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- Publikacje 1486 wyników po odfiltrowaniu
- Czasopisma 70 wyników po odfiltrowaniu
- Konferencje 110 wyników po odfiltrowaniu
- Osoby 127 wyników po odfiltrowaniu
- Projekty 1 wyników po odfiltrowaniu
- Zespoły Badawcze 1 wyników po odfiltrowaniu
- Kursy Online 68 wyników po odfiltrowaniu
- Wydarzenia 19 wyników po odfiltrowaniu
- Dane Badawcze 22 wyników po odfiltrowaniu
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Wyniki wyszukiwania dla: sztuczna inteligencja
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Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings
PublikacjaThe paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...
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Production planning and control methods in the intelligent manufacturing systems
PublikacjaNiniejszy rozdział prezentuje zagadnienia związane z planowaniem i sterowaniem wytwarzaniem w kontekście budowy i działania inteligentnych systemów produkcyjnych (ISP). Architektura ISP, będąca rozwinięciem elastycznych systemów produkcyjnych, integruje systemy wspomagania decyzji ze strukturami bazodanowymi oraz dodatkowymi modułami komunikacyjnymi. Systemy wspomagania decyzji budowane są w oparciu o mechanizmy tzw. inteligencji...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublikacjaThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Importance of artificial intelligence to support the process of anaerobicdigestion of kitchen waste with bioplastics / Znaczenie sztucznej inteligencji we wspomaganiu procesu beztlenowej fermentacji odpadów kuchennych zawierających bioplastiki
PublikacjaArtificial intelligence (AI) and machine learning were used to obtain more effective methods for conducting the digestion process and achieving final products. Data acquisition was carried out by an automatic monitoring and anal. research. The knowledge describing the anaerobic digestion process was summarized in the form of rules: IF (premise) THEN (conclusion). The compiled set of rules created a knowledge base of the expert...
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Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects
PublikacjaModern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimizationand model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systemsneeds a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work isdifficult to comprehend. Explainable artificial intelligence (XAI)...
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Automatic labeling of traffic sound recordings using autoencoder-derived features
PublikacjaAn approach to detection of events occurring in road traffic using autoencoders is presented. Extensions of existing algorithms of acoustic road events detection employing Mel Frequency Cepstral Coefficients combined with classifiers based on k nearest neighbors, Support Vector Machines, and random forests are used. In our research, the acoustic signal gathered from the microphone placed near the road is split into frames and converted...
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Rozdział 4. Cieplno-przepływowe relacje diagnostyczne ustabilizowanych cieplnie bloków energetycznych wykorzystujące sztuczne sieci neuronowe (SSN)
PublikacjaPodano przykłady relacji diagnostycznych budowanych dla bloków energetycznych pracujących w warunkach stabilizacji cieplnej. Należą one do metod off-line. Dobrze sprawdzają się w nich sztuczne sieci neuronowe. Przy modułowej strukturze relacji diagnostycznych wykorzystywane są z powodzeniem SSN zarówno z ciągłymi jak i skokowymi funkcjami przejścia, w zależności od oczekiwanego wyniku obliczeń neuronowych.
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Assessing the attractiveness of human face based on machine learning
PublikacjaThe attractiveness of the face plays an important role in everyday life, especially in the modern world where social media and the Internet surround us. In this study, an attempt to assess the attractiveness of a face by machine learning is shown. Attractiveness is determined by three deep models whose sum of predictions is the final score. Two annotated datasets available in the literature are employed for training and testing...
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Detecting Lombard Speech Using Deep Learning Approach
PublikacjaRobust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks...
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Klasyfikacja sygnału EKG przy użyciu konwolucyjnych sieci neuronowych
PublikacjaAutomation and improvement of diagnostic process is a vital element of medicine development and patient’s condition self-control. For a long time different ECG signal classification methods exist and are successfully applied, nevertheless their accuracy is not always satisfying enough. The lack of identification of an existing abnormality, which is very similar to a normal heartbeat is the biggest issue - for example premature...
