Wykaz publikacji wybranego autora

Michał Koziarski, mgr inż.

pracownik inżynieryjno-techniczny

Akademickie Centrum Komputerowe ,,Cyfronet'' AGH
ACK, Akademickie Centrum Komputerowe ,,Cyfronet'' AGH


Identyfikatory Autora

ORCID: 0000-0001-7707-9640 połącz konto z ORCID

ResearcherID: brak

Scopus: 57190133444





Liczba pozycji spełniających powyższe kryteria selekcji: 21, z ogólnej liczby 28 publikacji Autora


1
  • A study on pattern recognition with the histograms of oriented gradients in distorted and noisy images
2
  • Analysis of group evolution prediction in complex networks
3
  • Breast cancer classification on histopathological images affected by data imbalance using active learning and deep convolutional neural network
4
  • Classification of histopathological images using Scale-Invariant Feature Transform
5
6
  • Convolutional neural network-based classification of histopathological images affected by data imbalance
7
  • CSMOUTE: Combined Synthetic Oversampling and Undersampling Technique for imbalanced data classification
8
  • Image recognition with deep neural networks in presence of noise – Dealing with and taking advantage of distortions
9
  • Impact of low resolution on image recognition with deep neural networks: an experimental study
10
  • Marine snow removal using a fully convolutional 3D neural network combined with an adaptive median filter
11
  • Multicriteria classifier ensemble learning for imbalanced data
12
13
  • Radial-based oversampling for multiclass imbalanced data classification
14
15
  • Radial-Based Undersampling algorithm for classification of breast cancer histopathological images affected by data imbalance
16
17
  • RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
18
  • RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification
19
  • The choice of feature representation in small-scale MobileNet-based imbalanced image recognition
20
  • The impact of distortions on the image recognition with histograms of oriented gradients
21
  • Two-stage resampling for convolutional neural network training in the imbalanced colorectal cancer image classification