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    Wyświetlanie 1-11 z 11
    Tytuł:
    Improved Method of Searching the Associative Rules while Developing the Software
    Autorzy:
    Savchuk, Tamara O.
    Pryimak, Natalia V.
    Slyusarenko, Nina V.
    Smolarz, Andrzej
    Smailova, Saule
    Amirgaliyev, Yedilkhan
    Powiązania:
    https://bibliotekanauki.pl/articles/226118.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    software development
    classification
    C4.5 algorithm
    associated rules
    FPG-algorithm
    Opis:
    As the delivery of good quality software in time is a very important part of the software development process, it's a very important task to organize this process very accurately. For this, a new method of the searching associative rules were proposed. It is based on the classification of all tasks on three different groups, depending on their difficulty, and after this, searching associative rules among them, which will help to define the time necessary to perform a specific task by the specific developer.
    Źródło:
    International Journal of Electronics and Telecommunications; 2020, 66, 3; 425-430
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Online Training and Contests for Informatics Contestants of Secondary School Age
    Autorzy:
    NÉMETH,, Ágnes Erdősné
    ZSAKÓ, László
    Powiązania:
    https://bibliotekanauki.pl/articles/457559.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Rzeszowski
    Tematy:
    algorithm
    online contest
    online training
    classification
    IOI
    Opis:
    If you prepare students for programming contests carefully selected and widely available online training and contests offer help and diversity. If you teach about testing programs you have to know which sites offer downloadable tests or feedback with detailed test cases. If you want to make series of tasks for practicing you have to know which sites offer you categorized tasks of the appropriate level. In order to be able to choose from the available materials we need to categorize them. The previously defined and used criteria need some supplement criteria for better and sophisticated use of categorization from the teacher’s point of view. Online resources can be classified in general: what programming languages can be used, how often the contests are organized, in which languages they can be accessed, what types of problems a website deals with and at what level, what prior knowledge is required. We can group sites according to whether they help teachers to set tasks for their students, or get ideas for solutions or see the results of their students. Online contests can also be categorized regarding whether students can see each other's solutions. The aim of this paper is to supplement the categorization and describe some major portals according to the previously defined and supplemented criteria.
    Źródło:
    Edukacja-Technika-Informatyka; 2015, 6, 1; 273-280
    2080-9069
    Pojawia się w:
    Edukacja-Technika-Informatyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A novel fast feedforward neural networks training algorithm
    Autorzy:
    Bilski, Jarosław
    Kowalczyk, Bartosz
    Marjański, Andrzej
    Gandor, Michał
    Zurada, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/2031099.pdf
    Data publikacji:
    2021
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    neural network training algorithm
    QR decomposition
    Givens rotations
    approximation
    classification
    Opis:
    In this paper1 a new neural networks training algorithm is presented. The algorithm originates from the Recursive Least Squares (RLS) method commonly used in adaptive filtering. It uses the QR decomposition in conjunction with the Givens rotations for solving a normal equation - resulting from minimization of the loss function. An important parameter in neural networks is training time. Many commonly used algorithms require a big number of iterations in order to achieve a satisfactory outcome while other algorithms are effective only for small neural networks. The proposed solution is characterized by a very short convergence time compared to the well-known backpropagation method and its variants. The paper contains a complete mathematical derivation of the proposed algorithm. There are presented extensive simulation results using various benchmarks including function approximation, classification, encoder, and parity problems. Obtained results show the advantages of the featured algorithm which outperforms commonly used recent state-of-the-art neural networks training algorithms, including the Adam optimizer and the Nesterov’s accelerated gradient.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2021, 11, 4; 287-306
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Metrics and similarities in modeling dependencies between continuous and nominal data
    Autorzy:
    Grabowski, M.
