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Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering, PCBEE 2023

作者： Jurgas, Artur Wodzinski, Marek Atzori, Manfredo Müller, Henning Information Systems Institute Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031384295

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pathology

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High-temperature Corrosion of ∼ 30 Pct Porous FeCr Stainless Steels in Air: Long-Term Evaluation Up to Breakaway (vol 54, pg 2244, 2023)

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METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS science 2023年第8期54卷 3370-3370页

作者： Koszelow, D. Advanced Materials Centre Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology 80-233 Gdańsk Poland Laboratory for Nuclear Materials Laboratory for Synchrotron Radiation and Femtochemistry Paul Scherrer Institut 5232 Villigen Switzerland International Centre of Electron Microscopy for Materials Science Faculty of Metal Engineering and Industrial Computer Science AGH University of Science and Technology Al. Mickiewicza 30 30-059 Krakow Poland

In this work, a long-term (up to 6000 hours) corrosion evaluation of three porous (~ 30 pct of initial porosity) ferritic iron-chromium alloys with different Cr contents (20, 22, and 27 wt pct of Cr) was carried out at 600 °C, 700 °C, 800 °C, and 900 °C in air. Mass gain measurements and SEM analyses revealed that at temperatures above 600 °C, all alloys exhibit breakaway corrosion, whereas at 600 °C, none of the alloys were heavily oxidized even after 6000 hours. Based on the results, the diffusion character of the corrosion of porous chromia-forming alloys was identified. The microstructure changes at high temperatures in porous alloys containing 22 wt pct of Cr were determined in detail by transmission electron microscopy. The proposed prediction model indicated that the lifetimes of the Fe20Cr and Fe22Cr alloys were determined as 1250 hours (± 535 hours) and 1460 hours (± 640 hours), respectively. It is in agreement with the long-term oxidation experiment. For the Fe27Cr alloy, the deviation between predicted and observed lifetimes occurs. The proposed model allows for qualitative estimation of the porous alloys’ lifetime with experimentally validated accuracy.

关键词： Chromium alloys

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Polyp image segmentation based on improved planet optimization algorithm using reptile search algorithm

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Neural Computing and Applications 2025年第8期37卷 6327-6349页

作者： Abd Elaziz, Mohamed Al-qaness, Mohammed A. A. Al-Betar, Mohammed Azmi Ewees, Ahmed A. Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China Faculty of Computer Science and Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Computer Damietta University Damietta Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan

To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images. © The Author(s) 2025.

关键词： Diseases

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Analysis of the Possibility of Using Exploration and Learning Algorithms in the Production of Castings

Analysis of the Possibility of Using Exploration and Learnin...

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75th World Foundry Congress, WFC 2024

作者： Wilk-Kolodziejczyk, Dorota Witkowski, Mateusz Jaśkowiec, Krzysztof Malysza, Marcin Bitka, Adama Marcjan, Lukasz Department of Applied Computer Science and Modelling Faculty of Metals Engineering and Industrial Computer Science AGH University of Krakow Czarnowiejska 66 Krakow30-054 Poland Lukasiewicz – Krakow Institute of Technology Zakopiańska 73 Kraków30-418 Poland

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Optical Design for Automatic Inspection System of Defects on Disposable Surgical Scalpel Blades Using Machine Vision 9th

Optical Design for Automatic Inspection System of Defects on...

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9th International Conference on the Development of Biomedical Engineering in Vietnam, BME 2022

作者： Dau, Sy Hieu Dang, Thi Phuc Han, Doan Quang My Hy, Ta Chiu Khang, Le Nguyen An Dat, Khau Nguyen Thanh Department of Applied Physics Applied Sciences Faculty University of Technology Vietnam National University HCMC Ho Chi Minh City Viet Nam Department of Computer Science Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031446290

Nowadays, many products in the medical area are disposable, such as needles, daily contact lenses, disposable ultra-thin surgical scalpel blades, infusion tubes. With the increasing demand in the market, manufacturers are striving to enhance production output while maintaining acceptable quality standards. Consequently, the implementation of an automatic inspection system for these products has become imperative. The main challenge with automatic quality inspection systems is the input data, consisting mainly of images captured during production. Image processing algorithms or deep learning models are used to analyze these images, classifying the products as "pass" or "fail" based on the inspection results. To minimize inspection time, high-quality input images are crucial, clearly distinguishing defects from the background. The optical system used for inspection must be carefully designed, considering the unique characteristics of each product, such as surface properties and external features that may pose challenges. In this article, we present a research study in which an optical system is designed using Dark Field, Coaxial and Dome lighting techniques to highlight defects on disposable surgical scalpel blades with metallic reflection surface. The Dark Field technique effectively reveals missing portions, cracks, or fractures on the cutting edge, while Coaxial lighting detects faults on the blade body. The Dome lighting technique provides illumination, preventing reflections on the metallic surface. We employed the Dome technique to inspect the quality of printed details on the aluminum foil sheet of the sealed blade’s pocket. The resulting images enable efficient defect detection due to their well-highlighted nature. © The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024.

关键词： computer vision

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TinyML Model for Solar Cell Defect Classification Based on Electroluminescence Images 4th

TinyML Model for Solar Cell Defect Classification Based on E...

