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Search results for: multi-task learning
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Feature Reduction Using Similarity Measure in Object Detector Learning with Haar-like Features
PublicationThis paper presents two methods of training complexity reduction by additional selection of features to check in object detector training task by AdaBoost training algorithm. In the first method, the features with weak performance at first weak classifier building process are reduced based on a list of features sorted by minimum weighted error. In the second method the feature similarity measures are used to throw away that features...
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Shallow Water Communication with an Object Buried in Bottom Sediments
PublicationUnderwater acoustic communications (UAC) in shallow water applications is a very difficult task. This task becomes even more difficult when there is a need to ensure reliable communication with an object buried in bottom sediments. The article presents a simulation of an acoustic transmission channel in conditions of strong multi-path propagation to an object buried in bottom sediments. The impulse response method was used, supported...
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Strategic Flexibility as a Mediator in Relationship between Managerial Decisions and Organizational Learning: Ambidexterity Perspective
PublicationPurpose: The purpose of the article is to determine strategic flexibility in the relationship between managerial decisions and organizational learning. The analyses are conducted in the ambidexterity convection. Design/Methodology/Approach: The study was conducted at a textile company. The company is a leader in the textile recycling industry in Poland. Empirical data were collected using the PAPI technique. The survey questionnaire...
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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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Selection of Visual Descriptors for the Purpose of Multi-camera Object Re-identification
PublicationA comparative analysis of various visual descriptors is presented in this chapter. The descriptors utilize many aspects of image data: colour, texture, gradient, and statistical moments. The descriptor list is supplemented with local features calculated in close vicinity of key points found automatically in the image. The goal of the analysis is to find descriptors that are best suited for particular task, i.e. re-identification...
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Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models
PublicationMulti-objective optimization of antenna structures is a challenging task due to high-computational cost of evaluating the design objectives as well as large number of adjustable parameters. Design speedup can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation (RSA) models,...
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From Scores to Predictions in Multi-Label Classification: Neural Thresholding Strategies
PublicationIn this paper, we propose a novel approach for obtaining predictions from per-class scores to improve the accuracy of multi-label classification systems. In a multi-label classification task, the expected output is a set of predicted labels per each testing sample. Typically, these predictions are calculated by implicit or explicit thresholding of per-class real-valued scores: classes with scores exceeding a given threshold value...
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Improvement of Task Management with Process Models in Small and Medium Software Companies
PublicationSmall and medium software companies exhibit many special features that give reason for a dedicated approach to process improvement. They often cannot afford implementing maturity models or quality standards both in terms of time and money. Instead, they expect simpler solutions that can allow to run projects in more systematic and repeatable way, increase quality and knowledge management. In this paper, we present a method focused...
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Streaming Real-time Data in Distributed Dispatcher and Teleinformation Systems for Visualization of Multimedia Data of the Border Guard
PublicationSurveillance of the sea borders is a very important task for the Border Guard. Monitoring of country maritime border is an important task of the Border Guard. This task can be facilitated with the use of the technology enabling gathering information from distributed sources and its supervision and visualization. This task can be accomplished using a technology that allows to collect information from distributed sensors of different...
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Behavior Based Complete Coverage Task of Unknown Area by an Autonomous Mobile Robot SCORPION with Static Obstacles in Environment
PublicationIn the paper the behavior based control system of an autonomous mobile robot SCORPION is presented to execute the one of the most difficult navigation task, which is the complete coverage task of unknown area with static obstacles in the environment. The main principle assumed to design control system was that the robot should cover all area only once, if it possible, to optimize the length of path and energy consumption. All commercial...
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The System of the Supervision and the Visualization of Multimedia Data for BG
PublicationMonitoring of country maritime border is an important task of the Border Guard. This task can be facilitated with the use of the technology enabling gathering information from distributed sources and its supervision and visualization. The system presented in the paper is an extension and enhancement of the previously developed distributed system map data exchange system. The added functionalities allow supplementation of map data...
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Voice command recognition using hybrid genetic algorithm
PublicationAbstract: Speech recognition is a process of converting the acoustic signal into a set of words, whereas voice command recognition consists in the correct identification of voice commands, usually single words. Voice command recognition systems are widely used in the military, control systems, electronic devices, such as cellular phones, or by people with disabilities (e.g., for controlling a wheelchair or operating a computer...
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A Task-Scheduling Approach for Efficient Sparse Symmetric Matrix-Vector Multiplication on a GPU
PublicationIn this paper, a task-scheduling approach to efficiently calculating sparse symmetric matrix-vector products and designed to run on Graphics Processing Units (GPUs) is presented. The main premise is that, for many sparse symmetric matrices occurring in common applications, it is possible to obtain significant reductions in memory usage and improvements in performance when the matrix is prepared in certain ways prior to computation....
