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Search results for: deep learning, robotics
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E-Learning and Digital Media
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JOURNAL OF COMPUTER ASSISTED LEARNING
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Robust-adaptive dynamic programming-based time-delay control of autonomous ships under stochastic disturbances using an actor-critic learning algorithm
PublicationThis paper proposes a hybrid robust-adaptive learning-based control scheme based on Approximate Dynamic Programming (ADP) for the tracking control of autonomous ship maneuvering. We adopt a Time-Delay Control (TDC) approach, which is known as a simple, practical, model free and roughly robust strategy, combined with an Actor-Critic Approximate Dynamic Programming (ACADP) algorithm as an adaptive part in the proposed hybrid control...
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Development of thin film SPME sorbents based on deep eutectic solvents and their application for isolation and preconcentration of endocrine-disrupting compounds leaching from diapers to urine
PublicationIn this study, the thin film solid phase microextraction (TF-SPME) based on a novel sorbent composed of deep eutectic solvent (DES) is proposed. Among several synthesized sorbents the most efficient was DES containing trihexyl(tetradecyl)phosphonium chloride and docosanol mixed with polydimethylsiloxane (PDMS) in a molar ratio 1:3:0.25 to achieve a more stable structure. The proposed sorbent was applied on a fiberglass mesh and...
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Investigation of vortex assisted magnetic deep eutectic solvent based dispersive liquid–liquid microextraction for separation and determination of vanadium from water and food matrices: Multivariate analysis
PublicationA new and simple vortex assisted magnetic deep eutectic solvent dispersive liquid–liquid microextraction procedure (VA-MDES-DLLME) was developed for the determination of vanadium (V) in food and water samples by flame atomic absorption spectrometry (FAAS). In the extraction medium, a bis(acetylpivalylmethane) ethylenediimine (H2APM2en) was used for the complexation of V(V) in sample solution at pH 6. The VA-MDES-DLLME was optimized...
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Sensitive method for determination of benzoic acid in beverages and food samples using air–assisted hydrophobic deep eutectic solvent-based dispersive liquid-liquid microextraction
PublicationA simple, reliable and rapid air–assisted hydrophobic deep eutectic solvent-based dispersive liquid–liquid microextraction (AA-HDES-DLLME) was developed for analysis of benzoic acid in various beverages and food samples. The final determination stage was performed via UV–visible spectrophotometry. The key parameters (extraction time, HDES type and volume, dispersant volume, pH and sample volume) of the AA-HDES-DLLME method were...
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Machine learning techniques combined with dose profiles indicate radiation response biomarkers
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DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images
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Effects of mutual learning in physical education to improve health indicators of Ukrainian students
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Simulation Method for Scheduling Linear Construction Projects Using the Learning– Forgetting Effect
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Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems
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Learning from Imbalanced Data Streams Based on Over-Sampling and Instance Selection
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Machine Learning and data mining tools applied for databases of low number of records
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Overcoming “Big Data” Barriers in Machine Learning Techniques for the Real-Life Applications
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Driver’s Condition Detection System Using Multimodal Imaging and Machine Learning Algorithms
PublicationTo 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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Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches
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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublicationThe aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...
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Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction
PublicationNowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum...
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Szymon Zaporowski mgr inż.
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Integrating Statistical and Machine‐Learning Approach for Meta‐Analysis of Bisphenol A‐Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival
PublicationBisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are being tested in various experimental models, creating large expression datasets found in open access storage. The meta‐analysis of such datasets is, however, very complicated for various...
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Methylation of estrogen receptor 2 (ESR2) in deep paravertebral muscles and its association with idiopathic scoliosis
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Prediction of NOx Emission Based on Data of LHD On-Board Monitoring System in a Deep Underground Mine
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Lead-free bismuth-based perovskites coupled with g–C3N4: A machine learning based novel approach for visible light induced degradation of pollutants
PublicationThe use of metal halide perovskites in photocatalytic processes has been attempted because of their unique optical properties. In this work, for the first time, Pb-free Bi-based perovskites of the Cs3Bi2X9 type (X = Cl, Br, I, Cl/Br, Cl/I, Br/I) were synthesized and subjected to comprehensive morphological, structural, and surface analyses, and photocatalytic properties in the phenol degradation reaction were examined. Furthermore,...
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Book Review
PublicationActing over the last three decades as an Editor and Associate Editor for a number of international journals in the general area of cybernetics and AI, as well as a Chair and Co-Chair of numerous conferences in this field, I have had the exciting opportunity to closely witness and to be actively engaged in the stimulating research area of machine learning and its important augmentation with deep learning techniques and technologies. From...
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Journal of Automation, Mobile Robotics and Intelligent Systems - JAMRIS
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International Journal of Medical Robotics and Computer Assisted Surgery
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Edyta Gołąb-Andrzejak dr hab.
