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Search results for: KNOWLEDGE SHARING, NEURAL KNOWLEDGE DNA, DEEP NEURAL NETWORKS
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Robert Bajko mgr inż.
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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
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Optimal Selection of Input Features and an Acompanying Neural Network Structure for the Classification Purposes - Skin Lesions Case Study
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Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
PublicationAbstract Background In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments...
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Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings
PublicationThe 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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Tomasz Zubowicz dr inż.
PeopleTomasz Zubowicz has received his M.Sc. Eng. degree in Control Engineering from the Faculty of Electrical and Control Engineering at the Gda{\'n}sk University of Technology (GUT) in $2008$. He received his Ph.D. Eng. (Hons.) in the field of Control Engineering from the same faculty in $2019$. In $2012$ he became a permanent staff member at the Department of Intelligent Control and Decision Support Systems at GUT and a member of...
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Developing an Ontology from Set of Experience KnowledgeStructure
PublicationWhen referring to knowledge forms,collecting for all decision eventsin a knowledge-explicit way becomes a significant ask for any company. Set of experience knowledge structure can assis in accomplishing this purpose.However,after collecting,distributing and sharing that knowledge as adecisional DNA is even a more important advance.Distributing and sharing companies' decisional DNA through an efficient development of Ontologies...
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A digital repository of science resources of research institute as a source of knowledge from the area of production engineering for SMEs
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Pinus Species as Prospective Reserves of Bioactive Compounds with Potential Use in Functional Food—Current State of Knowledge
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Unknown Enemy and Psychopathological Responses: A Cross-Sectional Nationwide Study Assessing the Knowledge About COVID-19
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Cross-Lingual Knowledge Distillation via Flow-Based Voice Conversion for Robust Polyglot Text-to-Speech
PublicationIn this work, we introduce a framework for cross-lingual speech synthesis, which involves an upstream Voice Conversion (VC) model and a downstream Text-To-Speech (TTS) model. The proposed framework consists of 4 stages. In the first two stages, we use a VC model to convert utterances in the target locale to the voice of the target speaker. In the third stage, the converted data is combined with the linguistic features and durations...
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Computational intelligence methods in production management
PublicationThis chapter presents a survey of selected computational intelligence methods used in production management. This group of methods includes, among others, approaches based on the artificial neural networks, the evolutionary algorithms, the fuzzy logic systems and the particle swarm optimization mechanisms. From the abovementioned methods particularly noteworthy are the evolutionary and the particle swarm algorithms, which are successfully...
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Efkleidis Katsaros
PeopleEfklidis Katsaros received the B.Sc. degree in mathematics from the Aristotle University of Thessaloniki, Greece, in 2016, and the M.Sc. degree (cum laude) in data science: statistical science from Leiden University, The Netherlands, in 2019. He is currently pursuing the Ph.D. degree in deep video multi-task learning with the Department of Biomedical Engineering, Gdańsk University of Technology, Poland. Since 2020, he has been...
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Paweł Rościszewski dr inż.
PeoplePaweł Rościszewski received his PhD in Computer Science at Gdańsk University of Technology in 2018 based on PhD thesis entitled: "Optimization of hybrid parallel application execution in heterogeneous high performance computing systems considering execution time and power consumption". Currently, he is an Assistant Professor at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Poland....
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Genetic and biochemical determinants of serum concentrations of monocyte chemoattractant protein-1, a potential neural tube defect risk factor
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A new analyzer based on pellistor sensor with neural network data postprocessing for measurement of hydrocarbons in lower explosive limit range
PublicationW pracy przedstawiono rezultaty pierwszego etapu badań nad nowym typem analizatora do oznaczania stężenia wodoru i lotnych węglowodorów w zakresie dolnej granicy wybuchowości. Analizator ten zbudowano w oparciu o pojedynczy czujnik pelistorowy z układem przetwarzania danych wykorzystującym sztuczną sieć neuronową.
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Deep Learning: A Case Study for Image Recognition Using Transfer Learning
PublicationDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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Deep Learning
PublicationDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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Social learning and knowledge flows in cluster initiatives, In: Sanz S.C., Blanco F.P., Urzelai B. (Eds). Human and Relational Resources (pp. 44-45). the 4th International Conference on Clusters and Industrial Districts CLUSTERING, University of Valencia, Spain, May 23–24 (ISBN: 978-84-09-11926-4).
PublicationPurpose – The purpose of the paper is to explore how learning manifests and knowledge flows in cluster initiatives (CIs) due to interactions undertaken by their members. The paper addresses the research question of how social learning occurs and knowledge flows in CIs. Design/methodology/approach – The qualitative study of four cluster initiatives helped to identify various symptoms of social learning and knowledge flows in...
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Gender and Body-Fat Status as Predictors of Parental Feeding Styles and Children’s Nutritional Knowledge, Eating Habits and Behaviours
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Complete Mesogastric Excisions Involving Anatomically Based Concepts and Embryological-Based Surgeries: Current Knowledge and Future Challenges
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Graphene Oxide as Mine of Knowledge: Using Graphene Oxide To Teach Undergraduate Students Core Chemistry and Nanotechnology Concepts
PublicationThe aim of this laboratory experiment is to utilize graphene oxide (GO) material to introduce under-graduate students to many well-known concepts of general chemistry. GO is a new nanomaterial that has generated worldwide interest and can be easily produced in every well-equipped undergraduate chemical laboratory. An in-depth examination of GO synthesis, as well as a study of its structure and properties, allows students to familiarize...
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Natalia Sokół dr inż.