    Korpusik, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/91361.pdf
    Data publikacji:
    2013
    Wydawca:
    Warszawska Wyższa Szkoła Informatyki
    Tematy:
    k-nearest neighbors algorithm
    data metrics
    classification
    continuous data
    nominal data
    Opis:
    Classification theory analytical paradigm investigates continuous data only. When we deal with a mix of continuous and nominal attributes in data records, difficulties emerge. Usually, the analytical paradigm treats nominal attributes as continuous ones via numerical coding of nominal values (often a bit ad hoc). We propose a way of keeping nominal values within analytical paradigm with no pretending that nominal values are continuous. The core idea is that the information hidden in nominal values influences on metric (or on similarity function) between records of continuous and nominal data. Adaptation finds relevant parameters which influence metric between data records. Our approach works well for classifier induction algorithms where metric or similarity is generic, for instance k nearest neighbor algorithm or proposed here support of decision tree induction by similarity function between data. The k-nn algorithm working with continuous and nominal data behaves considerably better, when nominal values are processed by our approach. Algorithms of analytical paradigm using linear and probability machinery, like discriminant adaptive nearest-neighbor or Fisher’s linear discriminant analysis, cause some difficulties. We propose some possible ways to overcome these obstacles for adaptive nearest neighbor algorithm.
    Źródło:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2013, 7, 10; 25-37
    1896-396X
    2082-8349
    Pojawia się w:
    Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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
    Tytuł:
    Web–based framework for breast cancer classification
    Autorzy:
    Bruździński, T.
    Krzyżak, A.
    Fevens, T.
    Jeleń, Ł.
    Powiązania:
    https://bibliotekanauki.pl/articles/91866.pdf
    Data publikacji:
    2014
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    breast cancer
    classification
    cytological image
    aspiration biopsy
    feature vector
    classifier
    multilayer perceptron
    segmentation algorithm
    Opis:
    The aim of this work is to create a web-based system that will assist its users in the cancer diagnosis process by means of automatic classification of cytological images obtained during fine needle aspiration biopsy. This paper contains a description of the study on the quality of the various algorithms used for the segmentation and classification of breast cancer malignancy. The object of the study is to classify the degree of malignancy of breast cancer cases from fine needle aspiration biopsy images into one of the two classes of malignancy, high or intermediate. For that purpose we have compared 3 segmentation methods: k-means, fuzzy c-means and watershed, and based on these segmentations we have constructed a 25–element feature vector. The feature vector was introduced as an input to 8 classifiers and their accuracy was checked. The results show that the highest classification accuracy of 89.02 % was recorded for the multilayer perceptron. Fuzzy c–means proved to be the most accurate segmentation algorithm, but at the same time it is the most computationally intensive among the three studied segmentation methods.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 2; 149-162
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Monitoring of Land Surface Temperature from Landsat Imagery: A Case Study of Al-Anbar Governorate in Iraq
    Autorzy:
    Morsy, Salem
    Ahmed, Shaker
    Powiązania:
    https://bibliotekanauki.pl/articles/2203961.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    land surface temperature
    Landsat
    single channel algorithm
    NDVI
    land use
    land cover
    classification
    regression
    Opis:
    Land surface temperature (LST) estimation is a crucial topic for many applications related to climate, land cover, and hydrology. In this research, LST estimation and monitoring of the main part of Al-Anbar Governorate in Iraq is presented using Landsat imagery from five years (2005, 2010, 2015, 2016 and 2020). Images of the years 2005 and 2010 were captured by Landsat 5 (TM) and the others were captured by Landsat 8 (OLI/TIRS). The Single Channel Algorithm was applied to retrieve the LST from Landsat 5 and Landsat 8 images. Moreover, the land use/land cover (LULC) maps were developed for the five years using the maximum likelihood classifier. The difference in the LST and normalized difference vegetation index (NDVI) values over this period was observed due to the changes in LULC. Finally, a regression analysis was conducted to model the relationship between the LST and NDVI. The results showed that the highest LST of the study area was recorded in 2016 (min = 21.1°C, max = 53.2°C and mean = 40.8°C). This was attributed to the fact that many people were displaced and had left their agricultural fields. Therefore, thousands of hectares of land which had previously been green land became desertified. This conclusion was supported by comparing the agricultural land areas registered throughout the presented years. The polynomial regression analysis of LST and NDVI revealed a better coefficient of determination (R2) than the linear regression analysis with an average R2 of 0.423.
