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Search results for: DEEP LEARNING, ROBOTICS
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Journal of College Reading and Learning
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International Journal of Learning and Change
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Second Language Learning and Teaching
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Advances in Learning and Behavioral Disabilities
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Malaysian Journal of Learning & Instruction
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International Journal of Technologies in Learning
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Journal of Motor Learning and Development
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CONTINUUM Lifelong Learning in Neurology
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Journal of Formative Design in Learning
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Scholarship of Teaching and Learning in Psychology
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JOURNAL OF MACHINE LEARNING RESEARCH
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Journal of Applied Learning and Teaching
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Critical Studies in Teaching and Learning
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Higher Learning Research Communications
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Canadian Journal of Learning and Technology
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E-Learning and Digital Media
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Machine Learning and Knowledge Extraction
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Machine Learning-Science and Technology
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JOURNAL OF COMPUTER ASSISTED LEARNING
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Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction
PublicationRegarding the unpredictable and complex nature of seismic excitations, there is a need for vulnerability assessment of newly constructed or existing structures. Predicting the seismic limit-state capacity of steel Moment-Resisting Frames (MRFs) can help designers to have a preliminary estimation and improve their views about the seismic performance of the designed structure. This study improved data-driven decision techniques in...
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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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Szymon Zaporowski mgr inż.
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Overcoming “Big Data” Barriers in Machine Learning Techniques for the Real-Life Applications
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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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Simulation Method for Scheduling Linear Construction Projects Using the Learning– Forgetting Effect
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Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches
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Effects of mutual learning in physical education to improve health indicators of Ukrainian students
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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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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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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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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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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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The effect of deep pectoral myopathy on the properties of broiler chicken muscles characterised by selected instrumental techniques
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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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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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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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Expedited Machine-Learning-Based Global Design Optimization of Antenna Systems Using Response Features and Multi-Fidelity EM Analysis
PublicationThe design of antenna systems poses a significant challenge due to stringent per-formance requirements dictated by contemporary applications and the high com-putational costs associated with models, particularly full-wave electromagnetic (EM) analysis. Presently, EM simulation plays a crucial role in all design phases, encompassing topology development, parametric studies, and the final adjustment of antenna dimensions. The latter...
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Edyta Gołąb-Andrzejak dr hab.
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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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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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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...