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Klasyfikacja sygnału EKG przy użyciu konwolucyjnych sieci neuronowych
PublikacjaAutomation and improvement of diagnostic process is a vital element of medicine development and patient’s condition self-control. For a long time different ECG signal classification methods exist and are successfully applied, nevertheless their accuracy is not always satisfying enough. The lack of identification of an existing abnormality, which is very similar to a normal heartbeat is the biggest issue - for example premature...
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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublikacjaComplexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...
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Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions
PublikacjaWith 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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Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublikacjaOne of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was...
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Halucynacje chatbotów a prawda: główne nurty debaty i ich interpretacje
PublikacjaGeneratywne systemy sztucznej inteligencji (SI) są w stanie tworzyć treści medialne poprzez zastosowanie uczenia maszynowego do dużych ilości danych szkoleniowych. Te nowe dane mogą obejmować tekst (np. Bard firmy Google, LLaMa firmy Meta lub ChatGPT firmy OpenAI) oraz elementy wizualne (np. Stable Diffusion lub DALL-E OpenAI) i dźwięk (np. VALL-E firmy Micro- soft). Stopień zaawansowania tych treści może czynić je nieodróżnialnymi...
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Predicting bankruptcy with the use of macroeconomic variables
PublikacjaRegarding the current global financial crisis, the firms can expect the increased uncertainty of their existence. The relevant literature includes extensive studies on bankruptcy prediction. Studies show that the most popular method used for prediction of firms' failures are discriminant analyses (30,3% of all models), then logit and probit models (21,3%), which all three are parametric models. The nature, the structure of the...
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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
PublikacjaCurrent 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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Skuteczność systemu eksperckiego i sztucznej inteligencji w prognozowaniu upadłości firm
PublikacjaArtykuł ten dotyczy prognozowania upadłości przedsiębiorstw w Polsce. W artykule tym porównano dwie metody prognozowania zagrożeń firm upadłością: sztuczne sieci neuronowe oraz logikę rozmytą. W badaniach autor wykorzystał dane dotyczące 185 spółek notowanych na Warszawskiej Giełdzie Papierów Wartościowych. Populacja ta została podzielona na próbę uczącą i testową. Każde z analizowanych przedsiębiorstw opisanych zostało za pomocą...
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Prediction of Overall In Vitro Microsomal Stability of Drug Candidates Based on Molecular Modeling and Support Vector Machines. Case Study of Novel Arylpiperazines Derivatives
PublikacjaOther than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model...
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Algorytmy ewolucyjne w projektowaniu sieci MPLS
PublikacjaNiniejszy artykuł opisuje zrealizowane narzędzie, które umożliwia projektowanie sieci MPLS za pomocą Algorytmów Ewolucyjnych. Narzędzie to generuje ścieżki i optymalizuje alokację na nich przepływności żądań zapotrzebowań z uwzględnieniem klas obsługi strumieni ruchu z gwarancją zróżnicowanego QoS. Może także wybierać ścieżki do alokacji spośród danych wejściowych tak, aby wykorzystanie sieci było optymalne. Narzędzie to zostało...
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Multi-criterion decision making in distributed systems by quantum evolutionary algorithms
PublikacjaDecision making by the AQMEA (Adaptive Quantum-based Multi-criterion Evolutionary Algorithm) has been considered for distributed computer systems. AQMEA has been extended by a chromosome representation with the registry of the smallest units of quantum information. Evolutionary computing with Q-bit chromosomes has been proofed to characterize by the enhanced population diversity than other representations, since individuals represent...
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Neural network simulator's application to reference performance determination of turbine blading in the heat-flow diagnostics.
PublikacjaIn the paper, the possibility of application of artificial neural networks to perform the fluid flow calculations through both damaged and undamaged turbine blading was investigated. Preliminary results are presented and show the potentiality of further development of the method for the purpose of heat-flow diagnostics.
