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Search results for: CONSTANT LEARNING CULTURE, HIERARCHY, MATURITY, MISTAKES ACCEPTANCE, CHANGE ADAPTABILITY, ORGANISATIONAL LEARNING, SINGLE-LOOP LEARNING, DOUBLE-LOOP LEARNING, KNOWLEDGE WORKERS
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Tizard Learning Disability Review
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New Directions for Teaching and Learning
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Journal of College Reading and Learning
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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-Science and Technology
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JOURNAL OF COMPUTER ASSISTED LEARNING
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Loop-mediated isothermal amplification (LAMP) as a diagnostic tool in detection of infectious diseases
PublicationLoop-mediated isothermal amplification (LAMP) is a gene amplification method which amplifies DNA with high specificity and efficiency under isothermal conditions. Because of its rapidity and simplicity, it is a valuable diagnostic tool in the early detection and identification of infectious diseases. LAMP method is based on the use of a set of four to six specially designed primers spanning six to eight distinct sequences on the...
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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublicationThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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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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Power Hardware-in-the-Loop Approach In Power System Development
PublicationThe main objective of the research is the verification of the Power Hardware-In-The-Loop (PHIL) approach in power system analysis and design. The premise of the article is that using PHIL approach the performance of the power system in steady and transient state conditions can be analysed in real power system conditions. Models of induction machine were developed and real time simulations were performed. Simulation variables were...
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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublicationAtmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure,...
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Szymon Zaporowski mgr inż.
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Flow Boiling in Minigap in the Reversed Two-Phase Thermosiphon Loop
PublicationThe paper presents the results of experimental investigations of a model of a heat exchanger featuring a minigap, which is perceived as an evaporator for an inverted thermosiphon. The system works with a single component test fluid. The tested evaporator generates pumping power in the test loop in a way similar to the mammoth pump. The tests regarded a module of the heat exchanger, consisting of a hot leg and a cold leg with the...
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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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Effects of mutual learning in physical education to improve health indicators of Ukrainian students
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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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Improved estimation of dynamic modulus for hot mix asphalt using deep learning
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Simulation Method for Scheduling Linear Construction Projects Using the Learning– Forgetting Effect
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BIG DATA SIGNIFICANCE IN REMOTE MEDICAL DIAGNOSTICS BASED ON DEEP LEARNING TECHNIQUES
PublicationIn 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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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
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Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches
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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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Influence of Thermal Imagery Resolution on Accuracy of Deep Learning based Face Recognition
PublicationHuman-system interactions frequently require a retrieval of the key context information about the user and the environment. Image processing techniques have been widely applied in this area, providing details about recognized objects, people and actions. Considering remote diagnostics solutions, e.g. non-contact vital signs estimation and smart home monitoring systems that utilize person’s identity, security is a very important factor....
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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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Autonomous Perception and Grasp Generation Based on Multiple 3D Sensors and Deep Learning
PublicationGrasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...
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Solubility Characteristics of Acetaminophen and Phenacetin in Binary Mixtures of Aqueous Organic Solvents: Experimental and Deep Machine Learning Screening of Green Dissolution Media
PublicationThe solubility of active pharmaceutical ingredients is a mandatory physicochemical characteristic in pharmaceutical practice. However, the number of potential solvents and their mixtures prevents direct measurements of all possible combinations for finding environmentally friendly, operational and cost-effective solubilizers. That is why support from theoretical screening seems to be valuable. Here, a collection of acetaminophen...
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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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Investigation of the influence of capilary effect on operation of the loop heat pipe
PublicationIn the paper presented are studies on the inestigation of the capillary forces effect inducted in the porous structure of a loop heat pipe using water and ethanol ad test fluids. The potential application of such effects is for example in the evaporator of the domestic micro-CHP unit, where the reduction of pumping power could be obtained. Preliminary analysis of the results indicates water as having the best potential for developing...
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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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Edyta Gołąb-Andrzejak dr hab.
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Towards the 4th industrial revolution: networks, virtuality, experience based collective computational intelligence, and deep learning
PublicationQuo 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.