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Rozproszone usługi obliczeniowe na klastrach TASK z dostępem przez WWW i Web Services.
PublicationArtykuł omawia architekturę i doświadczenia wstępnych faz specyfikacji wymagań i analizy projektu, który umożliwi zdalne wykorzystanie równoległych klastrów i/lub superkomputerów sieci TASK przez użytkowników z rozproszonych geograficznie lokalizacji. System zapewni dotychczasowym i nowym użytkownikom klastrów i superkomputerów TASK zdalne uruchamianie i zarządzanie aplikacjami, bibliotekami równoległymi i sekwencyjnymi poprzez...
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Classification of Sea Going Vessels Properties Using SAR Satellite Images
PublicationThe aim of the project was to analyze the possibility of using machine learning and computer vision to identify (indicate the location) of all sea-going vessels located in the selected area of the open sea and to classify the main attributes of the vessel. The key elements of the project were to download data from the Sentinel-1 satellite [1], download data on the sea vessels [2], then automatically tag data and develop a detection...
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Investigating Feature Spaces for Isolated Word Recognition
PublicationMuch attention is given by researchers to the speech processing task in automatic speech recognition (ASR) over the past decades. The study addresses the issue related to the investigation of the appropriateness of a two-dimensional representation of speech feature spaces for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and timefrequency signal representation...
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Weakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech
PublicationWe propose a weakly-supervised model for word-level mispronunciation detection in non-native (L2) English speech. To train this model, phonetically transcribed L2 speech is not required and we only need to mark mispronounced words. The lack of phonetic transcriptions for L2 speech means that the model has to learn only from a weak signal of word-level mispronunciations. Because of that and due to the limited amount of mispronounced...
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Design of a microrobotic wrist for needle laparoscopic surgery
PublicationThe paper addresses the design of a micro wrist for needle laparoscopic surgery (needlescopy) using MEMS technology and an original 3 degree of freedom, 3D architecture. Advancement in needlescopy drives the development of multi-dof micro-tools 1-2mmin diameter with 3D mobility but standard available fabricationtechniques are for 2.5D structures. The paper discusses thedevelopment steps and design solutions for the realization...
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From Sequential to Parallel Implementation of NLP Using the Actor Model
PublicationThe article focuses on presenting methods allowing easy parallelization of an existing, sequential Natural Language Processing (NLP) application within a multi-core system. The actor-based solution implemented with the Akka framework has been applied and compared to an application based on Task Parallel Library (TPL) and to the original sequential application. Architectures, data and control flows are described along with execution...
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Training of Deep Learning Models Using Synthetic Datasets
PublicationIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Deep-Learning-Based Precise Characterization of Microwave Transistors Using Fully-Automated Regression Surrogates
PublicationAccurate 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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Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces – How to Face the Face Verification Task
PublicationThis paper describes the exhaustive tests of four known methods of linear transformations (Eigenfaces, Fisherfaces, Laplacianfaces and Marginfaces) in the context of face verification task. Additionally, we introduce a new variant of the transformation (Laplacianface + LDA), and the specific interval-based decision rule. Both of them improve the performance of face verification, in general, however, our experiments show that the...
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Updating a hospital building. A task for innovation design
PublicationRefurbishment of a hospital, especially located in a historical building, is a task that goes far beyond a standard framework of architectural practice. A concept of modularity in the architecture of the late nineteenth and early twentieth century was only just to happen, building system installations and technical equipment appeared as the simplest solutions. Inscribing complex functional solutions into such a space is an interesting...
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Intelligent Audio Signal Processing − Do We Still Need Annotated Datasets?
PublicationIn this paper, intelligent audio signal processing examples are shortly described. The focus is, however, on the machine learning approach and datasets needed, especially for deep learning models. Years of intense research produced many important results in this area; however, the goal of fully intelligent signal processing, characterized by its autonomous acting, is not yet achieved. Therefore, a review of state-of-the-art concerning...
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Parallel Computations of Text Similarities for Categorization Task
PublicationIn this chapter we describe the approach to parallel implementation of similarities in high dimensional spaces. The similarities computation have been used for textual data categorization. A test datasets we create from Wikipedia articles that with their hyper references formed a graph used in our experiments. The similarities based on Euclidean distance and Cosine measure have been used to process the data using k-means algorithm....
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Optymalizacja zasobów chmury obliczeniowej z wykorzystaniem inteligentnych agentów w zdalnym nauczaniu
PublicationRozprawa dotyczy optymalizacji zasobów chmury obliczeniowej, w której zastosowano inteligentne agenty w zdalnym nauczaniu. Zagadnienie jest istotne w edukacji, gdzie wykorzystuje się nowoczesne technologie, takie jak Internet Rzeczy, rozszerzoną i wirtualną rzeczywistość oraz deep learning w środowisku chmury obliczeniowej. Zagadnienie jest istotne również w sytuacji, gdy pandemia wymusza stosowanie zdalnego nauczania na dużą skalę...