PeopleBACKGROUND Master of Science in Light and Lighting (2008-2009/11) The UCL Bartlett School of Graduate Studies, Faculty of the Built Environment, London, UK, www.bartlett.ucl.ac.uk MA Degree in Interior Architecture (1999-2004), The Academy of Fine Arts, Poznan, Poland, www.uap.edu.pl MA Degree in Art Education (1997-2002), Academy of Fine Arts, Poznan, Poland, www.uap.edu.pl MAIN RESEARCH AREAS · ...
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Artificial Neural Networks in Engineering Conference
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European Symposium on Artificial Neural Networks
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IEEE International Conference on Neural Networks
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International Conference on Artificial Neural Networks
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Artificial neural network model of hardness, porosity and cavitation erosion wear of APS deposited Al2O3 -13 wt% TiO2 coatings
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Application of Generalized Regression Neural Network and Gaussian Process Regression for Modelling Hybrid Micro-Electric Discharge Machining: A Comparative Study
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Neural, Parallel and Scientific Computations
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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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Andrzej Stateczny prof. dr hab. inż.
PeopleProf. Dr. Andrzej Stateczny is a Professor of Gdansk Technical University Poland and President of Marine Technology Ltd. His research interests are mainly centered on navigation, hydrography and geoinformatics. Current RF research activities include radar navigation, comparative navigation, hydrography, artificial intelligence methods focused on image processing and multisensory data fusion. He has been the Principal Investigator...
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Olgun Aydin Dr
PeopleOlgun Aydin finished his PhD by publishing a thesis about Deep Neural Networks. He works as a Senior Data Scientist in PwC Poland, gives lectures in Gdansk University of Technology in Poland and member of WhyR? Foundation. Olgun is a very big fan of R and author of the book called “R Web Scraping Quick Start Guide” , two video courses are called “Deep Dive into Statistical Modelling using R” and “Applied Machine Learning and Deep...
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Prediction of protein structure using a knowledge-based off-lattice united-residue force field and global optimization methods
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Commitment-based human resource practices, job satisfaction and proactive knowledge-seeking behavior: The moderating role of organizational identification
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A new multi-process collaborative architecture for time series classification
PublicationTime series classification (TSC) is the problem of categorizing time series data by using machine learning techniques. Its applications vary from cybersecurity and health care to remote sensing and human activity recognition. In this paper, we propose a novel multi-process collaborative architecture for TSC. The propositioned method amalgamates multi-head convolutional neural networks and capsule mechanism. In addition to the discovery...
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Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples
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Data augmentation for improving deep learning in image classification problem
PublicationThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
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Experience oriented enhancement of smartness for Internet of Things
PublicationIn this paper, we propose a novel approach, the Experience-Oriented Smart Things that allows experiential knowledge discovery, storage, involving, and sharing for Internet of Things. The main features, architecture, and initial experiments of this approach are introduced. Rather than take all the data produced by Internet of Things, this approach focuses on acquiring only interesting data for its knowledge discovery process. By...
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Decisional DNA based framework for representing Virtual Engineering Objects
PublicationIn this paper, we propose a frame-work to represent the Virtual Engineering Objects (VEO) utilizing Set of Knowledge Experience Structure (SOEKS) and Decisional DNA. A VEO will enable the discovery of new knowledge in a manufacturing unit and the generation of new rules that drive reasoning. The proposed VEO framework will not only be knowledge based representation but it will also have its associated experience embedded within...
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Acceptance of a potential major bleeding among patients with venous thromboembolism on long-term oral anticoagulation: the knowledge of the disease and therapy matters
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Proceedings of the Rhine Province Assembly in Karl Marx’s articles as a source of knowledge about political and legal debates in the mid-nineteenth century
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Knowledge about coeliac disease: Results of survey conducted among persons screened using a self-administered transglutaminase-based test
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Bounding approach to parameter estimation without prior knowledge on modeling error and application to quality modeling in drinking water distribution systems
PublicationW artykule rozważana jest estymacja parametrów modelu autoregresji z ruchoma średnią i sygnałem wejściowym (ARMAX) z wykorzystaniem przedziałowego modelu błędu. Zakłada się, że granice błędu struktury modelu są nieznane, bądź znane, ale bardzo konserwatywne. Dla zmniejszenia tego konserwatyzmu proponowane jest idea modeli punktowo-parametrycznych, w której występują zbiory parametrów i błędu modelu odpowiadające wszystkim wejściom....
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European Society of Hypertension Working Group on Obesity Obesity-induced hypertension and target organ damage: current knowledge and future directions
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Mo-P5:335 Influence of life style knowledge on the occurrence of cardiovascular risk factors in adults living in Krakow - South Poland
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Rating by detection: an artifact detection protocol for rating EEG quality with average event duration
PublicationQuantitative evaluation protocols are critical for the development of algorithms that remove artifacts from real EEG optimally. However, visually inspecting the real EEG to select the top-performing artifact removal pipeline is infeasible while hand-crafted EEG data allow assessing artifact removal configurations only in a simulated environment. This study proposes a novel, principled approach for quantitatively evaluating algorithmically...
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Particle swarm optimization–artificial neural network modeling and optimization of leachable zinc from flour samples by miniaturized homogenous liquid–liquid microextraction
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From Individual to Collective: Intelligence Amplification with Bio-Inspired Decisional DNA and its Extensions
PublicationIn nature, deoxyribonucleic acid (DNA) contains the genetic instructions used in the development and functioning of all known living organisms. The idea behind our vision is to develop an artificial system, an architecture that would support discovering, adding, storing, improving and sharing information and knowledge among agents and organizations through experience. We propose a novel Knowledge Representation (KR) approach in...
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IEEE International Joint Conference on Neural Networks
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