmentation of Traffic Stereo-Pair Images
Here the results of application of several deep learning architectures (PSPNet and ICNet) for semantic image segmentation of traffic stereo-pair images are presented.
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.
Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions
The new method is proposed to monitor the level of current physical load and accumulated fatigue by several objective and subjective characteristics.
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared.
Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes
The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared.