    Źródło:
    Geomatics and Environmental Engineering; 2023, 17, 3; 61--81
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-Layer Perceptron Neural Network Utilizing Adaptive Best-Mass Gravitational Search Algorithm to Classify Sonar Dataset
    Autorzy:
    Mosavi, Mohammad Reza
    Khishe, Mohammad
    Naseri, Mohammad Jafar
    Parvizi, Gholam Reza
    Ayat, Mehdi
    Powiązania:
    https://bibliotekanauki.pl/articles/176971.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    MLP NN
    Multi-Layer Perceptron Neural Network
    ABGSA
    Adaptive Best Mass Gravitational Search Algorithm
    sonar
    classification
    Opis:
    In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.
    Źródło:
    Archives of Acoustics; 2019, 44, 1; 137-151
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessing the accuracy of the pixel-based algorithms in classifying the urban land use, using the multi spectral image of the IKONOS satellite (Case study, Uromia city)
    Autorzy:
    Safaralizade, E.
    Husseinzade, R.
    Pashazade, G.
    Khosravi, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/11078.pdf
    Data publikacji:
    2014
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    pixel-based algorithm
    urban land
    land use
    multispectral image
    IKONOS satellite
    classification
    urbanization
    urban planning
    Uromia city
    Opis:
    With the development of urbanization and expansion of urban land use, the need to up to date maps, has drawn the attention of the urban planners. With the advancement of the remote sensing technology and accessibility to images with high resolution powers, the classification of these land uses could be executed in different ways. In the current research, different algorithms for classifying the pixel-based were tested on the land use of the city of Urmia, using the multi spectral images of the IKONOS satellite. Here, in this method, the algorithms of the supervised classification of the maximum likelihood, minimum distance to mean and parallel piped were executed on seven land use classes. Results obtained using the error matrix indicated that the algorithm for classifying the maximum likelihood has an overall accuracy of 88/93 % and the Kappa coefficient of 0/86 while for the algorithms of minimum distance to mean and parallel piped , the overall accuracy are 05/79 % and 40/70 % respectively. Also, the accuracy of the producer and that of the user in most land use classes in the method of maximum likelihood are higher compared to the other algorithms.
    Źródło:
    International Letters of Natural Sciences; 2014, 06
    2300-9675
    Pojawia się w:
    International Letters of Natural Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A differential evolution approach to dimensionality reduction for classification needs
    Autorzy:
    Martinović, G.
    Bajer, D.
    Zorić, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/331498.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    classification
    differential evolution
    feature subset selection
    k-nearest neighbour algorithm
    wrapper method
    ewolucja różnicowa
    selekcja cech
    algorytm najbliższego sąsiada
    Opis:
    The feature selection problem often occurs in pattern recognition and, more specifically, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features, which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold cross-validation on the archive solutions and selecting the best one. Experimental analysis is conducted on several standard test sets. The classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis shows that the proposed approach successfully determines good feature subsets which may increase the classification accuracy.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 111-122
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Patient classification algorithm at urgency care area of a hospital based on the triage system
    Autorzy:
    Mondragon, N.
    Istrate, D.
    Wegrzyn-Wolska, K.
    Garcia, J. C.
    Sanchez, J.C.
    Powiązania:
    https://bibliotekanauki.pl/articles/951692.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    triage
    classification
    SET
    fuzzy logic
    decision trees
    patients
    urgency
    hospital emergency
    algorithm
    ocena stanu zdrowia rannych
    klasyfikacja
    logika rozmyta
    drzewa decyzyjne
    pacjenci
    pomoc szpitalna
    algorytm
    Opis:
    The time passed in the urgency zone of a hospital is really important, and the quick evaluation and selection of the patients who arrive to this area is essential to avoid waste of time and help the patients in a higher emergency level. The triage, an evaluation and classification structured system, allows to manage the urgency level of the patient; it is based on the vital signs measures and clinical data of the patient. The goal is making the classification in the shortest possible time and with a minimal error percentage. Levels are allocated according to the concept that what is urgent is not always serious and that what is serious is not always urgent. In this work, we present a computational algorithm that evaluates the patients within the fever symptomatic category, we use fuzzy logic and decision trees to collect and analyze simultaneously the vital signs and the clinical data of the patient through a graphical interface; so that the classification can be more intuitive and faster. Fuzzy logic allows us to process data and take a decision based on incomplete information or uncertain values, decision trees are structures or rules sets that classify the data when we have several variables.
    Źródło:
    Journal of Medical Informatics & Technologies; 2013, 22; 87-94
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-11 z 11

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