Wyniki wyszukiwania dla: covid-19, x-ray images, deep learning, convolutional neural network
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Improvement of speech intelligibility in the presence of noise interference using the Lombard effect and an automatic noise interference profiling based on deep learning
PublikacjaThe Lombard effect is a phenomenon that results in speech intelligibility improvement when applied to noise. There are many distinctive features of Lombard speech that were recalled in this dissertation. This work proposes the creation of a system capable of improving speech quality and intelligibility in real-time measured by objective metrics and subjective tests. This system consists of three main components: speech type detection,...
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Deep neural network architecture search using network morphism
PublikacjaThe paper presents the results of the research on neural architecture search (NAS) algorithm. We utilized the hill climbing algorithm to search for well-performing structures of deep convolutional neural network. Moreover, we used the function preserving transformations which enabled the effective operation of the algorithm in a short period of time. The network obtained with the advantage of NAS was validated on skin lesion classification...
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Digits Recognition with Quadrant Photodiode and Convolutional Neural Network
PublikacjaIn this paper we have investigated the capabilities of a quadrant photodiode based gesture sensor in the recognition of digits drawn in the air. The sensor consisting of 4 active elements, 4 LEDs and a pinhole was considered as input interface for both discrete and continuous gestures. Index finger and a round pointer were used as navigating mediums for the sensor. Experiments performed with 5 volunteers...
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Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors
PublikacjaIn the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore,...
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Face with Mask Detection in Thermal Images Using Deep Neural Networks
PublikacjaAs the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The...
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Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan
PublikacjaResearch background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the...
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Highlighting interlanguage phoneme differences based on similarity matrices and convolutional neural network
PublikacjaThe goal of this research is to find a way of highlighting the acoustic differences between consonant phonemes of the Polish and Lithuanian languages. For this purpose, similarity matrices are employed based on speech acoustic parameters combined with a convolutional neural network (CNN). In the first experiment, we compare the effectiveness of the similarity matrices applied to discerning acoustic differences between consonant...
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Categorization of emotions in dog behavior based on the deep neural network
PublikacjaThe aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular...
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Buried Object Characterization by Data-Driven Surrogates and Regression-Enabled Hyperbolic Signature Extraction
PublikacjaThis work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently...
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Remote learning among students with and without reading difficulties during the initial stages of the COVID-19 pandemic
PublikacjaThis article presents the results of a survey on yet under-researched aspects of remote learning and learning difficulties in higher education during the initial stage (March – June 2020) of the COVID-19 pandemic. A total of 2182 students from University of Warsaw in Poland completed a two-part questionnaire regarding academic achievements in the academic year 2019/2020, living conditions and stress related to learning and pandemic,...
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Olgun Aydin dr
OsobyOlgun Aydin finished his PhD by publishing a thesis about Deep Neural Networks. He works as a Principal Machine Learning Engineer in Nike, and works as Assistant Professor in Gdansk University of Technology in Poland. Dr. Aydin is part of editorial board of "Journal of Artificial Intelligence and Data Science" Dr. Aydin served as Vice-Chairman of Why R? Foundation and is member of Polish Artificial Intelligence Society. Olgun is...
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Detection of Cystic Fibrosis Symptoms Based on X-Ray Images Using Machine Learning- Pilot Study
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MODELLING OF CONCRETE FRACTURE AT AGGREGATE LEVEL USING DEM BASED ON X-RAY mu CT IMAGES OF INTERNAL STRUCTURE
PublikacjaThe paper describes two-dimensional meso-scale numerical results of fracture in notched concrete beams under quasi-static three-point bending. Concrete was modelled as a random heterogeneous 4-phase material composed of aggregate particles, cement matrix, interfacial transitional zones (ITZs) and air voids. As a numerical approach, the discrete element method (DEM) was used. The concrete micro-structure in calculations was directly...
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X-ray images of Baltic herring
Dane BadawczeA methodology for studying the geometric shape of Baltic herring swimbladders including the optimal way of catching, transporting and storing fish, the X-ray measurements and the X-ray image analysis, that does not change the natural shape of the fish swimbladder was developed. Fish for research was obtained in the area of the Polish coastal zone...
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Food Classification from Images Using a Neural Network Based Approach with NVIDIA Volta and Pascal GPUs
PublikacjaIn the paper we investigate the problem of food classification from images, for the Food-101 dataset extended with 31 additional food classes from Polish cuisine. We adopted transfer learning and firstly measured training times for models such as MobileNet, MobileNetV2, ResNet50, ResNet50V2, ResNet101, ResNet101V2, InceptionV3, InceptionResNetV2, Xception, NasNetMobile and DenseNet, for systems with NVIDIA Tesla V100 (Volta) and...
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Modeling and Simulation for Exploring Power/Time Trade-off of Parallel Deep Neural Network Training
PublikacjaIn the paper we tackle bi-objective execution time and power consumption optimization problem concerning execution of parallel applications. We propose using a discrete-event simulation environment for exploring this power/time trade-off in the form of a Pareto front. The solution is verified by a case study based on a real deep neural network training application for automatic speech recognition. A simulation lasting over 2 hours...
