81 citations · 144 across the 20 of their papers we have counts for
21 papers
Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification
Jan Krupiński, Maciej Wielgosz, Szymon Mazurek +12
This paper presents a computer-aided cytology diagnosis system designed for animals, focusing on image quality assessment (IQA) using Convolutional Neural Networks (CNNs). The syst…
Using simulation to calibrate real data acquisition in veterinary medicine
Krystian Strzałka, Szymon Mazurek, Maciej Wielgosz +12
This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs…
Assessing Dataset Quality Through Decision Tree Characteristics in Autoencoder-Processed Spaces
Szymon Mazurek, Maciej Wielgosz
In this paper, we delve into the critical aspect of dataset quality assessment in machine learning classification tasks. Leveraging a variety of nine distinct datasets, each crafte…
Using super-resolution for enhancing visual perception and segmentation performance in veterinary cytology
Jakub Caputa, Maciej Wielgosz, Daria Łukasik +11
The primary objective of this research was to enhance the quality of semantic segmentation in cytology images by incorporating super-resolution (SR) architectures. An additional co…
Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis
Jakub Grzeszczyk, Michał Karwatowski, Daria Łukasik +11
This paper shows the machine learning system which performs instance segmentation of cytological images in veterinary medicine. Eleven cell types were used directly and indirectly…
Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest
Maciej Wielgosz, Stefano Puliti, Phil Wilkes +1
This article introduces Point2Tree, a novel framework that incorporates a three-stage process involving semantic segmentation, instance segmentation, optimization analysis of hyper…
CARLA-BSP: a simulated dataset with pedestrians
Maciej Wielgosz, Antonio M. López, Muhammad Naveed Riaz
We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestri…
Modern Cybersecurity Solution using Supervised Machine Learning
Mustafa Sakhai, Maciej Wielgosz
Cybersecurity is essential, and attacks are rapidly growing and getting more challenging to detect. The traditional Firewall and Intrusion Detection system, even though it is widel…
Retrain or not retrain? -- efficient pruning methods of deep CNN networks
Marcin Pietron, Maciej Wielgosz
Convolutional neural networks (CNN) play a major role in image processing tasks like image classification, object detection, semantic segmentation. Very often CNN networks have fro…
Falls Prediction in eldery people using Gated Recurrent Units
Marcin Radzio, Maciej Wielgosz, Matej Mertik
Falls prevention, especially in older people, becomes an increasingly important topic in the times of aging societies. In this work, we present Gated Recurrent Unit-based neural ne…
Convolutional neural network compression for natural language processing
Krzysztof Wróbel, Marcin Pietroń, Maciej Wielgosz +2
Convolutional neural networks are modern models that are very efficient in many classification tasks. They were originally created for image processing purposes. Then some trials w…
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń +1
This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduc…
Improving text classification with vectors of reduced precision
Krzysztof Wróbel, Maciej Wielgosz, Marcin Pietroń +2
This paper presents the analysis of the impact of a floating-point number precision reduction on the quality of text classification. The precision reduction of the vectors represen…
A Conceptual Framework for Supporting a Rapid Design of Web Applications for Data Analysis of Electrical Quality Assurance Data for the LHC
Matej Mertik, Maciej Wielgosz
The Large Hadron Collider (LHC) is one of the most complex machines ever build. It is composed of many components which constitute a large system. The tunnel and the accelerator is…
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik
This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets. We used high r…
The observer-assisted method for adjusting hyper-parameters in deep learning algorithms
Maciej Wielgosz
This paper presents a concept of a novel method for adjusting hyper-parameters in Deep Learning (DL) algorithms. An external agent-observer monitors a performance of a selected Dee…
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik
The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used syste…
A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework
Matej Mertik, Maciej Wielgosz, Andrzej Skoczeń
This article presents a development of web application for quench prediction in \gls{te-mpe-ee} at CERN. The authors describe an ELectrical Quality Assurance (ELQA) framework, a pl…
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity
Maciej Wielgosz, Marcin Pietroń
This paper examines the performance of a Spatial Pooler (SP) of a Hierarchical Temporal Memory (HTM) in the task of noisy object recognition. To address this challenge, a dedicated…
OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory
Maciej Wielgosz, Marcin Pietroń
We present a method to classify objects in video streams using a brain-inspired Hierarchical Temporal Memory (HTM) algorithm. Object classification is a challenging task where huma…
Formal analysis of HTM Spatial Pooler performance under predefined operation conditions
M. Pietron, M. Wielgosz, K. Wiatr
This paper introduces mathematical formalism for Spatial (SP) of Hierarchical Temporal Memory (HTM) with a spacial consideration for its hardware implementation. Performance of HTM…