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Algorytmy genetyczne w wielokryterialnej optymalizacji obserwatorów detekcyjnych.
PublikacjaW rozdziale przedstawia się możliwości zastosowania podejścia genetycznego do zagadnień wielokryterialnej optymalizacji w przestrzeniach wielowymiarowych z wykorzystaniem koncepcji optymalności w sensie Pareto. Jako przykład ilustrujący rozważane podejście daje się zadanie syntezy obserwatorów stanu służących wykrywaniu błędów występujących w układzie sterowania bezzałogowego statku latającego oraz w układzie napędowym jednostki...
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LOS and NLOS identification in real indoor environment using deep learning approach
PublikacjaVisibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a deep learning (DL) model to classify LOS/NLOS...
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The Implementation of Fuzzy Logic in Forecasting Financial Ratios
PublikacjaThis paper is devoted to the issue of forecasting financial ratios. The objective of the conducted research is to develop a predictive model with the use of an innovative methodology, i.e., fuzzy logic theory, and to evaluate its effectiveness. Fuzzy logic has been widely used in machinery, robotics and industrial engineering. This paper introduces the use of fuzzy logic for the financial analysis of enterprises. While many current...
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Wpływ ruchu pojazdów ciężarowych na zniszczenia domów zlokalizowanych w pobliżu dróg przejazdowych
PublikacjaW niniejszym artykule przestawiono problem występowania drgań w budynkach, spowodowanych poruszającymi się pojazdami oraz ideę rozwiązania problemu pracochłonnych i kosztownych pomiarów takiego zjawiska. Autorzy starają się ukazać czynniki mające wpływ na wielkość drgań w świetle obowiązujących przepisów i norm. Główny wysiłek skupiają na ich pomiarze i interpretacji otrzymanych wyników w świetle propozycji budowy aplikacji wykorzystującej...
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Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublikacjaThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
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Predicting Compressive Strength of Cement-Stabilized Rammed Earth Based on SEM Images Using Computer Vision and Deep Learning
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When Neural Networks Meet Decisional DNA: A Promising New Perspective for Knowledge Representation and Sharing
PublikacjaABSTRACT In this article, we introduce a novel concept combining neural network technology and Decisional DNA for knowledge representation and sharing. Instead of using traditional machine learning and knowledge discovery methods, this approach explores the way of knowledge extraction through deep learning processes based on a domain’s past decisional events captured by Decisional DNA. We compare our approach with kNN (k-nearest...
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Raw data of AuAg nanoalloy plasmon resonances used for machine learning method
Dane BadawczeRaw data used for machine learning process. UV-vis measurements of AuAg alloyed nanostructures created from thin films. Plasmonic band position dependence on fabrication parameters. Small presentation reviewing achieved structures and their properties.
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Ship Resistance Prediction with Artificial Neural Networks
PublikacjaThe paper is dedicated to a new method of ship’s resistance prediction using Artificial Neural Network (ANN). In the initial stage selected ships parameters are prepared to be used as a training and validation sets. Next step is to verify several network structures and to determine parameters with the highest influence on the result resistance. Finally, other parameters expected to impact the resistance are proposed. The research utilizes...
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Bimodal deep learning model for subjectively enhanced emotion classification in films
PublikacjaThis research delves into the concept of color grading in film, focusing on how color influences the emotional response of the audience. The study commenced by recalling state-of-the-art works that process audio-video signals and associated emotions by machine learning. Then, assumptions of subjective tests for refining and validating an emotion model for assigning specific emotional labels to selected film excerpts were presented....
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublikacjaFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Sensors and Sensor’s Fusion in Autonomous Vehicles
PublikacjaAutonomous vehicle navigation has been at the center of several major developments, both in civilian and defense applications. New technologies such as multisensory data fusion, big data processing, and deep learning are changing the quality of areas of applications, improving the sensors and systems used. New ideas such as 3D radar, 3D sonar, LiDAR, and others are based on autonomous vehicle revolutionary development. The Special...