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Two-dimensional simulations of concrete fracture at aggregate level with cohesive elements based on X-ray lCT images
PublikacjaThe paper presents results of two-dimensional meso-scale simulations of fracture in notched concrete beams subjected to three-point bending test. Concrete was assumed as a 4-phase material composed of aggregate grains placed in the cement matrix, interfacial transitional zones (ITZs) and macro-voids. The particle distribution was taken from real concrete beams on the basis of X-ray lCT images. Comprehensive numerical analyses were carried...
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X-ray images of Baltic herring. Data analysis
Dane BadawczeBased on the developed methodology for the: (i) optimal method of catching, (ii) transporting and storing fish, (iii) measuring and (iv) analyzing X-rays images, the existing collection of X-ray images of Baltic herring, caught in October 2002 during the Swedish component of the Baltic International Acoustic Survey (BIAS) in the Baltic proper (ICES...
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BIOLOGICAL AGE ASSESSMENT ALGORITHMS BASED ON X-RAY IMAGES OF BONE TISSUE
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The influence of image masks definition onsegmentation results of histopathological imagesusing convolutional neural network
PublikacjaAbstract—In the era of collecting large amounts of tissue materials, assisting the work of histopathologists with various electronic and information IT tools is an undeniable fact. The traditional interaction between a human pathologist and the glass slide is changing to interaction between an AI pathologist with a whole slide images. One of the important tasks is the segmentation of objects (e.g. cells) in such images. In this...
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Fracture evolution in concrete compressive fatigue experiments based on X-ray micro-CT images
PublikacjaArtykuł omawia ewolucje pękania w betonie podczas cyklicznego ściskania betonu. Przestrzenną ewolucję pękania zobrazowano stosując mikro-tomograf rentgenowski. Zdjęcia wykonano dla różnych cykli zmęczeniowych. Wyniki porównano z testami monotonicznymi. Jakościowa ewolucja objętości pękania ze wzrostem zmęczeniowego zniszczenia pokazała silnie nieliniowy kształt.
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Selection of an artificial pre-training neural network for the classification of inland vessels based on their images
PublikacjaArtificial neural networks (ANN) are the most commonly used algorithms for image classification problems. An image classifier takes an image or video as input and classifies it into one of the possible categories that it was trained to identify. They are applied in various areas such as security, defense, healthcare, biology, forensics, communication, etc. There is no need to create one’s own ANN because there are several pre-trained...
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Shear fracture of longitudinally reinforced concrete beams under bending using Digital Image Correlation and FE simulations with concrete micro-structure based on X-ray micro-computed tomography images
PublikacjaThe paper presents experimental and numerical investigations of the shear fracture in rectangular concrete beams longitudinally reinforced with steel or basalt bar under quasi-static three point bending. Shear fracture process zone formation and development on the surface of beams was investigated by Digital Image Correlation (DIC) whereas thorough analyses of 3D material micro-structure, air voids, width and curvature of shear...
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Machine Learning for Control Systems Security of Industrial Robots: a Post-covid-19 Overview
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Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
PublikacjaAbstract Background In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments...
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The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video
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A CNN based coronavirus disease prediction system for chest X-rays
PublikacjaCoronavirus disease (COVID-19) proliferated globally in early 2020, causing existential dread in the whole world. Radiography is crucial in the clinical staging and diagnosis of COVID-19 and offers high potential to improve healthcare plans for tackling the pandemic. However high variations in infection characteristics and low contrast between normal and infected regions pose great challenges in preparing radiological reports....
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Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network
PublikacjaThe idea of training Articial Neural Networks to evaluate chess positions has been widely explored in the last ten years. In this paper we investigated dataset impact on chess position evaluation. We created two datasets with over 1.6 million unique chess positions each. In one of those we also included randomly generated positions resulting from consideration of potentially unpredictable chess moves. Each position was evaluated...
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A three-dimensional meso-scale approach with cohesive elements to concrete fracture based on X-ray μCT images.
PublikacjaArtykuł omawia wyniki numeryczne dotyczące pękania betonu uzyskane stosując trójwymiarowy model mezoskopowy z elementami kohezyjnymi. Obliczenia trójwymiarowe zostały wykonane dla zginanej belki betonowej. Beton został opisany jako model 3-fazowy. Mikrostruktura betonu odpowiadała zdjęciom tomograficznym. Wyniki numeryczne zostały porównane z wynikami doświadczalnymi. Uzyskano b. dobra zgodność między wynikami numerycznymi i doświadczalnymi.
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Diversity of Students’ Unethical Behaviors in Online Learning Amid COVID-19 Pandemic: An Exploratory Analysis
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Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Power Capping
PublikacjaIn this paper we demonstrate that it is possible to obtain considerable improvement of performance and energy aware metrics for training of deep neural networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were...
