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Wyniki wyszukiwania dla: learning from mistakes
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A Highly Scalable, Modular Architecture for Computer Aided Assessment e-Learning Systems
PublikacjaIn this chapter, the authors propose a novel e-learning system, dedicated strictly to knowledge assessment tasks. In its functioning it utilizes web-based technologies, but its design differs radically from currently popular e-learning solutions which rely mostly on thin-client architecture. The authors' research proved that such architecture, while well suited for didactic content distribution systems is ill-suited for knowledge...
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E-Learning Service Management System For Migration Towards Future Internet Architectures
PublikacjaAs access to knowledge and continuous learning are among the most valuable assets in modern, technological society, it is hardly surprising that e-learning solutions can be counted amongst the most important groups of services being deployed in modern network systems. Based on analysis of their current state-of-the-art, we decided to concentrate our research and development work on designing and implementing a management system...
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Continuous learning as a method of raising qualifications – the perspective of workers, employers and training organizations
PublikacjaContinuous learning is discussed in strategic documents of Poland and the European Union. In Poland, the idea of continuous learning is not very popular. However, in the context of strong competition in the labour market and the progressive globalization processes, the skills issue takes on new meaning — both for employees and employers. In order to adapt skills to labour market needs it is necessary to conduct adequate studies...
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Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
PublikacjaThis research is primarily concentrated on predicting the output of photovoitaic power, an essential field in the study of renewable energy. The paper comprehensively reviews various forecasting methodologies, transitioning from conventional physical and statistical methods to advanced machine learning (ML) techniques. A significant shift has been observed from traditional point forecasting to machine learning-based forecasting...
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Application of the Flipped Learning Methodology at a Business Process Modelling Course – A Case Study
PublikacjaFlipped learning has been known for a long time, but its modern use dates back to 2012, with the publication of Bergmann and Saams. In the last decade, it has become an increasingly popular learning method. Every year, the number of publications on implementing flipped learning experiments is growing, just as the amount of research on the effectiveness of this educational method. The aim of the article is to analyze the possibilities...
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Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings
PublikacjaInstalling photovoltaic (PV) systems in buildings is one of the most effective strategies for achieving sustainable energy goals and reducing carbon emissions. However, the requirement for efficient energy management, the fluctuating energy demands, and the intermittent nature of solar power are a few of the obstacles to the seamless integration of PV systems into buildings. These complexities surpass the capabilities of rule-based...
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Comparison of Deep Neural Network Learning Algorithms for Mars Terrain Image Segmentation
PublikacjaThis paper is dedicated to the topic of terrain recognition on Mars using advanced techniques based on the convolutional neural networks (CNN). The work on the project was conducted based on the set of 18K images collected by the Curiosity, Opportunity and Spirit rovers. The data were later processed by the model operating in a Python environment, utilizing Keras and Tensorflow repositories. The model benefits from the pretrained...
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BIG DATA SIGNIFICANCE IN REMOTE MEDICAL DIAGNOSTICS BASED ON DEEP LEARNING TECHNIQUES
PublikacjaIn this paper we discuss the evaluation of neural networks in accordance with medical image classification and analysis. We also summarize the existing databases with images which could be used for training deep models that can be later utilized in remote home-based health care systems. In particular, we propose methods for remote video-based estimation of patient vital signs and other health-related parameters. Additionally, potential...
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Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
PublikacjaIn this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KDbased methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublikacjaThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits
PublikacjaThe Contextual Multi-Armed Bandits (CMAB) framework is pivotal for learning to make decisions. However, due to challenges in deploying online algorithms, there is a shift towards offline policy learning, which relies on pre-existing datasets. This study examines the relationship between the quality of these datasets and the performance of offline policy learning algorithms, specifically, Neural Greedy and NeuraLCB. Our results...
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Looking through the past: better knowledge retention for generative replay in continual learning
PublikacjaIn this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. Nevertheless, such an approach does not come without limitations. In particular, we notice the degradation of the continually trained...
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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep Learning
PublikacjaIn this paper, the data, information, knowledge, and wisdom (DIKW) pyramid is revisited in the context of deep learning applied to machine learningbased audio signal processing. A discussion on the DIKW schema is carried out, resulting in a proposal that may supplement the original concept. Parallels between DIWK pertaining to audio processing are presented based on examples of the case studies performed by the author and her collaborators....