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Nina Rizun dr
OsobyNina Rizun jest adiunktem na Wydziale Zarządzania i Ekonomii Politechniki Gdańskiej. W październiku 1999 r. uzyskała stopień doktora nauk technicznych za specjalizacją Gospodarka przedsiębiorstwa i organizacja produkcji. W latach 1993–2000 pracowała na Wydziale Informatyki Ekonomicznej w Akademji Metalurgicznej, Dnipro, Ukraina. W latach 2000–2016 – na Wydziale Cybernetyki Ekonomicznej i Metod Matematycznych na Uniwersytecie Alfreda...
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An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublikacjaThis 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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How personality traits, sports anxiety, and general imagery could influence the physiological response measured by SCL to imagined situations in sports?
Dane BadawczeThe data were collected to understand how individual differences in personality (e.g. neuroticism), general imagery, and situational sports anxiety are linked to arousal measuring with skin conductance level (SCL) in situational imagery (as scripted for sport-related scenes). Thirty persons participated in the study, aged between 14 and 42 years, with...
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Exploring the influence of personal factors on physiological responses to mental imagery in sport
PublikacjaImagery is a well-known technique in mental training which improves performance efficiency and influences physiological arousal. One of the biomarkers indicating the amount of physiological arousal is skin conductance level (SCL). The aim of our study is to understand how individual differences in personality (e.g. neuroticism), general imagery and situational sport anxiety are linked to arousal measuring with SCL in situational...
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Knowledge Base Suitable for Answering Questions in Natural Language
PublikacjaThis paper presents three knowledge bases widely used by researchers coping with natural language processing: OpenCyc, DBpedia and YAGO. They are characterized from the point of view of questions answering system. In this paper a short description of the aforementioned system implementation is also presented.
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Machine learning approach to packaging compatibility testing in the new product development process
PublikacjaThe paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing...
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Computational Intelligence - 2023
Kursy OnlineWidening the students knowledge about the selected methods of artificial intelligence
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Computational Intelligence 2022
Kursy OnlineWidening the students knowledge about the selected methods of artificial intelligence
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Computational Intelligence - sem. 2022/23
Kursy OnlineWidening the students knowledge about the selected methods of artificial intelligence
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Computational Intelligence - 2023/2024 sem.
Kursy OnlineWidening the students knowledge about the selected methods of artificial intelligence
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Computational Intelligence - sem. 2023/2024
Kursy OnlineWidening the students knowledge about the selected methods of artificial intelligence
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Nowoczesne metody prognozowania zagrożenia finansowego przedsiębiorstw
PublikacjaMonografia przedstawia w sposób szczegółowy metody oraz etapy budowy modeli oceny zagrożenia przedsiębiorstw upadłością. Autor opisał bardzo dokładnie trzy techniki wykorzystywane do budowy tego typu modeli, a mianowicie: liniową analizę dyskryminacyjną, analizę logitową oraz sztuczne sieci neuronowe. Ponadto publikacja ta ukazuje metody stosowane w analizie porównawczej modeli oceny zagrożenia przedsiębiorstw upadłością oraz zawiera...
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Swarm Intelligence
Czasopisma -
A new library for construction of automata
PublikacjaWe present a new library of functions that construct minimal, acyclic, deterministic, finite-state automata in the same format as the author's fsa package, and also accepted by the author's fadd library of functions that use finite-state automata as dictionaries in natural language processing.
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Implementing fuzzy logic to generate user profile in decisional DNA television: the concept and initial case study
PublikacjaIn the paper the concept and case study of a novel approach that generates a television user's profile utilizing principles of fuzzy logic is presented.
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Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing
PublikacjaCirculating tumor cells (CTCs) are tumor cells that separate from the solid tumor and enter the bloodstream, which can cause metastasis. Detection and enumeration of CTCs show promising potential as a predictor for prognosis in cancer patients. Furthermore, single-cells sequencing is a technique that provides genetic information from individual cells and allows to classify them precisely and reliably. Sequencing data typically...