Filters
total: 6185
filtered: 4750
-
Catalog
- Publications 4750 available results
- Journals 706 available results
- Conferences 92 available results
- People 167 available results
- Projects 19 available results
- Research Teams 1 available results
- e-Learning Courses 219 available results
- Events 15 available results
- Open Research Data 216 available results
Chosen catalog filters
displaying 1000 best results Help
Search results for: KNOWLEDGE CULTURE, KNOWLEDGE SHARING, KNOWLEDGE HIDING, LEARNING CULTURE, MISTAKES ACCEPTANCE, LEARNING CLIMATE, HUMAN CAPITAL
-
A new multi-process collaborative architecture for time series classification
PublicationTime series classification (TSC) is the problem of categorizing time series data by using machine learning techniques. Its applications vary from cybersecurity and health care to remote sensing and human activity recognition. In this paper, we propose a novel multi-process collaborative architecture for TSC. The propositioned method amalgamates multi-head convolutional neural networks and capsule mechanism. In addition to the discovery...
-
Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublicationThis article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points...
-
Reversible Data Hiding in Encrypted DICOM Images Using Cyclic Binary Golay (23, 12) Code
PublicationIn this paper, a novel reversible data hiding method for encrypted images (RDHEI) is proposed. An efficient coding scheme based on cyclic binary Golay (23, 12) code is designed to embed additional data into the least significant bits (LSBs) of the encrypted image. The most significant bits (MSBs) are used to ensure the reversibility of the embedding process. The proposed scheme is lossless, and based on the receiver’s privileges,...
-
The Co-Culture of Staphylococcal Biofilm and Fibroblast Cell Line: The Correlation of Biological Phenomena with Metabolic NMR1 Footprint
Publication -
267: Mesenchymal cells regulate growth of intestinal crypts by a Wnt independent mechanism in 3D culture system
Publication -
Comparison of 2D and 3D culture models in the studies of the biological response induced by unsymmetrical bisacridines in cancer cells
PublicationMulticellular tumor spheroids are a good tool for testing new anticancer drugs, including those that may target cancer stem cells (CSCs), responsible for cancer progression, metastasis, and recurrence. Therefore, following the initial evaluation of the impact of antitumor unsymmetrical bisacridines (UAs) on lung and colon cancer cells using traditional monolayer cultures, I extended my investigations and applied the spherical model....
-
Fungal co-culture improves the biodegradation of hydrophobic VOCs gas mixtures in conventional biofilters and biotrickling filters
PublicationThe present study systematically evaluated the potential of Candida subhashii, Fusarium solani and their consortium for the abatement of n-hexane, trichloroethylene (TCE), toluene and α-pinene in biofilters (BFs) and biotrickling filters (BTFs). Three 3.2 L BFs packed with polyurethane foam and operated at a gas residence time of 77 s with an air mixture of hydrophobic volatile organic compounds (VOCs) were inoculated with C. subhashii,...
-
Kultura jakości, doskonałości i bezpieczeństwa w organizacji
PublicationKultura jakości, bezpieczeństwa oraz doskonałości to ważne, kategorie zasobów każdej organizacji, decydujące o jej niepowtarzalności. Kształtują one nie tylko tożsamość danej organizacji, ale także mają wpływ na jej zdolność do spełniania potrzeb różnych grup interesariuszy oraz na szeroko rozumianą reputację na rynku. Każda z tych kultur wyraża się poprzez swoiste założenia, normy, wartości oraz artefakty. Stanowią one fundament...
-
Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen
PublicationDeep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental...
-
Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen
PublicationDeep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental...
-
Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various Machine learning techniques
PublicationMachine Learning (ML) method is widely used in engineering applications such as fracture mechanics. In this study, twenty different ML algorithms were employed and compared for the prediction of the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) of various materials, including fibre-reinforced concrete, cement mortar, sandstone, white travertine, marble, and granite. A set of 401 specimens of “Brazilian...
-
Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
PublicationPredicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised Machine Learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained...