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Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
PublicationPlain weave composite is a long-lasting type of fabric composite that is stable enough when being handled. Open-hole composites have been widely used in industry, though they have weak structural performance and complex design processes. An extensive number of material/geometry parameters have been utilized for designing these composites, thereby an efficient computational tool is essential for that purpose. Different Machine Learning...
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Magnetic hydrophobic deep eutectic solvents for orbital shaker-assisted dispersive liquid-liquid microextraction (MAGDES-OS-DLLME) - determination of nickel and copper in food and water samples by FAAS
PublicationIn this work, a cheap and widely applicable dispersive liquid-liquid microextraction (DLLME) method was developed for the extraction of Ni(II) and Cu(II) from water and food samples and analysis using flame atomic absorption spectrometry. DLLME was assisted by orbital shaker, while ferrofluid as an extractant was based on deep eutectic solvent (DES). This ferrofluid was made of hydrophobic DES (hDES), composed of lauric acid and...
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Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing
PublicationAir pollution remains a considerable contemporary challenge affecting life quality, the environment, and economic well-being. It encompasses an array of pollutants—gases, particulate matter, biological molecules—emanating from sources such as vehicle emissions, industrial activities, agriculture, and natural occurrences. Nitrogen dioxide (NO2), a harmful gas, is particularly abundant in densely populated urban areas. Given its...
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Machine Learning for Control Systems Security of Industrial Robots: a Post-covid-19 Overview
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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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Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing
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Experimental Evaluation of the Agent-Based Population Learning Algorithm for the Cluster-Based Instance Selection
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E-LEARNING NA POLITECHNICE GDAŃSKIEJ - HISTORIA ROZWOJU W LATACH 1995-2020
PublicationInternet oraz kształcenie oparte na wykorzystaniu e-technologii stały się nieodłącznym elementem edukacji. Artykuł przedstawia zarys historii rozwoju e-learningu na Politechnice Gdańskiej, przykładowe rozwiązania technologiczne, elementy tworzenia struktur organizacyjnych oraz związanych z legislacją, a także wybrane projekty wykorzystujące szeroko pojęte e-technologie w edukacji akademickiej realizowanej na Uczelni
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Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems
PublicationTe 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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Open source learning management systems at civil engineering and environmental department: TeleCAD and Moodle.
PublicationW rozdziale zaprezentowano dwa systemy zarządzania kształceniem, służące do przygotowania i prowadzenia e-kursów. Pierwszy z nich TeleCAD został opracowany w ramach projektu Leonardo da Vinci (1998-2001). Ostanie użycie systemu miało miejsce w roku akademickim 2003/2004 i był on wykorzystany w projekcie CURE (V Program Ramowy, 2003-2006). W roku 2003 dzięki wsparciu projektu Leonardo da Vinci EMDEL (2001-2005) Centrum Edukacji...
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Sperm segmentation and abnormalities detection during the ICSI procedure using machine learning algorithms
Publication(1) About 15-20% of couples struggle with the problem of infertility. 30 to 40% of these cases are caused by abnormalities in the structure and motility of sperm. Sometimes the only possibility for such people is to use the procedure of artificial insemination. CASA systems are used to increase the efficiency of this procedure by selecting the appropriate sperm cell. (2) This paper presents an approach to the sperm classification...
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Comparative study on total nitrogen prediction in wastewater treatment 1 plant and effect of various feature selection methods on machine learning algorithms performance
PublicationWastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods are used in the pre-processing section for enhancing the model performance. This study aims to evaluate the effect of seven different FS methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen (TN) in the WWTP...
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Social learning and knowledge flows in cluster initiatives, In: Sanz S.C., Blanco F.P., Urzelai B. (Eds). Human and Relational Resources (pp. 44-45). the 4th International Conference on Clusters and Industrial Districts CLUSTERING, University of Valencia, Spain, May 23–24 (ISBN: 978-84-09-11926-4).
PublicationPurpose – The purpose of the paper is to explore how learning manifests and knowledge flows in cluster initiatives (CIs) due to interactions undertaken by their members. The paper addresses the research question of how social learning occurs and knowledge flows in CIs. Design/methodology/approach – The qualitative study of four cluster initiatives helped to identify various symptoms of social learning and knowledge flows in...
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Abdominoplasty Skin-Based Dressing for Deep Wound Treatment—Evaluation of Different Methods of Preparation on Therapeutic Potential
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Geochemical Changes in Aquatic Environment Caused by Deep Dredging - A Case Study: The Puck Bay (Baltic Sea)
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Microbial Diversity in Deep-Subsurface Hot Brines of Northwest Poland: from Community Structure to Isolate Characteristics
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Diversity of Students’ Unethical Behaviors in Online Learning Amid COVID-19 Pandemic: An Exploratory Analysis
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Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis—A Cross-Sectional Study
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SELECTING A REPRESENTATIVE DATA SET OF THE REQUIRED SIZE USING THE AGENT-BASED POPULATION LEARNING ALGORITHM
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