131 citations · 429 across the 14 of their papers we have counts for
14 papers
Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercises
Peng Gang, Wei Zeng, Yuri Gordienko +3
The assessment of energy expenditure in real life is of great importance for monitoring the current physical state of people, especially in work, sport, elderly care, health care,…
Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases
Yuriy Kochura, Yuri Gordienko, Vlad Taran +4
The impact of the maximally possible batch size (for the better runtime) on performance of graphic processing units (GPU) and tensor processing units (TPU) during training and infe…
Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions
Vlad Taran, Yuri Gordienko, Alexandr Rokovyi +2
Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The app…
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti
Nikita Gordienko, Peng Gang, Yuri Gordienko +4
Machine learning techniques are presented for automatic recognition of the historical letters (XI-XVIII centuries) carved on the stoned walls of St.Sophia cathedral in Kyiv (Ukrain…
Parallel Statistical and Machine Learning Methods for Estimation of Physical Load
Sergii Stirenko, Gang Peng, Wei Zeng +4
Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue . They are based on the statistical analys…
Gamification for Education of the Digitally Native Generation by Means of Virtual Reality, Augmented Reality, Machine Learning, and Brain-Computing Interfaces in Museums
Olga Barkova, Natalia Pysarevska, Oleg Allenin +7
Particularly close attention is being paid today among researchers in social science disciplines to aspects of learning in the digital age, especially for the Digitally Native Gene…
Performance Evaluation of Deep Learning Networks for Semantic Segmentation of Traffic Stereo-Pair Images
Vlad Taran, Nikita Gordienko, Yuriy Kochura +4
Semantic image segmentation is one the most demanding task, especially for analysis of traffic conditions for self-driving cars. Here the results of application of several deep lea…
Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation
Sergii Stirenko, Yuriy Kochura, Oleg Alienin +4
The results of chest X-ray (CXR) analysis of 2D images to get the statistically reliable predictions (availability of tuberculosis) by computer-aided diagnosis (CADx) on the basis…
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer
Yu. Gordienko, Yu. Kochura, O. Alienin +5
Efficiency of some dimensionality reduction techniques, like lung segmentation, bone shadow exclusion, and t-distributed stochastic neighbor embedding (t-SNE) for exclusion of outl…
Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions
Yuri Gordienko, Sergii Stirenko, Yuriy Kochura +3
The new method is proposed to monitor the level of current physical load and accumulated fatigue by several objective and subjective characteristics. It was applied to the dataset…
Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer
Yu. Gordienko, Peng Gang, Jiang Hui +5
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…
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
Yuriy Kochura, Sergii Stirenko, Oleg Alienin +2
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. Their c…
Performance Evaluation of Distributed Computing Environments with Hadoop and Spark Frameworks
Vladyslav Taran, Oleg Alienin, Sergii Stirenko +2
Recently, due to rapid development of information and communication technologies, the data are created and consumed in the avalanche way. Distributed computing create preconditions…
Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes
Yuriy Kochura, Sergii Stirenko, Anis Rojbi +3
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. Their c…