-
Low-Cost and Precise Automated Re-Design of Antenna Structures Using Interleaved Geometry Scaling and Gradient-Based Optimization
PublicationDesign of contemporary antennas is an intricate endeavor involving multiple stages, among others, tuning of geometry parameters. In particular, re-designing antennas to different operating frequencies, makes parametric optimization imperative to ensure the best achievable system performance. If the center frequency at the current design is distant from the target one, local tuning methods generally fail, whereas global algorithms...
-
Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
PublicationSatellite image classification is widely used in various real-time applications, such as the military, geospatial surveys, surveillance and environmental monitoring. Therefore, the effective classification of satellite images is required to improve classification accuracy. In this paper, the combination of Hierarchical Framework and Ensemble Learning (HFEL) and optimal feature selection is proposed for the precise identification...
-
Quality of Institutions for Knowledge-based Economy within New Institutional Economics Framework. Multiple Criteria Decision Analysis for European Countries in the Years 2000–2013
Publication -
Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces
PublicationPopularity of metasurfaces has been continuously growing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite...
-
Perceived technostress while learning a new mobile technology: Do individual differences and the way technology is presented matter?
Publication -
Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublicationOne of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was...
-
Car-sharing: The Impact on Metropolitan Spatial Structures
PublicationMany examples from the past show that new technologies designed to solve particular problems can also create side effects generating new problems. Some unforeseen or unwanted results may influence space use and spatial structures. Car-sharing is an invention to compete with car ownership. It drastically rise efficiency of car use, reducing the number of vehicles per users. Diffusion of car-sharing is going to accelerate in the...
-
Experience-Oriented Intelligence for Internet of Things
PublicationThe Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. The main reason behind this interest is the capabilities of the IoT for seamlessly integrating classical networks and networked objects, and hence allows people to create an intelligent environment based on this powerful integration. However, how to extract useful information from data produced by IoT and facilitate...
-
Exploration of the Solubility Hyperspace of Selected Active Pharmaceutical Ingredients in Choline- and Betaine-Based Deep Eutectic Solvents: Machine Learning Modeling and Experimental Validation
PublicationDeep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was...
-
Variable Data Structures and Customized Deep Learning Surrogates for Computationally Efficient and Reliable Characterization of Buried Objects
PublicationIn this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model has been used. The task is to predict simultaneously and independent of each characteristic parameters of a buried object of several radii at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable...
-
Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublicationNowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to...
-
Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning
PublicationConcrete machine foundations are structures that transfer loads from machines in operation to the ground. The design of such foundations requires a careful analysis of the static and dynamic effects caused by machine exploitation. There are also other substantial differences between ordinary concrete foundations and machine foundations, of which the main one is that machine foundations are separated from the building structure....
-
A fuzzy logic model for forecasting exchange rates
PublicationThis article is devoted to the issue of forecasting exchange rates. The objective of the conducted research is to develop a predictive model with the use of an innovative methodology - fuzzy logic theory - and to evaluate its effectiveness in times of prosperity and during the financial crisis. The model is based on sets of rules written by the author in the form of IF-THEN, where expert knowledge is stored. This model is the result...
-
Energy consumption optimization in wastewater treatment plants: Machine learning for monitoring incineration of sewage sludge
PublicationBiomass management in terms of energy consumption optimization has become a recent challenge for developed countries. Nevertheless, the multiplicity of materials and operating parameters controlling energy consumption in wastewater treatment plants necessitates the need for sophisticated well-organized disciplines in order to minimize energy consumption and dissipation. Sewage sludge (SS) disposal management is the key stage of...
-
Does Culture Moderate Gender Stereotypes? Individualism Predicts Communal (but Not Agentic) Prescriptions for Men Across 62 Nations
Publication -
Three-Dimensional Gastrointestinal Organoid Culture in Combination with Nerves or Fibroblasts: A Method to Characterize the Gastrointestinal Stem Cell Niche
Publication -
Comparison of siRNA-mediated silencing of glycosaminoglycan synthesis genes and enzyme replacement therapy for mucopolysaccharidosis in cell culture studies.
