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Search results for: SEMI-SUPERVISED LEARNING
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning 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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An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublicationThis 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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The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing
PublicationRubbers 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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Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set
PublicationThis work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning...
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Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework
PublicationThe rapid development of shipping trade pushes automated container terminals toward the direction of intelligence, safety and efficiency. In particular, the formulation of AGV scheduling tasks and the safety and stability of transportation path is an important part of port operation and management, and it is one of the basic tasks to build an intelligent port. Existing research mainly focuses on collaborative operation between...
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Investigating the drinking and surface water quality and associated health risks in a semi-arid multi-industrial metropolis (Faisalabad), Pakistan
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Comparison of the Antioxidant Properties of Green Macroalgae from Diverse European Water Habitats by Use of Several Semi-Quantitative Assays
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Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
PublicationCurrent Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim...
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E-learning - prawdziwa czy fikcyjna koncepcja edukacyjnego rozwoju uczelni
PublicationNie można zaprzeczyć, że wykorzystanie narzędzi multimedialnych oraz Internetu pozwala na dodanie istotnych, z punku widzenia dydaktyki, komponentów edukacyjnych tworzących kompetencje i umiejętności zawodowe, a także te czysto akademickie. Trzeba rozważyć, czy wszystkie strony procesu dydaktycznego na uczelni są przygotowane do e−learningu. Oczywistym wymogiem jest posiadanie odpowiedniej bazy sprzętowej i przygotowanej kadry...
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Reward Learning Requires Activity of Matrix Metalloproteinase-9 in the Central Amygdala
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IT support for OKNO broadband Internet-based distant learning system at WUT
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The Role of Dopaminergic Genes in Probabilistic Reinforcement Learning in Schizophrenia Spectrum Disorders
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Confirmation Bias in the Course of Instructed Reinforcement Learning in Schizophrenia-Spectrum Disorders
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Efficient sampling of high-energy states by machine learning force fields
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Learning from Imbalanced Data Using Over-Sampling and the Firefly Algorithm
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Deep learning approach for delamination identification using animation of Lamb waves
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Deep learning super-resolution for the reconstruction of full wavefield of Lamb waves
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OmicSelector: automatic feature selection and deep learning modeling for omic experiments
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Modular machine learning system for training object detection algorithms on a supercomputer
PublicationW pracy zaprezentowano architekturę systemu służącego do tworzenia algorytmów wykorzystujących metodę AdaBoost i służących do wykrywania obiektów (np. twarzy) na obrazach. System został podzielony na wyspecjalizowane moduły w celu umożliwienia łatwej rozbudowy i efektywnego zrównoleglenia implementacji przeznaczonej dla superkomputera. Na przykład, system może być rozszerzony o nowe cechy i algorytmy ich ekstrakcji bez konieczności...
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Beesybees-Agent-Based, Adaptive & Learning Workflow Execution Module for BeesyCluster
PublicationPrezentujemy projekt oraz implementację adaptacyjnego i uczącego się modułu przeznaczonego dowykonywania scenariuszy w środowisku BeesyCluster. BeesyCluster pozwala na modelowaniescenariuszy w formie acyklicznego grafu skierowanego, w którym wierzchołki oznaczają zadania,a krawędzie określają zależności między nimi. Przedstawiamy także kooperatywne wykonaniescenariusza przez grupę agentów zdolnych do zbierania, składowania i korzystania...
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A Prototype of Educational Agent in Distance Learning Environment - Virtual Student Assistant
PublicationW zdalnym nauczaniu pojawia się wiele systemów wspierających, z których niezwykle ciekawym przykładem są agenty edukacyjne. Wśród wielu rodzajów agentów edukacyjnych wyróżnia się osobistych asystentów, których rolą jest organizacyjna pomoc osobie zdobywającej wiedzę. Artykuł jest poświęcony zaimplementowanemu na Wydziale ETI Politechniki Gdańskiej prototypowi agenta edukacyjnego o nazwie WAS (Wirtualny Asystent Studenta). Pokazana...
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Stacking and rotation-based technique for machine learning classification with data reduction
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POPULATION-BASED MULTI-AGENT APPROACH TO SOLVING MACHINE LEARNING PROBLEMS
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Designing RBF Networks Using the Agent-Based Population Learning Algorithm
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Analysis of network infrastructure and QoS requirements for modern remote learning systems.
PublicationW referacie przedstawiono różne modele zdalnego nauczania. Podjęto próbę oceny wymagań nakładanych na infrastrukturę sieci. Ponadto przedstawiono mechanizmy QoS spotykane w sieciach teleinformatycznych oraz dokonano oceny możliwości ich współpracy w systemach edukacji zdalnej
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Developing ICT-rich lifelong learning opportunities trough EU-projects.
PublicationArtykuł opisuje doświadczenia Centrum Edukacji Niestacjonarnej Politechniki Gdańskiej od 1997 roku we wdrażaniu kształcenia na odległość. Głównym zadaniem CEN PG jest tworzenie dostępu do materiałów, skryptów, kursów i środowiska internetowego w sieciach LAN i WAN. Udostępniane moduły kursowe zostały opracowane głównie w międzynarodowych zespołach projektowych w wyniku realizowanych unijnych programów Leonardo da Vinci, Socrates...
