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Wyniki wyszukiwania dla: PREDICTIVE MACHINE LEARNING-BASED TOOLS
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Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems
PublikacjaTe 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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Data-Driven Surrogate-Assisted Optimization of Metamaterial-Based Filtenna Using Deep Learning
PublikacjaIn this work, a computationally efficient method based on data driven surrogate models is pro-posed for the design optimization procedure of a Frequency Selective Surface (FSS)-based filtering antenna (Filtenna). A Filtenna acts as a as module that simultaneously pre-filters unwanted sig-nals, and enhances the desired signals at the operating frequency. However, due to a typically large number of design variables of FSS unit elements,...
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Low-Cost and Highly-Accurate Behavioral Modeling of Antenna Structures by Means of Knowledge-Based Domain-Constrained Deep Learning Surrogates
PublikacjaThe 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...
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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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Project-Based Learning as a Method for Interdisciplinary Adaptation to Climate Change—Reda Valley Case Study
PublikacjaThe challenges of the global labour market require university authorities to extend traditional forms of education into more innovative and effective solutions. Project-based learning (PjBL) is one of highly effective methods for acquiring knowledge and teaching “soft” skills to future employees. This article describes an experimental use of PjBL at a university with a long history of teaching based on traditional methods—the Gdansk...
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Enhancing environmental literacy through urban technology-based learning. The PULA app case
PublikacjaThis study addresses the need to enhance environmental literacy, focusing on urban adults through mobile applications, based on the example of PULA app that engages early adopters in gamified pro- environmental activities, offering insights into informal learning. Grounded in 'urban pedagogy,' the study combines semi-structured interviews with 17 application testers and quantitative data analysis, unveiling motivations, user feedback,...
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Olfactory receptor-based biosensors as potential future tools in medical diagnosis
PublikacjaThe detection of biomarkers is the future of non-invasive medical diagnosis and screening. Discovery and identification of reliable disease specific volatile organic compounds is dependent on repeatable, accurate analysis of trace level gaseous analytes mainly in breath samples. Natural variety of the olfactory systems and the compounds capable of gas molecules binding creates wide possibilities of acquisition and implementation...
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Deep-Learning-Based Precise Characterization of Microwave Transistors Using Fully-Automated Regression Surrogates
PublikacjaAccurate models of scattering and noise parameters of transistors are instrumental in facilitating design procedures of microwave devices such as low-noise amplifiers. Yet, data-driven modeling of transistors is a challenging endeavor due to complex relationships between transistor characteristics and its designable parameters, biasing conditions, and frequency. Artificial neural network (ANN)-based methods, including deep learning...
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Quantitative Soil Characterization for Biochar–Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization
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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublikacjaAtmospheric 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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Endoscopy images classification with kernel based learning algorithms.
PublikacjaPrzedstawiono zastosowanie algorytmów opartych na wektorach wspierających zbudowanych na dwóch różnych funkcjach straty do klasyfikacji obrazów endoskopowych przełyku. Szczegółowo omówiono sposób ekstrakcji cech obrazów oraz algorytm klasyfikacji. Klasyfikator został zastosowany do problemu rozpoznawania zdjęć guzów złośliwych i łagodnych.
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Transfer learning in imagined speech EEG-based BCIs
PublikacjaThe Brain–Computer Interfaces (BCI) based on electroencephalograms (EEG) are systems which aim is to provide a communication channel to any person with a computer, initially it was proposed to aid people with disabilities, but actually wider applications have been proposed. These devices allow to send messages or to control devices using the brain signals. There are different neuro-paradigms which evoke brain signals of interest...
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An approach to machine classification based on stacked generalization and instance selection
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Grid-Forming Operation of Energy-Router Based on Model Predictive Control with Improved Dynamic Performance
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Tools, Methods and Services Enhancing the Usage of the Kepler-based Scientific Workflow Framework
PublikacjaScientific workflow systems are designed to compose and execute either a series of computational or data manipulation steps, or workflows in a scientific application. They are usually a part of a larger eScience environment. The usage of workflow systems, however very beneficial, is mostly not irrelevant for scientists. There are many requirements for additional functionalities around scientific workflows systems that need to be...
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Experimental Evaluation of the Agent-Based Population Learning Algorithm for the Cluster-Based Instance Selection
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Discrete-time predictive control design based on overparameterized delay-plant models and identified cancellation order.
PublikacjaPraca dotyczy uogólnionego sterowania predykcyjnego (GPC) obiektami opisanymi dyskretnoczasowymi modelami CARIMA z uproszczeniami (nieminimalnych, przeparametryzowanych) oraz o niezerowym opóźnieniu transportowym. Optymalne sterowanie predykcyjne wyznacza się na podstawie minimalnowariancyjnego oszacowania przyszłej odpowiedzi sterowanego obiektu. Poprzez analizę warunków rozwiązywalności zadania syntezy sterownika GPC, sformułowano...
