Autor: Kisiel-Dorohinicki, Marek - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: An explainable AI approach to agrotechnical monitoring and crop diseases prediction in Dnipro region of Ukraine
Autorzy:: Laktionov, Ivan
Diachenko, Grigorii
Rutkowska, Danuta
Kisiel-Dorohinicki, Marek
Powiązania:: https://bibliotekanauki.pl/articles/23944837.pdf
Data publikacji:: 2023
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: IoT
ANFIS
explainable AI
agrotechnical monitoring
disease prediction
crop
Opis:: The proliferation of computer-oriented and information digitalisation technologies has become a hallmark across various sectors in today’s rapidly evolving environment. Among these, agriculture emerges as a pivotal sector in need of seamless incorporation of highperformance information technologies to address the pressing needs of national economies worldwide. The aim of the present article is to substantiate scientific and applied approaches to improving the efficiency of computer-oriented agrotechnical monitoring systems by developing an intelligent software component for predicting the probability of occurrence of corn diseases during the full cycle of its cultivation. The object of research is non-stationary processes of intelligent transformation and predictive analytics of soil and climatic data, which are factors of the occurrence and development of diseases in corn. The subject of the research is methods and explainable AI models of intelligent predictive analysis of measurement data on the soil and climatic condition of agricultural enterprises specialised in growing corn. The main scientific and practical effect of the research results is the development of IoT technologies for agrotechnical monitoring through the development of a computer-oriented model based on the ANFIS technique and the synthesis of structural and algorithmic provision for identifying and predicting the probability of occurrence of corn diseases during the full cycle of its cultivation.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 4; 247--272
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 2.

Tytuł:: Fine tuning of agent-based evolutionary computing
Autorzy:: Mizera, Michal
Nowotarski, Pawel
Byrski, Aleksander
Kisiel-Dorohinicki, Marek
Powiązania:: https://bibliotekanauki.pl/articles/91820.pdf
Data publikacji:: 2019
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: multi-agent systems
metaheuristics
evolutionary computing
Opis:: Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 2; 81-97
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 3.

Tytuł:: Towards a very fast feedforward multilayer neural networks training algorithm
Autorzy:: Bilski, Jarosław
Kowalczyk, Bartosz
Kisiel-Dorohinicki, Marek
Siwocha, Agnieszka
Żurada, Jacek
Powiązania:: https://bibliotekanauki.pl/articles/2147135.pdf
Data publikacji:: 2022
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: neural network training algorithm
QR decomposition
scaled Givens rotation
approximation
classification
Opis:: This paper presents a novel fast algorithm for feedforward neural networks training. It is based on the Recursive Least Squares (RLS) method commonly used for designing adaptive filters. Besides, it utilizes two techniques of linear algebra, namely the orthogonal transformation method, called the Givens Rotations (GR), and the QR decomposition, creating the GQR (symbolically we write GR + QR = GQR) procedure for solving the normal equations in the weight update process. In this paper, a novel approach to the GQR algorithm is presented. The main idea revolves around reducing the computational cost of a single rotation by eliminating the square root calculation and reducing the number of multiplications. The proposed modification is based on the scaled version of the Givens rotations, denoted as SGQR. This modification is expected to bring a significant training time reduction comparing to the classic GQR algorithm. The paper begins with the introduction and the classic Givens rotation description. Then, the scaled rotation and its usage in the QR decomposition is discussed. The main section of the article presents the neural network training algorithm which utilizes scaled Givens rotations and QR decomposition in the weight update process. Next, the experiment results of the proposed algorithm are presented and discussed. The experiment utilizes several benchmarks combined with neural networks of various topologies. It is shown that the proposed algorithm outperforms several other commonly used methods, including well known Adam optimizer.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 3; 181--195
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 4.

Tytuł:: Multi-population-based algorithm with an exchange of training plans based on population evaluation
Autorzy:: Łapa, Krystian
Cpałka, Krzysztof
Kisiel-Dorohinicki, Marek
Paszkowski, Józef
Dębski, Maciej
Le, Van-Hung
Powiązania:: https://bibliotekanauki.pl/articles/2147148.pdf
Data publikacji:: 2022
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: population-based algorithm
multi-population algorithm
hybrid algorithm
island algorithm
subpopulation evaluation
training plan
Opis:: Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such structure. As PBAs have great application possibilities, the aim is to develop more and more effective search formulas used in them. An interesting approach is to use multiple populations and process them with separate PBAs (in a different way). In this paper, we propose a new multi-population-based algorithm with: (a) subpopulation evaluation and (b) replacement of the associated PBAs subpopulation formulas used for their processing. In the simulations, we used a set of typical CEC2013 benchmark functions. The obtained results confirm the validity of the proposed concept.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 239--253
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Skocz do pozycji: 5.

Tytuł:: Detecting anomalies in advertising web traffic with the use of the variational autoencoder
Autorzy:: Gabryel, Marcin
Lada, Dawid
Filutowicz, Zbigniew
Patora-Wysocka, Zofia
Kisiel-Dorohinicki, Marek
Chen, Guang Yi
Powiązania:: https://bibliotekanauki.pl/articles/2147149.pdf
Data publikacji:: 2022
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: anomaly detection
web traffic
ad fraud
variational autoencoder
Opis:: This paper presents a neural network model for identifying non-human traffic to a website, which is significantly different from visits made by regular users. Such visits are undesirable from the point of view of the website owner as they are not human activity, and therefore do not bring any value, and, what is more, most often involve costs incurred in connection with the handling of advertising. They are made most often by dishonest publishers using special software (bots) to generate profits. Bots are also used in scraping, which is automatic scanning and downloading of website content, which actually is not in the interest of website authors. The model proposed in this work is learnt by data extracted directly from the web browser during website visits. This data is acquired by using a specially prepared JavaScript that monitors the behavior of the user or bot. The appearance of a bot on a website generates parameter values that are significantly different from those collected during typical visits made by human website users. It is not possible to learn more about the software controlling the bots and to know all the data generated by them. Therefore, this paper proposes a variational autoencoder (VAE) neural network model with modifications to detect the occurrence of abnormal parameter values that deviate from data obtained from human users’ Internet traffic. The algorithm works on the basis of a popular autoencoder method for detecting anomalies, however, a number of original improvements have been implemented. In the study we used authentic data extracted from several large online stores.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 4; 255--266
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

Artykuł

Zmień widok

na półce

Informacja

Wyszukujesz frazę "Kisiel-Dorohinicki, Marek" wg kryterium: Autor

Źródło danych

Dostawca treści

Kolekcja

Rok wydania

Wydawca

Temat

Autor

Typ dokumentu

Język