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Autonomous pick-and-place system based on multiple 3Dsensors 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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Evaluation of aspiration problems in L2 English pronunciation employing machine learning
PublicationThe approach proposed in this study includes methods specifically dedicated to the detection of allophonic variation in English. This study aims to find an efficient method for automatic evaluation of aspiration in the case of Polish second-language (L2) English speakers’ pronunciation when whole words are analyzed instead of particular allophones extracted from words. Sample words including aspirated and unaspirated allophones...
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Using deep learning to increase accuracy of gaze controlled prosthetic arm
PublicationThis paper presents how neural networks can be utilized to improve the accuracy of reach and grab functionality of hybrid prosthetic arm with eye tracing interface. The LSTM based Autoencoder was introduced to overcome the problem of lack of accuracy of the gaze tracking modality in this hybrid interface. The gaze based interaction strongly depends on the eye tracking hardware. In this paper it was presented how the overall the...
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A Note on Knowledge Management Education: Towards Implementing Active Learning Methods
PublicationKnowledge Management as an area of education is still a big challenge for teachers and practitioners. Nevertheless, there are several useful teaching methods in active education, especially oriented towards courses where innovation and delivering dynamic knowledge are critical. The goal of the paper is to present and discuss criteria relevant in the selection of active educational methods supporting knowledge management courses....
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Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
Publicationconvolutional neural network (CNN) which is a class of deep, feed-forward artificial neural network. We decided to analyze audio signal feature maps, namely spectrograms, linear and Mel-scale cepstrograms, and chromagrams. The choice was made upon the fact that CNN performs well in 2D data-oriented processing contexts. Feature maps were employed in the Lithuanian word recognition task. The spectral analysis led to the highest word...
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DEEP LEARNING BASED ON X-RAY IMAGING IMPROVES COXARTHROSIS DETECTION
PublicationObjective: The purpose of the study was to create an Artificial Neural Network (ANN) based on X-ray images of the pelvis, as an additional tool to automate and improve the diagnosis of coxarthrosis. The research is focused on joint space narrowing, which is a radiological symptom showing the thinning of the articular cartilage layer, which is translucent to X-rays. It is the first and the most important of the radiological signs...
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Deep learning approach on surface EEG based Brain Computer Interface
PublicationIn this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...
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Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning
PublicationTheophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally...
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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor
PublicationNitrous oxide (N2O) is a key parameter for evaluating the greenhouse gas emissions from wastewater treatment plants. In this study, a new method for predicting liquid N2O production during nitrification was developed based on a mechanistic model and machine learning (ML) algorithm. The mechanistic model was first used for simulation of two 15-day experimental trials in a nitrifying sequencing batch reactor. Then, model predictions...
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Pharmacological Classification and Activity Evaluation of Furan and Thiophene Amide Derivatives Applying Semi-Empirical ab initio Molecular Modeling Methods
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Activity Evaluation and Selection of Some Classes of Antibiotics with the use of Semi-Empirical Quantum Mechanics and Quantitative Structure- Activity Relationships Approach
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Badania właściwości akustycznych komór semi-bezechowychprzyczep do badania hałasu opon: Tiresonic MK2, Tiresonic MK3 oraz Slipsonic
PublicationPrzyczepy badawcze: Tiresonic MK2, Tiresonic MK3 oraz SlipSonic służą do badania hałasu opon do samochodów osobowych i dostawczych. Wszystkie przyczepy wyposażone są w komory semo-bezechowe, osłaniające koła z badanymi oponami. omówiono konstrukcję poszczególnych komór. Przedstawiono eksperyment badawczy, którego celem było wyznaczenie własności akustycznych tych komór. Podano i omówiono wyniki tych badań.
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Enhancing seismic performance of steel buildings having semi-rigid connection with infill masonry walls considering soil type effects
PublicationUnpreventable constructional defects are the main issues in the case of steel Moment-Resisting Frames (MRFs) that mostly occur in the rigidities of beam-to-column connections. The present article aims to investigate the effects of different rigidities of structures and to propose Infill Masonry Walls (IMWs) as retrofitting strategy for the steel damaged buildings. A fault or failure to meet a certain consideration of the soil type...
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Phosphorus removal by application of natural and semi-natural materials for possible recovery according to assumptions of circular economy and closed circuit of P
PublicationIn the last fewyears the idea of circular economy has become essential. Thus, designing methods of nutrients removal should be based on usingmaterials that make it possible to recover those nutrients. Recently,methods applied in wastewater treatment plants cannot provide optimal results; moreover, the application of commercial coagulants like ferric chloride and polyaluminumchloride can cause difficulties in potential recovery...
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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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Widening Participation and lifelong learning
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Learning Disabilities Research & Practice
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Journal of Interactive Learning Research
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Australian Journal of Adult Learning
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Lifelong Learning in Europe (LLinE)
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British Journal of Learning Disabilities
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Academy of Management Learning & Education
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Journal of Interactive Online Learning
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Learning Culture and Social Interaction
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