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Deep learning-based waste detection in natural and urban environments
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DentalSegmentator: robust deep learning-based CBCT image segmentation
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Agent-Based Population Learning Algorithm for RBF Network Tuning
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Employing a biofeedback method based on hemispheric synchronization in effective learning
PublikacjaIn this paper an approach to build a brain computer-based hemispheric synchronization system is presented. The concept utilizes the wireless EEG signal registration and acquisition as well as advanced pre-processing methods. The influence of various filtration techniques of EOG artifacts on brain state recognition is examined. The emphasis is put on brain state recognition using band pass filtration for separation of individual...
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Molecular Diffusion Simulation on ARUZ – Massively-parallel FPGA-based Machine
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Control system based on the modified multiscalar model for the double fed machine
PublikacjaPrzedstawiono układ sterowania maszyną asynchroniczą dwustronnie zasilaną wykorzystujący zmodyfikowany model multisaklarny. Wprowadzone nielinowe sprzężenia pozwalają na podział systemu na dwa niezależne podukłady. Pododuje to brak zauważalnych sprzężeń przy regulacji mocy czynnej i biernej regulowanej przez kontrolery PI. W układzie wyeliminowano konieczność stosowania kaskadowego połączenia regulatorów. Przedstawiono wyniki symulacji.
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Aptamer based tools for environmental and therapeutic monitoring: A review of developments, applications, future perspectives
PublikacjaNucleic acids in the form of aptamers play a growing and significant role in the targeted and rapid analysis of environmental sample composition and medical analyses. In this paper, the review of both aptamers synthesis methods as well as application of these short chain oligonucleotides (with critical comments on their strong and weak features) are given. The first ones include: systematic evolution of ligands by exponential enrichment...
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Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework
PublikacjaThe 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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Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage
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Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
Publikacjaconvolutional 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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Designing RBF Networks Using the Agent-Based Population Learning Algorithm
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IT support for OKNO broadband Internet-based distant learning system at WUT
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Beesybees-Agent-Based, Adaptive & Learning Workflow Execution Module for BeesyCluster
PublikacjaPrezentujemy 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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DEEP LEARNING BASED ON X-RAY IMAGING IMPROVES COXARTHROSIS DETECTION
PublikacjaObjective: 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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Autonomous pick-and-place system based on multiple 3Dsensors and deep learning
PublikacjaGrasping 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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Deep learning approach on surface EEG based Brain Computer Interface
PublikacjaIn 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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Methodology for developing Web-Based applications from reusable components using open source tools
PublikacjaGlobalna sieć stwarza nowe mozliwości dla projektantów IT. Pozwala na budowanie aplikacji z wielu części zwanych komponentami i rozproszonych w różnych miejscach sieci. metodologia wskazuje jak zbudować wszystkie elementy systemu: warstwę logiki biznesowej, dostępu do danych oraz prezentacji z gruboziarnistych komponentów. Takie mozliwości daje język Java i powiązane z nim technologie open-source: EJB do tworzenia komponentów biznesowych...
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Robust-adaptive dynamic programming-based time-delay control of autonomous ships under stochastic disturbances using an actor-critic learning algorithm
PublikacjaThis 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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Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning
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Determinants of anxiety levels among young males in a threat of experiencing military conflict–Applying a machine-learning algorithm in a psychosociological study
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Artificial Intelligence-Based Weighting Factor Autotuning for Model Predictive Control of Grid-Tied Packed U-Cell Inverter
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The comparative study of micellar TLC and RP-TLC as potential tools for lipophilicity assessment based on QSRR approach
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Assessing therapeutic relevance of biologically interesting, ampholytic substances based on their physicochemical and spectral characteristics with chemometric tools
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A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine
PublikacjaRNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA....
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A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine
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BIG DATA SIGNIFICANCE IN REMOTE MEDICAL DIAGNOSTICS BASED ON DEEP LEARNING TECHNIQUES
PublikacjaIn 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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Learning from Imbalanced Data Streams Based on Over-Sampling and Instance Selection
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Innovative e-learning approach in teaching based on case studies - Innocase project
PublikacjaThe article presents the application of innovative e-learning approach for the creation of case study content. Case study methodology is becoming more and more widely applied in modern education, especially in business and management field. Although case study methodology is quite well recognized and used in education, there are still few examples of developing e-learning content on the basis of case studies. This task is to be...
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublikacjaIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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Influence of Thermal Imagery Resolution on Accuracy of Deep Learning based Face Recognition
PublikacjaHuman-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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Autonomous Perception and Grasp Generation Based on Multiple 3D Sensors and Deep Learning
PublikacjaGrasping 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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Statistical Data Pre-Processing and Time Series Incorporation for High-Efficacy Calibration of Low-Cost NO2 Sensor Using Machine Learning
PublikacjaAir pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given...