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Modelling of concrete fracture at aggregate level using FEM and DEM based on X-ray uCT images of internal structure
PublikacjaArtykuł podejmuje problem pękania w zginanych belkach betonowych. Proces pękania był obserwowany przy zastosowaniu mikrotomografii . Zaobserwowany proces był symulowany numerycznie przy zastosowaniu metody elementów skończonych i metody elementów dyskretnych. Beton był opisany jako materiał 4-fazowy. Otrzymano dobrą zgodność wyników numerycznych z doświadczalnymi.
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Modelling of concrete facture at aggregate level using DEM based on x-ray mu CT images of internal structure
PublikacjaW artykule pokazano wyniki dyskretne DEM dla pękania betonu poddanego zginaniu. Beton został symulowany jako ośrodek 4-fazowy. Mikrostrukturę betonu określono na podstawie zdjęć uzyskanych mikro-tomografem. Szczególną uwagę zwrócono na zjawiska mikrostrukturalne podczas zarysowania na poziomie kruszywa.
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A three-dimensional meso-scale approach to concrete fracture based on combined DEM with x-ray micro-CT images
PublikacjaArtykuł omawia wyniki numeryczne uzyskane stosując 3-wymiarowy mezoskopowy model do opisu pękania w betonie na poziomie kruszywa w belce z nacięciem podczas zginania. Do obliczeń użyto metodę elementów dyskretnych. Beton został opisany jako materiał 4-fazowy złożony z kruszywa, zaprawy, makro-porów i stref przejściowych miedzy kruszywem a zaprawą. Kształt i położenia kruszywa przyjęto na podstawie skanów z mikro-tomografu. Uzyskano...
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Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
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Predicting Compressive Strength of Cement-Stabilized Rammed Earth Based on SEM Images Using Computer Vision and Deep Learning
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Big Data from Sensor Network via Internet of Things to Edge Deep Learning for Smart City
PublikacjaData from a physical world is sampled by sensor networks, and then streams of Big Data are sent to cloud hosts to support decision making by deep learning software. In a smart city, some tasks may be assigned to smart devices of the Internet of Things for performing edge computing. Besides, a part of workload of calculations can be transferred to the cloud hosts. This paper proposes benchmarks for division tasks between an edge...
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Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning
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Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship
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Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland – Swietokrzyskie Voivodeship
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E-Learning as a Factor Optimizing the Amount of Work Time Devoted to Preparing an Exam for Medical Program Students during the COVID-19 Epidemic Situation
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SEM (Scanning Electron Microscopy) and SEM-EDS (Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy) images of nickel (Ni) foam as received, after photocatalysis and after oxidation at 500_C.
Dane BadawczeThis dataset contains SEM (Scanning Electron Microscopy) and SEM-EDS (Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy) images of Ni (nickel) foam as received from the supplier, after photocatalytic treatment and after oxidation at 500C. The detailed equipment and measurement data was described in "readme SEM.txt" file
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Deep Learning Basics 2023/24
Kursy OnlineA course about the basics of deep learning intended for students of Computer Science. It includes an introduction to supervised machine learning, the architecture of basic artificial neural networks and their training algorithms, as well as more advanced architectures (convolutional networks, recurrent networks, transformers) and regularization and optimization techniques.
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Piotr Szczuko dr hab. inż.
OsobyDr hab. inż. Piotr Szczuko w 2002 roku ukończył studia na Wydziale Elektroniki, Telekomunikacji i Informatyki Politechniki Gdańskiej zdobywając tytuł magistra inżyniera. Tematem pracy dyplomowej było badanie zjawisk jednoczesnej percepcji obrazu cyfrowego i dźwięku dookólnego. W roku 2008 obronił rozprawę doktorską zatytułowaną "Zastosowanie reguł rozmytych w komputerowej animacji postaci", za którą otrzymał nagrodę Prezesa Rady...
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Efkleidis Katsaros
OsobyEfklidis Katsaros received the B.Sc. degree in mathematics from the Aristotle University of Thessaloniki, Greece, in 2016, and the M.Sc. degree (cum laude) in data science: statistical science from Leiden University, The Netherlands, in 2019. He is currently pursuing the Ph.D. degree in deep video multi-task learning with the Department of Biomedical Engineering, Gdańsk University of Technology, Poland. Since 2020, he has been...
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Self-Supervised Learning to Increase the Performance of Skin Lesion Classification
PublikacjaTo successfully train a deep neural network, a large amount of human-labeled data is required. Unfortunately, in many areas, collecting and labeling data is a difficult and tedious task. Several ways have been developed to mitigate the problem associated with the shortage of data, the most common of which is transfer learning. However, in many cases, the use of transfer learning as the only remedy is insufficient. In this study,...
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Paweł Rościszewski dr inż.
OsobyPaweł Rościszewski received his PhD in Computer Science at Gdańsk University of Technology in 2018 based on PhD thesis entitled: "Optimization of hybrid parallel application execution in heterogeneous high performance computing systems considering execution time and power consumption". Currently, he is an Assistant Professor at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Poland....
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Tagged images with bees
Dane BadawczeImages taken from bee hive with tagged bees. The images are prepared for training yolo5 deep neural network (supplied with the data).
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Paweł Możejko dr hab.
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Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach
PublikacjaTo improve the user's localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application....