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Bibliografia publikacji pracowników
Państwowej Szkoły Wyższej w Białej Podlaskiej

Baza tworzona przez Bibliotekę Akademii Bialskiej im. Jana Pawła II.


Zapytanie: ARTIFICIAL INTELLIGENCE
Liczba odnalezionych rekordów: 4



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Nr opisu: nicznych^oAndrzejuk Wojciech^p3958211^t0000024634^80000-0002-0687-4812^w ^32^42;1^6tak^aSzafraniec Małgorzata^b000^c^d000^e ^f000^g^kSzafraniec Małgorzata^2Szafraniec^5Małgorzata^iSzafraniec^jMałgorzata^1Małgorzata^9M^oSzafraniec Małgorzata^t0000034652^33^40;0^6nie^aKachel Magdalena^b000^c^d000^e ^f000^g^kKachel Magdalena^2Kachel^5Magdalena^iKachel^jMagdalena^1Magdalena^9M^oKachel Magdalena^t0000034653^34^40;0^6nie^aHunek Robert^b000^c^d000^e ^f000^g^kHunek Robert^2Hunek^5Robert^iHunek^jRobert^1Robert^9R^oHunek Robert^t0000034654^35^40;0^6nie^a5^bWNT:2;3:2;3:1;4^cUNIT:2;3:2;3:1;4^d5^e2^f1^gtak^aBarnat-Hunek
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Punktacja ministerstwa: potential for generating incorrect answers. Additionally, the article highlights the importance of privacy concerns when using Chat GPT. Ultimately, the analysis of the article focuses on examining both the benefits and potential challenges associated with integrating Chat GPT into people's professional endeavors. The aim of this paper is to present the concept of Chat GPT and its impact on various professions. Design/Methodology/Approach: The article employed the following methods: literature review and analysis of the impact of the Chat GPT tool on the job market. The focus was on examining various perspectives and considering the main concepts related to Chat GPT. Findings: Based on the conducted analyses, it was determined that Chat GPT has assisted professionals in their work, but at this moment, it is not capable of replacing humans in their respective occupa
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Nr opisu: /IDAACS.2019.8924243^aGolovko, Vladimir^cy^aartificial intelligence^aKopia dostępna w Sekcji Bibliometrii.^a10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)^aMetz^dFR^b2019.09.18^c2019.09.21^aThis paper presents and explains an implementation of an Artificial Neural Network approach for sport activities (gestures) detection and recognition using PIQ ROBOT device. Tennis was chosen as an example of sports activities. The development of artificial intelligence has given rise to gesture-recognition-based devices. The global gesture recognition market size was valued at USD 6.22 billion in 2017 and it is likely to reach USD 30.6 billion by 2025. This paper starts our ambitious research in the area of artificial neural networks for activity recognition in the sport.^aartificial neural networks^agesture recognition^asport activity recognition^atime series processing
Autorzy: , , 99999920.000PUNKTACJA UWM 009999.000 003 Proceedings of the 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems : Technology and Applications, Institute of Electrical and Electronics Engineers Inc. 2019.09.18 998899009999.000003Artificial Intelligence for Sport Activity RecognitionIDAACS'2019S. 628-632[Piscataway]978-1-7281-4068-12018/201910.1109/IDAACS.2019.8924243Golovko, Vladimirartificial intelligenceKopia dostępna w Sekcji Bibliometrii.10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)MetzThis paper presents and explains an implementation of an Artificial Neural Network approach for sport activities (gestures) detection and recognition using PIQ ROBOT device. Tennis was chosen as an example of sports activities. The development of artificial intelligence has given rise to gesture-recognition-based devices. The global gesture recognition market size was valued at USD 6.22 billion in 2017 and it is likely to reach USD 30.6 billion by 2025. This paper starts our ambitious research in the area of artificial neural networks for activity recognition in the sport.artificial neural networksgesture recognitionsport activity recognitiontime series processing.
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Nr opisu: y and an energy production using the satellite photos. For this purpose, a Convolutional Neural Network was used. For training and testing dataset consists of low-quality Google satellite images was used. The experimental results show a high rate accuracy of detection with low rate incorrect classifications of the proposed approach. The proposed approach has enormous implementation and can be improved in future.^acomputer vision^aconvolutional neural network^aenergy production^ageospatial data^apower capacity^asatellite photos^asolar panels detection
Autorzy: , , K 003 Conference Proceedings 2018 International Scientific-Practical Conference Institute of Electrical and Electronics Engineers (IEEE) 2018.10.09 003Development of Solar Panels DetectorProblems of Infocommunications Science and Technology (PIC S&T' 2018)KharkivS. 761-7642018/201910.1109/INFOCOMMST.2018.8632132Golovko, Vladimirxartificial intelligenceInternational Scientific-Practical Conference - Problems of Infocommunications Science and Technology (PIC S&T)KijówThe paper describes the method of detection of roof-installed solar photovoltaic panels in low-quality satellite photos. It is important to receive the geospatial data (such as country, zip code, street and home number) of installed solar panels, because they are connected directly to the local power. It will be helpful to estimate a power capacity and an energy production using the satellite photos. For this purpose, a Convolutional Neural Network was used. For training and testing dataset consists of low-quality Google satellite images was used. The experimental results show a high rate accuracy of detection with low rate incorrect classifications of the proposed approach. The proposed approach has enormous implementation and can be improved in future.computer visionconvolutional neural networkenergy productiongeospatial datapower capacitysatellite photossolar panels detection, Conference Proceedings 2018 International Scientific-Practical Conference Institute of Electrical and Electronics Engineers (IEEE) 2018.10.09 Development of Solar Panels DetectorProblems of Infocommunications Science and Technology (PIC S&T' 2018)KharkivS. 