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Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning
PublikacjaEvery year in many countries, there are fatal unintentional drownings in different water reservoirs like swimming pools, lakes, seas, or oceans. The existing threats of this type require creating a method that could automatically supervise such places to increase the safety of bathers. This work aimed to create methods and prototype solutions for detecting people bathing in water reservoirs using a multimodal imaging system and...
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Technology-Enhanced Environmental Learning: Co-design of Educational Mobile Application Case
PublikacjaThe process of co-creating an educational mobile application to support environmentally friendly behavior is presented in this paper. The research material consisted of quantitative data collected on the application during the first testing phase by early adopters. The results suggest that the most frequently used features of the app were related to transport and educational activities. While women tended to split their time between...
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Driver’s Condition Detection System Using Multimodal Imaging and Machine Learning Algorithms
PublikacjaTo this day, driver fatigue remains one of the most significant causes of road accidents. In this paper, a novel way of detecting and monitoring a driver’s physical state has been proposed. The goal of the system was to make use of multimodal imaging from RGB and thermal cameras working simultaneously to monitor the driver’s current condition. A custom dataset was created consisting of thermal and RGB video samples. Acquired data...
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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
PublikacjaBiochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts,...
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Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data
PublikacjaThis paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost...
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Machine learning approach to packaging compatibility testing in the new product development process
PublikacjaThe paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing...
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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublikacjaComplexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...
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LOS and NLOS identification in real indoor environment using deep learning approach
PublikacjaVisibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a deep learning (DL) model to classify LOS/NLOS...
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Zastosowanie systemów e-learningu w szkolnictwie wyższym
PublikacjaWzrost dostępności internetu oraz rozwój systemów informatycznych przyczyniły się do zmian w zakresie edukacji. Poprzez wykorzystanie narzędzi informatycznych coraz popularniejszą formą nauczania stał się e-learning. Obecnie większość uczelni wyższych stosuje wybrany przez siebie system e-learningowy, którego głównym zadaniem jest wsparcie procesu dydaktycznego. Celem pracy jest analiza zastosowania kilku przykładowych systemów...
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Przestrzeń i Forma
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Brushing with additional cleaning restrictions
PublikacjaIn graph cleaning problems, brushes clean a graph by traversing it subject to certain rules. We consider the process where at each time step, a vertex that has at least as many brushes as incident, contaminated edges, sends brushes down these edges to clean them. Various problems arise, such as determining the minimum number of brushes (called the brush number) that are required to clean the entire graph. Here, we study a new variant...
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A surface-exposed DraD protein of uropathogenic Escherichia coli bearing Dr fimbriae may be expressed and secreted independently from DraC usher and DraE adhesin
PublikacjaOperon dra uropatogennych szczepów Escherichia coli Dr+ odpowiedzialny jest za proces bakteryjnej adhezji i inwazji. Operon dra kodujący fimbrie Dr zawiera sześć otwartych ramek odczytu - draA, draB, draC, draD, draP oraz draE. Fimbrie Dr zlokalizowane na powierzchni komórki bakteryjnej, zbudowane są z podjednostek białka DraE. Niewiele wiadomo na temat powierzchniowej ekspresji białka DraD oraz jego roli w procesie biogenezy fimbrii...
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Edyta Gołąb-Andrzejak dr hab.
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Chemometric analysis of UV-Vis spektra of associated forms of Amphotericin B and its derivatives
PublikacjaPrzedmiotem badań był proces agregacji nowych pochodnych antybiotyku polienowego Amfoterycyny B. Do śledzenia procesu agregacji wykorzystano spektroskopię absorpcyjną w zakresie widzialnym i bliskiego nadfioletu. Zastosowanie zaawansowanych technik chemometrycznych pozwoliło na dogłębną analizę agregacji tych pochodnych, co pozwoliło na wyciągniecie następujacych wniosków: i) w przypadku wszystkich pochodnych przy stężeniach poniżej...