Publication -
Becoming a Learning Organization Through Dynamic Business Process Management
Publication -
DentalSegmentator: robust deep learning-based CBCT image segmentation
Publication -
Agent-Based Population Learning Algorithm for RBF Network Tuning
Publication -
An A-Team Approach to Learning Classifiers from Distributed Data Sources
Publication -
An A-Team approach to learning classifiers from distributed data sources
Publication -
Employing a biofeedback method based on hemispheric synchronization in effective learning
PublicationIn this paper an approach to build a brain computer-based hemispheric synchronization system is presented. The concept utilizes the wireless EEG signal registration and acquisition as well as advanced pre-processing methods. The influence of various filtration techniques of EOG artifacts on brain state recognition is examined. The emphasis is put on brain state recognition using band pass filtration for separation of individual...
-
The detection of Alternaria solani infection on tomatoes using ensemble learning
Publication -
Scheduling Repetitive Construction Processes Using the Learning-Forgetting Theory
Publication -
Generation of microbial colonies dataset with deep learning style transfer
Publication -
Meta-Design and the Triple Learning Organization in Architectural Design Process
Publication -
Deep learning-based waste detection in natural and urban environments
Publication -
Playback detection using machine learning with spectrogram features approach
PublicationThis paper presents 2D image processing approach to playback detection in automatic speaker verification (ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients (HOG) with support vector machines (SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks (CNN) were compared on different data partitions in respect...
-
Digital competence learning in secondary adult education in Finland and Poland
Publication -
Didaktische simulationsmodelle fur E-learning in der IK-ausbildung.
PublicationPrzedstawiono dydaktyczne modele symulacyjne wykorzystywane w zdalnym kształceniu z zakresu informatyki i technik komunikacyjnych. Pokazano na przykładach zbudowanych symulatorów, w jaki sposób zrealizować lub dostosować modele symulacyjne do zdalnego nauczania. Opisano doświadczenia autorów w wykorzystaniu modeli symulacyjnych w zdalnym nauczaniu.
-
Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
-
Security Information Sharing for the Polish Power System
PublicationThe Polish Power System is becoming increasingly more dependent on Information and Communication Technologies which results in its exposure to cyberattacks, including the evolved and highly sophisticated threats such as Advanced Persistent Threats or Distributed Denial of Service attacks. The most exposed components are SCADA systems in substations and Distributed Control Systems in power plants. When addressing this situation...
-
On Decision-Making Strategies for Improved-Reliability Size Reduction of Microwave Passives: Intermittent Correction of Equality Constraints and Adaptive Handling of Inequality Constraints
PublicationDesign optimization of passive microwave components is an intricate process, especially if the primary objective is a reduction of the physical size of the structure. The latter has become an important design consideration for a growing number of modern applications (mobile communications, wearable/implantable devices, internet of things), where miniaturization is imperative due to a limited space allocated for the electronic circuitry....
-
An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublicationThis study presents an advanced algorithm for selecting machine learning (ML) models for nitrous oxide (N2O) emission prediction in wastewater treatment plants (WWTPs) employing the activated sludge process. The examined ML models comprised multivariate adaptive regression spline (MARS), support vector machines (SVM), and extreme gradient boosting (XGboost). The study explores the concept that involves new criteria to select the...
-
Data Model Development for Security Information Sharing in Smart Grids
PublicationThe smart grid raises new security concerns which require novel solutions. It is commonly agreed that to protect the grid, the effective collaboration and information sharing between the relevant stakeholders is prerequisite. Developing a security information sharing platform for the smart grid is a new research direction which poses several challenges related to the highly distributed and heterogeneous character of the grid. In...
-
The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing
PublicationRubbers combine the flexibility with mechanical strength, supporting myriad applications, but suffer from inherent flammability. Formulation and production of flame-retardant rubber composites (FRRCs) have intensively been practiced over years, but not comprehensively reviewed. This necessity has outlined collecting, analyzing, screening, classifying, and interpreting the literature with the aim of classifying the FRRCs. We quantified...
-
Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set
PublicationThis work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning...