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Search results for: ensamble
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Ensemble Classifier for Mining Data Streams
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Agent-Based Data Reduction Using Ensemble Technique
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EBE: elastic blob ensemble for coarse human tracking
PublicationProponujemy nowy probabilistyczny algorytm śledzenia oparty na elastycznym zespole kropelkowym (EBE), który ma zastosowanie przy śledzeniu obiektów elastycznych. Wynikiem jest wskazówka nt. zgrubnego ruchu w postaci lokalizacji i orientacji obiektu wraz z lokalizacją kropelki. Głównym założeniem jest to, że orientacja całego obiektu nie zmienia się znacznie między sąsiednimi klatkami. Dyskretna przestrzeń rozwiązań jest tworzona...
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Electrochemistry from first-principles in the grand canonical ensemble
PublicationProgress in electrochemical technologies, such as automotive batteries, supercapacitors, and fuel cells, depends greatly on developing improved charged interfaces between electrodes and electrolytes. The rational development of such interfaces can benefit from the atomistic understanding of the materials involved by first-principles quantum mechanical simulations with Density Functional Theory (DFT). However, such simulations are...
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Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection
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The detection of Alternaria solani infection on tomatoes using ensemble learning
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SMOTE-Based Homogeneous Ensemble Methods for Software Defect Prediction
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Ensemble-Based Logistic Model Trees for Website Phishing Detection
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Estimation of a Stochastic Burgers' Equation Using an Ensemble Kalman Filter
PublicationIn this work, we consider a difficult problem of state estimation of nonlinear stochastic partial differential equations (SPDE) based on uncertain measurements. The presented solution uses the method of lines (MoL), which allows us to discretize a stochastic partial differential equation in a spatial dimension and represent it as a system of coupled continuous-time ordinary stochastic differential equations (SDE). For such a system...
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Divide and not forget: Ensemble of selectively trained experts in Continual Learning
PublicationClass-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are usually trained all at once using whole task data, which makes them all prone to forgetting and increasing computational burden. To address this limitation,...
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Multidimensional Feature Selection and Interaction Mining with Decision Tree Based Ensemble Methods
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Tree-based homogeneous ensemble model with feature selection for diabetic retinopathy prediction
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Study of preference for surround microphone techniques, used in the recording of choir and instrumental ensemble
PublicationThe aim of this paper is to describe the process of choosing the best surround microphone technique for recording of choir with an instrumental ensemble. First, examples of multichannel microphone techniques including those used in the recording are described. Then, the assumptions and details of music recording in Radio Gdansk Studio are provided as well as the process of mixing of the multichannel recording. The extensive subjective...
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Segmentation-Based BI-RADS ensemble classification of breast tumours in ultrasound images
PublicationBackground: The development of computer-aided diagnosis systems in breast cancer imaging is exponential. Since 2016, 81 papers have described the automated segmentation of breast lesions in ultrasound images using arti- ficial intelligence. However, only two papers have dealt with complex BI-RADS classifications. Purpose: This study addresses the automatic classification of breast lesions into binary classes (benign vs. ma- lignant)...
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Ensemble Online Classifier Based on the One-Class Base Classifiers for Mining Data Streams
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Likelihood of Transformation to Green Infrastructure Using Ensemble Machine Learning Techniques in Jinan, China
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Comparison of single best artificial neural network and neural network ensemble in modeling of palladium microextraction
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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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Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction
PublicationIn recent times, customer churn has become one of the most significant issues in business-oriented sectors with telecommunication being no exception. Maintaining current customers is particularly valuable due to the high degree of rivalry among telecommunication companies and the costs of acquiring new ones. The early prediction of churned customers may help telecommunication companies to identify the causes of churn and design...
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Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
PublicationSatellite image classification is widely used in various real-time applications, such as the military, geospatial surveys, surveillance and environmental monitoring. Therefore, the effective classification of satellite images is required to improve classification accuracy. In this paper, the combination of Hierarchical Framework and Ensemble Learning (HFEL) and optimal feature selection is proposed for the precise identification...
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Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
PublicationThis research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story...
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DIAGNOSIS OF MALIGNANT MELANOMA BY NEURAL NETWORK ENSEMBLE-BASED SYSTEM UTILISING HAND-CRAFTED SKIN LESION FEATURES
PublicationMalignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task...
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Fault diagnosis of marine 4-stroke diesel engines using a one-vs-one extreme learning ensemble
PublicationThis paper proposes a novel approach for intelligent fault diagnosis for stroke Diesel marine engines, which are commonly used in on-road and marine transportation. The safety and reliability of a ship's work rely strongly on the performance of such an engine; therefore, early detection of any type of failure that affects the engine is of crucial importance. Automatic diagnostic systems are of special importance because they can...