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4th International Conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Boubaker, S. Moussaoui, C. Mellit, Adel Benghanem, M. Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah21959 Saudi Arabia Faculty of Sciences and Technology University of Jijel Jijel18000 Algeria Department of Physics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia

ISBN: (纸本)9789819606436

Being largely utilized as alternative sources of energy, photovoltaic (PV) panels are being deployed in many locations. However, those panels are extensively being prone to faults. A careful detection and diagnosis of those faults becomes a must for solar energy engineers, practitioners and decision-makers in order to extract the maximum of energy from them. The integration of artificial intelligence (AI) in the domain of solar energy management has become a trend in the few previous years because of the high potential provided by machine learning (ML) and deep learning (DL) algorithms in handling issues related to PV systems such as control and fault detection and isolation (FDI). In this paper, TinyML models for defect classification of photovoltaic modules based on electroluminescence (EL) images were developed. An open-source platform, Edge Impulse, was used to design and deploy the models. Two convolutional neural network models (mobileNetV1 and mobileNetV2) have been used and compared. A dataset of EL with three anomalies was used to develop the models. The validation test shows an accuracy of 88.44 and 76.38% for respectively MobileNet-V1 and MobileNet-V2. The quantized (Int8) model has been integrated into a microcontroller (Arduino Portenta H7) for real-time application. The obtained results can be considered as promising and can be extended to other case studies with more defects and larger datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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Modelling of diffraction of X-rays in perovskite superlattices 7

Modelling of diffraction of X-rays in perovskite superlattic...

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7th International Conference of Computational Methods in Engineering science, CMES 2022

作者： Kokosza, L. Mitura, Z. Department of Applied Computer Science and Modelling Faculty of Metals Engineering and Industrial Computer Science AGH University of Science and Technology al. Mickiewicza 30 Kraków30-059 Poland

Nowadays, different perovskite structures are widely investigated because they can be potentially used in many branches of industry. If two materials are deposited alternately on a substrate, then a superlattice having some unique properties can be formed. However, X-ray intensity distributions for perovskite superlattices, especially for very thin films, are difficult to be computationally determined, because many different parameters need to be considered to reproduce a superstructure of a sample. The main purpose of this work is to demonstrate how basic intensity distributions can be generated for perovskites, XRD technique and how to improve results using specific numerical methods. The computer application with a user-friendly graphical interface has been developed employing Unity software (with 3D graphics tools) and Oracle Database (through Oracle Cloud Infrastructure). The calculations are based on the kinematical theory of diffraction. The values of some important parameters and the results are stored in the assigned database. It is assumed that epitaxial superlattices can be prepared on substrates using molecular beam epitaxy or pulsed laser deposition methods. The desktop application allows for designing samples of different thicknesses and combinations of monolayers. The results are more realistic if Monte Carlo simulations are done although longer computation time is required. The software developed should be helpful to compare in detail virtual model results with experimental data. © 2022 Institute of physics Publishing. All rights reserved.

关键词： Perovskite

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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

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Journal of Electronic science and technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Detection of decision-making manipulation in the pairwise comparisons method

arXiv

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arXiv 2024年

作者： Strada, Michal Ernst, Sebastian Szybowski, Jacek Kulakowski, Konrad AGH University of Krakow Department of Applied Computer Science Poland AGH University of Krakow Faculty of Applied Mathematics Poland

Most decision-making models, including the pairwise comparison method, assume the decision-makers honesty. However, it is easy to imagine a situation where a decision-maker tries to manipulate the ranking results. This paper presents three simple manipulation methods in the pairwise comparison method. We then try to detect these methods using appropriately constructed neural networks. Experimental results accompany the proposed solutions on the generated data, showing a considerable manipulation detection level. © 2024, CC BY.

关键词： Machine learning

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Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams

Towards Differentiating Between Failures and Domain Shifts i...

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2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024

作者： Wojak-Strzelecka, Natalia Bobek, Szymon Nalepa, Grzegorz J. Stefanowski, Jerzy Jagiellonian Human-Centered AI Lab Mark Kac Center for Complex Systems Research Institute of Applied Computer Science Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. prof. Stanislawa Lojasiewicza 11 Krakow30-348 Poland Institute of Computing Science Poznań University of Technology Poznań60-965 Poland

Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the data do not always represent abnormal system states. Such changes may be recognized incorrectly as failures, while being a normal evolution of the system, e.g. referring to characteristics of starting the processing of a new product, i.e. realizing a domain shift. Therefore, distinguishing between failures and such"healthy" changes in data distribution is critical to ensure the practical robustness of the system. In this paper, we propose a method that not only detects changes in the data distribution and anomalies but also allows us to distinguish between failures and normal domain shifts inherent to a given process. The proposed method consists of a modified Page-Hinkley changepoint detector for identification of the domain shift and possible failures and supervised domain-adaptation-based algorithms for fast, online anomaly detection. These two are coupled with an explainable artificial intelligence (XAI) component that aims at helping the human operator to finally differentiate between domain shifts and failures. The method is illustrated by an experiment on a data stream from the steel factory. © 2024 Copyright for this paper by its authors.

关键词： Data streams

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