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Multi-agent graph searching and exploration algorithms
PublicationA team of mobile entities, which we refer to as agents or searchers interchangeably, starting from homebases needs to complete a given task in a graph.The goal is to build a strategy, which allows agents to accomplish their task. We analyze strategies for their effectiveness (e.g., the number of used agents, the total number of performed moves by the agents or the completion time).Currently, the fields of on-line (i.e., agents...
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AITP - AI Thermal Pedestrians Dataset
PublicationEfficient pedestrian detection is a very important task in ensuring safety within road conditions, especially after sunset. One way to achieve this goal is to use thermal imaging in conjunction with deep learning methods and an annotated dataset for models training. In this work, such a dataset has been created by capturing thermal images of pedestrians in different weather and traffic conditions. All images were manually annotated...
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A Generative Approach to Hull Design for a Small Watercraft
PublicationIn the field of ocean engineering, the task of spatial hull modelling is one of the most complicated problems in ship design. This study presents a procedure applied as a generative approach to the design problems for the hull geometry of small vessels using elements of concurrent design with multi-criteria optimisation processes. Based upon widely available commercial software, an algorithm for the mathematical formulation of...
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Robust and Efficient Machine Learning Algorithms for Visual Recognition
PublicationIn visual recognition, the task is to identify and localize all objects of interest in the input image. With the ubiquitous presence of visual data in modern days, the role of object recognition algorithms is becoming more significant than ever and ranges from autonomous driving to computer-aided diagnosis in medicine. Current models for visual recognition are dominated by models based on Convolutional Neural Networks (CNNs), which...
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Investigating Feature Spaces for Isolated Word Recognition
PublicationThe study addresses the issues related to the appropriateness of a two-dimensional representation of speech signal for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and time-frequency signal representation converted to the investigated feature spaces. In particular, waveforms and fractal dimension features of the signal were chosen for the time domain, and...
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Poprawa jakości klasyfikacji głębokich sieci neuronowych poprzez optymalizację ich struktury i dwuetapowy proces uczenia
PublicationW pracy doktorskiej podjęto problem realizacji algorytmów głębokiego uczenia w warunkach deficytu danych uczących. Głównym celem było opracowanie podejścia optymalizującego strukturę sieci neuronowej oraz zastosowanie uczeniu dwuetapowym, w celu uzyskania mniejszych struktur, zachowując przy tym dokładności. Proponowane rozwiązania poddano testom na zadaniu klasyfikacji znamion skórnych na znamiona złośliwe i łagodne. W pierwszym...
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Performance Evaluation of the Parallel Codebook Algorithm for Background Subtraction in Video Stream
PublicationA background subtraction algorithm based on the codebook approach was implemented on a multi-core processor in a parallel form, using the OpenMP system. The aim of the experiments was to evaluate performance of the multithreaded algorithm in processing video streams recorded from monitoring cameras, depending on a number of computer cores used, method of task scheduling, image resolution and degree of image content variability....
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Surface sliding in human abdominal wall numerical models: Comparison of single-surface and multi-surface composites
PublicationDetermining mechanical properties of abdominal soft tissues requires a coupled experimental-numerical study, but first an appropriate numerical model needs to be built. Precise modeling of human abdominal wall mechanics is difficult because of its complicated multi-layer composition and large variation between specimens. There are several approaches concerning simplification of numerical models, but it is unclear how far one could...
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Dysfunctional prefrontal cortical network activity and interactions following cannabinoid receptor activation.
PublicationCoordinated activity spanning anatomically distributed neuronal networks underpins cognition and mediates limbic-cortical interactions during learning, memory, and decision-making. We used CP55940, a potent agonist of brain cannabinoid receptors known to disrupt coordinated activity in hippocampus, to investigate the roles of network oscillations during hippocampal and medial prefrontal cortical (mPFC) interactions in rats. During...
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Tolerance-Aware Multi-Objective Optimization of Antennas by Means of Feature-Based Regression Surrogates
PublicationAssessing the immunity of antenna design to fabrication tolerances is an important consideration, especially when the manufacturing process has not been predetermined. At the same time, the antenna parameter tuning should be oriented toward improving the performance figures pertinent to both electrical (e.g., input matching) and field properties (e.g., axial ratio bandwidth) as much as possible. Identification of available trade-offs...
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The Application of Vibration Recording and Analysis in Tribological Research on Sliding Friction
PublicationThe paper reports on a tribological research on the macroscopic manifestation and characteristics of sliding friction. The aim of the task was to measure friction in lubricated sliding contacts and test the interactions between the environment (the test rig) and the experimental friction contact. Friction-induced vibrations were observed and studied as a manifestation of the process. The typical set of velocity and force/torque...