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Underground Water Level Prediction in Remote Sensing Images Using Improved Hydro Index Value with Ensemble Classifier
PublicationThe economic sustainability of aquifers across the world relies on accurate and rapid estimates of groundwater storage changes, but this becomes difficult due to the absence of insitu groundwater surveys in most areas. By closing the water balance, hydrologic remote sensing measures offer a possible method for quantifying changes in groundwater storage. However, it is uncertain to what extent remote sensing data can provide an...
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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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Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI): An approach for learning from imbalanced data streams
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Ensemble fits of restrained peptides’ conformational equilibria to NMR data. Dependence on force fields: AMBER/8 ff03 versus ECEPP/3
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imPlatelet classifier: image‐converted RNA biomarker profiles enable blood‐based cancer diagnostics
PublicationLiquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia...
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Can Evaluation Patterns Enable End Users to Evaluate the Quality of an e-learning System? An Exploratory Study.
PublicationThis paper presents the results of an exploratory study whose main aim is to verify if the Pattern-Based (PB) inspection technique enables end users to perform reliable evaluation of e-learning systems in real work-related settings. The study involved 13 Polish and Italian participants, who did not have an HCI background, but used e-learning platforms for didactic and/or administrative purposes. The study revealed that the participants...
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White light thermoplasmonic activated gold nanorod arrays enable the photo-thermal disinfection of medical tools from bacterial contamination
PublicationThe outspread of bacterial pathogens causing severe infections and spreading rapidly, especially among hospitalized patients, is worrying and represents a global public health issue. Current disinfection techniques are becoming insufficient to counteract the spread of these pathogens because they carry multiple antibiotic-resistance genes. For this reason, a constant need exists for new technological solutions that rely on physical...
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Recent Developments in Data-Assisted Modeling of Flexible Proteins
PublicationMany proteins can fold into well-defined conformations. However, intrinsically-disordered proteins (IDPs) do not possess a defined structure. Moreover, folded multi-domain proteins often digress into alternative conformations. Collectively, the conformational dynamics enables these proteins to fulfill specific functions. Thus, most experimental observables are averaged over the conformations that constitute an ensemble. In this...
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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
PublicationExamining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass...
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Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift
PublicationWhile recent computer vision algorithms achieve impressive performance on many benchmarks, they lack robustness - presented with an image from a different distribution, (e.g. weather or lighting conditions not considered during training), they may produce an erroneous prediction. Therefore, it is desired that such a model will be able to reliably predict its confidence measure. In this work, uncertainty estimation for the task...
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Model-free and Model-based Reinforcement Learning, the Intersection of Learning and Planning
PublicationMy doctoral dissertation is intended as the compound of four publications considering: structure and randomness in planning and reinforcement learning, continuous control with ensemble deep deterministic policy gradients, toddler-inspired active representation learning, and large-scale deep reinforcement learning costs.
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Shape-Based Pose Estimation of Robotic Surgical Instruments
PublicationWe describe a detector of robotic instrument parts in image-guided surgery. The detector consists of a huge ensemble of scale-variant and pose-dedicated, rigid appearance templates. The templates, which are equipped with pose-related keypoints and segmentation masks, allow for explicit pose estimation and segmentation of multiple end-effectors as well as fine-grained non-maximum suppression. We train the templates by grouping examples...
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Controlled English Interface for Knowledge Bases
PublicationThis paper describes the approach to interface for OWL Knowledge Bases based on Controlled English that is transformed directly to OWL 2 expressions and back. Two possible modes enable usage of the interface to specify Terminology, World Description as well as Integrity Constrains. Knowledge Base then can be validated against the Integrity Constrains to enable its continuous validation.
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Transmitting Alarm Information in DAB+ Broadcasting System
PublicationThe main goal of digital broadcasting is to deliver high-quality content with the lowest possible bitrate. This paper is focused on transmitting alarm information, such as emergency warning and alerting, in the DAB+ (Digital Audio Broadcasting plus) broadcasting system. These additional services should be available at the lowest possible bitrate, in order to provide a clear and understandable voice message to people. Furthermore, additional...
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Exploring the Solubility Limits of Edaravone in Neat Solvents and Binary Mixtures: Experimental and Machine Learning Study
PublicationThis study explores the edaravone solubility space encompassing both neat and binary dissolution media. Efforts were made to reveal the inherent concentration limits of common pure and mixed solvents. For this purpose, the published solubility data of the title drug were scrupulously inspected and cured, which made the dataset consistent and coherent. However, the lack of some important types of solvents in the collection called...
