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Wyniki wyszukiwania dla: ORACLE ILEARNING
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Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublikacjaOne 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...
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Cost-Efficient Measurement Platform and Machine-Learning-Based Sensor Calibration for Precise NO2 Pollution Monitoring
PublikacjaAir quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung...
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Variable Data Structures and Customized Deep Learning Surrogates for Computationally Efficient and Reliable Characterization of Buried Objects
PublikacjaIn 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...
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Detection of Cystic Fibrosis Symptoms Based on X-Ray Images Using Machine Learning- Pilot Study
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Employing Subjective Tests and Deep Learning for Discovering the Relationship between Personality Types and Preferred Music Genres
PublikacjaThe purpose of this research is two-fold: (a) to explore the relationship between the listeners’ personality trait, i.e., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms. We assume that this may help match songs according to the listener’s personality in social music networks....
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Towards the 4th industrial revolution: networks, virtuality, experience based collective computational intelligence, and deep learning
PublikacjaQuo vadis, Intelligent Enterprise? Where are you going? The authors of this paper aim at providing some answers to this fascinating question addressing emerging challenges related to the concept of semantically enhanced knowledge-based cyber-physical systems – the fourth industrial revolution named Industry 4.0.
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Early Predictors of Learning a Foreign Language in Pre-school – Polish as a First Language, English as a Foreign Language
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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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Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
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Experimental investigations and prediction of WEDMed surface of nitinol SMA using SinGAN and DenseNet deep learning model
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Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning
PublikacjaConcrete 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....
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Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various Machine learning techniques
PublikacjaMachine 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...
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Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries
PublikacjaOptical fiber sensorsusing low-coherence interferometry require processing ofthe output spectrum or interferogramto quickly and accurately determine the instantaneous value of the measured quantity, such as temperature.Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber temperature sensoris demonstrated.Using aZnO-coated...
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Technological vs. Non-Technological Mindsets: Learning From Mistakes, and Organizational Change Adaptability to Remote Work
PublikacjaThe permanent implementation of the change in working methods, e.g., working in the virtual space, is problematic for some employees and, as a result, for management leaders. To explore this issue deeper, this study assumes that mindset type: technological vs. non-technological, may influence the organizational adaptability to change. Moreover, the key interest of this research is how non-technological mindsets...
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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublikacjaNowadays, 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...
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NLITED - New Level of Integrated Techniques for Daylighting Education: Preliminary Data on the Use of an E-learning Platform
PublikacjaProject NLITED – New Level of Integrated Techniques for Daylighting Education - is an educational project for students and professionals. The project's objective is to create and develop an online eLearning platform with 32 eModules dedicated to daylight knowledge. The project also offers e-learners two summer school training where the theory is put into practice. The platform was launched on January 31, 2022. The paper...
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Reliable computationally-efficient behavioral modeling of microwave passives using deep learning surrogates in confined domains
PublikacjaThe importance of surrogate modeling techniques has been steadily growing over the recent years in high-frequency electronics, including microwave engineering. Fast metamodels are employed to speedup design processes, especially those conducted at the level of full-wave electromagnetic (EM) simulations. The surrogates enable massive system evaluations at nearly EM accuracy and negligible costs, which is invaluable in parameter...
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An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublikacjaThis 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...
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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data
PublikacjaWhether the computer is driving your car or you are, advanced driver assistance systems (ADAS) come into play on all levels, from weather monitoring to safety. These modern-day ADASs use various assisting tools for drivers to keep the journey safe; these sophisticated tools provide early signals of numerous events, such as road conditions, emerging traffic scenarios, and weather warnings. Many urban applications, such as car-sharing...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublikacjaTreatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by...
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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods
PublikacjaOne of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at...
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Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
PublikacjaSatellite 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...
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Fast Fading Influence on the Deep Learning-Based LOS and NLOS Identificationin Wireless Body Area Networks
PublikacjaIn the article, the fast fading influence on the proposed DL (Deep Learning) approach for LOS (Line-of-Sight) and NLOS (Non-Line-of-Sight) conditions identification in Wireless Body Area Networks is investigated. The research was conducted on the basis of the off-body communication measurements using the developed mobile measurement stand, in an indoor environment for both static and dynamic scenarios. The measurements involved...
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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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Uczenie na błędach w nauczaniu programowania w systemie e-learningu
PublikacjaJedną z kluczowych umiejętności, które muszą posiąść adepci programowania, stanowi umiejętność poprawiania kodu programu zawierającego błędy. Jest to działanie bardzo złożone, wymagające znajomości składni języka, rozumienia semantyki kodu, znajomości zasad testowania oraz rozumienia działania algorytmu. W artykule autor proponuje własną metodę kształtowania umiejętności poprawiania kodu programu wykorzystującą narzędzia do nauczania...
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Oral and Maxillofacial Infections—A Bacterial and Clinical Cross-Section
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Therapy with acetylsalicylic acid does not interfere with oral surgery
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Prace badawcze realizowane w Katedrze Technologii Materiałów Maszynowych i Spawalnictwa Politechniki Gdańskiej
PublikacjaW publikacji zaprezentowano prace badawcze prowadzone aktualnie w KTMMiS PG. Prezentacja obejmuje tematykę prac, nazwiska prowadzących oraz krótkie omówienie tematyki prac i ich zaawansowania i planu dalszej realizacji.