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Approximate and analytic flow models for leak detection and identification
PublicationThe article presents a comprehensive quantitative comparison of four analytical models that, in different ways, describe the flow process in transmission pipelines necessary in the task of detecting and isolating leaks. First, the analyzed models are briefly presented. Then, a novel model comparison framework was introduced along with a methodology for generating data and assessing diagnostic effectiveness. The study presents basic...
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Data Partitioning and Task Management in the Clustered Server Layer of the Volunteer-based Computation System
PublicationWhile the typical volunteer-based distributed computing system focus on the computing performance, the Comcute system was designed especially to keep alive in the emergency situations. This means that designers had to take into account not only performance, but the safety of calculations as well. Quadruple-layered architecture was proposed to separate the untrusted components from the core of the system. The main layer (W) consists...
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How to Design Affect-aware Educational Systems – the AFFINT Process Approach
PublicationComputer systems, that support learning processes, can adapt to the needs and states of a learner. The adaptation might directly address the knowledge deficits and most tutoring systems apply an adaptable learning path of that kind. Apart from a preliminary knowledge state, there are more factors, that influence education effectiveness and among those there are fluctuating emotional states. The tutoring systems may recognize or...
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Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions
PublicationWith the technology advancements in smart home sector, voice control and automation are key components that can make a real difference in people's lives. The voice recognition technology market continues to involve rapidly as almost all smart home devices are providing speaker recognition capability today. However, most of them provide cloud-based solutions or use very deep Neural Networks for speaker recognition task, which are...
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Variable Data Structures and Customized Deep Learning Surrogates for Computationally Efficient and Reliable Characterization of Buried Objects
PublicationIn this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model has been used. The task is to predict simultaneously and independent of each characteristic parameters of a buried object of several radii at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable...
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Generalized Formulation of Response Features for Reliable Optimization of Antenna Input Characteristics
PublicationElectromagnetic (EM)-driven parameter adjustment has become imperative in the design of modern antennas. It is necessary because the initial designs rendered through topology evolution, parameter sweeping, or theoretical models, are often of poor quality and need to be improved to satisfy stringent performance requirements. Given multiple objectives, constraints, and a typically large number of geometry parameters, the design closure...
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Reduced-cost optimization-based miniaturization of microwave passives by multi-resolution EM simulations for internet of things and space-limited applications
PublicationStringent performance specifications along with constraints imposed on physical dimensions, make the design of contemporary microwave components a truly onerous task. In recent years, the latter demand has been growing in importance, with the innovative application areas such as Internet of Things coming into play. The need to employ full-wave electromagnetic (EM) simu-lations for response evaluation, reliable yet CPU heavy, only...
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Quantitative risk assessment of new ship designs in damage conditions
PublicationThe paper is devoted to safety of ships in damage conditions. The novel contribution of the paper is connected with a new Multi-Task ship (MT-ship) design at the preliminary stage of design. There are a few problems at the preliminary stage that should be considered. One problem is connected with if the quantitative risk-based method is a reliable and formal method for safety assessment of such the new design (MT-ship) in damage...
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Advanced Ship Control Methods
PublicationThe chapter presents two main streams of research in vessel control at sea: dynamic positioning (DP) of the vessel and decision support in case of collision at sea. The control structure and basic requirements for the DP system are defined. Selected issues of automatic control of a dynamically positioned vessel are discussed. A review of advanced methods of controlling a DP ship is carried out, taking into account the tasks of...
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The principles of arturbain.fr for teaching sustainable urban design
PublicationThe article is an attempt to show how important it is in the contemporary education of architecture students to use an ordered repertoire of principles and concepts of a universal nature, at the same time, pointing to the new directions of solutions and answers to the problems of the 21st century. This task is implemented by the French Association L'Arturbain dans les Territoires. It is accompanied by an idea consisting in observing...
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Teaching High–performance Computing Systems – A Case Study with Parallel Programming APIs: MPI, OpenMP and CUDA
PublicationHigh performance computing (HPC) education has become essential in recent years, especially that parallel computing on high performance computing systems enables modern machine learning models to grow in scale. This significant increase in the computational power of modern supercomputers relies on a large number of cores in modern CPUs and GPUs. As a consequence, parallel program development based on parallel thinking has become...
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Moduł Warsztaty - narzędzie w procesie edukacji na uczelni wyższej
PublicationObecnie istnieje bardzo szeroka gama narzędzi informatycznych, które wspierają proces edukacji przy wykorzystaniu internetu na uczelniach wyższych. Wśród nieodpłatnych narzędzi powszechnie znana jest platforma Moodle. W artykule zaprezentowano jeden z jej modułów – Warsztaty. Przedstawiono jego funkcjonalność. Opisano jego zalety i wady w nauczaniu łączącym techniki online i tradycyjne na uczelni wyższej (blended-learning). W artykule...
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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)...