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SUBIEKTYWNA OCENA MULTIPLEKSU RADIOFONII LOKALNEJ DAB+ DZIAŁAJĄCEJ W GDAŃSKU I WROCŁAWIU
PublicationStandard DAB+ (Digital Audio Broadcasting plus) jest wiodącym systemem naziemnej radiofonii cyfrowej. W porównaniu do analogowej radiofonii FM wszystkie usługi, obejmujące tradycyjne programy radiowe oraz usługi transmisji danych, grupowane są w zbiór (ensemble). Praca ta przedstawia proces rekonfiguracji polskiego multipleksu na przykładzie lokalnej radiofonii DAB+ w Gdańsku i Wrocławiu. Opisuje wyniki badań subiektywnych dotyczących...
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Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks
PublicationOne of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying...
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Examples of Original Descriptive Geometry Task Items
PublicationThe paper presents several descriptive geometry drawing tasks and a comprehensive approach to their evaluation. The results as the task profile include the most important features and enable interpretation of the tasks in the educational system.
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Architecture Design of a Networked Music Performance Platform for a Chamber Choir
PublicationThis paper describes an architecture design process for Networked Music Performance (NMP) platform for medium-sized conducted music ensembles, based on remote rehearsals of Academic Choir of Gdańsk University of Technology. The issues of real-time remote communication, in-person music performance, and NMP are described. Three iterative steps defining and extending the architecture of the NMP platform with additional features to...
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The influence of image masks definition onsegmentation results of histopathological imagesusing convolutional neural network
PublicationAbstract—In the era of collecting large amounts of tissue materials, assisting the work of histopathologists with various electronic and information IT tools is an undeniable fact. The traditional interaction between a human pathologist and the glass slide is changing to interaction between an AI pathologist with a whole slide images. One of the important tasks is the segmentation of objects (e.g. cells) in such images. In this...
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Problems of modelling toxic compounds emitted by a marine internal combustion engine in unsteady states
PublicationContemporary engine tests are performed based on the theory of experiment. The available versions of programmes used for analysing experimental data make frequent use of the multiple regression model, which enables examining effects and interactions between input model parameters and a single output variable. The use of multi-equation models provides more freedom in analysing the measured results, as those models enable simultaneous...
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Applying case studies to teaching architectural investment
PublicationCase studies enable students to encounter practical issues during their education. Experiments conducted in class employing this method often feature simplified models of real-world situations. However, they still enable students to encounter actual problems, to which theoretical knowledge is applied. In architectural education, students carrying out semester projects usually rely on data provided by the teacher, without wondering...
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Features extraction from the electrocatalytic gas sensor responses
PublicationOne of the types of gas sensors used for detection and identification of toxic-air pollutant is an electrocatalytic gas sensor. The electrocatalytic sensors are working in cyclic voltammetry mode, enable detection of various gases. Their response are in the form of I-V curves which contain information about the type and the concentration of measured volatile compound. However,...
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Model neuronowy jako alternatywa dla numerycznego modelu okołodźwiękowego przepływu pary przez palisadę turbinową.
PublicationWystępowanie skośnej fali uderzeniowej w przepływie pary przez palisadę turbinową stanowi zagrożenie dla bezpiecznej pracy turbiny oraz dla jej elementów konstrukcyjnych. Detekcja oraz lokalizacja fali uderzeniowej, a także rozpoznanie przyczyny jej powstawania, nie są możliwe do osiągnięcia na drodze pomiarowej. Analizę zjawisk zachodzących wewnątrz kanału przepływowego umożliwiają natomiast modele numeryczne oraz neuronowe. Zaletą...
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Knowledge Societies Policy Handbook
PublicationThe Handbook builds upon existing knowledge and practices to provide policy-makers with an actionable conceptual framework for understanding and assessing the relationships between the Sustainable Development Goals and Knowledge Societies. By identifying gaps as well as strengths, the Handbook will enable countries to more effectively deploy resources and implement appropriate policy measures.
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Breast MRI segmentation by deep learning: key gaps and challenges
PublicationBreast MRI segmentation plays a vital role in early diagnosis and treatment planning of breast anomalies. Convolutional neural networks with deep learning have indicated promise in automating this process, but significant gaps and challenges remain to address. This PubMed-based review provides a comprehensive literature overview of the latest deep learning models used for breast segmentation. The article categorizes the literature...
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Next Generation Digital
PublicationThe paper outlines the major objectives of the MENAID research project, eimed at novel architectures of digital documents. Such documents will enable reduction of information overflow and strain, a major threat to the growth of a digital society. They will be forward compatible, technology neutral and lightweight, allowing workers of network organizations to use personal devices of any type.