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Application of Intelligent Conversational Agents in E-learing Environments
PublikacjaThe paper concerns intelligent tutoring systems and concentrates on apliaction of virtual mentors in distance learning environments. Different educational agent types are described. The prototype of an agent developed at Gdansk University of Technology is also presented, as well as its internal representation and construction. Benefits of virtual mentors applied in distance learning are also pointed out.
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Effect of Sieve Drum Inclination Angle on Wheat Grain Cleaning in a Novel Rotary Cleaning Device
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Analysis of the Sieve Unit Inclination Angle in the Cleaning Process of Oat Grain in a Rotary Cleaning Device
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Beata Krawczyk-Bryłka dr
OsobyPsycholog, doktor nauk humanistycznych w dziedzinie zarządzania, adiunkt w Katedrze przedsiębiorczości. 2018 - 2021: Kierownik projektu NCN: „Efektuacyjny model zespołu przedsiębiorczego. Jak działają przedsiębiorcze zespoły odnoszące sukces" od 2016: Quality Standards Lead filaru People management & personal development na studiach MBA Politechniki Gdańskiej 2008 – 2012: Prodziekan ds kształcenia Wzydziału Zarządzania i Ekonomii...
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Porównanie efektywności wybranych technik ekstrakcji/ługowania metabolitów wtórnych z suchego materiału roślin owadożernych z hodowli in vitro = Acomparison of effciency of secondary metabolites extraction/leaching techniques from carnivorous plants dry material from in vitro culture
PublikacjaW roślinach owadożernych za efekt terapeutyczny odpowiadają głównie pochodne 1,4-naftochinonu (juglon), oraz flawonoidy. Interesujące właściwości tych związków oraz możliwość synergicznego działania wielu innych metabolitów na efekt terapeutyczny, podjęto pracę przygotowania technologii efektywnego wyodrębniania metabolitów. W tej publikacji porównano efektywności technik ekstrakcji/ługowania. Jako materiał badany posłużyły wysuszone...
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Deep learning model for automated assessment of lexical stress of non-native english speakers
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Machine Learning for Control Systems Security of Industrial Robots: a Post-covid-19 Overview
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The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video
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Likelihood of Transformation to Green Infrastructure Using Ensemble Machine Learning Techniques in Jinan, China
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Edu Inspiracje WZiE: Active Learning, czyli o mocy aktywnego przetwarzania informacji
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Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines
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COMPARATIVE ANALYSIS OF COPING STRATEGIES WITH STRESS OF STUDENTS IN DIFFERENT LEARNING CONDITIONS DURING THE PANDEMIC
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Urban Food Self-Production in the Perspective of Social Learning Theory: Empowering Self-Sustainability
PublikacjaUrban food production is becoming an increasingly significant topic in the context of climate change and food security. Conducting research on this subject is becoming an essential element of urban development, deepening knowledge regarding the benefits, challenges, and potential for the development of urban agriculture as an alternative form of food production. Responding to this need, this monograph presents the results of...
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DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation
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Machine learning goes global: Cross-sectional return predictability in international stock markets
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Experimental Evaluation of the Agent-Based Population Learning Algorithm for the Cluster-Based Instance Selection
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Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems
PublikacjaTe feld of Big Data is rapidly developing with a lot of ongoing research, which will likely continue to expand in the future. A crucial part of this is Knowledge Discovery from Data (KDD), also known as the Knowledge Discovery Process (KDP). Tis process is a very complex procedure, and for that reason it is essential to divide it into several steps (Figure 1). Some authors use fve steps to describe this procedure, whereas others...
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Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)
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Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing
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Reinforcement Learning Algorithm and FDTD-based Simulation Applied to Schroeder Diffuser Design Optimization
PublikacjaThe aim of this paper is to propose a novel approach to the algorithmic design of Schroeder acoustic diffusers employing a deep learning optimization algorithm and a fitness function based on a computer simulation of the propagation of acoustic waves. The deep learning method employed for the research is a deep policy gradient algorithm. It is used as a tool for carrying out a sequential optimization process the goal of which is...
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COVID-19 severity forecast based on machine learning and complete blood count data
PublikacjaProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
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COVID-19 severity forecast based on machine learning and complete blood count data
PublikacjaProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
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Performance Analysis of Machine Learning Methods with Class Imbalance Problem in Android Malware Detection
PublikacjaDue to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for detecting malware...