761-7642018/201910.1109/INFOCOMMST.2018.8632132Golovko, Vladimirxartificial intelligenceInternational Scientific-Practical Conference - Problems of Infocommunications Science and Technology (PIC S&T)KijówThe paper describes the method of detection of roof-installed solar photovoltaic panels in low-quality satellite photos. It is important to receive the geospatial data (such as country, zip code, street and home number) of installed solar panels, because they are connected directly to the local power. It will be helpful to estimate a power capacity and an energy production using the satellite photos. For this purpose, a Convolutional Neural Network was used. For training and testing dataset consists of low-quality Google satellite images was used. The experimental results show a high rate accuracy of detection with low rate incorrect classifications of the proposed approach. The proposed approach has enormous implementation and can be improved in future.computer visionconvolutional neural networkenergy productiongeospatial datapower capacitysatellite photossolar panels detection.
Tytuł równoległy: Development of Solar Panels DetectorProblems of Infocommunications Science and Technology (PIC S&T' 2018)KharkivS. 761-7642018/201910.1109/INFOCOMMST.2018.8632132Golovko, Vladimirxartificial intelligenceInternational Scientific-Practical Conference - Problems of Infocommunications Science and Technology (PIC S&T)KijówThe paper describes the method of detection of roof-installed solar photovoltaic panels in low-quality satellite photos. It is important to receive the geospatial data (such as country, zip code, street and home number) of installed solar panels, because they are connected directly to the local power. It will be helpful to estimate a power capacity and an energy production using the satellite photos. For this purpose, a Convolutional Neural Network was used. For training and testing data : Conference Proceedings 2018 International Scientific-Practical Conference : Institute of Electrical and Electronics Engineers (IEEE) : 2018.10.09
Seria: Development of Solar Panels DetectorProblems of Infocommunications Science and Technology (PIC S&T' 2018)KharkivS. 761-7642018/201910.1109/INFOCOMMST.2018.8632132Golovko, Vladimirxartificial intelligenceInternational Scientific-Practical Conference - Problems of Infocommunications Science and Technology (PIC S&T)KijówThe paper describes the method of , Conference Proceedings 2018 International Scientific-Practical Conference, Institute of Electrical and Electronics Engineers (IEEE), 2018.10.09, 2018, y, 2018.10.12 ; UA, 3320838414, 3320799106
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Index Copernicus: y (PIC S&T' 2018)^bConference Proceedings 2018 International Scientific-Practical Conference^aKharkiv^bInstitute of Electrical and Electronics Engineers (IEEE)^c2018^aS. 761-764^a2018/2019^a10.1109/INFOCOMMST.2018.8632132^aGolovko, Vladimir^cy^ax^aartificial intelligence^aInternational Scientific-Practical Conference - Problems of Infocommunications Science and Technology (PIC S&T)^aKijów^dUA^b2018.10.09^c2018.10.12^aThe paper describes the method of detection of roof-installed solar photovoltaic panels in low-quality satellite photos. It is important to receive the geospatial data (such as country, zip code, street and home number) of installed solar panels, because they are connected directly to the local power. It will be helpful to estimate a power capacity and an energy production using the satellite photos. For this purpose, a Convolutional Neural Network was used. For training and testing dataset consists of low-quality Google satellite images was used. The experimental results show a high rate accuracy of detection with low rate incorrect classifications of the proposed approach. The proposed approach has enormous implementation and can be improved in future.^acomputer vision^aconvolutional neural network^aenergy production^ageospatial data^apower capacity^asatellite photos^asolar panels detection
Słowa kluczowe ang.: an energy production using the satellite photos. For this purpose, a Convolutional Neural Network was used. For training and testing dataset consists of low-quality Google satellite images was used. The experimental results show a high rate accuracy of detection with low rate incorrect classifications of the proposed approach. The proposed approach has enormous implementation and can be improved in future.^acomputer vision^aconvolutional neural network^aenergy production^ageospatial data^apower capacity^asatellite photos^asolar panels detection ; an energy production using the satellite photos. For this purpose, a Convolutional Neural Network was used. For training and testing dataset consists of low-quality Google satellite images was used. The experimental results show a high rate accuracy of detection with low rate incorrect classifications of the proposed approach. The proposed approach has enormous implementation and can be improved in future.^acomputer vision^aconvolutional neural network^aenergy production^ageospatial data^apower capacity^asatellite photos^asolar panels detection
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