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eLearning im Wandel der Zeit am Beispiel der TU Gdansk und der FH Flensburg
PublikacjaW referacie omówiono zmiany systemów eLearning na przykładzie ich rozwoju na Politechnice Gdańskiej i w Wyższej Szkole Zawodowej we Flensburgu. Dokonano analizy pierwszych rozwiązań, w tym własnych systemów i narzędzi, jak również dokonanego na obu uczelniach zwrotu w kierunku otwartych systemów LMS. Podkreślono rolę oceny jakości(QoS) systemów zdalnego nauczania.
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Light4Health eLearning Course: health research for interior lighting design. Re-thinking design approaches based on science
PublikacjaThis paper presents the results of 'Light4Health' (L4H), a three-year EU Erasmus+ Strategic Partnership grant project (2019-2021), which investigated, systematized and taught health-related research on the impact of natural and artificial light on human health and well-being relevant to indoor lighting design. The objective was to re-think evidence-based lighting design approaches for residential, working/educational, and healthcare...
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Oral Surgery Oral Medicine Oral Pathology Oral Radiology and Endodontology
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“Fearing your own queer self”: Depictions of Diasporic Queer Experience in Grace Lau’s Poetry
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Automated analyser for monitoring trace amounts of volatile chloro-organic compounds in recirculated industrial water
PublikacjaOpisano konstrukcję oraz badania laboratoryjne modelu nowego automatycznego analizatora śladowych ilości lotnych związków chlorowco-organicznych w wodzie do celów przemysłowych (o wysokiej czystości). Praca analizatora oparta jest na: - przeciwprądowym ciągłym wypłukiwaniu analitów ze strumienia próbki, - okresowym zatrzymywaniu uwolnionych analitów na złożu sorbenta, - desorpcji termicznej, - oznaczaniu sumy związków chlorowco-organicznych...
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Research on Temporal Leachability of Trace Elements from Opoka-Rocks in The Aspect of Geochemical Environmental Indicators
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Main complications connected with detection, identification and determination of trace organic constituents in complex matrix samples
PublikacjaIt is well known that some problems with the determination of organic analytes at trace level can occur. This issue is connected with contamination during each stage of the analytical procedure from sampling to sample preparation up to chromatographic analysis, which often leads to false-positive or overestimated results. Another problem associated with determination of analytes occurs at trace- and ultra-trace level is a background...
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Effects of biotransport and hydro-meteorological conditions on transport of trace elements in the Scott River (Bellsund, Spitsbergen)
PublikacjaThe shaping of surface water chemistry in the Svalbard Archipelago is strongly dependent on the geology of the catchment and the process of long-range transport of atmospheric pollutants (LRATP). It was found that the dissolved trace elements in the Scott River, which catchment is characterized by a decreasing degree of glaciation, were of the natural origin (i.a. weathering and dissolution of local geological substratum). The...
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Cold atmospheric plasma-induced chemical vapor generation in trace element analysis by spectrometric methods
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Serum trace elements in animal models and human depression: Part III. Magnesium. Relationship with copper
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Determination of PAHs, PCBs, minerals, trace elements and fatty acids in Rapana thomasiana as an indication of pollution
PublikacjaW trakcie badań wykorzystano różne metodyki analityczne do oznaczania zawartości szerokiego spektrum składników (anality z grupy WWA i PCB), metali ciężkich i innych pierwiastków oraz kwasów tłuszczowych w próbkach tkanek miękkich mięczaków morskich pochodzących z miejsc o różnym poziomie antropopresji (obszary zanieczyszczone i niezanieczyszczone w bułgarskiej strefie przybrzeżnej Morza Czarnego).Analiza statystyczna uzyskanych...
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Twierdza Wisłoujście w Gdańsku - prace konserwatorskie wlatach 2002-2005 - problemy i wnioski
PublikacjaTekst przedstawia prace konserwatorskie prowadzone w obiektach twierdzy Wisłoujście.
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New achievements in the field of extraction of trace analytes from samples characterized by complex composition of the matrix
PublikacjaWithout any doubt, the monitoring of compounds present in samples at trace or ultra-trace level usually requires a preliminary step of isolation and/or enrichment of analytes due to the fact that majority of analytical techniques are not sensitive enough for direct determination of trace compounds. On the other hand, sample preparation is considered as crucial part of whole analytical procedures, in particular in samples characterized...
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Analysis of friction ridge evidence for trace amounts of paracetamol in various pharmaceutical industries by Raman spectroscopy
PublikacjaThe detection of potentially harmful substances presents a multifaceted challenge. On one hand, it can directly save lives, on the other, it can significantly aid and enhance police work, thereby increasing the effectiveness of investigations. The research conducted in this study primarily aims to identify paracetamol in fingerprints, considering situations involving direct contact of a person with paracetamol either chronically...
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Trace Metal Contamination of Bottom Sediments: A Review of Assessment Measures and Geochemical Background Determination Methods
PublikacjaThis paper provides an overview of different methods of assessing the trace metal (TM) contamination status of sediments affected by anthropogenic interference. The geochemical background determination methods are also described. A total of 25 papers covering rivers, lakes, and retention tanks sediments in areas subjected to anthropogenic pressure from the last three years (2019, 2020, and 2021) were analysed to support our examination...
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Perceived technostress while learning a new mobile technology: Do individual differences and the way technology is presented matter?
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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
PublikacjaExamining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass...
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublikacjaDesigning 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...
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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
PublikacjaWetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral...
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The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing
PublikacjaRubbers 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...
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublikacjaLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls
PublikacjaEfficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and post-processing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced...