Filters
total: 1294
displaying 1000 best results Help
Search results for: INFORMAL WORKPLACE LEARNING
-
Learning Disabilities-A Multidisciplinary Journal
Journals -
Australian Journal of Learning Difficulties
Journals -
IEEE Transactions on Learning Technologies
Journals -
Journal of Unschooling and Alternative Learning
Journals -
Psychology Learning and Teaching-PLAT
Journals -
Journal of Practice Teaching and Learning
Journals -
Tizard Learning Disability Review
Journals -
New Directions for Teaching and Learning
Journals -
Journal of College Reading and Learning
Journals -
International Journal of Learning and Change
Journals -
Second Language Learning and Teaching
Journals -
Advances in Learning and Behavioral Disabilities
Journals -
Malaysian Journal of Learning & Instruction
Journals -
International Journal of Technologies in Learning
Journals -
Journal of Motor Learning and Development
Journals -
CONTINUUM Lifelong Learning in Neurology
Journals -
Journal of Formative Design in Learning
Journals -
Scholarship of Teaching and Learning in Psychology
Journals -
JOURNAL OF MACHINE LEARNING RESEARCH
Journals -
Journal of Applied Learning and Teaching
Journals -
Critical Studies in Teaching and Learning
Journals -
Higher Learning Research Communications
Journals -
Canadian Journal of Learning and Technology
Journals -
E-Learning and Digital Media
Journals -
Machine Learning and Knowledge Extraction
Journals -
Machine Learning-Science and Technology
Journals -
JOURNAL OF COMPUTER ASSISTED LEARNING
Journals -
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...
-
Low-Cost and Highly-Accurate Behavioral Modeling of Antenna Structures by Means of Knowledge-Based Domain-Constrained Deep Learning Surrogates
PublicationThe awareness and practical benefits of behavioral modeling methods have been steadily growing in the antenna engineering community over the last decade or so. Undoubtedly, the most important advantage thereof is a possibility of a dramatic reduction of computational expenses associated with computer-aided design procedures, especially those relying on full-wave electromagnetic (EM) simulations. In particular, the employment of...
-
Overcoming “Big Data” Barriers in Machine Learning Techniques for the Real-Life Applications
Publication -
Machine learning techniques combined with dose profiles indicate radiation response biomarkers
Publication -
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images
Publication -
Improved estimation of dynamic modulus for hot mix asphalt using deep learning
Publication -
Effects of mutual learning in physical education to improve health indicators of Ukrainian students
Publication -
Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
Publication -
Simulation Method for Scheduling Linear Construction Projects Using the Learning– Forgetting Effect
Publication -
Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems
Publication -
Learning from Imbalanced Data Streams Based on Over-Sampling and Instance Selection
Publication -
Machine Learning and data mining tools applied for databases of low number of records
Publication -
Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches
Publication -
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...
-
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...
-
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...
-
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....
-
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...
-
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...
-
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...
-
Szymon Zaporowski mgr inż.
People -
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...
-
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,...